Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Li, Ziyu
Master of Applied Science - MASc
University of British Columbia
This research examined the use of fluorescence spectroscopy as a rapid contaminant detection method. Identifying and quantifying environmental contaminants in surface waters has always been challenging after transient events such as spills or storm events. Previous research has shown fluorescence spectroscopy to be a promising tool for continuous monitoring of water quality due to its real-time and sensitive detection capabilities. However, persistent challenges with fluorescence approaches remain and limit its use in contaminant monitoring. In this study, research was carried out to identify possible data analysis techniques that improve the separation of overlapping fluorescence signals from background noise to accurately identify and quantify contaminants of interest. Algorithm-based dimensionality reduction methods, including parallel factor analysis (PARAFAC), principal component analysis (PCA), and autoencoder neural networks, were compared to a basic peak-picking method. It was observed that contaminant detection was most accurate using dimensionality reduction methods that maximally explain dataset variance, such as PCA, although at the cost of component interpretability. Due to the interference from background organic matter and other environmental impacts on fluorescence signals, fluorescence-based contaminant detection models are highly source-specific and require significant effort and resources for calibration. The novel application of data processing techniques was investigated to enable the transfer of fluorescence detection models from one water source to another. A contaminant detection model from a relatively consistent and low organic background source was successfully transferred to a second source with higher organic concentration and distinct water quality. Only a few additional fluorescence spectra of the background water quality and contaminants of interest were required to successfully transfer the model without the need for labelled samples in the new source. Notable differences in peak location and spectral shape of identical compounds were found in source-specific models between the two water sources, implying variability in fluorescence signals resulting from environmental conditions. Despite the impact of environmental conditions, features identified by principal component analysis (PCA) or an autoencoder produced sensitive transferred models capable of addressing the spatial and temporal variability in source waters.
DEVELOPMENT OF MACHINE LEARNING METHODS FOR IMPROVED FLUORESCENCE-BASED MONITORING OF ENVIRONMENTAL CONTAMINANTS IN SURFACE WATERS by Ziyu Li B. Eng, Hohai University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE In THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) September 2021 © Ziyu Li, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: DEVELOPMENT OF MACHINE LEARNING METHODS FOR IMPROVED FLUORESCENCE-BASED MONITORING OF ENVIRONMENTAL CONTAMINANTS IN SURFACE WATERS submitted by Ziyu Li in partial fulfillment of the requirements of the degree of Master of Applied Science . Dr. Nicolás Peleato, School of Engineering Supervisor Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Deborah Roberts, University of Northern British Columbia Supervisory Committee Member Dr. Mohammad Zarifi, School of Engineering University Examiner iii Abstract This research examined the use of fluorescence spectroscopy as a rapid contaminant detection method. Identifying and quantifying environmental contaminants in surface waters has always been challenging after transient events such as spills or storm events. Previous research has shown fluorescence spectroscopy to be a promising tool for continuous monitoring of water quality due to its real-time and sensitive detection capabilities. However, persistent challenges with fluorescence approaches remain and limit its use in contaminant monitoring. In this study, research was carried out to identify possible data analysis techniques that improve the separation of overlapping fluorescence signals from background noise to accurately identify and quantify contaminants of interest. Algorithm-based dimensionality reduction methods, including parallel factor analysis (PARAFAC), principal component analysis (PCA), and autoencoder neural networks, were compared to a basic peak-picking method. It was observed that contaminant detection was most accurate using dimensionality reduction methods that maximally explain dataset variance, such as PCA, although at the cost of component interpretability. Due to the interference from background organic matter and other environmental impacts on fluorescence signals, fluorescence-based contaminant detection models are highly source-specific and require significant effort and resources for calibration. The novel application of data processing techniques was investigated to enable the transfer of fluorescence detection models from one water source to another. A contaminant detection model from a relatively consistent and low organic background source was successfully transferred to a second source with higher organic concentration and distinct water quality. Only a few additional fluorescence spectra of the background water quality and contaminants of interest were required to successfully transfer the model without the need for labelled samples in the new source. Notable differences in peak location and spectral shape of identical compounds were found in source-specific models between the two water sources, implying variability in fluorescence signals resulting from environmental conditions. Despite the impact of environmental conditions, features identified by principal component analysis (PCA) or an autoencoder produced sensitive transferred models capable of addressing the spatial and temporal variability in source waters. iv Lay Summary Monitoring water quality is crucial to the effective management of environmental and human health. Sewer overflows, chemical spills, and urban or agricultural run-off during storm events are examples of unexpected and transient environmental contamination events that can negatively impact water quality but are challenging to detect. Fluorescence spectroscopy is a promising technique for rapidly detecting select organic contaminants and could be used to identify exceedances of regulated water quality standards in short time frames. However, two persistent challenges limit the adoption of real-time fluorescence contaminant monitoring: difficulties in separating signals of interest from background noise leading to poor quantification accuracy and the source-specificity of fluorescence models that dictate the need for extensive calibration before use. In this thesis, several data analysis techniques were applied to fluorescence data to improve the detection limits of toxic contaminants. Furthermore, a methodology to generate global or non-source-specific detection models was developed using data-driven algorithms. This study highlights the potential of using machine learning methods to improve fluorescence-based monitoring of pollutants and identifies new approaches to help contaminant detection in surface waters be more effective, easier, and less labour-intensive. v Preface This thesis is based on the research work completed in the School of Engineering at the University of British Columbia, Okanagan, under the supervision and guidance of Dr. Nicolás Peleato. All published works are included in this thesis. Chapter 4 is based on the following published papers and used with permission of chemosphere: â¢ Li, Ziyu, and Nicolas M Peleato. 2021. Comparison of Dimensionality Reduction Techniques for Cross-Source Transfer of Fluorescence Contaminant Detection Models. Chemosphere:130064. Chapter 5 is based on the following published papers and used with permission of springer: â¢ Li, Ziyu, and Nicolas M Peleato. Rapid and cross-source detection of naphthenic acids and phenol using fluorescence spectroscopy. 2021 Annual Conference of the Canadian Society for Civil Engineering. Springer (in press) vi Table of Contents Abstract ........................................................................................................................................ iii Lay Summary ............................................................................................................................... iv Preface .......................................................................................................................................... v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................. x List of Figures .............................................................................................................................. xi Nomenclature .............................................................................................................................. xiii Acknowledgements .................................................................................................................... xvii Chapter 1: Introduction ................................................................................................................. 1 1.1 Background ................................................................................................................................. 1 1.2 Objectives .................................................................................................................................... 4 Chapter 2: Literature Review ........................................................................................................ 6 2.1 Surface water quality ................................................................................................................. 6 2.2 Fluorescence spectroscopy ...................................................................................................... 7 2.2.1 Fluorescence theory .......................................................................................................... 7 2.2.2 Fluorophores relevant to water quality .......................................................................... 10 2.3 Fluorescence data analysis .................................................................................................... 12 2.3.1 Data pre-processing ......................................................................................................... 12 2.3.2 Spectral decomposition ................................................................................................... 14 2.3.3 Regression analysis ......................................................................................................... 17 vii 2.4 Research gaps .......................................................................................................................... 17 2.4.1 Previous work ................................................................................................................... 17 2.4.2 Research challenges ....................................................................................................... 18 Chapter 3: Materials and Methods .............................................................................................. 20 3.1 Selection of contaminants ....................................................................................................... 20 3.2 Fluorescence pre-processing ................................................................................................. 20 3.3 Fluorescence dimensionality reduction ................................................................................. 22 3.3.1 Parallel factor analysis (PARAFAC) .............................................................................. 22 3.3.2 Principal component analysis (PCA) ............................................................................. 28 3.3.3 Autoencoder ...................................................................................................................... 29 3.4 Regression analysis ................................................................................................................. 30 3.4.1 Multivariate linear regression .......................................................................................... 30 3.4.2 Random forest .................................................................................................................. 31 3.4.3 Neural networks ................................................................................................................ 31 3.5 Limit of detection (LOD) .......................................................................................................... 32 Chapter 4: Cross-Source Detection of PAHs and Pesticides using Fluorescence Spectroscopy .................................................................................................................................................... 33 4.1 Introduction ................................................................................................................................ 33 4.2 Materials and methods ............................................................................................................ 36 4.2.1 Natural water samples ..................................................................................................... 36 4.2.2 Mixing experiments .......................................................................................................... 36 viii 4.2.3 Fluorescence ..................................................................................................................... 37 4.2.4 Dimensionality reduction and model transfer ............................................................... 37 4.2.5 Regression analysis ......................................................................................................... 40 4.2.6 Cross-source model transfer .......................................................................................... 40 4.3 Results and discussion ............................................................................................................ 41 4.3.1 Dimensionality reduction ................................................................................................. 41 4.3.2 Contaminant detection in cross-source water .............................................................. 47 4.3.3 Fluorescence components importance for contaminant detection ........................... 52 4.4 Summary ................................................................................................................................... 53 Chapter 5: Detection of Highly Overlapping Naphthenic Acids and Phenol using Fluorescence Spectroscopy .............................................................................................................................. 55 5.1 Introduction ................................................................................................................................ 55 5.2 Materials and methods ............................................................................................................ 57 5.2.1 Samples and mixing experiments .................................................................................. 57 5.2.2 Fluorescence ..................................................................................................................... 58 5.2.3 Cross-source model transfer .......................................................................................... 58 5.3 Results and discussion ............................................................................................................ 59 5.3.1 Fluorescence characterization of the organic matter in water samples ................... 59 5.3.2 Calibration modelling ....................................................................................................... 64 5.3.3 Prediction of NAs and phenol in surface waters .......................................................... 64 5.3.4 Contaminant detection in cross-source water .............................................................. 66 ix 5.4 Summary ................................................................................................................................... 68 Chapter 6: Conclusions ............................................................................................................... 70 6.1 Summary of contributions ....................................................................................................... 70 6.2 Limitations and suggestions for future research .................................................................. 72 References .................................................................................................................................. 74 Appendix ..................................................................................................................................... 85 x List of Tables Table 3.1 Pre-processing steps to correct the fluorescence EEMs ............................................ 21 Table 3.2 Schematic of the steps to build a valid PARAFAC model for EEMs analysis ............. 23 Table 3.3. Algorithm for PCA in dimensionality reduction of sample EEMs ................................ 28 Table 4.1 Contaminant fluorophores and the excitation/emission (ex/em) wavelengths selected by the peak-picking method ........................................................................................................ 38 Table 4.2 PARAFAC component locations (ex/em) from Otonabee River and Lake Ontario with similarity score compared with previous studies ......................................................................... 43 Table 4.3 Tucker congruence coefficient (TCC) of PARAFAC components with similar peak positions from Otonabee River and Lake Ontario. TCC â¥ 0.95 as minimal match criterium ....... 46 Table 5.1 Theoretical excitation and emission positions for NAs, phenol, and some NOM in water ........................................................................................................................................... 60 Table 5.2 PARAFAC component locations (ex/em) from Lake Okanagan and Mission Creek water with similarity score compared with previous studies ........................................................ 63 Table 5.3 Mean absolute error (MAE) for peak-picking, PARAFAC and PCA dimensionality reduction analysis with varying regressors to predict naphthenic acids (NAs) and phenol in site-specific waters (Lake Okanagan and Mission Creek water) ....................................................... 66 Table 5.4 Regression Error (MAE) for multivariate linear regression with varying dimensionality reduction techniques to predict naphthenic acids (NAs) and phenol in site-specific waters and in transfer water source .................................................................................................................. 67 xi List of Figures Figure 2.1 An example of fluorescence EEMs collected from Lake Ontario ................................. 8 Figure 2.2 Diagram of a cuvette from the top showing incident light (I0), tramsmitted light (I), and the fluorescent radiation at 90 degrees. Primary and secondary inner-filter effects are shown with bule rhombus and yellow rounds ........................................................................................... 9 Figure 2.3 A sketch of the scattering occurring in a fluorescence EEM ...................................... 13 Figure 3.1 Visualize EEMs plots of first 9 samples for dataset ± 15, ± 15, ± 20 and ± 20 nm .... 25 Figure 3.2 Spectral loadings of components during exploratory phase for dataset ± 15, ± 15, ± 20 and ± 20 nm ........................................................................................................................... 26 Figure 3.3 Some likely outliers according to their leverage values from a PARAFAC model ..... 27 Figure 3.4 Example loadings plot for a successful split-half validation ....................................... 27 Figure 3.5 Loading plots of PARAFAC components for the Lake Okanagan (C1-4) .................. 28 Figure 4.1 Roadmap for Model Setup using Fluorescence EEMs to predict the concentration of PAHs and pesticides in the Otonabee River. (experiment A): retrain and repurpose an existing lake model using the loadings of the new dataset, (experiment B): fit the new dataset using the original loadings of lake dataset .................................................................................................. 42 Figure 4.2 Peak location comparison among PARAFAC models from two surface waters: Lake Ontario, green solid for excitation spectra and green dashed line for emission spectra, and the Otonabee River, red solid for excitation spectra and dashed line for emission spectra. Similarity scores of components (C1 - C7) between the Lake and River model are reported in Table 2. .. 44 Figure 4.3 Loading plots of PCA components (PC1-7) and the latent maps of nodes in autoencoder (AE1-7) for the Otonabee River ............................................................................. 47 Figure 4.4 Mean absolute error for (A): retraining and repurposing the existing lake model using the loadings of the new dataset, (B): fitting the new dataset using the original loadings, (Baseline) site-specific model, and no transfer (note distinct scale). PCA: principal component analysis. PARAFAC: parallel factors analysis. PP: peak picking. LR: linear regression. Note: xii MAE from No transfer models are given on a different scale (0 to 10 µg L-1) than MAE from other models (0 to 3 µg L-1). ........................................................................................................ 49 Figure 4.5 Measured vs predicted PAHs and pesticides for test samples (the river source) using various data techniques according to the results from experiment A and No transfer. Note: Peak-picking models were trained based on intensities at specific peak positions and neglect impacts such as interaction with NOM, water matrices among sources. Thus, it is hard to remap or transfer peak-picking models according to the cross-source transfer methods described in this study. .................................................................................................................................... 52 Figure 5.1 Roadmap for model setup using Fluorescence EEMs to predict the concentration of NAs and phenol in the target water samples .............................................................................. 59 Figure 5.2 Loading plots from 4 component PARAFAC models for Okanagan Lake and Mission Creek .......................................................................................................................................... 61 Figure 5.3 Loading plots of the 4 component PCA model (PC1-4) within EEMs from Okanagan Lake. PCs 1 to 4 represented > 99.70% of the variance in the dataset. ..................................... 62 Figure 5.4 Measured vs predicted PAHs and pesticides for test samples (the creek source) using peak-picking or PCA approaches coupled with linear regression ..................................... 68 xiii Nomenclature % Percent ð´ð´ðððð Area under Raman peak ð´ð´ðð Absorbance at wavelength ðð ð¹ð¹ðððððððððððððð Blank EEMs measured using Milli-Q® water ðð450/ðð500 Fluorescence index at excitation 370 nm ðððððð Emission wavelength ðððððð Excitation wavelength ABA Absorbance-based approach AE Autoencoder ANN Artificial neural networks BOD5 Five-day biochemical oxygen demand CCME Canadian Council of Ministers of the Environmental COD Chemical oxygen demand DOM Dissolved organic matter EEMs Fluorescence excitation-emission matrices em Emission EOCs Emerging organic compounds EU European Union ex Excitation ex/em Excitation-emission pair xiv FRI Fluorescence regional integration FTIP Fourier transform infrared GC Gas chromatography h Hour HPLC High-performance liquid chromatography I Transmitted light I0 Incident light IFE Inner filtering effect L Liter LC Liquid chromatography mg Milligrams mg/L Milligrams per liter MLR Multiple linear regression MS Mass spectrometry n Number of samples NAs Naphthenic acids NOM Natural organic matter ºC Degrees Celsius OLS Ordinary least squares OSPW Oil sands process-affected waters PAHs Polycyclic aromatic hydrocarbons xv PARAFAC Parallel factors analysis PCA Principal component analysis PCR Principal component regression PCs Principal components PLS Partial least square regression PPCPs Pharmaceuticals and personal care products R2 Coefficient of determination ðððððððð Residual for sample I, emission j, and excitation k S0 Electron from the singlet ground S1,S2 Upper excited singlet state SSE Sum of squared errors SVM Support vector machine t Time T Temperature TCC Tucker congruence coefficients THM Trihalomethane TN Total nitrogen TOC Total organic carbon TP Total phosphorus WTPs Drinking water treatment plants μg Micrograms xvi μg/L Micrograms per liter μL Microliter ð´ð´.ðð. Arbitrary Units ð ð .ðð. Raman Units ðð Predictor matrix ðð Response matrix ðð Residual matrix ðð Wavelength xvii Acknowledgements First of all, I deeply acknowledge my supervisor Nicolás Peleato for his advice, encouragement, and guidance over the years. Not only did he grant me a great freedom in my study, but he also delivered me lots of academic knowledge. Thanks for his support every step of the way. I would also like to thank the lab colleagues who kindly give suggestions and assistance in my experimental works. Thanks go to Mehrnaz Soleimanpour, Atefeh Aliashrafi and Hirra Zafar that Iâve had pleasure of working with. Especially thanks to Ye Zheng Yang for sharing data with me and his previous research. This work would not have been possible without my boyfriend Chengkai for collecting samples and giving me the support and encouragement. Sincere thanks also given to my family for their greatly help in different ways.1 Chapter 1: Introduction 1.1 Background Environmental water quality monitoring is crucial to safeguard the environment against chemical contaminants from anthropogenic diffuse emissions and point sources. Sewer overflows, urban or agriculture run-off, chemical spills, and open burning are examples of unexpected and transient events that lead to potential contamination by a range of toxic, mutagenic, and carcinogenic compounds. For example, Seopela et al. (2020) reported that total average concentrations of polycyclic aromatic hydrocarbons (PAHs) and n -alkanes varied from 150 to 50,000 ng/L and 574 to 19,000 ng/L in surface water in South Africa. Contamination by organic pollutants from anthropogenic stressors was highlighted as a contributing factor for mass mortalities of aquatic organisms recorded in the aquatic systems in various regions (Seopela et al., 2020). The result was also consistent with research by Khuman et al. (2018) in Eastern and Northeastern India, where incomplete combustion, industrial effluents, and harbour or port activities caused higher PAHs and higher risk from edible fish species due to carcinogenic potency. Therefore, there is an urgent need for assessing those prioritized and emerging contaminants rapidly to identify exceedances of regulatory limits and inform management efforts. Common measurement methods used to quantify targeted contaminants include gas or liquid chromatography coupled to mass spectrometry (GC-MS, LC-MS) (Ferretto et al., 2014). Limitations of current laboratory-based methods primarily relate to sample acquisition and preparation which is costly and time-consuming (Ferretto et al., 2014; Vuckovic, 2012; Yang et al., 2019). It is imperative to have rapid detection methods since the levels of contaminants in aquatic environments are dependent on short time-scale events, such as storm/rainfall or leaks (Stedmon et al., 2011a). Fluorescence spectroscopy is a promising analytical method that can be utilized for monitoring surface water quality. Fluorescence-based water quality analysis is typically completed by measuring fluorescence intensities at iterated excitation and emission wavelengths, and data are presented as excitation-emission matrices (EEMs) (Hudson et al., 2007). Several molecules will have distinct fluorescence properties, including optimal excitation and emission wavelengths, allowing them to be uniquely identified in a fluorescence EEM. Furthermore, the intensity of fluorescence emissions is correlated with concentrations of the specific molecules, allowing for the quantification of distinct molecules in a mixture (Murphy et al., 2014a). Data collection using fluorescence methods is relatively easy and straightforward. 2 After development and calibration of a fluorescence-based model with NOM and expected contaminant, estimation of unknown contaminant concentrations can be completed rapidly (5 â 20 minutes). Timing is mostly dependent on the scanning speed of the fluorescence spectrophotometer and data processing time, both of which are typically on the order of minutes. Compared with fluorescence methods, conventional analytical methods such as chromatographic methods require large volumes of water (1â5 L) and possibly several pre-treatment steps, such as extraction, purification or concentration. Therefore, they are time-consuming, taking days to weeks to complete, and cannot be applied for a large number of samples (Ferretto et al., 2014). Previous studies have successfully detected specific types of dissolved organic matter (DOM) to monitor sewage contamination, trihalomethane (THM) precursors to monitor the formation of disinfection by-products (DBP) in drinking water, and predicted the concentrations of ibuprofen, caffeine, lopinavir to monitor pharmaceutical contamination (Henderson et al., 2009; Peleato et al., 2018). Some common aquatic pollutants can also be detected using fluorescence spectroscopy. PAHs and pesticides are among the most common environmental pollutants in aquatic environments and threaten public and environmental health (Yang et al., 2019). Some PAHs and pesticides are toxic, carcinogenic, and can bioaccumulate in the food chain due to the poor biodegradability and recalcitrant characteristics (Abdel-Shafy and Mansour, 2016; Foo and Hameed, 2010). Therefore, PAHs and pesticides are listed as the priority regulated pollutants by various international organizations (Keith, 2014; United States et al.). Previous studies have successfully identified select fluorescing PAHs and pesticides from controlled background solutions (ultrapure water, methanol, and spiked humic substances) using fluorescence techniques (Alostaz et al., 2008; Ferretto et al., 2014). Besides PAHs and pesticides, acid extractable organics in oil sands process-affected waters (OSPW), known as naphthenic acids (NAs), are harmful contaminants that have received increasing attention. NAs are considered the primary toxic compounds in OSPW, and other pollutants, like phenol, contribute to the overall toxicity (Mahaffey and Dubé, 2017). Impact on the environment could occur due to loss of containment, spills, storm events, and other transient events. However, using conventional analytical methods and low-frequency monitoring programs, it can be challenging to catch short-term events, leading to potentially undetected exceedances of regulatory limits. Compared with conventional analytical techniques such as chromatography and mass spectrometry, little sample preparation and rapid acquisition times 3 for EEM-based approaches have the potential to provide timely data for continuously rapid contamination detection (Carstea et al., 2016; Vuckovic, 2012; Yang et al., 2019). Fluorescence techniques are considered to have the potential to rapidly assess prioritized and emerging contaminants and work as an early warning to avoid undetected exceedances of regulatory limits (Bridgeman et al., 2011). However, the high dimensionality of fluorescence excitation-emission matrices (EEMs) is a refractory problem for data handling. Thousands of fluorescence intensities are obtained concurrently to make up an EEM, and the resulting high dimensionality of EEMs complicates the extraction of meaningful information for identifying contaminants. For example, fluorescence signals from background natural organic matter (NOM), including humic-like and protein-like fluorophores, cause spectral overlapping and interaction with the relevant contaminant signals, making interpretation difficult and may overestimate the contaminant concentration (Ferretto et al., 2014). Several dimension reduction techniques have been investigated to simplify and interpret the fluorescence signals. Parallel factors analysis (PARAFAC) and principal component analysis (PCA) are the most popular multiway statistical techniques for dimensionality reduction of spectra (Mirnaghi et al., 2018). Based on the expected linear correlations between fluorescence intensities and concentration of related compounds, dimensionality reduction coupled with linear regression has been successfully used to quantify contaminants in controlled studies (Ferretto et al., 2014; Yao et al., 2011). Common linear regression approaches to get the information out of the fluorescence spectra are multiple linear regression (MLR), principal component regression (PCR), and partial least square regression (PLS). In addition to these linear methods, there are a number of applications of non-linear techniques, including artificial neural networks (ANN), random forest, and other machine learning methods. A remaining challenge of fluorescence applications is the diversity of background water quality in different sources. Frequent sampling and calibration are commonly required to develop specific correlations between relevant contaminant concentration and fluorescence intensities obtained from variable hydraulic conditions, environmental conditions, and background water quality in the different water sources. However, source and contaminant-specific calibration implies a significant burden on data collection and sampling, limiting a wider implementation of fluorescence-based models for rapid and sensitive detection of contaminants in various water sources. Therefore, there is a need to develop data analysis techniques that can facilitate the application of existing fluorescence models to overcome the source variability of background water quality, signal overlapping, and possible peak shifts or distortion on new surface waters. 4 Source transfer methods could help to make fluorescence detection more effective, easier, and less labour-intensive. 1.2 Objectives The focus of this work was to combine machine learning and fluorescence techniques to detect the organic contaminants of interest in surface waters and investigate the method to transfer the existing fluorescence model to other surface waters. The specific objectives of this thesis are listed below: 1) Investigate the application of fluorescence spectroscopy as a monitoring tool for pollution in different source waters under the interference of natural organic matters (NOM). Develop fluorescence detection models for selected contaminants, including polycyclic aromatic hydrocarbons (PAH), pesticides, naphthenic acids (NAs), and phenol in surface waters. Some toxic contaminants in water have received increasing attention in recent years. Fluorescence techniques have advantages to rapidly and even continuously monitor contaminants, compared to the conventional analytical methods and low-frequency monitoring programs. Recent studies have shown fluorescence to provide corresponding feedbacks of some contaminants in controlled background matrices, such as ultrapure water, but with more limited discussion focused on natural water. As such, issues related to the overlapping and interacting signals between NOM and contaminants must be addressed. Several data analysis techniques were investigated in this thesis to interpret the fluorescence EEMs and improve the overall prediction accuracy. PAHs and pesticides can be introduced into water by several means, such as sewer overflows, urban or agriculture run-off during storm events, and chemical spills. NAs and phenol are toxic compounds in OSPW and are a potential source of contamination to the aquatic environment. Different molecules of organic compounds are hypothesized to have distinct fluorescence properties allowing them to be uniquely identified using fluorescence EEMs. Those organic contaminants were selected to investigate the possible applications of fluorescence spectroscopy as a pollution impact monitoring technique. To date, research on this application has relied on the identified wavelength pairs, and little discussion involved the impacts from fluorescence signals of NOM on detection of NAs and phenol. Furthermore, contaminants that fluoresce in similar spectral regions and are 5 highly convoluted with each other such as NAs and phenol, have not been extensively studied. In this thesis, selected contaminants were spiked in different surface waters to explore the applicability and sensitivity of the fluorescence technique to detect target contaminants entered into the water. Furthermore, in addition to linear regression, some non-linear regressors were utilized in Chapter 6 to help account for NOM interference and build robust fluorescence-based detection models. 2) Investigate cross-source fluorescence detection methods to detect contaminants. The challenge with using a fluorescence approach is that the resulting models are source-specific. Every water source has a slightly different composition of organic compounds, which can impact the contaminant signals. The spatial and temporal variability of NOM in surface waters and potential shifts in peak location would require frequent calibration and dictate a need to build site-specific models. However, this implies a significant sampling and calibration burden using standards directly added to the source water being analyzed. In this thesis, a novel application for fluorescence model transfer was developed and described in Chapter 5. Machine learning algorithms were used to addresses the challenge of source-specific models through mapping their similarities. A fluorescence model built with data from one water source was used to learn general features and then was repurposed to a different environment of a second distinct surface water. 6 Chapter 2: Literature Review 2.1 Surface water quality Surface water is critical to the health of local people and aquatic ecosystems (Simeonov et al., 2003). Anthropogenic impacts and natural events such as increased storm events or erosion due to climate changes would cause degradation of surface water (Ouyang et al., 2006). Therefore, it is necessary to have a monitoring program to provide regular assessments of surface water quality. Physical, chemical, and microbiological tests are carried out to ensure the water quality meets the standards. A large number of physicochemical parameters are used to assess the water quality, including five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP) (Wang et al., 2017). High concentration of BOD5 and COD represents a high level of some organic compounds within bodies of water, while a high concentration of TN, TP, and NH3+-N in surface water indicate high nutrients, which may lead to excessive growth of algae and macrophytes (Mouri et al., 2011) The potential risks associated with pollution in drinking water sources is recently gaining attention. Some waterborne diseases became associated with microorganisms from wastewater contaminations, such as unintentional leakage or mixing with drinking water sources (Daneshvar et al., 2012). Parameters such as thermotolerant coliforms, and more recently Escherichia coli are widely recognized as the indicator of fecal contamination in drinking water sources (Daneshvar et al., 2012; Gasser et al., 2010). Apart from microorganisms, the presence of some refractory contaminants in treated wastewater effluents would also pose an unknown threat to surface water and drinking water sources (Gasser et al., 2010). For example, Daneshvar et al. (2012) reported that pharmaceuticals and personal care products (PPCPs), pesticides, and herbicides entering surface water can still be detected in treated drinking waters, indicating low removal rates provided by the available water treatment processes. However, it remains difficult for current water quality analysis techniques to achieve continuous monitoring. Most analysis methods are grab samples followed by laboratory-based analysis, such as chromatography and mass spectrometry (Ferretto et al., 2014). Limitations of traditional water quality monitoring techniques are related to the costly and time-consuming acquisition and sample preparation processes (Ferretto et al., 2014; Vuckovic, 2012; Yang et al., 2019). The expected monitoring programs need to be capable of handling a large number of 7 samples in continuous monitoring, dictating rapid measurement and quick analysis within hours or even less. 2.2 Fluorescence spectroscopy 2.2.1 Fluorescence theory Optical methods, such as fluorescence, commonly characterize compounds relying on measuring the absorption and possibly emission of energy by the molecules. When a substance receives an external source of light, a photon is absorbed by the molecule. The electron from the singlet ground (S0) is promoted to the upper excited singlet state (i.e. electronic singlet states S1, S2), and the excited electron is paired to an electron in the ground state. The fluorescence emission occurs when the electron in the excited state returns to the ground state and emits a photon, which happens quickly and is typically within 10 ns (10 x 10-9) from excitation (Lakowicz, 2006). Although fluorescence is a reversible process, a portion of the energy is lost in non-radiative decay between excitation and emission. Therefore, the emitted light is always red-shifted compared to the excitation light, and the incident excitation typically has shorter wavelengths than the emission, known as Stokeâs Shift (Hudson et al., 2007). Furthermore, Stokeâs shifts can be caused or influenced by the molecular environment, such as the polarity of solvents, other excited-state reactions, complex formation, and/or energy transfer to other molecules (Lakowicz, 2006). The emission spectrum is measured when the photon is emitted across a wide range of wavelengths upon fixed excitation wavelength. Fluorescence excitation excitation-emission matrices (EEMs) will occur when measuring fluorescence intensities at iterated excitation and emission wavelengths. Compounds with aromatic rings, conjugated double bonds, or rigid structures have higher probabilities of fluorescing due to the electron structures. Other factors such as molecular weight and functional groups in the compounds would also contribute to fluorescence properties (Bridgeman et al., 2011). The EEM can be thought of as a âfingerprintâ, displaying peak locations and intensities that can be used to compare molecular composition and quantification. For example, Figure 1 shows an example of fluorescence EEMs of one water sample collected from Lake Ontario, Ontario, Canada. The location of any given peak indicates the type of molecule (fluorophore) emitting the fluorescence, while the peak intensities of fluorophores represent concentrations of corresponding compounds in the sample. For example, the peak at approximately ex/em 310 nm/ 425 nm can be cross-referenced with previous studies and standard compounds in order to identify a specific type of compound present in the sample 8 (humic-like material). Although fluorescence EEMs can provide detailed fingerprint of water quality, it is still difficult to estimate exact fluorophore concentrations. The challenges come from many factors, such as interference with additional compounds, quenching and other factors influencing fluorescence yield (Hudson et al., 2007). Figure 2.1 An example of fluorescence EEMs collected from Lake Ontario 2.2.1.1 Matrix effects There are several factors referred to as matrix effects impacting fluorescence that do not originate from the analytes themselves. The typical matrix effects include inner filtering effects (IFE), fluorescence quenching, changes in pH, changes in temperature, and metal ions (Henderson et al., 2009). Inner filtering effect is the most common matrix effect caused by the sample matrix, where molecules in the sample can absorb the incident or excitation light and emitted radiation resulting in an apparent decrease in fluorescence intensity and shape distortion (Figure 2.1) (Henderson et al., 2009). Primary inner filtering decreases the excitation light source and second inner filtering reabsorb the emitted photon after fluorescence (Kothawala et al., 2013). Especially for samples with high organic matter content, both primary inner filtering and secondary inner filtering (reabsorption) should be accounted for in the fluorescence analysis. Without inner filter corrections, the relationship between concentration and fluorescence intensity would no longer be linear and would not conform to the Beer-Lambert Law. Some researchers believed that the Excitation (nm)Emission (nm) 200 250 300 350250300350400450500550600Intensity0501001502002503009 inner filtering effects only need to be accounted for in samples with high optical densities. However, some degree of IFE occurs as long as the fluorophores are measurable (Murphy et al., 2013). It may be possible for fluorophores with high quantum yields (having high efficiency to emit the incident radiation) to reduce the IFE by keeping concentrations low (Murphy et al., 2013). Figure 2.2 Diagram of a cuvette from the top showing incident light (I0), tramsmitted light (I), and the fluorescent radiation at 90 degrees. Primary and secondary inner-filter effects are shown with bule rhombus and yellow rounds To correct the fluorescence intensity affected by IFE, sample dilution has been applied to reduce the absorption in the sample matrix (Hudson et al., 2007). Sample dilution is recommended for samples with high fluorescence intensities (>c.400 IU) or a TOC concentration higher than 25 mg/L to negate the IFE on fluorescence (Hudson et al., 2008). The laboratory distilled water is used to dilute the sample until the intensities are measurable. Another method that is simpler and more popular is the absorbance based correction approach. Using two parameters: excitation (ð´ð´ðððððð) and emission (ð´ð´ðððððð) absorbance a matrix of correction factors can be calculated (Lakowicz, 2013). 10 2.2.1.2 Scattering effects There are three types of light scattering that can affect fluorescence EEMs: Rayleigh, Tyndall and Raman scattering (Zepp et al., 2004). Rayleigh scattering is caused by particles or interaction between an incident photon and molecules smaller than the wavelength of the light (Hudson et al., 2007). The intensity of Rayleigh scattering is approximately in inverse proportion to the fourth power of the incident photon wavelength (Zepp et al., 2004). Rayleigh scattering can be categorized as first order or second order. First order Rayleigh scattering emission peaks appear at the wavelength of the incident wavelength, and second order Rayleigh scattering emission is twice the wavelength of incident wavelength. Tyndall scattering originates from reflection of incident photon from particulate matter in the samples where the peaks are at the same wavelength as the excitation wavelength. Raman scattering is much less intensive than Rayleigh scattering and is primarily caused by water molecules in the context of water samples. While Rayleigh scattering originates from direct scattering of the incident light, Raman scattering is due to non-elastic scatter at a higher wavelength than the incident light (Lawaetz and Stedmon, 2009). Energy loss (i.e. increasing of emission wavelength) from Raman scattering proportional to the excitation wavelength (Lakowicz, 2013). Because both of the Rayleigh scattering types do not represent the features of organic matters in the sample and have the potential to interfere with organic fluorophores in EEMs, they are removed in most cases prior to EEM analysis (Bahram et al., 2006); Søndergaard et al. (2003)). Similar to Rayleigh scattering, Raman scattering lines are removed when carrying out water quality analysis to limit interference. 2.2.2 Fluorophores relevant to water quality 2.2.2.1 Natural organic matter (NOM) in surface water Several previous studies have investigated the potential of fluorescence spectroscopy as a monitoring tool for organic matter from various sources in a range of aquatic systems. The majority of studies have focused on characterizing and describing the changes to dissolved organic matter (DOM) (Hudson et al., 2007). Some natural DOM in the aquatic system is created in situ in the water body (autochthonous), and some others is transported from outside sources (allochthonous) (Wang et al., 2017). Autochthonous DOM is commonly created from polymerization and degradation of existing DOM or is generated by microbial activity. Allochthonous DOM is formed outside the water body (e.g. in soil, on forest floors, etc.) and transported to the water body 11 through direct discharge or surface runoff (Hudson et al., 2007; McDowell and Likens, 1988). Due to the varied origin and extent of decomposition, natural DOM is a complex heterogeneous mixture in water. The surrounding environment and location conditions influence the composition and concentration, including soil type, catchment, vegetation, and climate. Fluorescence has been extensively used to characterize DOM in a wide range of natural environments. Based on the locations of DOM fluorophores in the spectra, individual compound types can be identified, such as humic-like and protein-like fractions (Carstea, 2012; Li et al., 2020b; Murphy et al., 2011). Previous studies have defined some specific regions or peaks: two humic-like peaks (λex/λem = 350/420â480 nm, or λex/λem =260/380â460 nm), and two protein-like peaks including âtryptophan-likeâ (λex/λem = 275/340 nm) and âtyrosine-likeâ (λex/λem = 275/310 nm) (Coble, 1996). Numerous studies have proven that the information provided by fluorescence techniques allows characteristics of DOM and aquatic systems. For example, âPeak Câ fluorescence intensity in humic-like regions (λex: 300 â 360 nm, λem: 400 â 480 nm) correlated well with total organic carbon, showing a linear relationship with compounds with aromatic structures (McKnight et al., 2001) and were also found to relate with organic matter content (Stewart and Wetzel, 1981). Peak T or tryptophan-like fluorescence has been found to have strong relationships with biological oxygen demand (BOD) (Bieroza et al., 2009). Shutova et al. (2014) found correlations between OM removal and fluorescence component ratios to characterize DOM changes and treatability in drinking water treatment plants (WTPs). 2.2.2.2 Environmental organic contaminants in surface water In addition to the humic substances and proteins that are naturally present in the water sources, other common aquatic pollutants can also be detected by fluorescence spectroscopy. According to the fluorescence signatures from those environmental contaminants from wastewaters that are distinct from unimpacted surface waters, fluorescence measurements could provide useful surrogate parameters to track, characterize, and quantitate their behavior in the impacted water system. For example, most petroleum products contain aromatic hydrocarbon structures and have been reported to exhibit strong fluorescence properties due to the unpaired electrons (Mirnaghi et al., 2018; Riley et al., 2018). Monoaromatic compounds such as benzene, toluene, xylene, and phenols are reported to have emission wavelengths around 250 â 320 nm. Two and three aromatic rings (naphthalene and phenanthrenes) have emission peaks between 310 â 355 nm (Carstea, 2012). Yang et al. (2019) and Ferretto et al. (2014) successfully identified and quantified polycyclic aromatic hydrocarbons (PAHs), pesticides, and environmental hormones in 12 surface water, which are toxic and persist in the environment (soil, surface water, groundwater). Emerging organic compounds (EOCs) including pharmaceuticals (i.e., ibuprofen, carbamazepine, diclofenac, sulfamethoxazole, and trimethoprim), a commonly used insect repellant (DEET), caffeine and sucralose were all successfully identified and showed good correlations with florescence indexes obtained for variable hydraulic conditions (Sgroi et al., 2017b). Several other studies have also demonstrated successful use of appropriate fluorescence indexes based on prior defined excitation/emission wavelengths to detect and differentiate in real-time the presence of contaminants released from wastewater discharge or monitor their removal in wastewater treatment plants (Sgroi et al., 2017a; Wasswa et al., 2019). 2.3 Fluorescence data analysis 2.3.1 Data pre-processing Fluorescence spectroscopy is an attractive method because of the easy sample preparation and straightforward data collection process. Pre-processing and correction of fluorescence data is recommended to eliminate artifacts (bias and error) from a range of sources, including the sample constituents (concentration and matrix effects) and different instruments or measurement settings (Murphy et al., 2010). Generally, data pre-processing involves absorption based IFE correction, scattering masking, and intensity normalization. Inner filtering effect correction refers to the process of correcting any possible IFE due to the absorption of excitation or emission light within the cuvette. The absorbance-based approach (ABA) (Lakowicz, 2013) is the most commonly applied method and provides adequate correction without prior dilution for most natural waters (Kothawala et al., 2013). Corrected intensities (ð¹ð¹ðððððððððððððð ) at any given excitation and emission pair (ðððððð, ðððððð) can be calculated using eq. 2.1. ð´ð´ðððððð and ð´ð´ðððððð are the absorbance at each pair of excitation and emission wavelengths. As conveyed by Lakowicz (2013), the sum of ð´ð´ðððððð and ð´ð´ðððððð is the measured absorbance at any point in the EEM. Therefore, the ð´ð´ðððððð and ð´ð´ðððððð multiplied by 0.5 was assumed to represent the absorbance and fluorescence occurring at the mid-point for intensity correction. ð¹ð¹ðððððððððððððð = ð¹ð¹ðððððððððððð Ã 10(0.5ï¿½ð´ð´ðððððð+ð´ð´ððððððï¿½) (eq.2.1) As introduced in Chapter 2.2, scattering does not represent features about compounds of interest and is caused by the interference between incident light and the solvent. In the EEMs, 13 the scattering appears as diagonal lines of intensity at points where the emission wavelength is equal to the excitation wavelength or is twice the excitation wavelength (Figure 2.2). Based on the fact that the spectral bandpass is approximately 5 nm, commonly 10 â 15 nm scattering regions above and below the peak emission would be removed or replaced with missing data flags from EEMs. Removing the whole regions where ðððððð < ðððððð can also eliminate scattering effects and speed up the statistical analysis of EEMs. Figure 2.3 A sketch of the scattering occurring in a fluorescence EEM Since optical components, detectors, and settings vary between labs or experiments, and influence the intensities measured, it is necessary to normalize the fluorescence EEMs to a standard for easier comparison. To deal with this issue, Lawaetz and Stedmon (2009) reported that Raman scattering was consistently observed in water and could therefore be used for intensity calibration in aqueous samples. The integral of Raman scattering peaks (ð´ð´ðððð) in pure water is used to correct the fluorescence intensity (ð¼ð¼ðððððð,ðððððð(ð´ð´.ðð. )) to the same scale of Raman units (ð¹ð¹ðððððð,ðððððð(ð ð .ðð. )). The equation used to calculate the Raman scattering peak position (ðððððð) is listed as following, where ðððððð = 350 nm as the Raman peak follow the 350 nm excitation in pure water. ðððððð (ðððð) = 1 Ã 107 ï¿½1 Ã 107ðððððð â 3400ï¿½â1 (eq.2.2) Rayleigh Scatter 14 ð´ð´ðððððððððð = ï¿½ ð¼ð¼ðððððððððððð2ðððððð1dðððððð (eq.2.3) ð¹ð¹ðððððð,ðððððð(ð ð .ðð. ) = ð¹ð¹ðððððð,ðððððð(ð´ð´.ðð. )ð´ð´ðððð (eq.2.4) 2.3.2 Spectral decomposition Fluorescence EEMs are high-dimensional (i.e. potentially thousands of excitation/emission pairs) and present a significant analysis challenge. Therefore, to facilitate interpretation and use in subsequent analysis or models, a wide range of approaches dimensionality reduction methods have been applied for EEM analysis. Generally, the predominant EEM interpretation methods started from the two simplest analytic methods: peak picking and fluorescence regional integration (FRI) (Li et al., 2020b). Both of the two methods only make use of partial data to extract information from fluorescence spectra. Peak picking analysis selects the peak excitation and emission locations identified in previous studies that typically use standards in pure water. FRI is similar and is calculated from the volume contained by fluorescence peaks (Chen et al., 2003; Coble, 1996). More recently, studies have turned to algorithm-based methods to translate EEMs from high dimensions to lower dimensions to better understand the information provided in fluorescence spectra. Parallel factor analysis (PARAFAC) is the predominant method, with some application of principal component analysis (PCA) (Bridgeman et al., 2011). These methods can provide not only more detailed qualitative analysis but also semiquantitative analysis. PCA decomposes the high-dimensional data sets into fewer uncorrelated principal components by identifying the directions of maximum variance (Peiris et al., 2010). In this way, the data can be simplified by PCA to a manageable level while preserving the original features to account for much of the total variance (Persson and Wedborg, 2001). Compared with PCA, PARAFAC can decompose fluorescence data into trilinear components. It is assumed that fluorescence data should be trilinear (i.e. linear relationships of intensity with excitation wavelength, emission wavelength, and concentration). As such, PARAFAC has been widely applied to decompose EEMs into individual fluorophores. 2.3.2.1 Peak-picking Manually choosing the intensities at peak excitation and emission wavelengths is the most straightforward method to determine fluorescence features from large data sets of EEMs. Due to 15 differences in molecular structures, each organic fluorophore has a unique absorbance and emission response (Li et al., 2020a). However, similar fluorescence signals can be observed from distinct molecules, limiting the ability of peak-picking approaches to identify and capture the properties of individual fluorophores confidently. Similar fluorescence signals can also impact other analysis methods such as PARAFAC (Murphy et al., 2011). Overlapping signals with background NOM would also cause the correlation between peak fluorescence intensities and concentration levels to deviate from linearity (Yu et al., 2015). NOM in natural waters, such as humic substances, is a primary influencing factor hindering the detection of contaminants. Humic substances have strong fluorescence signals (Blough and Del Vecchio, 2002) and can be easily interacted with or bound with organic contaminant fluorescence signals (Akkanen et al., 2012). The uncertainty of both specific peak excitation and emission wavelengths decreases the ability of the peak-picking method to be used as a metric for identifying and detecting changes in fluorophores to monitor contaminants of interest (Korak et al., 2014; Murphy et al., 2011). Correctly determining peak locations is essential for interpreting EEMs to identify compositional differences or develop composition-specific quantification relationships. Therefore, in peak-picking analysis, special care needs to be taken in selecting peak positions, which can be challenging when adjacent to more dominant neighbouring fluorophores or intermolecular interactions (Korak et al., 2014). 2.3.2.2 PARAFAC Parallel factor analysis (PARAFAC) is a trilinear decomposition method applied to fluorescence EEMs using the eq.(2.5). ð¥ð¥ðððððð is the measured fluorescence corresponding to the ððð¡ð¡â samples at ððð¡ð¡â emission wavelength and ððð¡ð¡â excitation wavelength. The parameter ðð-value represents the score for each sample decomposed based on the loadings ðð-value and ðð-value. The value of parameter ðð is directly proportional to the concentration of the ððð¡ð¡â PARAFAC component in the sample ðð , while the value of loadings ðð, ðð is the scaled estimate of emission and excitation spectrum of the ððð¡ð¡â PARAFAC component. ðððððððð represents the model residual. ð¥ð¥ðððððð = ï¿½ðððððððððððððððððð + ððððððððð¹ð¹ðð=1 (eq.2.5) Where ðð = 1, â¦ , ð¼ð¼; ðð = 1, â¦ , ð½ð½; ðð = 1, . . . ,ð¾ð¾ 16 ðð, ðð and ðð are the sample number, emission and excitation wavelength ðð is the decomposed PARAFAC component (total of F components). A successfully PARAFAC model is assumed to have emission spectra invariant across excitation wavelengths and excitation spectra invariant across emission wavelengths. After linear superposition of fluorophores, the total signal is supposed to equal the observed total fluorescence EEM (Murphy et al., 2013). Therefore, PARAFAC does not have problems of rotational ambiguity after the model is successfully validated with a correct number of components, giving the results both mathematically and chemically meanings (Murphy et al., 2013). 2.3.2.3 PCA Principal component analysis (PCA) is a multivariate analysis method that is widely used to interpret fluorescence EEMs to lower dimensions and reduce the number of variables. Several independent variables decomposed from fluorescence EEMs, referred to as principal components (PCs), can substitute the original dataset and account for a large proportion of the total variance (Murphy et al., 2014a). The results of the decomposition of the data matrix X from PCA are shown in Eq. (2.6), usually given as outer products of score vectors ð¡ð¡ðð, loading vectors ðððð and a noise residual matrix ðð (Geladi et al., 1989). Loading vectors indicate the relative importance of excitation-emission wavelength combinations for specific principal components. Scores are analogous to concentrations of the relative importance of each principal component to a specific sample. ðð = ï¿½ð¡ð¡ðððððð + ðððððð=1 (eq.2.6) The loading vectors ðððð identify the spectral regions represented by the PCA components (i.e. PCs). The score vectors ð¡ð¡ðð can be interpreted as a data transformation for the projection of the fluorescence data matrix onto a lower-dimensional feature space spanned by the PCA components. Fluorescence spectral variables (EEMs) in the original space in X matrix transformed to PCA components in the PCA space are expressed by the loading vectors. In the PCA loading plots for EEMs, the loadings show the relative importance of excitation-emission wavelength combinations for the model. By examining the loading plots for each PCA component, the dominant fluorophores in the X matrix can be identified. 17 2.3.3 Regression analysis Fluorescence intensity is assumed to have a linear relationship with concentrations of the fluorophores (Korak et al., 2014). As such, to predict the concentration level of contaminants, linear regression has been widely applied as a straightforward tool to find the correlation between specific fluorescence features and pollutants. A quantitative response Y is predicted based on a group of predictors (ðð1,ðð2,ðð3, â¦ ,ðððð ), which is supposed to have an approximately linear relationship between them. However, some researchers were suspicious of whether a linear relationship between concentration and fluorescence intensity is sufficient to explain the complex fluorescence mechanics and irregular phenomena in natural water. For example, Korak et al. (2014) found the linear relationship between fluorescence and concentration is only valid across a narrow concentration range and is composition-specific, which can also easily be influenced by non-ideal intermolecular interactions among compositions in water. Recently, neural networks and machine learning concepts have been introduced to the field, reflecting new advances in 3D fluorescence spectroscopy (Cuss et al., 2016; Muchlinski et al., 2017; Wang et al., 2016). By analyzing the underlying relationship between fluorescence spectra and organic contaminants of interest, these regression models allow using the nonlinear relationship to expose latent correlations for the accurate prediction of organic pollutants in water. 2.4 Research gaps 2.4.1 Previous work Almost three decades ago, Reynolds (1995) and Tartakovsky (1996) demonstrated the possibilities of using fluorescence spectroscopy for both offline and online monitoring of DOM during wastewater treatment. Previous research focused on implementing fluorescence EEMs for DOM characterization and process management in WTP. It is now of ongoing interest for tracking water contaminants for source water control using fluorescence techniques (Wasswa et al., 2019). The potential for EEM analysis to be easily adapted for rapid detection presents a promising method for monitoring quantitative and qualitative changes in contaminants, and therefore, acts as an early warning for contamination events. Some researchers have investigated fluorescence spectroscopy as a rapid detection for monitoring contaminants as an early warning for oil spills, sewer overflows, wastewater impacts, and urban or agriculture run-off during storm events (Ferretto et al., 2014; Mirnaghi et al., 2018; Yang et al., 2019). Peak-picking and PARAFAC are 18 common techniques for dimensionality reduction for simplifying datasets, and especially the latter method can further give chemically meaningful results. 2.4.2 Research challenges The use of PARAFAC has improved contaminant detection and improved the interpretability of component signals (Yang et al., 2019). However, the dimensionality reduction process via PARAFAC is time-consuming and commonly requires days to weeks to be developed by skilled researchers due to the method's sensitivity to outliers, non-linear effects, and computational complexity. Furthermore, potential impacts of environmental conditions such as pH or temperature on organic fluorescence may invalidate the assumption of a linear relationship between fluorophore concentrations and fluorescence intensities. As such, although it may reduce detection accuracy, many studies have recommended peak-picking for real-time fluorescence analysis (Carstea et al., 2016; Yu et al., 2015). Some studies have examined other dimensionality reduction techniques, such as PCA and autoencoder neural networks. PCA is similar to PARAFAC bye linearly transforming the original date by projecting it onto a lower-dimensional hyperplane. However, PCA is much faster and computationally cheaper. Autoencoders allow for non-linear transformations, which could possibly result in a more accurate reconstruction of the dataset. However, autoencoders have seen very limited use in EEM analysis (Peleato et al., 2018). Although PCA and autoencoders cannot provide a direct chemical interpretation, as from PARAFAC analysis, they are less constrained and can potentially represent a greater degree of variance within the dataset. Furthermore, they also show the potential to provide more rapid and user-friendly analysis. Recently machine learning approaches such as random forests and neural network (NN) are gaining traction and show superior means to utilize features identified by dimensionality reduction techniques (Cuss et al., 2016; Muchlinski et al., 2017; Wang et al., 2016). Furthermore, there has been increasing discussion about using machine learning algorithms coupled with unprocessed EEMs without the use of a dimensionality reduction process (Boulesteix et al., 2012; Peleato et al., 2018). Thus, machine learning methods present an opportunity to simplify developing fluorescence detection models for contaminants in the water and improve the feasibility of implementing fluorescence techniques. Fluorescence models developed based on one water source often have poor performance when directly applied to new surface water due to variation in background water composition, 19 especially natural organic matter (NOM) (Carstea et al., 2016; Yu et al., 2015). The resulting models are site-specific and require frequent calibration to address spatial and temporal variability in water quality. However, frequent calibration requirements imply significant sampling and lab analysis of contaminants to ensure the validity of fluorescence detection models. Data-driven model transfer or domain adaptation approaches are promising methods to address the challenge of site-specific models. In general, domain adaptation aims to utilize labelled data from a source domain to develop models on a study or target domain where only unlabeled data is available (Ganin and Lempitsky, 2015; Pan et al., 2010). This method could decrease costly annotation for fluorescence EEMs to calibrate a fluorescence model to a new source. For example, an existing and pre-trained fluorescence model built with one water source could be used as the starting point to learn general features and then be repurposed to a different environment. 20 Chapter 3: Materials and Methods 3.1 Selection of contaminants In Chapter 4, polycyclic aromatic hydrocarbons (PAHs) and pesticides were chosen as the common aquatic pollutants to investigate the applicability of fluorescence spectroscopy for rapid water quality assessment in cross-source waters. Some PAHs and pesticides are toxic, carcinogenic, and persist in the food chain, many of which are regulated but not monitored regularly in water systems (Akkanen et al., 2012; Ferretto et al., 2014; Yang et al., 2019). They can enter surface waters from anthropogenic points and non-point emissions, resulting in adverse environmental and public health effects. The PAHs used in this study included pyrene, fluoranthene and naphthalene. Carbaryl and thiabendazole were selected according to their wide usage as insecticide and fungicide. According to the occurrence and concentration of PAHs and pesticides in tributaries to the Great Lakes, PAHs and pesticides were mixed in the range from 0.1 to 7 µg/L spiked in natural water samples conformed to the low µg/L concentration level reported in previous studies (Baldwin et al., 2016; Yang et al., 2019). In addition to PAHs and pesticides, naphthenic acids (NAs) and phenol were also studied in Chapter 5 of this thesis. NAs and phenol are constituents of oil sands process-affected waters (OSPW) produced from the bitumen extraction in the oil industry (Marentette et al., 2015). NAs are considered the primary toxic compounds in OSPW and can be persistent in the environment. Phenol also contributes to the overall toxicity. Due to the toxicity and unknown compositions in OSPW, these waters are retained on-site in the tailing ponds rather than released to receiving waters (Grewer et al., 2010), which increases the risk to nearby water systems. Recent research has provided evidence of migration of OWSP and enter into surface water and groundwater nearby (Frank et al., 2014). NAs and phenol were also selected since relatively few studies used fluorescence to detect these contaminants, and their fluorescence signals are highly convoluted. As such, they present a significant challenge to detection and source-transfer methods. 3.2 Fluorescence pre-processing As identified in the literature review, fluorescence data needs to be corrected and pre-processed prior to use in contaminant detection methods. Recommended practices were carried out, including blank-subtraction, inner-filter effect correction, and intensity standardization to water-Raman scattering intensity. The detailed pre-processing steps prior to analysis are shown in Table 3.1. 21 Table 3.1 Pre-processing steps to correct the fluorescence EEMs Pre-processing Steps 1. Measure EEMs of a blank sample using Milli-Q® water (ð¹ð¹ðððððððððððððð ) 2. Subtract EEMs of the blank from the measured EEMs for each sample (ð¹ð¹ðððððððððððððð ) ð¹ð¹ððððððððððððð ð = ð¹ð¹ðððððððððððððð â ð¹ð¹ðððððððððððððð 3. Correct the EEMs data for inner-filter effects (IFE). ð´ð´ðððððð and ð´ð´ðððððð are the absorbance at each pair of excitation and emission wavelengths. ð¹ð¹ðððððððððððððð = ð¹ð¹ððððððððððððð ð Ã 10(0.5ï¿½ð´ð´ðððððð+ð´ð´ððððððï¿½) 4. Standardization and normalization to water-Raman scattering intensity The integral of Raman scattering peak (ð´ð´ðððð) was generated from the built-in function of Aqualog® spectrometer, calculating by summing the intensity at every wavelength below the Raman peak using Milli-Q® or ultrapure water. Location of Raman peak (an excitation wavelength of 350 nm) : ðððððððððððð,ðððð (ðððð) = 1 Ã 107 ï¿½1 Ã 107ðððððð â 3400ï¿½â1 The integral of Raman scattering peaks used to correct the fluorescence intensity to the same scale of Raman units: ð´ð´ðððððððððð = ï¿½ ð¼ð¼ðððððððððððð2 =416ðððððð1 =378dðððððð ð¹ð¹ðððððð,ðððððð(ð ð .ðð. ) = ð¼ð¼ðððððð,ðððððð(ð´ð´.ðð. )ð´ð´ðððð 22 The EEMs of blank in step 1 and the integral of Raman peak in step 4 were measured each day to minimize the instrumental variability. Furthermore, an excitation wavelength of 350 nm was used as a relatively broad fixed band to calculate the location of Raman peak for the integral of scattering peak (ð´ð´ðððð). This Raman peak resulting from excitation at a wavelength of 350 nm was also suggested for signal-to-noise determinations and included with the range of many fluorescence spectrophotometers. As such, the location of emission Raman peak was calculated at 396 nm, and the fluorescence signal of Raman scattering spanned from 378 nm to 416 nm. ð´ð´ðððð was determined daily by calculating the area under the Raman peak, which was then used to normalize the measured fluorescence of a sample at any wavelength into Raman Units (R.U.). Raman intensity normalization could help normalize the measured EEMs in a universal scale that should be independent of instrumental parameters and easy to compare when samples were measured in different time and locations (Lawaetz and Stedmon, 2009). 3.3 Fluorescence dimensionality reduction Dimensionality reduction refers to the process of interpreting the EEMs to remove the irrelevant and redundant variables to find the underlying components of fluorophores. In addition to simplifying the data, dimensionality reduction could help feature selection and address the signal interference from background fluorescence from natural organic matters (NOM) in water sources. Itâs often an important step as some other fluorescent components in the background water quality, such as humic-like or protein-like compounds, often interact and overlap with fluorescence signals resulted from the contaminants of interest in the water sample. The advantages of this process include improving the prediction, reducing data complexity, reducing training time, and helping to interpret the spectrum for a better understanding of the underlying fluorophores. 3.3.1 Parallel factor analysis (PARAFAC) PARAFAC analysis was conducted using the drEEM toolbox provided by Murphy et al. (2013). PARAFAC belongs to multi-way methods and allows decomposition of three-way EEMs (sample Ã excitation wavelength Ã emission wavelength) into a set of trilinear terms and a residual array (Murphy et al., 2014a). There are proportional relationships between ðððððð and the concentration, ðððððð and the emission spectrum, and ðððððð with the absorption coefficients of the ððð¡ð¡â analyte in sample ðð. 23 ð¥ð¥ðððððð = ï¿½ðððððððððððððððððð + ððððððððð¹ð¹ðð=1 (eq.3.1) Where ðð = 1, â¦ , ð¼ð¼; ðð = 1, â¦ , ð½ð½; ðð = 1, . . . ,ð¾ð¾ ðð, ðð and ðð are the sample number, emission and excitation wavelength ðð is the decomposed PARAFAC component (total of F components). The typical steps to generate a valid PARAFAC model showed in Table 3.2, which could be roughly classified into 5 parts: 1) assemble the dataset, 2) preprocess including scatter and normalization, 3) random-initialization, 4) validation and 5) interpretation. Table 3.2 Schematic of the steps to build a valid PARAFAC model for EEMs analysis Steps involved in PARAFAC analysis 1. Load all the sample EEMs in subfolder in a .csv format with emission units in the first column, excitation units in the first row with each EEM row-wise pasted together 2. Preprocessing the dataset with drEEM Resize the dataset to remove possible noisy spectrum using subdataset() Remove Rayleigh and Raman scattering using smootheem() Determine outlier samples using outliertest() based on sample leverage and remove them 3. Random-initialization 4. Split half validation Split the dataset into smaller subsets, often 4 different piles and make 6 different combinations of 2 piles then compare models made for those different datasets Eliminating non-trilinear data within EEMs is required before constructing the preliminary model and successfully validated the PARAFAC model. Scatterings resulted from the interference between incident light and the solvent, including Rayleigh and Raman scattering do not conform 24 to the trilinear assumption of PARAFAC and can hinder the modelling. In this study, the selected contaminants were dissolved in methanol to ensure solubility when mixed with the unfiltered natural water samples, which significantly increased the scatterings measured with sample EEMs. As such, the suggested method to reduce the Raman scattering regions by blank subtraction of pure water (i.e. Milli-Q® water) from the measured sample might not work very well. Other typical solutions for minimizing the influence include remove 10 â 15 nm scattering regions above and below the scatter peaks or excise those regions by either missing data or the interpolating value according to the adjacent regions (Murphy et al., 2013). The function smootheem() in drEEM toolbox is designed for independently dealing with each of the primary and secondary Rayleigh and Raman scatterings. ðððð = ððððð ð ð ð ð¡ð¡âðððððð(ððððððððð¡ð¡,ð ð ððð ð 1 = [ððððð ð ðððð ððððððð ð ðð],ð ð ðððð1 = [ððððð ð ðððð ððððððð ð ðð],ð ð ððð ð 2= [ððððð ð ðððð ððððððð ð ðð],ð ð ðððð2 = [ððððð ð ðððð ððððððð ð ðð], [ððððððð¡ð¡ðððð1 ððððððð¡ð¡ðððð2 ððððððð¡ð¡ðððð3 ððððððð¡ð¡ðððð4]); Where ð ð ððð ð 1,ð ð ððð ð 2,ð ð ðððð1,ð ð ðððð2 are the first, second order Rayleigh regions, and the first, second order Raman regions. The values within [ððððð ð ðððð ððððððð ð ðð] represent the regions above and below the centered scattering emission wavelength to remove or to interpolate. [ððððððð¡ð¡ðððð1 ððððððð¡ð¡ðððð2 ððððððð¡ð¡ðððð3 ððððððð¡ð¡ðððð4] means to turn interpolation on/off for [ð ð ððð ð 1,ð ð ððð ð 2,ð ð ðððð1,ð ð ðððð2] by using 1=on, 0=off. Sometimes, special care needs to be taken to deal with the scattering effects in order to avoid hamper PARAFAC modelling. For example, in Chapter 5 the fluorescence signals of phenol in the mixtures were close to the first order Rayleigh scatter and were impacted by the first order Raman scatter. Furthermore, phenolâs fluorophore interacted and overlapped with the signals from NAs, making it vulnerable and easily affected by scattering effects during the decomposition in PARAFAC analysis. Because the poorly separated fluorophores may negatively influence all other components, the incompletely separated fluorophore of phenol may also result in poor representation of NAs. Figure 3.1 showed ± 15, ± 15, ± 20 and ± 20 nm were used for first and secondary Rayleigh and Raman scatter. Rayleigh bands were replaced by a band of missing values, while Raman bands were interpolated with adjacent values to reduce information loss. Care must be taken to deal with the signals bordering interpolated bands because inappropriate interpolation might cause narrow peaks due to fluorophores or residual scatter to be broader. For 25 example, the displayed plots of sample EEMs showed evident residuals of scatter or artifacts due to interpolation cross the edges of the scatter band after applying the above steps. The scatter peaks that were not completely removed during model training led to poorly defined preliminary loadings for each component, as shown in Figure 3.2. Most results did not conform to the âstoke shiftâ phenomenon that the emission spectrum should have a red shift relative to its excitation spectrum due to the energy loss in the excited state. Therefore, decomposed loadings lacked characteristics of the assumption that fluorophores would be independent and non-interacting. Function parameters, such as the width determined for scatter regions and whether to interpolate with adjacent values or directly excise, should be adjusted for each of the first and second order Rayleigh and Raman scatter. Large widths will cause information loss and uncertainty in the removed or interpolated area, whereas narrow widths will result in the inclusion of residual scatter in the model. Those parameters should be carefully considered and treated well to improve modelling. Figure 3.1 Visualize EEMs plots of first 9 samples for dataset ± 15, ± 15, ± 20 and ± 20 nm used for first and secondary Rayleigh and Raman scatter Residuals or artifacts 26 Figure 3.2 Spectral loadings of components during exploratory phase for dataset ± 15 nm, ± 15 nm, ± 20 nm and ± 20 nm band removed to account for first and secondary Rayleigh and Raman scatter, respectively. Identifying and removing outliers that might be unrepresentative or poor quality data is also suggested during PARAFAC modelling to obtain the best possible dataset. Outliers can be present due to several reasons including sampling or analytical errors. Function outliertest() was applied to examine each sample to determine outliers according to their leverage values as shown in Figure 3.3. The higher the leverage value is, the more different it is compared to other samples. It may need to repeat several times until the optimal loadings, number of components, and preliminary developed model are appropriate for validation. Under-specific or over-specific models could all cause failure in obtaining the valid chemical interpretation since the wrong number of components could not one-to-one represent the water compositions in the sample. After determining the number of components included in the model, the next step is random-initialization and split-half validation to build the final PARAFAC model. Different random initializations were conducted to choose the run with the lowest error or reconstruction. Split-half validation was used to verify that the established PARAFAC model was appropriate and robust through obtaining identical models from all the subdataset. The common approach taken in 27 PARAFAC analysis is to split the whole dataset into 4 piles named A, B, C and D, and then combine two piles to compare against each other, which is âS4C6T3â (Splits: 4, Combinations: 6, Tests: 3). The 4 splits are A, B, C, and D, 6 combinations for 3 tests are AB vs. CD, AC vs. BD, and AD vs. BC. The established PARAFAC model was only considered valid when the loadings were matched for all the subdatasets (Figure 3.4), and Figure 3.5 shows the loading plots of PARAFAC components for the Lake Okanagan. Figure 3.3 Example of likely outliers (sample 61) according to their score values from a PARAFAC model Figure 3.4 Example loadings plot for a successful split-half validation 28 Figure 3.5 Loading plots of PARAFAC components for the Lake Okanagan (C1-4) 3.3.2 Principal component analysis (PCA) Table 3.3. Algorithm for PCA in dimensionality reduction of sample EEMs Parameters in PCA pca = PCA(n_components=n_components, # define number of components to keep svd_solver='randomized', # input shape is larger than 200Ã 49956, and number is set to be 4, 'randomized' method is more efficient whiten=True). # ensure uncorrelated outputs with unit component-wise variances fit(input) # define the input PCA was conducted to identify the most important directions of variation in the EEM dataset. The PCA model decomposed the sample EEMs into a set of bilinear terms and a residual array. 29 ðð = ï¿½ð¡ð¡ðððððð + ðððððð=1 (eq.3.2) where the loading vectors ðððð describe the contribution of each variable for each principal component and the score vectors ð¡ð¡ðð are analogous to concentrations of the relative importance of each principal component to a specific sample. Scikit-learn, an open-source package for machine learning in python, was used for PCA analysis (Pedregosa et al., 2011). The implementation of PCA to reduce the dimensionality of EEMs is presented in Chapters 4 and 5. 3.3.3 Autoencoder Autoencoders are self-supervised neural networks that can encode an input vector to a lower-dimensional space and then decode the decomposed vector to approximate the given input (Pathirage et al., 2018). In the context of this work, the autoencoder can be thought of as a multi-layer neural network that is given fluorescence spectra and attempts to recreate the identical spectra as an output. Dimensionality reduction is achieved by imposing a constraint on the middle hidden layer, referred to as the bottleneck, to a number of nodes less than the input. The number of nodes in the bottleneck determines the reduced dimensionality since the network is forced to learn a way to recreate any given input through the constrained hidden layer. The cost function is typically used to ensure input reconstruction as accurately as possible through the latent layer, which is defined as the sum of squared errors (SSE) of reconstruction (JE): ð½ð½ð¸ð¸(ðð) = ï¿½(ð¥ð¥ð¤ð¤ï¿½ â ð¥ð¥ðð)2ðððð=1 (eq.3.1) Where, ðð is the set of weights in the network ðð is the number of the training samples ð¥ð¥ð¤ð¤ï¿½ is the reconstructed input ðð ð¥ð¥ðð is the original input ðð The network structure was chosen based on sequential trial and error to minimize the cost reconstruction (Figure 3.6). The encoder received input ð¥ð¥ and was defined by ðð(ð¥ð¥) = ð§ð§ to 30 generate the projection output ð§ð§ of the network. The decoder defined by ðð(ð§ð§) = ð¥ð¥ï¿½, received the encoded vector ð§ð§ as input and outputs the reconstructed input ð¥ð¥ï¿½. Figure 3.6. The diagrammatic sketches of example autoencoder structure with input ð¥ð¥, one hidden layer, latent layer ð§ð§ with two nodes and reconstructed input as output ð¥ð¥ï¿½ (Figure adapted from Peleato et al. (2018)). The learned weights connected to nodes, referred as latent maps, provided a visualization of the features that captured from the input. Hidden layers and nodes in each hidden layer were tested until we found consistently lower reconstruction error and have latent maps most resembling organic matter fluorescence. Latent maps were visualized from the network weight connected to each latent node independently, which were then compared to the spectral representation of each derived component in other dimensionality reduction methods. As a basis for comparison to PARAFAC and PCA, in this thesis, the size of latent nodes was constrained based on the optimum lower dimension from PARAFAC. The autoencoder was trained using Googleâs TensorFlow, an open-source library for machine learning in Python (Abadi et al., 2016). 3.4 Regression analysis 3.4.1 Multivariate linear regression Many studies indicated a strong linear relationship between the spectra components and organic composition concentrations as an underlying assumption in fluorescence (Hudson et al., 2007; Hudson et al., 2008; Korak et al., 2014). As such, multivariate linear regression is widely applied 31 as a very straightforward approach in spectra analysis for predicting a quantitative response Y based on a group of predictors (ðð1,ðð2,ðð3, â¦ ,ðððð). ðð is the residual representing the variability not accounted for by the model. β0, β1, β2,β3, â¦ , Î²ðð are the regression coefficients of the linear regression model. The equation to formulate the MLR is listed as following: Y = β0 + Î²1ðð1 + Î²2ðð2 + Î²3ðð3 â¦ + Î²ðððððð + ðð (eq.3.3) 3.4.2 Random forest Random forest is one of the tree-based models prevalent in learning tasks, and it can produce accurate predictions with less overfitting (Breiman, 2001). Random forest attempts to grow the aggregation of many decision trees, and each tree has several nodes by drawing a subset of fluorescence data in replication from the sample with replacement, known as bootstrapping. When the fluorescence data was fed into random forest regression, the decision, the concentration value, was taken at the leaf end of the decision tree. A new fluorescence dataset was evaluated against all decision trees, and each tree produces the predicted contaminant concentration. The final decision was made by averaging the class assignment probabilities calculated by all trees. The data drawn into the sample is known as the in-bag data, whereas the portion not drawn is the out-of-bag data. The latter is not used to build or prune any tree but provides better estimates of node error and other generalization errors for bagged predictors (Breiman, 2001). The out-of-bag samples can be used to calculate an unbiased error rate and variable importance, eliminating the need for a test set or cross-validation. 3.4.3 Neural networks Neural networks are powerful computation tools in modelling studies(Andrews and Lieberman, 1994; Daliakopoulos et al., 2005). Neural networks have special inter-connected layers consisting of the number of the single processing elements (nodes, neurons). Active neuron multiplies each input vector by its weight, sums the products and passes the sum through a transfer function to produce the output (Bieroza et al., 2011). Compared with linear regression, neural networks show capabilities for allowing using nonlinear relationship to expose latent correlations between fluorescence and contaminant concentrations. In this study, neural networks were constructed and trained using Google's TensorFlow, an open source library for machine learning in Python (Abadi et al., 2016). 32 3.5 Limit of detection (LOD) Limit of detection (LOD) is shown in Eq.(3.5), calculated as the sum of the limit of blank (LOB) and test replicates of a sample known to contain a low concentration of the analyte (Armbruster and Pry, 2008). LOB is defined by measuring replicates of a blank sample and calculating the mean result and the standard deviation (SD). A traditional approach to estimate LOD is to measure replicates of a blank sample or zero calibrator, then calculating LOD as the mean plus 2 times of SD (Armbruster and Pry, 2008). This method assumes that a greater signal will be given from analyte than the analytical noise in the absence of analyte. However, this assumption is not proved that analyte at a low concentration will indeed produce a signal distinguishable from blank. ð¿ð¿ð¿ð¿ð¿ð¿ = ðððððððððððððððððð + 1.645(ðððððððððððððð) (eq.3.4) ð¿ð¿ð¿ð¿ðð = ð¿ð¿ð¿ð¿ð¿ð¿ + 1.645(ðððððððððð ððððððððððððð¡ð¡ððððð¡ð¡ðððððð ð ð ððððððððððð ð ) (eq.3.5) Compared with the traditional LOD estimate approach, which only consists of the mean value and standard deviation of blank sample, the approach used in this study could produce a signal to distinguish its presence from its absence when analyte exists. In this study, the number of LOD samples to be used for NAs and phenol is 20, including 10 blank samples and 10 samples consisting low concentration of analytes. Considering the concentration ranges that NAs and phenol spiked in water, low levels of NAs (n=5) and phenol (n=5) at 5 mg/L and 5 µg/L are used to make the low concentration samples, respectively. Assuming a Gaussian distribution of low concentration samples, we used 1.645 SD. Therefore, no more than 5% of low concentration samples will erroneously appear to contain no analyte. 33 Chapter 4: Cross-Source Detection of PAHs and Pesticides using Fluorescence Spectroscopy 4.1 Introduction Polycyclic Aromatic Hydrocarbons (PAHs) and pesticides are widespread organic contaminants in aquatic environments threatening public and environmental health. PAHs can be introduced into water by several means, such as incomplete combustion of organic matter and petroleum leaks or spills (Abdel-Shafy and Mansour, 2016; Meador, 2003). Pesticides are widely applied in agricultural activities and can enter surface waters from runoff. Some PAHs and pesticides are toxic, carcinogenic, and can bioaccumulate in the food chain (Abdel-Shafy and Mansour, 2016; Foo and Hameed, 2010). Many international organizations have promulgated PAHs and pesticides as priority pollutants: they are listed among the priority regulated substances in aquatic and terrestrial ecosystems by U.S. Environmental Protection Agency (Keith, 2014; United States et al.) and are among 45 priority regulated substances by the European Union (European Commission, 2013). Therefore, there is an urgent need for assessing those prioritized and emerging contaminants to avoid undetected exceedances of regulatory limits. Common measurement methods used to quantify targeted contaminants include gas or liquid chromatography coupled to mass spectrometry (GC-MS, LC-MS) (Ferretto et al., 2014). (Ferretto et al., 2014). Limitations of current laboratory-based methods mostly relate to the acquisition and sample preparation, such as extraction, purification or concentration, which is costly and time-consuming (Ferretto et al., 2014; Vuckovic, 2012; Yang et al., 2019). It is imperative to have rapid detection methods since the levels of contaminants in aquatic environments are dependent on short time-scale events, such as storm/rainfall or leaks (Stedmon et al., 2011a). Fluorescence spectroscopy is a promising analytical method that can be utilized for monitoring surface waters. Fluorescence analysis is typically completed through measuring fluorescence intensities at iterated excitation and emission wavelengths and data is presented as excitation-emission matrices (EEMs) (Hudson et al., 2007). Several molecules will have distinct fluorescence properties including optimal excitation and emission wavelengths allowing them to be uniquely identified in a fluorescence EEM. Furthermore, the intensity of fluorescence emissions is correlated with concentrations of the specific molecules, allowing for quantification of distinct molecules in a mixture (Murphy et al., 2014a). Previously, PAHs and pesticides have been successfully quantified in water based on the fluorescence properties of their aromatic structures (Ferretto et al., 2014; Yang et al., 2019). Compared with conventional analytical 34 methods such as chromatography and mass spectrometry, little sample preparation and rapid acquisition times for EEM-based approaches have the potential to provide timely data for continuously rapid contamination detection (Carstea et al., 2016; Vuckovic, 2012; Yang et al., 2019). While fluorescence spectra can represent a detailed fingerprint of background water quality and some contaminants, the high-dimensional data (i.e. potentially thousands of excitation/emission pairs) produced presents a significant analysis challenge. As such, there has been an emphasis on the application of algorithms to better leverage the collected data and realize lower detection limits for contaminants. Several dimensionality reduction techniques have been utilized to simplify the overlapping and convoluted fluorescence signals, with parallel factors analysis (PARAFAC) and principal component analysis (PCA) being the most popular (Mirnaghi et al., 2018). In addition to reducing data complexity, these methods have the potential to address signal interference from background fluorescence from natural organic matters (NOM) in water sources. NOM consists of a complex mixture of organic compounds, and exhibit distinct composition and concentration due to the degradation of terrestrial and aquatic organisms in different sources. (Thurman, 2012). As Ferretto et al. (2014) reported, humic substances caused overlapping peaks and molecularly interaction with PAHs and pesticides in the contaminated natural water, which made interpretation difficult and may overestimate the contaminant concentration. Previous results from Yang et al. (2019) also demonstrate the importance of considering interfering NOM signals for accurate detection of PAHs and pesticides in water by fluorescence. Although dimensionality reduction techniques can improve detection by accounting for background NOM signals, a remaining challenge is that the resulting models are site-specific and require frequent calibration to address the spatial and temporal variability in water quality. The initial fluorescence model often has poor performance when directly applied to new surface water due to variation in background water composition, especially natural organic matter (NOM). Yu et al. (2015) compared a global PARAFAC model, developed for monitoring dissolved organic matter (DOM) at several sources, with models developed for individual sites, and observed that the source diversity caused peak shifts that undermined the accuracy of the global model. Murphy et al. (2011) and Shutova et al. (2014) also identified that systematically biased estimates of fluorescence intensities and fluorescence ratios might be generated between individual models and the global model, implying the impossibility of building a universal global model suitable for all the sources. Carstea et al. (2016) reported that there is no consensus regarding an optimum 35 model that is universal for global datasets because of the NOM diversity, as well as impacts from environmental conditions, such as pH, on fluorescence signals. As such, source-specific fluorescence detection models are preferable to maximize accuracy. However, this implies a significant sampling and calibration burden using standards directly added into the source water being analyzed. Domain adaptation is a promising approach to address the challenge of site-specific models. In general, domain adaptation aims to utilize labelled data from a source domain to develop models on a study or target domain where only unlabeled data is available (Ganin and Lempitsky, 2015; Pan et al., 2010). This method could decrease the need for costly annotation for fluorescence EEMs to calibrate a fluorescence model to a new source. An existing and pre-trained fluorescence model built with one water source could be used as the starting point to learn general features and then be repurposed to a different environment. This work demonstrates the novel application of dimensionality reduction techniques to remap or transfer fluorescence detection models between two water sources. Specifically, this paper reports on transfer of PAHs and pesticides quantification models from a lake source to a river with dissimilar NOM characteristics. The objective was to use only a few additional unlabeled fluorescence EEMs to extract and transfer the features of new surface water, without the demand for quantifying the contaminants in the samples. The PAHs used for this study included: pyrene, considered as the highest risk among PAHs (United States et al., 1993), fluoranthene, and naphthalene. They are among the most common and toxic PAHs detected in tributaries to the Great Lakes in the low µg/L concentration range (Baldwin et al., 2016; Yang et al., 2019). The pesticides chosen in the study were carbaryl and thiabendazole. Carbaryl is a widely used carbamate insecticide for pest control and has been reported for its occurrence and distribution in southern Ontario surface water (Struger et al., 2016). Thiabendazole is one of the most popular fungicides to control postharvest decay on fruit, vegetables, and other crops. Several methods to obtain underlying fluorescence components and transfer between sources are compared, including PARAFAC, PCA, and autoencoders. The potential application to transfer the existing fluorescence model to other surface waters could aid in the wider implementation of fluorescence-based contamination detection models in different water sources. This work contributes to making rapid contaminant detection in surface waters more effective, easier, and less labour-intensive. 36 4.2 Materials and methods 4.2.1 Natural water samples The data used in this study were collected as part of a previous study by Yang et al. (2019) and are used in this work with permission from the authors. Data was generated from natural water samples were taken from Lake Ontario (Ajax, Ontario, Canada) and the Otonabee River (Peterborough, Ontario, Canada) over three months from February to May. Samples were taken using high-density polyethylene containers. Containers were rinsed with ultrapure water and respective surface water before sampling and then stored at 4 °C in the dark for a maximum of 14 days. Total organic carbon (TOC) for Lake Ontario and Otonabee River was ranged from 2.07 to 2.26 mg L-1 and 5.20 to 5.66 mg L-1, respectively, with similar pH conditions (Lake Ontario: 8.00â8.10, Otonabee River: 8.02â8.23). 4.2.2 Mixing experiments PAHs and pesticides were spiked at known concentrations (low µg/L ranges) into natural water sampled to imitate introduction of PAHs and pesticides in contamination events in surface waters. The concentration range chosen was based on the studies from Struger et al. (2016) and Baldwin et al. (2016) that reported PAHs and pesticides at low µg/L ranges in Great Lake tributaries. The selected PAHs and pesticides purchased from Sigma-Aldrich (â¥98%) were used to first prepare each individual 100 mg/L stock solutions in methanol (HPLC grade, Sigma-Aldrich) without further purification. From those stock solutions, a further diluted solution of 25 µg/L were prepared daily in Milli-Q water. Each compound's final concentrations were in the range from 0.1 to 7 µg/L in mixtures spiked in natural water samples. A total of 972 mixing samples were collected in the mixing experiments and then split into two parts: the training sets and the testing sets for each water source (Lake Ontario and Otonabee River). The natural water samples from lake and river sources have varying levels of TOC which resulted in variation of organic carbon concentrations and other characteristics of each water source in the datasets. This could help examine the transferred models' performance better when applied in cross-source water to detect the target contaminants. Furthermore, to accurately estimate model performance for predicting contaminant concentration, the testing sets contained complete and separate sampling days that were not included in the training sets. 37 4.2.3 Fluorescence The experiment used previously collected data that was produced from a continuous mixing and analysis set-up (Yang et al., 2019). The method is summarized below with further details on the bench-scale system and how the data was collected reported in . This system continuously spiked PAHs and/or pesticides into natural water samples to imitate short term contamination events. Each mixing sample was then automatically measured by a modified Agilent Cary Eclipse fluorescence spectrophotometer. The EEMs were collected with excitation and emission wavelengths that ranged from 220â360 nm with 5 nm increments and 280â500 nm with 2 nm increments, resulting in 3219 coordinate points (spectral variables). After the spectrum of the blank of pure Milli-Q water was subtracted, Raman correction was achieved using the Raman band to convert the fluorescence data into Raman Units (R.U.) (Lawaetz and Stedmon, 2009). As described by Yang et al. (2019), an inner-filter correction was not included in the continuous measurements due to challenges associated with implementing an additional continuous UV analyzer. However, for select grab samples, a correction method described by Lakowicz (2006) was used to examine the impacts due to inner-filter effects. Absorbance correction for inner-filter effects from both lake and river sources (n=5 and 8, respectively) showed relatively small changes in contaminant fluorescence (±10%) with respect to those in Milli-Q water. This suggests relatively minor matrix effects on fluorescence intensity. Furthermore, differences in the fluorescence intensity to concentration ratios of contaminants in each of the two water sources were not found to be significant (p > 0.05), except for fluoranthene (p < 0.01). 4.2.4 Dimensionality reduction and model transfer 4.2.4.1 Peak-picking The peak-picking analysis was carried out using in-house developed Python scripts. Peak locations for 5 contaminants (fluoranthene, naphthalene, pyrene, carbaryl and thiabendazole) were selected according to previous studies (Table 4.1). Peak-picking is considered the most basic method to interpret the fluorescence EEMs based on the expected peak position of fluorophores. Because the molecule structures of NOM and organic contaminants are unique, corresponding fluorescence response at particular excitation/emission pairs can be a basis to distinguish them. Therefore, in peak-picking analysis, those specific excitation/emission regions could be used to identify target organic compounds. The intensity of fluorescence is assumed to have a linear relationship with component concentration. 38 Table 4.1 Contaminant fluorophores and the excitation/emission (ex/em) wavelengths selected by the peak-picking method Fluorophores Excitation wavelength (nm) Emission wavelength (nm) Reference Fluoranthene 285 464 Maie et al. (2012) Naphthalene 220 332 Yang et al. (2019) Pyrene 235 392 Ferretto et al. (2014) Carbaryl 220 332 Ferretto et al. (2014) Thiabendazole 300 362 RodrÄ±Ìguez-Cuesta et al. (2003) 4.2.4.2 Parallel factor analysis (PARAFAC) Parallel factor analysis (PARAFAC) was carried out using MATLAB r2019b with drEEM toolbox and analysis procedure described by (Murphy et al., 2013). PARAFAC analysis can uniquely separate the real underlying chemical spectra by using the alternating least squares (ALS) for trilinear decomposition (Bro, 1997). Data pre-processing, including removing Rayleigh and Raman scattering regions and missing values were smoothed via interpolation to eliminate non-trilinear data in PARAFAC. Signal normalization, with respect to each excitation/emission pair across all samples, was also implemented to give similar weighting to fluorescence responses at each ex/em pair. A total of 31 samples were identified and removed as âoutlier samplesâ that were considered as unrepresentative or poor quality data based on sample error residuals and leverage. PARAFAC models were fitted using non-negativity constraints on all components. Split-half validation was carried out as an important step to combine four-quarter splits in six combinations and to form 3 unique comparisons of dataset halves for validation tests. The robustness of PARAFAC model was determined when identical models were obtained from all the subsamples of the dataset. 39 4.2.4.3 Principal component analysis (PCA) PCA is a multivariate analysis method for information extraction from a large data matrix based on achieving smaller variables that are mutually independent. The new variables, commonly referred to as principal components (PCs), are calculated as the directions of maximum variance in the mean-centered data set in descending order (Peiris et al., 2010). Through using fewer uncorrelated variables (PCs) to explain high-dimensional interrelated variables, the dimensionality can be reduced while accounting for a large proportion of the total variance (Persson and Wedborg, 2001). All the PCA analysis was carried out using Scikit-learn, an open-source package for machine learning in Python (Pedregosa et al., 2011). Because PCA is a two-way method, prior to analysis, the fluorescence EEMs with three-way structure were unfolded that the rows represent samples and the columns represent the excitation/emission pairs. The Raman and Rayleigh scattering were removed, and the dataset was normalized so as to be identical to the data analyzed in PARAFAC. 4.2.4.4 Autoencoder The structure of the autoencoder was determined based on trial and error. Hidden layers and nodes in each hidden layer were added until mean squared error or reconstruction did not significantly change (< 1% change). The autoencoder used was seven layers (including the bottleneck) with 100 â 20 â 10 â 7 â 10 â 20 - 100 nodes each layer, respectively. Node activations were rectified linear units (ReLU), and the cost function was mean squared error with an added L1 penalty to network weights (i.e. absolute value of the weights). Other parameters for network learning such as learning rate and scalars in the loss function were determined by grid search to minimize overall network error. Best practices to reduce overfitting and improve training such as dropout (20% of input) and batch normalization were used. Network structure (number of nodes and layers) and learning rate were determined through a grid-search method. The network was trained over 10 epochs and 100 steps in each epoch until a stable mean squared error of reconstruction was reached. The autoencoder was trained using Keras (TensorFlow backend), an open-source package for machine learning in Python. As a point of comparison to other dimensionality reduction methods, the network weights in the decoder associated with each latent node independently were taken as representations of autoencoder loadings. 40 4.2.5 Regression analysis In this work, linear regression was employed using scores (PCA/PARAFAC), latent activations (autoencoder), or peak intensities as input and output the accuracy to predict the concentration of spiked contaminants. Throughout the study, Mean Absolute Error (MAE) and Coefficient of Determination (R2) are used to evaluate the model performance to detect and predict the concentration of PAHs and pesticides. MAE is the most common and conventional measurements of regression models, and Coefficient of Determination (R2) is always used for robustness evaluation. The lower values of MAE and higher value of R2 indicate the better performance of the model. The equations are listed below where ð ð ðð is the true concentration values of measured sample ðð, and ð ð ð¤ð¤ï¿½ is the predicted concentration value. MAE = 1ððï¿½|ð ð ðð â ð ð ï¿½ðð| ðððð=1 (eq.4.1) ð ð 2 = 1 â â (ð ð ðð â ð ð ï¿½ðð)ðððð=1â (ð ð ï¿½ðð â ð ð ï¿½ðð)ðððð=1 (eq.4.2) 4.2.6 Cross-source model transfer For this study, the goal was to predict concentrations of PAHs and pesticides in Otonabee River samples using a reference model from Lake Ontario. Several data analysis techniques were then applied to remap or develop features using the lake data (including labeled samples) and unlabeled EEMs. It was hypothesized that a remapping process could reduce errors due to peak shifts and other various interactions between the contaminants and the background water matrix. For PARAFAC, the number of components or dimensionality of the latent space has to be pre-defined. As such, PARAFAC was treated as the baseline method, and the optimal number of components for PARAFAC when applied to Lake Ontario data was used to define the number of features extracted via other dimensionality reduction approaches. This allows a more direct comparison of the methods. We examined two approaches to remap features from the lake for cross-source model transfer: experiment A generated a regression model using labeled data from the lake source and features extracted from the river source EEMs (Figure 4.1). For this approach, the unsupervised feature 41 extraction techniques were applied to extract features from the river EEMs. Lake EEMs were then processed through this dimensionality reduction model (i.e. lake EEMs interpreted using river features). The resulting scores were used as the input variables for training a regression model using labeled data from the lake. The hypothesis is that the remapped fluorescence EEMs on another latent feature space of similar nature but from a different environment allow limited information loss, while keeping the original features. This is based on the fluorescence spectrum characteristic that the same compounds provide fluorophores exhibiting high degree of similarity except slightly peak shifts and shape distortion due to environmental variations. Therefore, the remapping should not radically change the whole data structure. Experiment B (Figure 4.1) fits the new dataset (river) using the original features derived from the lake data. Both methods aimed to have similar latent variables after dimensionality reduction prior to regression training. However, with regard to the various NOM in the samples from lake and river environments, remapped signals may not be conducive to discriminating NOM fluorophores' peak positions and spectra shape. Although the fluorophores of NOM are not directly sensitive to the identification and quantification of PAHs and pesticides, special attention is still encouraged to be paid in case of any overlapping signals between NOM and contaminants. 4.3 Results and discussion 4.3.1 Dimensionality reduction Several dimensionality reduction methods were applied to the fluorescence EEMs to identify underlying components and define a latent feature space. Compared to other dimensionality reduction techniques, the PARAFAC loadings provide a more direct representation of fluorophore signals expected in natural waters. The number of PARAFAC components was constrained to 7 based on the residual analysis and split-half analysis (C1-C7) (Figure S1). The wavelength peaks identified in PARAFAC loading components (except for component 6) (Table 4.2) conform well to previously published models that are included in the OpenFluor database, an online repository of PARAFAC models of aquatic systems (Murphy et al., 2014b). Component 1 (C1) derived from the Otonabee River matched 33 models recorded and the one derived from Lake Ontario matched 40 models in the OpenFluor database. This component showed high similarity scores identified as the humic-like or terrestrial humic-like compounds ubiquitous in the lakes and rivers (Osburn et al., 2018; Shutova et al., 2014). C2 had fluorescence characteristics similar to humic or microbial humic-like materials existing abundant in the surface waters (Derrien et al., 2020; Kulkarni et al., 2017). C3 was highly consistent with the models from agricultural catchments and 42 Figure 4.1 Roadmap for Model Setup using Fluorescence EEMs to predict the concentration of PAHs and pesticides in the Otonabee River. (experiment A): retrain and repurpose an existing lake model using the loadings of the new dataset, (experiment B): fit the new dataset using the original loadings of lake dataset urbanized areas, and matched the characteristic of anthropogenic microbial humic-like or tryptophan-like compounds reported by Lambert et al. (2017) and Graeber et al. (2012). C4 matched the characteristic of terrestrial humic-like materials from models ranging from estuarine waters at the land-sea interface to open ocean (Søndergaard et al., 2003; Stedmon et al., 2011b). C5 resembled protein-like compounds (Chen et al., 2018). C6 had primary excitation <240 nm, secondary excitation at 270 nm, third excitation > 330 nm, and emission at 390 nm. However, no match was found in the OpenFluor database. C7 matched several models from municipal recycled water, allochthonous litters like grass and leaves, and agricultural areas. This component associated with the fluorescence characteristic of protein- and tryptophan-like organic matters. 43 Table 4.2 PARAFAC component locations (ex/em) from Otonabee River and Lake Ontario with similarity score compared with previous studies Components Similarity score Components identified Reference Expected compound Component 1 River: 0.99 Humic-like Osburn et al. (2018) Humic-like Lake: 0.99 Terrestrial humic-like Shutova et al. (2014) Component 2 River: 0.97 Humic-like Kulkarni et al. (2017) Humic-like Lake: 0.95 Microbial humic-like Derrien et al. (2020) Component 3 River: 0.98 Anthropogenic microbial humic-like Lambert et al. (2017) Thiabendazole Lake: 0.98 Protein- and tryptophan-like Graeber et al. (2012) Component 4 River: 0.95 Terrestrial humic-like Fluoranthene Lake: 0.96 Terrestrial humic-like Stedmon et al. (2011b) Component 5 River: 0.98 Protein- and tryptophan-like Murphy et al. (2011) Protein-like Lake: 0.98 Protein-like Chen et al. (2018) Component 6 Pyrene Component 7 River: 0.99 Tryptophan-like Murphy et al. (2011) Naphthalene/ Carbaryl Lake: 0.97 Protein- and tryptophan-like Jørgensen et al. (2011a) The PARAFAC component scores of C3, C4, C6, and C7 changed coincidently with the levels of 5 PAHs and pesticides mixed in the samples. Furthermore, according to previous studies and known fluorescing signatures of the contaminants being studied, peak locations of C3, C4, C6, and C7 matched the fluorescence characteristic of thiabendazole (ex/em 300/362), fluoranthene (ex/em 285/464), pyrene (235/392), and a mixture of signals from naphthalene and carbaryl (ex/em 220/330). C3, C4, as well as C7 were matched as NOM in the OpenFluor database. However, due to the controlled nature of how contaminants were introduced into the samples analyzed, these matches with NOM are certainly incorrect. Notably, models highly matched with C3, C4, and C7 were collected from agricultural or urbanized areas (Lambert et al., 2017; Søndergaard et al., 2003), and some of them were sampled from municipal recycled water or allochthonous litters like grass (DâAndrilli et al., 2019; Murphy et al., 2011). While there is some 44 possibility that components have been mislabeled as NOM when actually representative of pesticides or PAHs, it is unlikely considering typical background levels of these compounds. Furthermore, poor component separation, similar fluorescence signatures, and the lack of sample size may also cause the incorrect high similarity scores (Yang et al., 2019). However, the mislabeling of components demonstrates the care needed and ambiguity around ascribing labels to fluorescence signals in natural waters. Figure 4.2 Peak location comparison among PARAFAC models from two surface waters: Lake Ontario, green solid for excitation spectra and green dashed line for emission spectra, and the Otonabee River, red solid for excitation spectra and dashed line for emission spectra. Similarity scores of components (C1 - C7) between the Lake and River model are reported in Table 2. Although seven PARAFAC components generated from river and lake datasets exhibit high similarity compared to previous work, there are notable differences in peak location and spectral shape between the river and lake source (Figure 4.2). The differences were primarily noted in the two humic-like fluorophores, thiabendazole, and fluoranthene signals (C1, C2, C3, and C4). Spectral shifts of approximately 50 nm were observed in the emission wavelengths of C1 humic-like and C3 thiabendazole fluorophore, and their excitation spectra shapes were also 45 varied. Similar shape deformation was also identified in the excitation spectra of C2 humic-like materials and C4 fluoranthene fluorophore. The tucker congruence coefficients (TCC), which indicates the degree of similarity between matrices, of components from the river and lake PARAFAC model are listed in Table 4.3. The TCC results confirm observations of variation in component characteristics based on loading plots. Only C6 and C7 have both TCC of excitation and emission wavelengths larger than 0.95, which meet the default minimal match criterium in the OpenFluor database (Murphy et al., 2014b). Shifts in location and shape of excitation and emission spectra are likely due to changes in environmental conditions or matrix effects (Coble, 1996). Factors that vary between samples in different environments known to impact fluorescence signals include pH, temperature, and interaction or quenching with other compounds (Henderson et al., 2009; Spencer et al., 2007). However, the exact causable factors and magnitude of impacts on fluorescence response are complex and still not completely defined (Li et al., 2020b). Due to these effects, similar compounds could have different fluorescence responses in different waters. In this study, both water sources had similar pH levels (Lake Ontario: 8.00â8.10, Otonabee River: 8.02â8.23), and fluorescence data was collected at a consistent temperature. The river source had higher absolute concentration of NOM as well as the variability of concentration between sample days. Furthermore, the composition of NOM would be unique between water sources. The interaction of contaminants with NOM may result in band shape distortions, causing differences in the compounds identified between sources. As such, we hypothesize that shifts in fluorescence peaks are due to interactions with NOM or generally due to matrix effects. The observed changes in spectral shapes highlight the site-specificity of detection models and variability in fluorescence signals due to differences in the water matrix, including NOM composition. The number of PCA components (PCs) was constrained to 7, to facilitate comparison to PARAFAC results, with cumulative explained variance (>99.5%) (Figure S2). The PCA loading plots derived from the target water site (Otonabee River) are shown in Figure 4.3. PC1 and PC2 showed spectral characteristics similar to pyrene. The excitation/emission regions of PC3 and PC4 are typical for naphthalene and carbaryl (ex/em 220/330 nm), which were also observed in PC5 and PC6. Fluoranthene was observed in PC5 at 285/414 nm. Peaks representative of humic-like fluorophores were observed in PC5. Compared to PARAFAC components, PCA components are less interpretable but likely provide more accurate reconstructions of the original data. Independent and uncorrelated PCs can be used to substitute original interrelated variables in 46 fluorescence EEMs. Therefore, the characteristics of multiple mixed fluorophores can be seen in one PCA component. Table 4.3 Tucker congruence coefficient (TCC) of PARAFAC components with similar peak positions from Otonabee River and Lake Ontario. TCC â¥ 0.95 as minimal match criterium Components Peak location (Max ex/em) Similarity scores between river and lake components ex em Component 1 river: 230/460 0.943 0.940 lake: 240/438 Component 2 river: 220/406 0.828 0.978 lake: 220/404 Component 3 (Thiabendazole) river: 245/400 0.939 0.797 lake: 235/368 Component 4 (Fluoranthene) river: 285/464 0.784 0.985 lake: 220/460 Component 5 river: 220/336 0.998 0.947 lake: 220/336 Component 6 (Pyrene) river: 235/390 0.998 0.991 lake: 235/392 Component 7 (Naphthalene/Carbaryl) river: 220/334 0.993 0.992 lake: 220/334 The loading plots or latent features from the autoencoder were analogous to that of PCA components, where physical interpretation of fluorophores is limited. The visualized autoencoder loading components (AE) are similarly difficult to interpret because of the ambiguity of multiple fluorophores represented. Figure 4.3 shows the trained network weights with respect to each latent node from a 7-component autoencoder. AE1/AE3/AE7 identified signals that conform well to the PAH pyrene around 235/392 nm, and AE6 possibly show representation of the wavelength peaks of PAH naphthalene and pesticide carbaryl. AE2 and AE5 emphasized the high emission 47 regions in fluorescence EEMs that do not necessarily conform to any of the 5 PAHs and pesticides' characteristics. Those regions may represent humic-like (ex/em 230/450) NOM in the river source. While autoencoders and PCA may provide a distinct representation of underlying features, they are challenging to interpret, and individual autoencoder nodes are unlikely to represent individual fluorophores in the water. Furthermore, there are no explicit constraints on individual latent autoencoder nodes to describe individual components, which further contributes to difficulties interpreting the latent space's representations. Figure 4.3 Loading plots of PCA components (PC1-7) and the latent maps of nodes in autoencoder (AE1-7) for the Otonabee River 4.3.2 Contaminant detection in cross-source water After applying dimensionality reduction, the latent variables were expressed by the loading values or the weights showing the relative importance of spectral regions. To remap the features and generate similar latent variables between sources a remapping type of approach was taken. Previously identified latent variables were modified based on new information from the target water source. By remapping, we hypothesized that the impact of background NOM variability and peak shifts between sources would be minimized. Furthermore, it may help to remap the distribution of fluorescence response and overcome peak shifts which may be caused by matrix effects varying between samples in different environmental conditions such as pH, temperature, and quenching. Linear regressors were trained on the remapped labelled source dataset to investigate the quantification accuracy for 5 contaminants. The input training dataset was in four versions: 1) output or activation of autoencoder nodes, 2) PCA component scores, 3) PARAFAC component scores, and 4) peak-picking fluorescence intensities. The model built on the lake was then tested on the target river with the concentration of contaminants spiked in the samples (n=336). Furthermore, to compare the performance of cross-source models (Lake Ontario to the 48 Otonabee River), baseline models were also created using the labelled river EEMs samples (n=336) and tested on samples of the same source, which were collected on different sampling dates. The mean absolute error (MAE) was used to measure the performance of cross-source and baseline regression models (Figure 4.4). In experiment A, the existing lake model was retrained and refitted to the new dataset, while the new river dataset was remapped to fit the existing model for contaminant detecting in B experiment. It is noted in Figure 4.2 that PARAFAC components represented as thiabendazole and fluoranthene all showed the most apparent peak shift and shape distortion. It is believed that the peak shifts limited transferability based on observations of lowest cross-source prediction accuracy using PARAFAC for thiabendazole and fluoranthene (Experiment A MAE: 1.02 and 2.67 µg L-1; Experiment B MAE: 0.66 and 2.61 µg L-1). In contrast, the components of naphthalene/carbaryl and pyrene, which have consistent peak wavelengths and almost identical shapes had relatively higher prediction accuracy both in two transfer methods (MAE: 0.051 - 0.153 µg L-1). These results indicated that the level of the conformity among similar PARAFAC components derived from the existing EEMs dataset and the new dataset may influence the behaviour of the transferred model. However, spatial variability of peak location and spectral shape is quite common due to source diversity. Both Fellman et al. (2009) and Murphy et al. (2011) reported this problem that similar fluorophores in different sources are found to have peak shifts that may arise from either mathematical decomposition, matrix effects, or variation of environmental conditions. In this study, the variation of similar fluorophores among different surface waters negatively affect transferability from one to another and, therefore, hindered the utilization of PARAFAC components for cross-source model transfer from previously collected data. However, PARAFAC decomposition is still a promising approach for site-specific modelling (Baseline MAE: 0.061 â 0.242 µg L-1), which is also advantageous to interpret and visualize underlying components depending on different purposes (Li et al., 2020b; Murphy et al., 2014a). Unlike PARAFAC, where poorly separated fluorophores may negatively influence all other components (Murphy et al., 2011), autoencoder and PCA may have improved model transfer through a better overall representation of variance in the dataset and simultaneous representation of multiple fluorophores in each LM or PC. As shown in A and B subplots in Figure 4.4, both PCA and autoencoder-based model transfer had an improved performance compared to PARAFAC, which implied a more substantial capability to discover universal cross-source underlying components. In PCA decomposition, the fluorescence EEMs were projected on a strictly 49 orthogonal coordinate system constituted by a set of linearly independent eigenvectors in the direction of maximum variance. PCA could better represent the data and give a better fit than PARAFAC to reduce information loss. Unlike multi-linear decomposition in PCA and PARAFAC, the autoencoder can represent non-linear features and have a strong generalization of fluorescence EEMs (Hinton and Salakhutdinov, 2006). As such, PCA and autoencoder characteristics might help transform the previously collected data with new representations to refit the existing lake model to the new dataset from the new task. Figure 4.4 Mean absolute error for (A): retraining and repurposing the existing lake model using the loadings of the new dataset, (B): fitting the new dataset using the original loadings, (Baseline) site-specific model, and no transfer (note distinct scale). PCA: principal component analysis. PARAFAC: parallel factors analysis. PP: peak picking. LR: linear regression. Note: MAE from No transfer models are given on a different scale (0 to 10 µg L-1) than MAE from other models (0 to 3 µg L-1). The predictability of PAHs and pesticides using the autoencoder or PCA features was stable and robust between both model transfer experiments, showing only a small variation in 50 prediction accuracy (0.05 â 8.5%). Overall, the autoencoder had similar or better performance than PCA (PCA MAE: 0.150 to 0.591 µg L-1, autoencoder MAE: 0.170 to 0.403 µg L-1). Improved accuracy may be due to the non-linear feature representations in autoencoder, which could more efficiently and accurately represent complex relationships (Vincent et al., 2010). Fluorescence excitation and emission spectra resulting from contaminants and background organic matters exhibit complex relationships that also influenced by various environmental effects. Our study agrees with previous work observing improvements when applying autoencoder and possibly capturing non-linear effects (Peleato et al., 2018). This is also evident from Figure 4.5 based on the measured vs predicted concentration of PAHs and pesticides when transferring the existing lake model to the river. For comparison, the performance of transferring models without remapping was also analyzed. The lake regression model was directly applied to predict contaminant concentrations in the river source (Figure 4.4). For all the selected PAHs and pesticides, prediction MAE without remapping was consistently higher (Autoencoder: 187 â 924%; PCA: 7 â 761%; PARAFAC: 134 â 1652%) compared to transferred methods. Predictive performance is further visualized based on measured and predicted concentrations of PAHs and pesticides in Figure 4.5. The weak correlation from no transfer models, especially for PARAFAC method, indicated low sensitivity to predict concentrations of target contaminants in the river source using only lake data. This is likely due to signal changes such as peak shift and shape distortion as well as differences in background water quality among two water. The poor performance of non-transferred models emphasizes the site-specificity of fluorescence-based contaminant detection and supports the motivation of finding cross-source detection methods. Dimensionality reduction with peak picking showed the worst performance when predicting the contaminant concentration in the river using the existing lake model. Challenges with peak-picking, including neglecting possible variation in peak positions and impacts of NOM has been widely acknowledged and reported by Ferretto et al. (2014), and Li et al. (2020b). The site-specific baseline models' performance was consistently better than the transferred models, which implied the advantages of in-situ modelling or site-specific calibration that can effectively obtain site-specific relationships between variables. However, the performance of cross-source transferred models using PCA or autoencoder was still competitive compared to baseline models, and showed significant improvements compared with no transfer models. Considering the significantly burden for data collection to generate site-specific models, 51 a cross-source model transfer approach may be a viable approach to more widespread implementation of fluorescence detection methods. 52 Figure 4.5 Measured vs predicted PAHs and pesticides for test samples (the river source) using various data techniques according to the results from experiment A and No transfer. Note: Peak-picking models were trained based on intensities at specific peak positions and neglect impacts such as interaction with NOM, water matrices among sources. Thus, it is hard to remap or transfer peak-picking models according to the cross-source transfer methods described in this study. 4.3.3 Fluorescence components importance for contaminant detection To further explore the performance of cross-source transferred models, the significant predictors among decomposed and transferred components from different data-processed techniques were determined. To that end, we used ordinary least squares (OLS) to estimate the strength of the given variables and the predicted contaminants in linear regression by the principle of least squares (Hutcheson, 2011). All the tested contaminants had a high correlation (R2 > 0.95) with the transferred components, indicating the linear relationship observed between the fluorescence response and the tested contaminants (Table S5). Through PARAFAC, EEMs were decomposed into physically interpretable fluorophores. After transfer, the tested contaminants were mostly explained by the individual corresponding transferred fluorophores with higher coefficients than other components. Strong negative coefficients were also noted between contaminants and humic-like as well as protein-like fluorophores. This suggests potential interference and overlapping signals between NOM and PAHs as well as pesticides. Like PARAFAC, the intensities at fluorescence peaks of contaminants in peak-picking showed a predominant positive connection individually with each contaminant. In the transferred models based on the autoencoder and PCA, the repurposed lake components were observed to have positive associations with PAHs and pesticides from Otonabee River (Table S2, Table S3). For example, the transferred LM1/LM7 had high positive coefficients with PAHs while the transferred LM3/LM6 were associated with pesticides. The LM2 and LM5 were identified to represent natural organic matters in the river samples Figure 4.3. Negative associations with tested contaminants were found in the transferred LM2/LM5, showing the capability of the model to separate the contaminant signals from that of NOM. This is also true for PCA. Through comparison to peak-picking and PARAFAC, each component from autoencoder and PCA had high connections with multiple contaminants expected to be detected. We hypothesize that this may make the fluorescence models more robust and sensitive during cross-source model transfer. In contrast, the poor performance of transferred models using peak-picking and PARAFAC highlighted the problems in relying on one-to-one representations when facing 53 signal changes between sources. Prediction accuracy is very likely to be hindered especially if the identified peak locations from previous work or the PARAFAC loadings generated from one water source do not completely and accurately represent the spectra collected from a new water source. 4.4 Summary An approach to transfer fluorescence-based contaminant detection models between water sources is presented in this study. Based on an already established model (from a lake), only a few additional unlabeled fluorescence EEMs from a new source were required. Two transfer methods were investigated to achieve this goal. One is to retrain the lake regression model using the remapped EEMs and concentration labels to fit the river features. The second method was to transform the new river EEMs based on the lake loadings/weights to fit the lake source features. As identified, after remapping and transformation, the lake regression model had enhanced river site-specific features, improving the predictability of concentration in the river source compared to no transfer. Results showed that comparable predictability was produced from the cross-source models using autoencoder or PCA compared to the baseline source-specific models (MAE < 0.5 µg L-1). PCA represents a greater degree of variance and autoencoders can capture non-linear feature representations in the dataset. It is hypothesized that these characteristics may help unify the distribution of fluorescence response among different surface waters and facilitate transferring fluorescence models between water sources. Compared to autoencoder and PCA approaches, PARAFAC produced more interpretable components that more directly represented organic fluorophores. However, the constrained output may lead to information loss, resulting in less transferability among water sources. These repurposed and refitted models could overcome the spatial background NOM variability, pH and other variable parameters among different water systems. Similarly, seasonal variations in NOM composition and concentration could also be addressed through model transfer. Since there is no need to quantify the samples for calibration, a great burden would be reduced for data collection, and only additional unlabeled EEMs were required for extracting and transferring the features of new surface water. However, the new dataset collected from the target site has to include the target contaminants, which could be simply water samples known to be contaminated. Otherwise, some information would be missing during the cross-source transfer. 54 This work could help lessen the burden of data collection for frequent calibration or building site-specific models and accelerate the set-up process for online and real-time fluorescence monitoring. 55 Chapter 5: Detection of Highly Overlapping Naphthenic Acids and Phenol using Fluorescence Spectroscopy 5.1 Introduction Oil sands process-affected waters (OSPW) are produced in surface mining to recover bitumen from a mixture of sand, water, and clay (Li et al., 2017). With the growing volume of OSPW stored in tailing ponds, increasing attention is being paid to the potential migration of toxic substances and contamination in surface water and groundwater. Grewer et al. (2010) indicated that the exposure of OSPW might lead to multiple physiological problems to a variety of organisms, such as compromised immunological function and impaired reproduction (Kavanagh et al., 2012; Rogers et al., 2002). Despite the variety in the composition of OSPW, the primary source of the toxic components is believed to be acid extractable organics, known as naphthenic acids (NAs) (Mahaffey and Dubé, 2017). NAs are a mixture of organic compounds described by the general formula CnH2n+zO2, where n represents the number of carbon atoms and z represents hydrogen deficiency (classical NAs), or CnH2n+ZOx, where x=2 to 5 (oxidized NAs) (Grewer et al., 2010; Samanipour et al., 2020). Other contaminants such as phenols also significantly contribute to the toxicity of OSPW. The ambient levels of NAs and phenol in natural surface waters in the oil sand regions are typically less than 1 mg/L and 1.6 µg/L, respectively (Belosevic et al., 2014). However, as reported, the concentration of NAs and phenol ranged widely from 12 â 43 mg/L and 8 â 83 µg/L in OSPW, which highly exceeds the Canadian Council of Ministers of the Environmental (CCME) guidelines (1 mg/L and 4 µg/L for NAs and phenol in surface waters, respectively) (Belosevic et al., 2014). Recent research has provided evidence of migration of OWSP and enter into surface water and groundwater nearby (Frank et al., 2014). Currently, standard methods to quantify NAs and phenol include high-performance liquid chromatography (HPLC), gas chromatography coupled to mass spectrometry, and Fourier transform infrared (FTIR) spectroscopy (Hsu et al., 2000; Lu et al., 2013; Mohamed et al., 2008). Although those methods are precise and selective to measure the contaminants, they are costly, and the steps for acquisition and sample preparation are time-consuming. Therefore, it is imperative to regularly monitor those contaminants releasing into the surface waters within the oil sand regions to protect public and aquatic ecosystem health. Fluorescence spectroscopy is a fast and promising optical method that can characterize and quantify organic compounds and contaminants in the water. High dimensional fluorescence 56 excitation-emission matrices (EEMs) measured at iterated excitation and emission wavelengths show the inherent fluorescence properties of organic matter constituents (Bridgeman et al., 2011; Murphy et al., 2014a). The increased fluorescence intensity in particular regions within the EEMs could indicate the presence of specific fluorophores, allowing characterizing some organic matter composition in water. Previous studies have reported the varying degrees of success of EEM-based models for observing the behavior of dissolved organic matter (DOM) and estimating fluorophore concentrations in natural or drinking water samples (Antunes and Esteves da Silva, 2005; BengraÄ±Ìne and Marhaba, 2003; Marhaba et al., 2000). Selected fluorescence intensity at peak wavelengths or derived from PARAFAC components have been successfully correlated with some conventional water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved organic carbon (DOC), and total microbial activity (Cohen et al., 2014; Henderson et al., 2009). However, the fluorescence of environmental pollutants such as NAs and phenol that are toxic and harmful to nearby water systems have not been extensively studied. In particular, studies of fluorescence used for quantifying pollutants natural surface waters, have been more limited. Detection of pollutants in natural surface waters is challenging, since contaminants, such as NAs and phenol, fluoresce in similar spectral regions (270/330 nm and 272/298 nm for NAs and phenol), resulting in highly overlapped signals (Lu et al., 2013; Mohamed et al., 2008). Furthermore, several background compounds, such as humic-like and protein-like substances from NOM, will interfere with fluorescence measurements of contaminants (Korak et al., 2014). The highly overlapping fluorophores and the background interference can be addressed by applying statistical algorithms to identify individual components in full fluorescence spectra or EEMs. Several fluorescence analysis techniques are reported in literature to interpret the high-dimensional EEMs and extract relevant information (Carstea et al., 2010; Wasswa et al., 2019). Among those data analysis techniques, tracking fluorescence peak intensity at pre-defined wavelength individually (peak-picking) to evaluate relative changes is the most straightforward and simplistic method to measure organic matter quantity and quality. However, this method only focuses on the key wavelengths and neglects the possible overlapping with background NOM or distinct organic compounds with similar fluorescence structures (Korak et al., 2014; Yu et al., 2015). Furthermore, traditional modelling approaches, such as linear regression, are based on the assumptions that fluorescence intensities change linearly across the range of concentrations, have limitations when dealing with nonlinearity within the dataset (Carstea et al., 2010). Therefore, to handle a large set of fluorescence data, there is a demand for more comprehensive approaches 57 to interpret the fluorescence EEMs and develop valid correlations between various variables and the corresponding contaminant concentrations. Advanced EEM analysis techniques include multiway parallel factor analysis (PARAFAC) and principal components analysis (PCA) from chemometric analysis; some computation and modelling techniques, for example, random forest and artificial neural networks (ANNs) (Bieroza et al., 2011). PARAFAC allows the separation of individual fluorophores in highly convoluted fluorescence spectra to elucidate the underlying components within the EEMs (Murphy et al., 2013), while PCA is a fast and most often used exploratory method decomposing the EEMs into the most important uncorrelated variations. The predictive models are established by multivariate calibration relating the fluorescence intensity of a fluorophore with its property of interest (concentration) from a set of reference samples to build the mathematical model to quantify the component concentration from the unknown samples (Bieroza et al., 2011). This study investigates fluorescence spectroscopy as a contaminant detection tool to monitor NAs and phenol in surface waters. Identification and separation of highly overlapping NAs and phenol under background interference was investigated. Several dimensionality reduction methods were tested on the basis of predicting the concentrations of NAs and phenol in the water. The most basic and widely applied method peak-picking approach with intensities at fixed wavelengths were compared to several algorithm-based dimensionality reduction approaches (PARAFAC and PCA). Furthermore, to consider the significant sampling and calibration burden using standards directly added into the source water being analyzed, the ability to transfer model knowledge from one source to another was also examined in this study. 5.2 Materials and methods 5.2.1 Samples and mixing experiments Natural water samples used in this study were collected from Lake Okanagan (Kelowna, Canada) and Mission Creek (Kelowna, Canada) over five months from October 2020 to February 2021. The study objective was to explore the applicability and sensitivity of the fluorescence technique for detecting target contaminants with interference from NOM in surface waters. Two water sources were used to examine the impact of varying NOM characteristics on detection methods. NOM variability over seasons could impact results, and a longer-term sampling program was planned for future work. High-density polyethylene containers were used for sampling and were 58 triple rinsed using the respective natural water before sampling. All the raw samples were stored in the dark at 4 °C for a maximum of 20 days. NAs stock solution of 10,000 mg/L and phenol stock solution of 10 mg/L were prepared by dissolving pure NAs and phenol purchased from Sigma-Aldrich (â¥98%) in methanol (HPLC grade, Sigma-Aldrich) and Milli-Q® pure water, respectively. Afterward, NAs and phenol were spiked in the range from 0 â 50 mg/L and 0 â 100 µg/L into natural water samples in the mixing experiments. The concentration range was chosen based on the studies from Holowenko et al. (2002), and Belosevic et al. (2014) to explore using fluorescence techniques to detect target contaminants. 5.2.2 Fluorescence Fluorescence spectra were collected using an Aqualog spectrometer (Aqualog, HORIBA, Japan). The EEMs were collected with excitation and emission wavelengths that ranged from 240â400 nm in steps of 2 nm and 250â600 nm in steps of 2 nm. The blank of pure Milli-Q® water was subtracted from each sample to eliminate the fluorescence impacts from the background solvent. Inner-filter corrections were automatically applied using Aqualog® package software. Fluorescence EEMs were then normalized and converted into Raman Units (R.U.) using the integrated area under Raman peak (ex=350) measured from the blank Milli-Q® pure water (Lawaetz and Stedmon, 2009). 5.2.3 Cross-source model transfer While fluorescence spectra can represent a detailed fingerprint of contaminants in water, a remaining challenge is that the resulting models are site-specific and require frequent calibration to address the spatial and temporal variability in water composition. This study investigated a cross-source model transfer method to transfer a detection model from one water source to another. PCA was applied to the fluorescence EEMs of the target water to extract features in an unsupervised manner. The fluorescence EEMs of the source water were then interpreted using the target water's features. Therefore, both source water and target water would have similar latent variables after dimensionality reduction. The transfer approach is based on the hypothesis that fluorescence spectral characteristics of any given compound should be similar, but not identical, between water sources. Peak shifts and shape distortions possible due to environmental variations. Therefore, after applying features from another surface water, the whole data structure should not be changed radically and contribute to simialr features between two surface waters. 59 Figure 5.1 Roadmap for model setup using Fluorescence EEMs to predict the concentration of NAs and phenol in the target water samples 5.3 Results and discussion 5.3.1 Fluorescence characterization of the organic matter in water samples For fluorescence analyses of the surface water, peak-picking is considered the easiest and the most basic method to identify fluorescence components within the high-dimensional fluorescence EEMs. Due to differences in molecular structures, each organic fluorophore has a unique absorbance and emission response (Li et al., 2020a). Fluorescence intensity and corresponding concentration level are considered to have a linear relationship with each other, which could be used as a basis to distinguish and quantify the levels of NAs and phenol in the samples. However, under the interference with naturally occurring background fluorescence, it could be noticed that humic-like, protein-like substances and the contaminant signals responded in similar spectral regions, making them overlap with each other and increasing the difficulty of peak-picking analysis Table 5.1. The picked intensity at key wavelength pre-defined for NAs and phenol could cause concentration overestimation due to other overlapping signals. Therefore, as discussed in Carstea et al. (2010), the peak-picking method might show success in measuring specific natural substances for freshwater monitoring. However, when monitoring the complex mixtures in contamination events, multiway data analysis techniques were recommended to reduce data complexity and address the signal interference from background fluorescence. 60 Table 5.1 Theoretical excitation and emission positions for NAs, phenol, and some NOM in water Compounds of Interest Classification Excitation Wavelength (nm) Emission Wavelength (nm) Naphthenic Acids OSPW 270 330 Phenol OSPW 272 298 Humic-like NOM 237-260 400-500 NOM 300-370 400-500 Protein-like NOM 225-237, 275 309-321, 310 NOM 225-237, 275 340-381, 340 To better interpret the sample EEMs and separate individual fluorophore within the underlying components, PARAFAC was applied to the lake and creek water sources with 120 mixing samples collected from 4 sample dates, respectively. PARAFAC models of 4 components for both Okanagan Lake and Mission Creek were established based on the criteria and split-half validation described by Murphy et al. (2013). The loading plots from the lake and creek PARAFAC models are shown in Figure 5.2. Using the Lake Okanagan water, two components were identified as humic-like substances based on the match to other studies in the OpenFluor (Murphy et al., 2014b). Lake component 1 (Clake1) with excitation maximum <250 and 305 nm and emission maximum at 445 nm is interpreted as humic-like substances, characterized as a relatively aromatic and high molecular weight compound (Kothawala et al., 2012; Lin and Guo, 2020). Lake component 2 (Clake2) has an excitation maximum at 275 nm and emission maximum at 325 nm, which matched tryptophan-like fluorescence and comprised the protein-like region in some studies (Baker, 2001; Bianchi et al., 2014). However, we noted that Podgorski et al. (2018) also indicated this compound (Clake2) might associate with petroleum-derived dissolved organic matter, such as alkyl-substituted naphthalene derivatives and aromatic amino acids in petroleum compounds. It is also difficult to characterize lake component 3 (Clake3) as it has excitation at 245 nm and board excitation analogous to humic-like fluorescence. However, the qualified humic substances typically have emission maximum at around 380 â 460 nm (Coble, 1996; Jørgensen 61 et al., 2011b). Clake3 has emission at 360 nm, which is less than that of humic substance and more in accord with fluorescence characteristics of tryptophan. Lake component 4 (Clake4) has low maximum excitation and emission wavelengths (excitation maximum at 260 â 270 nm, emission maximum at 300 â 310 nm) matched cresol (methyl phenol) reported by D'Andrilli et al. (2017) and also near regions associated with fluorescence characteristics of fluorophore peak B (tyrosine-like). As shown in Figure 5.2, analysis of the Mission Creek samples also resulted in 4 components that exhibit high similarity compared to the four lake components. Figure 5.2 Loading plots from 4 component PARAFAC models for Okanagan Lake and Mission Creek The identification of each PARAFAC component within the EEMs is conducted by matching our spectra loadings with the data from other previous publishes literature uploaded in the OpenFluor. Tucker congruence coefficients (TCC) of spectra from our loadings and previous studies are used to indicate the degree of similarity between matrices (Murphy et al., 2014b). A total of 140 PARAFAC components with 0.95 minimum TCC similarity scores were found to match four components validated for the Lake Okanagan water (n = 76) and Mission Creek water (n = 62 64). Clake1 and Ccreek1 had the most matches from previous data, which show high similarity with typical fluorescence characterizations of humic substances. Notably, although some studies believed our Clake2 and Ccreek2 analogous to protein-like substances, those two PARAFAC components also matched several studies analyzing oil impacted water. Around 10 studies have high similarity scores â¥ 0.98 with Clake2 and Ccreek2, including the datasets published by Zito et al. (2019) investigating heavy and light oil, Bianchi et al. (2014) analyzing Deepwater Horizon Oil spill contaminated water, and Podgorski et al. (2018) analyzing crude oil impacted groundwater. Furthermore, the PARAFAC component scores changed coincidently with the levels of naphthenic acids mixed in the samples. Their identified peak locations (ex/em = 275/325 nm) also conformed with the fluorescing signatures identified in a previously published study (ex/em = 270/330 nm) (Mohamed et al., 2008). As such, all the evidence suggests that Clake2 and Ccreek2 might be oil-derived compounds which are NAs and phenol, rather than naturally occurring compounds in the water. Although there is some possibility to misjudge and mislabel some PARAFAC components if only relying on their spectral loadings, it is unlikely to occur in the modeling considering the typical background levels of those contaminants and the correlation between component scores and their concentration. In this study, fluorescence from NAs and phenol matched that for terrestrial, microbially derived, and protein-like DOM. Similar fluorescence signals were observed from distinct molecules, but it does not mean that they are in the same class. Murphy et al. (2011) also reported contradictory chemical interpretations of similar-appearing PARAFAC components in different published studies, for example, mislabelling âterrestrialâ components and âmarineâ components. Therefore, it is still worthwhile to taking care around ascribing labels to fluorescence signals in natural waters. Figure 5.3 Loading plots of the 4 component PCA model (PC1-4) within EEMs from Okanagan Lake. PCs 1 to 4 represented > 99.70% of the variance in the dataset. 63 Table 5.2 PARAFAC component locations (ex/em) from Lake Okanagan and Mission Creek water with similarity score compared with previous studies Components Ex (nm) Em (nm) Description Similarity score1 Reference Reference corresponding compounds Clake1 250 445 Humic-like 0.99 Lin and Guo (2020) Humic-like Clake2 275 325 Naphthenic acids 0.98 Podgorski et al. (2018) Oil-derived compounds, Protein-like Clake3 245 360 Humic-like 0.97 Jørgensen et al. (2011b) Humic-like Clake4 265 310 Phenol 0.99 D'Andrilli et al. (2017) Methyl phenol Ccreek1 250 450 Humic-like 0.99 Chen et al. (2017) Humic-like Ccreek2 275 330 Naphthenic acids 0.97 Zito et al. (2019) Oil-derived compounds, Protein-like Ccreek3 245 360 Humic-like 0.96 Lambert et al. (2016) Humic-like Ccreek4 265 310 Phenol 0.97 D'Andrilli et al. (2017) Protein-like 1 High tucker congruence coefficients (TCC) (>0.95) with identified components in published studies in OpenFluor. Highest similarity scores are shown (>0.97). PCA was applied to the fluorescence EEMs as a dimensionality reduction method to identify the underlying components. The 4 component PCA model had cumulative explained variance >99.70% in the dataset. Loading plots of the 4 component PCA model derived from the creek water showed in Figure 5.2. Most PC components showed spectra characteristics at excitation/emission regions of 240/350 nm, which matched the characteristics of protein-like compounds ubiquitous in the lakes and rivers (Osburn et al., 2018; Shutova et al., 2014). PC 1 also identified signals around 270/330 nm (ex/em) that conform well to characteristics of NAs. the low emission regions emphasized by PC4 may represent fluorophores of phenol around 272/298 nm. However, each PCA component is unlikely to be a one-to-one representation of individual fluorophores in the water, and therefore, most of them are challenging to interpret. Those decomposed components are the reconstructions of the original data to substitute original interrelated variables in fluorescence EEMs. The latent variables are expressed by the loading values showing the relative importance of spectral regions. Coefficients, or PCA scores, are 64 analogous to individual PC importance or scores for any given sample. Scores were used in subsequent regression models. 5.3.2 Calibration modelling The bench-scale mixing experiments with NAs and phenol were performed to examine fluorescence spectroscopy as a surrogate for measuring contaminant concentrations in surface water. This hypothesis stems from an underlying assumption that excitation-emission maxima of fluorophores, which depended on compounds' molecular structure, weight, functional groups, etc., are linearly related to the concentration (Murphy et al., 2014a; Peleato et al., 2018). A total of 360 mixing samples were collected (180 mixing lake samples and 180 mixing creek samples) and a 5-fold cross-validation approach was applied to the dataset to assess the model prediction performance accurately. This approach iteratively splits the original dataset into randomized training and test subsets but utilizes all available data. 5-fold was chosen to maintain a common split of 80/20 for training/testing. The validation sets were not included in the dimensionality reduction and model building, making the model capable of testing its ability to predict new data that was not included in the modeling The predictability of the peak-picking approach, PARAFAC and PCA approaches, was compared to determine if the algorithm-based dimensionality reduction approach had better performance than fluorescence intensity at fixed wavelengths. Linear regressor, neural network, and random forest were trained to develop site-specific correction on each water source using the outputs from peak-picking PARAFAC component scores and PCA analysis: 1) peak-picking fluorescence intensity, 2) PARAFAC component scores, 3) PCA component scores. Mean absolute error (MAE) was used to measure the performance of the site-specific regression models (Table 2). It should be noted that MAE from naphthenic acids was given on a different scale (mg/L) than MAE from phenol (µg/L) to account for the different concentration ranges spiked in samples. The concentration of NAs was randomized over a range of 0.1 â 50 mg/L, whereas the concentration of phenol was from 0.1 â 100 µg/L. 5.3.3 Prediction of NAs and phenol in surface waters When calibration modelling, the training dataset included the contaminant concentration as reference measurements for the properties of interest and some measurement attributes believed to reflect these properties. The best relationship between the measurement attributes and concentration as properties of interest was identified in the calibration model for prediction. Table 65 5.3 presents the regression error for the validation sets that were not used in dimensionality reduction analysis or modeling of concentration. Models constructed with PARAFAC and PCA components scores performed better than with peak intensities for both NAs and phenol (2-55% and 25-82% decrease in MAE for NAs and phenol, respectively). It conformed with previous research that in complex mixtures using peak-picking was unlikely to quantify highly overlapping and convoluted fluorophores individually accurately. Phenolâs fluorescence signature overlapped with the signals from NAs as well as the background humic-like substances, making it easily overestimated if only relying on fluorescence intensities at specific excitation/emission pairs (Figure 5.2). For predicting NAs, PARAFAC as a predominant method in fluorescence spectral interpretation was observed comparable performance with PCA method but had consistently lower predictability for phenol in both lake and creek water (MAE: 14.033â21.287 mg/L). For models constructed using PARAFAC component scores, component separation is crucial for prediction accuracy. Because of the high level of NAs in the OSPW, NAs were spiked at mg L-1 levels in the bench-scale mixing experiments, resulting in much higher fluorescence intensities within the sample EEMs compare with phenol at µg/L levels. The inclusion of NAs caused interference and overlapped signals from phenol, making it challenging for PARAFAC to separate phenol from NAs-predominant fluorophores. As such, the poorly separated fluorophores can negatively represent corresponding compounds in EEMs, and cause loss of information related to phenol during PARAFAC dimensionality reduction and constraints of interpretable components. Neural networks were applied in comparison with a conventional multivariate linear regression and random forest. Prediction accuracy associated with lake and creek site-specific models was shown in Table 5.3. Overall, although all the regressors with dimensionality reduction, except peak-picking showed comparable MAE for predicting NAs (lake MAE: 1.381â1.798 mg/L, creek MAE: 1.525â2.001 µg/L), neural networks showed slight improvement compared with linear regression and random forest. However, for phenol prediction, neural networks were observed to have a noticeable increase in prediction accuracy, especially PCA-neural network with 16â84% and 26â71% increase in lake and creek compared with other pre-processing and regressor combinations. PCA could better represent the data and give a better fit than PARAFAC to reduce information loss, while neural network could represent the non-linear features. PCA coupled with neural networks was observed to result in the most sensitive and robust fluorescence detection model. The limit of detection (LOD) in Lake Okanagan water were 1.11 mg/L and 4.62 µg/L for NAs and phenol, respectively, while they were 1.85 mg/L and 5.03 µg/L in Mission Creek water. 66 Table 5.3 Mean absolute error (MAE) for peak-picking, PARAFAC and PCA dimensionality reduction analysis with varying regressors to predict naphthenic acids (NAs) and phenol in site-specific waters (Lake Okanagan and Mission Creek water) Lake site-specific models Creek site-specific models Dimensionality reduction Linear regression Neural network Random forest Linear regression Neural network Random forest Naphthenic acids (mg/L) Peak-picking 3.06 2.70 2.36 2.47 1.89 1.70 PARAFAC 1.79 1.59 1.52 1.75 1.76 1.52 PCA 1.38 1.40 1.67 2.00 1.54 1.66 Phenol (µg/L) Peak-picking 31.06 30.74 35.24 29.66 29.64 24.43 PARAFAC 14.03 16.70 20.28 21.28 15.75 18.37 PCA 6.79 5.66 10.26 12.99 8.61 11.69 It should be noted that while data analysis techniques influenced the prediction accuracy, other factors, for example, the source variability of background water quality, may also impact modeling. Overall, the lake detection models provided similar or better performance than the creek detection models. The reduced accuracy may be due to spatial variability of NOM between two water sources. Yang et al. (2019) reported in comparison to a lake source, a higher MAE in regression error was observed in a river source impacted by poor separation of the spiked contaminants from NOM fluorescence. 5.3.4 Contaminant detection in cross-source water In this study, model transfer methods were also investigated to reduce the burden on collecting data for developing site-specific models. A remapping type of approach was taken to have similar features of two surface waters using feature loadings derived from target water fluorescence EEMs. We hypothesized that background NOM variability and peak shifts between sources would be minimized after remapping the features. Therefore, linear regressors trained on the remapped source fluorescence EEMs could also provide sensitive identification and quantification of contaminants on the target water. We developed the detection model in Lake Okanagan water, transferring and testing it in the creek source. MAE was applied to compare the transfer models' predictability to the site-specific baseline models and no transfer situation (Table 5.4). The 'no transfer' represents directly 67 applying the site-specific models trained in the source dataset (lake) and predicting contaminant concentrations in the target water samples (creek). Peak-picking models were trained on the intensities at fixed and constant wavelengths for both sources, and therefore the concept of model transfer is not applicable. Table 5.4 Regression Error (MAE) for multivariate linear regression with varying dimensionality reduction techniques to predict naphthenic acids (NAs) and phenol in site-specific waters and in transfer water source Dimensionality reduction Site-specific (Creek) With Transfer No Transfer Naphthenic acids (mg/L) Peak-picking 2.47 â 5.70 PARAFAC 1.75 1.78 4.49 PCA 2.00 2.90 7.60 Phenol (µg/L) Peak-picking 29.66 â 32.23 PARAFAC 21.28 30.19 27.89 PCA 12.99 13.84 28.27 After directly using a site-specific detection model to new surface water, no transfer method resulted in the poorest performance to predict the concentration of NAs and phenol (NAs: 4.49 â 7.60 mg/L, phenol: 27.89 â 32.23 µg/L), which is also reflected in Figure 5.4. Based on the variation in background water composition, especially NOM, those models could not capture the relationship between the contaminants and background signals in another water site. Interference from NOM also explains the consistently better model performance of the site-specific models and the advantages of in-situ modelling or site-specific calibration to obtain site-specific relationships between variables effectively. However, cross-source transferred models using PCA were competitive compared to site-specific models and showed significant improvements compared with no transfer models (transferred models: NAs: 2.9 mg/L, phenol: 13.84 µg/L; no transfer: 7.6 mg/L, phenol: 28.27 µg/L). It implied the substantial capability of PCA-based model transfer to discover universal cross-source underlying components. Predictive performance visualized in Figure 5.4 further confirmed that the transferred model more accurately quantified contaminants in the target water after remapping the features. The lowest detection limits of the transferred model using PCA-neural 68 network approach was 1.01 mg/L and 5.61 µg/L for NAs and phenol in Mission Creek water, respectively. The measured detection limits of the transferred model were comparable to the site-specific results and had the potential to be used to detect exceedance of guideline levels. A cross-source model transfer approach may be a viable approach to the widespread implementation of fluorescence detection methods to address the significant burden for data collection to generate site-specific models, Figure 5.4 Measured vs predicted PAHs and pesticides for test samples (the creek source) using peak-picking or PCA approaches coupled with linear regression 5.4 Summary There is increasing concern about the growing oil sands process-affected waters (OSPW) produced from bitumen extraction. The application of fluorescence technique to quantify known NAs and phenol in surface waters was investigated in this study. Results showed that the PCA-69 neural network fluorescence model could provide sensitive detection with MAE < 1.54 mg/L and 8.61 µg/L for predicting concentrations of NAs and phenol in two water sources, respectively. However, adverse effects due to overlapping and interacting signals in complex mixtures hindered regression prediction. Although PARAFAC identifies more interpretable signals, incomplete component separation impacted by overlapping fluorophores and background noise could cause loss of information and significantly reduce regression error. Furthermore, the dimensionality reduction procedures of PARAFAC analysis were time-consuming and required a few weeks to take many iterations due to the mathematical complexity of separating fluorophores. An approach to transfer fluorescence-based contaminant detection models between water sources was also studied. Based on an already established model (from a source water), only a few additional unlabeled fluorescence EEMs from new target waters were required to remap features. The transferred models have comparable predictability to predict NAs and phenol in water samples and reduce the burden for data collection. The potential application to transfer the existing fluorescence model to other surface waters could aid in the broader implementation of fluorescence-based contamination detection models. 70 Chapter 6: Conclusions 6.1 Summary of contributions In this thesis, fluorescence excitation-emission matrices (EEM) were collected and analyzed to identify and quantify contaminants in the natural waters. Common aquatic pollutants including PAHs, pesticides, naphthenic acids, and phenol were studied in four distinct surface waters under the interference of natural organic matters (NOM). Several fluorescence analysis techniques were applied to interpret the high-dimensional EEMs and extract relevant information. The contributions of this thesis can be summarized as follows. 1. Fluorescence spectroscopy was successfully applied as a monitoring tool to identify some contaminants of concern in surface waters in the presence of NOM. Polycyclic aromatic hydrocarbons (PAHs) and pesticides are among the most common environmental pollutants in aquatic environments and threaten public and environmental health. Other toxic compounds such as naphthenic acids (NAs) and phenol present in oil process-affected waters (OSPW) produced from bitumen extraction have also increased attention. Detection of these toxic compounds in natural water was investigated using a fluorescence approach. Several background compounds, such as humic-like and protein-like substances from NOM interacted with the signals of contaminants. Furthermore, some pollutants such as NAs and phenol fluoresced on top of each other, making the underlying signals challenging to identify and separate. Peak-picking analysis with intensities at fixed wavelengths and several algorithm-based dimensionality reduction approaches were compared to determine how best to interpret the fluorescence excitation-emission matrices (EEMs). Multivariate linear regression (MLR), random forest, and neural networks were then applied to components derived from peak-picking, parallel factors analysis (PARAFAC), and principal component analysis (PCA) to quantify and predict concentration changes in those contaminants spiked into natural waters. Based on prediction accuracy, the PCA-neural network fluorescence model provided sensitive detection utilizing high-dimensional EEMs, with mean absolute error (MAE) < 1.54 mg/L and 8.61 µg/L to quantify NAs and phenol in two water sources, respectively. For PAHs and pesticides, both PCA and autoencoder coupled with multivariate linear regression could predict contaminant concentrations at low µg/L levels with MAE ranging from 0.021 to 0.21 µg/L for lake, and 0.049 to 0.42 µg/L for river water. 71 PARAFAC identified more interpretable signals to contrast with other dimensionality reduction approaches to interpret the underlying components. However, incomplete component separation in dimensionality reduction with PARAFAC due to convoluted fluorophores and background noise resulted in a loss of information and significantly reduced regression error. Furthermore, organic fluorophores with similar chemical structures were not easily distinguished using PARAFAC analysis. It was observed that some contaminants could also be mislabeled as humic-like or fulvic-like signals in fluorescence databases such as OpenFluor. 2. A cross-source fluorescence detection method was investigated to detect contaminants. A bench-scale fluorescence system was utilized to collect the real-time fluorescence excitation-emission matrices (EEM) to identify and quantify contaminants in the water. Previous work has shown the possible use of fluorescence to detect and quantify low levels of polycyclic aromatic hydrocarbons (PAHs) and fluorescing pesticides. The objective of the work was to investigate using different dimensionality reduction techniques for cross-source transfer of fluorescence-based detection models. This approach is contrary to previous studies that calibrate source-specific models. It was expected that data processing techniques would enable the transfer of fluorescence detection models from one water source to another. Based on the developed approaches presented in chapters 4 and 5, only a few additional fluorescence spectra of the background water quality and contaminants of interest were required, with no need for measuring and labelling new samples for calibration. Source variability of background water quality and difference in peak location and spectral shape of identical compounds were observed between two surface waters due to impacts of environmental conditions. When remapping the features and overcome peak shifts caused by various factors, including matrix effects, dimensionality reduction techniques showed different capabilities for fluorescence model transfer. The level of conformity among similar PARAFAC components between two surface water influenced the behaviour of the transferred model. For example, source variations among thiabendazole and fluoranthene showed negative effects on cross-source transferability. The observed source variation in contaminant signals is also evidence of interaction between the contaminants themselves and the water matrix, or interference of their component signals. In contrast, autoencoders and PCA improved transferability through a better overall representation of variance in the 72 dataset and simultaneous representation of multiple fluorophores in each LM or PC (PCA MAE: 0.150 to 0.591 µg L-1, autoencoder MAE: 0.170 to 0.403 µg L-1). Results showed comparable predictability provided by the site-specific models and the transferred models using autoencoder or PCA. For NAs and phenol, the detection model transferred from the lake source and tested in the creek source also showed comparable predictability compared with the site-specific creek model. Although those site-specific models have a consistently better performance, supporting the advantages of source-specific calibration, the cross-source model transfer method has significant benefits. Since there is no need to quantify the samples for calibration, a great burden would be reduced for data collection. This study could help to overcome spatial and temporal variability of background water quality, pH, and other variable parameters due to source variability. 6.2 Limitations and suggestions for future research The NA and phenol fluorescence data used for the cross-source model transfer was collected in two different water sources that are relatively similar in characteristics. This similarity may have impacted the assessment of how the background water quality hindered the information transfer in cross-source waters. Although weak correlation and poor performance from non transfered models indicated sufficient source variability to warrant transfer methods or source-specific models. A remaining challenge is the continuous monitoring using fluorescence spectroscopy. Long-term continuous organic characterization of water compositions is proposed to understand the seasonal dynamics of background water quality and interference from NOM. Specific to the detection of organic contaminants, it is suggested to focus on the potential peak shifts and signal shape distortion due to changes in environmental conditions or matrix effects. Besides, in situ analysis using water samples collected from continuous monitoring that contain the examined contaminants is recommended in future studies. Contaminants under interference from the temporal and spatial NOM variation should also be examined. Another limitation was using naphthenic acid standards in this thesis rather than the naturally occurring sources from OSPW water samples. While our objective is to detect the toxic contaminants due to OSPW, the changes in water character impacted by OSPW water have not been captured. As such, the next step in the development of these methods should be in-situ 73 analysis of water samples that contain the OSPW and environmental contaminants of concern. 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Environ Sci Technol 53(14), 8235-8243. 85 Appendix Chapter 4 Supplementary Data Figure S1. Loading plots of PARAFAC components for the Otonabee River (C1-7) Figure S2. Loading plots of PARAFAC components for the Lake Ontario (C1-7) 86 Table S1. Strength of regression (coefficient) between the transferred lake PARAFAC components and contaminants. PAHs and pesticides in Otonabee River samples Fluoranthene Naphthalene Pyrene Carbaryl Thiabendazole transferred C1 -0.21 -0.08 0.01 0.10 -0.15 transferred C2 0.04 -0.16 -0.01 0 0.03 transferred C3 (Thiabendazole) 0.01 -0.02 0.05 -0.02 0.08 transferred C4 (Fluoranthene) 0.26 0.01 0 -0.14 0.01 transferred C5 0.03 0.02 0 -0.02 0.02 transferred C6 (Pyrene) 0.01 0.05 0.21 0.11 0 transferred C7 (Naphthalene/ Carbaryl) -0.02 0.95 -0.05 0.17 0 Table S2. Strength of regression (coefficient) between the transferred lake autoencoder components and contaminants from Otonabee River. PAHs and pesticides in Otonabee River samples Fluoranthene Naphthalene Pyrene Carbaryl Thiabendazole transferred LM1 0.71 0.50 0.06 -0.41 -0.52 transferred LM2 -0.08 -0.06 -0.03 -0.13 -0.18 transferred LM3 -0.08 -0.08 0.02 3.21 4.35 transferred LM4 -0.04 -0.04 -0.02 0.08 0.07 transferred LM5 -0.19 -0.21 -0.21 -0.61 -0.71 transferred LM6 0.09 0.11 -0.50 2.96 -3.28 transferred LM7 0.98 0.98 1.15 -0.16 -0.21 Table S3. Strength of regression (coefficient) between the transferred lake PCA components and contaminants. PAHs and pesticides in Otonabee River samples Fluoranthene Naphthalene Pyrene Carbaryl Thiabendazole transferred PC1 1.13 1.09 1.11 -0.23 -0.15 transferred PC2 0.37 0.35 0.34 0.16 0.25 transferred PC3 -0.12 -0.01 -0.09 1.06 1.41 transferred PC4 0.06 0.05 0.03 0.08 -0.02 transferred PC5 0.18 0.12 0.03 -0.13 -0.34 transferred PC6 -0.06 -0.04 -0.01 -0.12 -0.19 transferred PC7 -0.09 -0.06 0.02 0.01 -0.17 87 Table S4. Strength of regression (coefficient) between the transferred lake peak-picking components and contaminants. PAHs and pesticides in Otonabee River samples Fluoranthene Naphthalene Pyrene Carbaryl Thiabendazole PP Fluoranthene 0.16 0.10 -0.03 -0.16 -0.21 PP Naphthalene 0.01 0.31 0.00 0.04 0.03 PP Pyrene 0.03 0.04 0.17 -0.02 -0.01 PP Carbaryl 0.01 0.01 0.00 0.36 0.03 PP Thiabendazole -0.22 -0.17 -0.09 0.04 0.90 Table S5. R square (R2) between preprocessing dataset and predicted contaminants in linear regression models Fluoranthene Naphthalene Pyrene Carbaryl Thiabendazole PARAFAC 0.999 0.999 0.999 0.993 0.972 autoencoder 0.990 0.992 0.994 0.967 0.982 PCA 0.988 0.998 0.999 0.995 0.984 Peak-picking 0.995 0.996 0.998 0.985 0.976 88 Chapter 5 Supplementary Data Figure S3. Loading plots of PARAFAC components for Okanagan Lake (C1-4) Figure S4. Loading plots of PARAFAC components for Mission Creek (C1-4)
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Peleato, Nicolas M.
Development of machine learning methods for improved fluorescence-based monitoring of environmental contaminants in surface waters
Text