Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
Proposed NAPS Monitor Classification
Brauer, Michael (Of University of British Columbia)
Hystad, Perry
Developing a Monitor Classification System for the National Air Pollution Surveillance System Prepared for Environment Canada by: Michael Brauer Perry Hystad School of Population and Public Health, The University of British Columbia Proposed NAPS Monitor Classification March 30th 2012 2 Preface This report has been prepared by its authors based on a scope of work prepared by the National Air Pollution Surveillance (NAPS) contractor. While the report has been reviewed by representatives of NAPS as sponsors of the study, the findings and conclusions expressed in this report are the opinions on the consultant authors, and do not necessarily represent the opinion of NAPS or Environment Canada. Provided along with this report are: • One Excel file containing all NAPS monitoring stations with derived classification variables. Proposed NAPS Monitor Classification March 30th 2012 3 Table of Contents Executive Summary ................................................................................................................. 6 Background .............................................................................................................................. 9 Literature Review ....................................................................................................................11 United States Air Quality Monitor Classification .....................................................................11 European Union Air Quality Monitor Classification .................................................................15 Australia and New Zealand Air Quality Monitor Classification ................................................18 Classification System for the Canadian NAPS Network.......................................................20 Available Canadian Spatial Data ...........................................................................................20 Proposed NAPS Monitor Classifications ................................................................................22 Level 1: Urbanization .........................................................................................................23 Level 2: Neighbourhood Population Characterization .........................................................24 Level 3: Local Land Use Characterization ..........................................................................26 Level 4: Local Source Characterization ..............................................................................29 Final NAPS Monitor Classification .........................................................................................35 Spatial Representativeness of Monitor Classifications........................................................40 Conclusions ............................................................................................................................51 References ..............................................................................................................................53 Appendices .............................................................................................................................56 Proposed NAPS Monitor Classification March 30th 2012 4 List of Figures Figure 1. Project workflow for the development of a monitor classification system for the NAPS Network.....................................................................................................................................10 Figure 2. Recommended distances and elevations of PM sampler inlets from heavily traveled roadway (EPA, 1997). ...............................................................................................................15 Figure 3. Satellite image of NAPS station 50128 with a 400m buffer and corresponding DMTI government and industrial land use classification. .....................................................................27 Figure 4. Satellite image of NAPS station 60106 with a 400m buffer and corresponding DMTI government and industrial land use classification. .....................................................................27 Figure 5. Agricultural classification based on DMTI and NALC land use. ..................................29 Figure 6. Proximity of NAPS monitors to major roads. ...............................................................31 Figure 7. Proximity of NAPS monitors to NPRI emission sources..............................................33 Figure 8. Illustration of NAPS classification structure. ...............................................................37 Figure 9. Grouping of annual NO2 averages by the four levels of NAPS classifications. ............42 Figure 10. Grouping of summer O3 averages by the four levels of NAPS classifications. ..........43 Figure 11. Grouping of annual PM2.5 averages by the four levels of NAPS classifications. ........44 Figure 12. Grouping of annual CO averages by the four levels of NAPS classifications. ...........45 Figure 13. Spatial representativeness of 2006 annual average NO2 levels (ppb) from NAPS monitor 100314.. .......................................................................................................................50 Figure 14. Location of NAPS monitors and watersheds that could be modified to represent representative airsheds. ............................................................................................................51 Proposed NAPS Monitor Classification March 30th 2012 5 List of Tables Table 1. Summary of the EPA AQS monitor siting variables. ....................................................12 Table 2. PAMS O3 monitoring network site classifications (EPA, 2010). ....................................13 Table 3. Spatial scales of monitor representativeness. ..............................................................14 Table 4. Primary spatial scales of specific pollutants (modified from EPA, 2007). .....................14 Table 5. Summary of the EUROAIRNET monitor classification. ................................................16 Table 6. Summary of additional EUROAIRNET monitor meta-data. ..........................................17 Table 7. Area of representativeness for different monitor classifications. ..................................18 Table 8. Australia and New Zealand Monitor Classifications. ....................................................18 Table 9. Monitor classifications for the New Zealand environmental performance indicators. ....19 Table 10. Available spatial data for use in the development of a NAPS monitor classification. ..20 Table 11. DMTI Road classifications and associated AADT (mean) counts in Vancouver. ........21 Table 12. Proposed NAPS monitor classification. .....................................................................22 Table 13. Population centre size classes by province and territory (Statistics Canada, 2011). ..24 Table 14. Summary of urbanization classifications for the NAPS monitor network. ...................24 Table 15. Categories of residential population within 4km of NAPS stations and the modified urbanization class codes. ..........................................................................................................25 Table 16. Summary of predominant and secondary land use within 400m of NAPS monitors. ..28 Table 17. Summary of monitor type classifications. ...................................................................34 Table 18. Summary of NAPS monitor classification, including variables/codes/definitions. .......36 Table 19. NAPS classification based on monitor type, urbanization class and local land use. ...38 Table 20. Summary of PM2.5, O3 and black carbon spatial variability by different geographic scales (Brauer et al., 2010). ......................................................................................................46 Table 21. Spatial scales of specific pollutants (modified from EPA, 2007) and monitor classification variables associated with each scale and pollutant. .............................................48 Proposed NAPS Monitor Classification March 30th 2012 6 Executive Summary We have developed a hierarchical monitor classification framework for the National Air Pollution Surveillance (NAPS) network. The classification includes variables derived in a GIS and captures urbanization, neighborhood population, local land use, and monitor type characteristics. This information will aid in the use, grouping, interpretation and extrapolation of NAPS measurement data. The excel file provided with this report contains the final monitor characterization for all 309 NAPS monitoring stations included in the database that was provided by Environment Canada. Recommendations: • An important limitation of most monitor classifications is that they are not pollutant specific, due to the trade-offs between usability and complexity. We have tried to address this issue by using a hierarchical classification that captures different scales of pollution sources. Monitor siting characteristics relevant to O3 are particularly unique, due to atmospheric photochemical reactions, and other national monitoring agencies have developed monitor classifications specific to O3. This could also be conducted for the NAPS network. • Traffic influenced monitors were classified using road classes as surrogates for traffic volume and emissions. Incorporating traffic counts (and composition) would improve the classification of traffic influenced monitors. • Industrial influence is pollutant specific and we have therefore classified industrial influenced monitors broadly, using a combination of GIS methods as well as existing industrial classified monitors and classifications based on VOC and SO2 measurement data. To standardize the classification of industrial influenced monitors, a Proposed NAPS Monitor Classification March 30th 2012 7 framework/decision tree should be established to guide industrial classification that incorporates pollutant type, GIS calculations and local knowledge. • The NAPS station file received on February 29th 2011 was used to derive station characteristics. While this is the most up to date location file, some monitors (e.g. NAPS ID# 104003 and 100304) may not be precisely located. It was outside the scope of this contract to validate all NAPS monitor latitude and longitude coordinates; however, it is recommended that monitor coordinates be calibrated using Google Earth (positional accuracy should be ±20m for NAPS latitude and longitude coordinates). • Land use and population density changes over time and it is recommended that NAPS periodically evaluate and update monitor siting characteristics with each 5-year census (e.g. 2011, 2016, 2021, etc.). In addition to creating the NAPS classification system, we have also examined how the resulting classifications can inform the spatial representativeness of monitor measurements as well as guide population exposure assessment. The spatial scales that were incorporated into this classification system (i.e. urbanization, neighborhood population and local land use) correspond to the widely used urban (4-50km), neighborhood (0.5-5km) and middle (0.1-0.5km) spatial scales defined by the U.S. EPA (2007). These variables provide the basis for a first estimate of the representativeness of a monitor's measurements, based on a quantitative assessment of a limited number of parameters influencing pollution levels. Recommendations: • While the monitor classification can be used to guide interpretation, use and extrapolation of NAPS measurements, spatial representativeness is unique to each pollutant or group of pollutants with similar characteristics, and also dependent upon the Proposed NAPS Monitor Classification March 30th 2012 8 monitors local context. Ideally, a representative area should be defined for each pollutant and NAPS monitor, either using measurements or modeling approaches. • Only non-specific source (background) monitor types should be extrapolated based on geographic distance (e.g. inverse distance weighting interpolation). • Industrial monitors are unique to the industrial emissions source, and local meteorology and topography and cannot be extrapolated. • Traffic monitors represent geographic areas parallel to major roadways (±100m) and extrapolation can be made, within the same airshed, to areas surrounding similar roads (i.e. traffic volume and composition and local topography). Geographic extrapolation (e.g. inverse distance weighting interpolation) of traffic monitors should not be conducted. • Airsheds should also be delineated and assigned to NAPS monitors to improve interpretation and extrapolation of measurements. Proposed NAPS Monitor Classification March 30th 2012 9 Background Environment Canada, in collaboration with the provincial, territorial and regional monitoring agencies, operates and manages the National Air Pollution Surveillance (NAPS) ambient monitoring network across Canada. The purpose of this Program is to: report on air quality indices and forecasting; characterize air quality trends; develop and evaluate emission control strategies; estimate health related exposure risks; evaluate source-receptor relationships; provide data for input and model evaluation; and measure the overall progress of air pollution control programs in Canada. The application of the NAPS network to these objectives is dependent on the spatial representativeness of NAPS monitor data. It does not matter how precise or unbiased the measurement values are if monitor sites are unrepresentative of the population or area they are presumed to represent (EPA, 2008). We previously examined the spatial representativeness of fine particulate matter (PM2.5) and ozone (O3) measurements from NAPS monitors and found significant within-city variability (Brauer et al., 2010). Assessing the spatial representativeness of monitoring data and classifying monitors are related tasks and we recommended that the existing NAPS monitor classifications be refined to provide better information on source influences and representativeness (Brauer et al. 2010). Currently, NAPS monitoring stations are assigned a code to identify dominant land use within a square kilometre of each monitor. This coding system was first introduced when NAPS was originally formed over 40 years ago. As new sites have been added or existing stations moved to other locations, the NAPS agency where the monitor is located has been responsible for assigning the land use category for these stations. Because these land use codes are not explicitly defined, the assignment of codes has been somewhat subjective. In addition, the land use surrounding NAPS monitors can change over time, and the current classifications have not been updated. Proposed NAPS Monitor Classification March 30th 2012 10 The increased availability of geospatial data and geographic information systems (GIS) can be used to quantitatively evaluate monitor siting characteristics, such as land use and nearby emission sources, which can be used to develop a comprehensive monitor classification system. Here we develop such a monitor classification system for the NAPS network. Figure 1 illustrates the project workflow. First we reviewed existing fixed-site monitoring network classification schemes and other relevant information to guide an updated Canadian NAPS monitor classification system. The literature review, in combination with a review of available spatial data in Canada, was used to develop definitions for the NAPS monitor classification. A GIS was then used to derive the variables needed to implement the system for all NAPS stations (n=309). In addition, we examine whether a spatial representative zone can be applied to the resulting monitor classifications, as well as provide guidance on how the classification system could be used to guide population exposure assessment. Figure 1. Project workflow for the development of a monitor classification system for the NAPS Network. Proposed NAPS Monitor Classification March 30th 2012 11 Literature Review Here we present a review of monitor classification schemes used in national ambient air pollution networks similar to NAPS, including those in the United States (US), European Union (EU), New Zealand, and Australia. Due to the different nature of emission sources, monitoring networks in developing countries were not reviewed. United States Air Quality Monitor Classification The U.S. Environmental Protection Agency’s (EPA's) Air Quality System (AQS) is EPA's repository of ambient air quality data. The majority of air quality monitoring performed in the US is carried out by state, local, and tribal agencies in four major networks of monitoring stations: State and Local Air Monitoring Stations (SLAMS), Photochemical Assessment Monitoring Stations (PAMS), PM2.5 Chemical Speciation Network (CSN), and air toxics monitoring stations (Canada-U.S. Air Quality Agreement, 2010). There are a number of monitor siting variables included in the AQS meta-data (see the AQS (2010) data coding manual for a full description); however, there is no specific monitor classification system. The AQS metadata should be used with caution as there are no formal requirements for the responsible state or local air monitoring agencies to quality assure or update metadata at any frequency (EPA, 2010). The one variable calculated using a GIS-based approach is urbanized area. An urbanized area is defined as a US Census Bureau demographic entity that comprises a place and the adjacent densely-settled surrounding territory that together have a minimum population of 50,000 people. This field is automatically derived from the EPA master geospatial database from the user-provided location. An additional location setting variable is also present that includes urban and centre city, suburban or rural; however, a clear definition of these classifications and how they are determined is not provided. Detailed information on the nearest road is also Proposed NAPS Monitor Classification March 30th 2012 12 collected in the AQS meta-data, including: the road name, type (i.e. local, highway, interstate, etc), daily traffic counts, year traffic counts were obtained, source of the traffic counts, and direction from monitor site to street. A subjective classification of the dominant land use type within 1/4 miles (~400m) of monitors is also recorded. It unclear whether there are set land use classes; the AQS (2010) coding manual provides examples such as agricultural, blighted areas, commercial, desert, forest, industrial, military, reservation, mobile residential, etc. A site comments field is also available to describe special features of a site. Table 1 summarizes monitor siting variables available for AQS monitors. Table 1. Summary of the EPA AQS monitor siting variables. Characteristic Variables Definitions Urban Urban > 50,000 individuals Location Setting Urban and centre city No definitions provided Suburban Rural Traffic Nearest road Distance to nearest road and associated road attributes (type, traffic counts, direction) Land use No set variables (Examples given: agricultural, blighted areas, commercial, desert, forest, residential, etc.) Dominant land use within 1/4 miles (~400 meters) The PAMS monitoring network has different site types (shown in Table 2) that specifically classify O3 related monitors. In 2007, the EPA determined that the number of required PAMS sites could be reduced. Only one Type 2 site is required per area regardless of population; Type 4 sites would not be required; and only one Type 1 or one Type 3 site would be required per area. Proposed NAPS Monitor Classification March 30th 2012 13 Table 2. PAMS O3 monitoring network site classifications (EPA, 2010). Monitor Type Measurement Scale Description 1 Urban Upwind and background characterization to identify those areas which are subjected to overwhelming incoming transport of O3. These sites are located in the predominant morning upwind direction from the local area of maximum precursor emissions and at a distance sufficient to obtain urban scale measurements. Typically, these sites will be located near the upwind edge of the photochemical grid model domain. 2 Neighbourhood Maximum O3 precursor emissions impact site. These sites are established to monitor the magnitude and type of precursor emissions in the area where maximum precursor emissions representative of the MSA/CMSA are expected to impact and are suited for the monitoring of urban air toxic pollutants. The #2 Sites are located immediately downwind (using the same morning wind direction as for locating Site #1) of the area of maximum precursor emissions and are typically placed near the downwind boundary of the central business district (CBD) or primary area of precursor emissions mix to obtain neighbourhood scale measurements. 2a Neighbourhood A second #2 Site may be required depending on the size of the area, and should be placed in the second-most predominant morning wind direction 3 Urban Maximum O3 concentration site. These sites are intended to monitor maximum O3 concentrations occurring downwind from the area of maximum precursor emissions. Locations for #3 Sites should be chosen so that urban scale measurements are obtained. Typically, these sites are located 10 to 30 miles from the fringe of the urban area 4 Urban Extreme downwind monitoring site. These sites are established to characterize the extreme downwind transported O3 and its precursor concentrations exiting the area and will identify those areas which are potentially contributing to overwhelming O3 transport into other areas. The #4 Sites are located in the predominant afternoon downwind direction from the local area of maximum precursor emissions at a distance sufficient to obtain urban scale measurements. Typically, these sites will be located near the downwind edge of the photochemical grid model domain. The measurement scales referenced in Table 2 have been defined for monitoring station siting purposes by the US EPA (1997) and have also been incorporated into the ambient air monitoring protocol for the Canada-wide standards (CWS) for particulate matter and O3 (CCME, 2011). The CWS states that "the goal in siting stations is to match the spatial scale represented Proposed NAPS Monitor Classification March 30th 2012 14 by the sample of monitored air with the spatial scale most appropriate for the monitoring objective of the station" (CCME, 2011). These spatial scales (summarised in Table 3) are therefore important to consider when developing a monitor classification system. Table 3. Spatial scales of monitor representativeness. Category of Spatial Scale Range of Air Volume of Concentration Micro Usually several metres to 0.1 km. Middle Typical of areas up to several city blocks ranging in size from about 0.1 to 0.5 kilometres. Neighbourhood Usually within some extended area of the city that has relatively uniform land use with dimensions in the 0.5 to 4.0 kilometres range. Urban Entire metropolitan area ranging in size from 4 to 50 kilometres Regional Usually a rural area of reasonably homogeneous geography and extends from 10s to 1,000s of kilometres. Global Represent concentrations characterizing a nation and the globe as whole. The US EPA (1997) report on "Guidance for network design and optimum site exposure from PM2.5 and PM10" provides further information on how to match categories of spatial scales to different pollutants as well as guidance for traffic-related monitors located in different spatial scale zones. Table 4 summarizes the associated spatial scales of different pollutants and Figure 2 illustrates the recommended distances and elevations of PM sampler inlets from heavily traveled roadways. This information is useful for classifying traffic-influenced monitors that are located in different scale representative zones. Table 4. Primary spatial scales of specific pollutants (modified from EPA, 2007). Category of Spatial Scale SO2 CO O3 NO NO2 Pb PM10 PM2.5 PM1.0-2.5 Micro * * * * * * * * * Middle * * * * * * * * Neighbourhood * * * * * * Urban * * * Regional * * * Global * * * Proposed NAPS Monitor Classification March 30th 2012 15 Figure 2. Recommended distances and elevations of PM sampler inlets from heavily traveled roadway (EPA, 1997). European Union Air Quality Monitor Classification In 1997 the European Union Council Decision 97/101/EC (Garber et al., 2002) established the European-wide air quality monitoring network (EUROAIRNET) with the goal of harmonising information on ambient air quality data between EU countries. The resulting information system, AIRBASE, provides access to air quality monitoring data from all EU countries. Currently, monitoring stations are classified according to the station type (traffic, industry, background) and the zone (urban, suburban, rural) and a new characterisation of zone (e.g. residential, industrial, commercial, etc.) is being added. Table 5 summarises the monitor classification variables available in EUROAIRNET. Other variables are also provided in the monitor's meta-data (see Table 6), including subclasses of regional background stations, further characterization of traffic monitors based on street width (e.g. narrow or canyon) and traffic volume, and a further characterization of industrial monitors based on the type and distance to Proposed NAPS Monitor Classification March 30th 2012 16 industries. Monitors also contain a representative area field which is the radius of the area of representativeness, or the length of road represented by traffic monitors. Table 5. Summary of the EUROAIRNET monitor classification. Monitor Classification Variables Definitions/Methods Type of Station Traffic Stations located such that their pollution level is influenced mainly by emissions from a nearby road/street. Industrial Stations located such that their pollution level is influenced mainly by nearby single industrial sources or industrial areas Background Stations that are neither traffic nor industrial. Zone Urban Continuously built-up area. Suburban Largely built-up area: continuous settlement of detached buildings mixed with non-urbanised areas (small lakes, woods, agricultural). Rural All areas that not fulfil the criteria for urban/suburban areas. Characterization of Zone Land use characterization (i.e. residential, commercial, industrial, agricultural, nature, res/com, com/in, ind/res, res/com/ind, agr/natural). Not defined. Proposed NAPS Monitor Classification March 30th 2012 17 Table 6. Summary of additional EUROAIRNET monitor meta-data. Characteristic Variables Definitions/Methods Regional, Background Stations Near city Located in rural/agricultural areas, with a distance of 3-10 km from built-up areas and other major sources. Regional Located in rural/agricultural areas, with a distance of 10-50 km from built-up areas and other major sources. Remote Located in rural/natural areas, with a minimum distance of 50 km to built-up areas and other major sources. Traffic Stations Traffic Volumes (AADT) Not defined Distance from kerb Heavy duty fraction of traffic Traffic speed Distance between and height of building facades (street canyons) Width of street/road (non-canyon streets) Industry Stations Type of industry(ies) Not defined. Distance to source/source area Area of representativeness Area of representativeness (radius). For traffic stations, the length of street/road that the station represents. Not defined. The area of representativeness in the EUROAIRNET meta-data is a qualitative evaluation of the area that the monitors' measurements represent. Typically, representativeness is defined as areas within ±20% of long term monitor measurements (Blanchard et al., 1999). The guidelines for monitor reporting (European Council, 1997) state that: "the area of representativeness is not easily determined. It requires either extensive monitoring at several adjacent sites covering an area around the station, or detailed dispersion model calculations based upon detailed emission inventories, both for the area in question and the larger surrounding area. In practice, such determinations are rarely performed". Table 7 summaries typical ranges of the area of representativeness (radius of area) for the various station classes (Type and Zone) currently used in EUROAIRNET (Larssen et al., 1999). Proposed NAPS Monitor Classification March 30th 2012 18 Table 7. Area of representativeness for different monitor classifications. Monitor Classification Radius of Area of Representativeness Traffic Station *Refers to length of road not area Industrial Station 10-100m Background Station - Urban Background 100-1000m - Near-City Background 1-5km - Regional Background 25-150km - Remote Background 200-500km Australia and New Zealand Air Quality Monitor Classification A new monitor siting standard for Australia and New Zealand (AS/NZS 3580.1.1:2007) was updated in 2007 and recommends a combination of New Zealand, Australian and US EPA site classifications (Ministry for the Environment, 2009). The purpose of the site classification system is to distinguish between different types of monitoring sites and to allow data sets to be compared nationally and internationally. The monitor siting classification and associated spatial scales are summarized in Table 8. Categories include traffic, industrial, residential and special. Industrial and residential classifications are further subdivided into peak and neighbourhood. Table 8. Australia and New Zealand Monitor Classifications. Site Category Site Scale Typical Area Traffic Peak (meters to 10s of meters) Typically very close to high-traffic-use roads and intersections. Site should be 2-5m from the roadside. Industrial Peak (meters to 10s of meters) Close to one large point source or fugitive emission. Typically used for compliance monitoring. Neighbourhood (10s of metres to 0.5 km) Large and varied point source industry emissions and high population density. Such areas may contain heavy commercial and processing industries. Residential Peak (meters to 10s of meters) Site located somewhere not truly representative (so it is not neighbourhood scale) but does not exactly fit the traffic or industrial peak site description. Proposed NAPS Monitor Classification March 30th 2012 19 Neighbourhood (10s of metres to 0.5 km) Suburban areas in larger cities with a relatively high population density, but not in the immediate vicinity of congested roads or industry. This category also includes residential areas in smaller towns in rural areas. Special (site description) Regional (10s to 100s of kilometers) Airsheds that are distinct in their geographical, meteorological and emissions characteristics. National National background sites that contribute to the global network. New Zealand has another monitor classification method for their environment performance indicators (EPI) (Ministry of the Environment, 1997). Site categories are based upon a mixture of the land use surrounding the station, the predominant sources of emissions, the spatial scale the site is intended to represent, and to some extent the station’s objectives. Table 9 summarises the EPI monitor classifications. Table 9. Monitor classifications for the New Zealand environmental performance indicators. Type Description Land Use Scale Emissions Source Type 1 (a) Adjacent to a busy roadway at street level. Any Micro, dense Single motor vehicle line source Type 1 (b) Adjacent to one particular source. Any Micro, dense Single point source Type 2 (a) In residential area where the main sources of contaminants would be domestic activities and similar proportion of vehicle emissions. Residential Neighbourhood Residential and motor vehicle Type 2 (b) Large industrial area where there are significant emissions from industry and smaller proportion from domestic fires and vehicles. Industrial Neighbourhood Mainly industrial + some motor vehicle and residential Type 3 Rural areas or national parks where the air quality may be influenced by natural sources with only a small proportion coming from human activities. Rural Regional Mainly background levels of pollutants Type 4 Pristine areas that provide an indication of air quality before it flows over New Zealand - ie,. the contaminants in the air as it flows off the sea. Pristine National No anthropogenic emissions Proposed NAPS Monitor Classification March 30th 2012 20 Classification System for the Canadian NAPS Network Available Canadian Spatial Data A wide-range of spatial data is available in Canada that can be used within a GIS to classify NAPS monitors. Table 10 summarises spatial data sources that capture variables typically included in national monitor classifications. The geographic coverage of the data source is also included as some datasets are available for urban areas on Canada only, or there are important scale and accuracy differences between urban and rural areas. Table 10. Available spatial data for use in the development of a NAPS monitor classification. Variable Type Source Geographic Coverage Road Networks • Source: DMTI Inc. 2010 road network • Format: Line file with road classification attributes (5 cartographic codes). • National • Source: Statistics Canada 2011 Road Network File. • Format: Line file with road classification attributes (5 street rank codes). • National Industrial Point Sources • Source: National Pollutant Release Inventory (NPRI) 2009 data. • Format: Point file with amount and type of emissions to air. • National Small Industrial Point Sources • Source: Dunn and Bradstreet 2007 Commercial Database Format: Point file with business type and number of employees. • National Population Density • Source: Statistics Canada 2006 Block Point File • Format: Point file with population counts. • National Populated Location File • Source: Statistics Canada 2011 Census dissemination area population counts • Format: Polygon file with 3 urban classifications • National Statistical Area Classifications • Source: Statistics Canada 2011 Census Sub-Division (CSD) classifications • Format: Polygon file with 5 classifications based on CSD geography • National Land Use • Source: DMTI Inc. 30m land use • Format: Polygon file with land use classifications (e.g. residential, industrial, commercial, • Urban locations Proposed NAPS Monitor Classification March 30th 2012 21 government, institutional, parks, water, open) • Source: North American Land Cover (NALC) derived from MODIS Satellite data (250m resolution) • Format: Polygon file with land use classifications (e.g. developed/built up, forested, water, agricultural, etc.) • National Greenness Index • Source: Derived from Landsat Satellite data • Format: Raster file with greenness index • National Elevation • Source: DMTI Inc. 30 and 90 m elevation • Format: Raster tiles by NTS index grid. • National Elevation • Source: Data: GTOPO30 global DEM ~1kmx1km • Format: Raster • Meteorology • Source: National Climate Data and Information • Format: Point file with annual meteorological variables. • National The data sources in Table 10 capture the different scales of pollution emission sources typically present in air pollution network classifications; however, one of the major data shortcomings is information on road traffic volumes. Other national monitoring networks have collected meta-data information on traffic conditions, including traffic annual average daily traffic (AADT) volumes and the fraction of heavy duty traffic on the nearest road. This information is unavailable in Canada in a national, standardized format and road classifications are therefore typically used as surrogates for traffic levels. Table 11 summarizes DMTI road classes and approximate AADT counts for each class (BC MOE, 2006b) based on data from Vancouver. Table 11. DMTI Road classifications and associated AADT (mean) counts in Metro Vancouver. DMTI Road Class AADT (mean) Local 6,500 Major 15,000 Highway Secondary 18,000 Highway Principal 21,000 Expressway >115,000 Proposed NAPS Monitor Classification March 30th 2012 22 Proposed NAPS Monitor Classifications The proposed NAPS monitor classification is shown in Table 12. The classification uses a hierarchical structure, similar to that of the EUROAIRNET monitor classification, and provides flexibility in terms of classifying monitors based on regional, neighbourhood and local scales of population density, land use characteristics and emission sources. Table 12. Proposed NAPS monitor classification. Monitor Class Variables Definition Urbanization Large Urban Area Large population centre (population ≥100,000) Medium Urban Area Medium population centre (population between 30,000-99,999) Small Urban Area Small population centre (population between 1,000-29,999) Non-Urban Non-urban area (population ≤999) Neighbourhood Population Population Q1 Categories of residential population within 4km. Population categories roughly correspond to population quartiles within each urbanization class. Population Q2 Population Q3 Population Q4 Local Land Use Agricultural The dominant land use category within a 400m radius area. A secondary land use code is included if greater than 1/3 of the area is also covered by another land use category. Commercial Industrial Open Parks Residential Water Monitor Type Traffic Monitors within 100m of a major road (defined as all freeways, highways, and arterial and collector roads) and located in an urban region (LU, MU, or SU). Point Source Monitors within 400m of a major industrial emissions source (identified from the NPRI database), subjective classification of industrial monitors in the original NAPS monitor meta-data, and industrial classified based on VOC and SO2 measurement data. Traffic and Point Source Traffic and point source influenced monitors. Non-Specific Not classified as traffic or industrial influenced (non-specific monitor type). This classification is based on a number of criteria, which will considerably enhance its utility over a simple classification structure with few criteria (Spangle et al., 2007), and since it is Proposed NAPS Monitor Classification March 30th 2012 23 derived in a GIS using available geospatial data it does not require additional effort from network managers and can be updated periodically to incorporate changes in land use and population density. Level 1: Urbanization The largest spatial scale of the classification is the degree of urbanization around each monitor (level 1). Various approaches are available for classifying urbanization and no consistent definition was found within the reviewed monitor classifications. For example, EUROAIRNET defines suburban as largely built-up areas with continuous settlement of detached buildings mixed with non-urbanised areas, while the US AQS does not define a suburban area. We use the Statistics Canada population centre classification to define different levels of urbanization in Canada. This classification does a poor job of differentiating between urban core and suburban areas (Turcotte, 2008); however, the neighborhood population classification (level 2) will differentiate between high and low population density locations. A population centre has a minimum population concentration of 1,000 persons and a population density of at least 400 persons per square kilometre, based on the 2011 census population count. All areas outside population centres are classified as non-urban (rural) areas. Taken together, population centres and rural areas cover all of Canada. Population centres are classified into three groups, depending on the size of their population: • Large urban population centres, with a population of 100,000 or more • Medium population centres, with a population between 30,000 and 99,999 • Small population centres, with a population between 1,000 and 29,999 Table 13 summarizes the number of different population classes by province; nationally there are 861 small population centres, 54 medium population centres and 32 large urban Proposed NAPS Monitor Classification March 30th 2012 24 population centres. Maps of the level 1 urbanization classifications are shown for the major urban areas of Canada in Appendix 1. Table 13. Population centre size classes by province and territory (Statistics Canada, 2011). Class Canada NL PEI NS NB Que Ont Man Sask Alta BC YT NWT NVT Large 32 1 1 1 6 14 1 2 2 4 Medium 54 1 1 2 13 19 1 2 6 9 Small 861 29 6 35 30 224 237 42 59 101 87 1 3 7 Total 947 30 7 37 33 243 270 44 63 109 100 1 3 7 Table 14 summarises the number of NAPS monitors within each urbanization class. A 250m buffer was used to capture monitors (n=6) that were on the border of urbanization regions but not contained within the population location boundaries. In total, 254 (82%) monitors were located in urban areas of Canada. Table 14. Summary of urbanization classifications for the NAPS monitor network. Urbanization Class Definition Code NAPS Monitors Large Urban Area Large population centre (population ≥100,000) LU 100 (32%) Medium Urban Area Medium population centre (population between 30,000-99,999) MU 36 (12%) Small Urban Area Small population centre (population between 1,000-29,999) SU 116 (38%) Non-Urban Non-urban area (population ≤999) NU 57 (18%) Level 2: Neighbourhood Population Characterization The second level of monitor classification summarizes the size of residential population located within 4km of NAPS monitors. A distance of 4km was selected as this represents the maximum distance associated with a neighbourhood scale of spatial representativeness (EPA, 1997). The 2006 Census block point population file (representing the finest spatial resolution of the location of the Canadian population: one block point represents on average 89 individuals) was used to determine the residential population within 4km. The 2011 block point population Proposed NAPS Monitor Classification March 30th 2012 25 file is currently unavailable. While a minimum population count and density threshold is incorporated in the urbanization category, it is at a different spatial scale than the neighborhood population class and there is significant variability in neighbourhood populations covered by the NAPS monitors in the different urbanization classes. Table 15 summarizes the urbanization classes modified with neighborhood (within 4km) population categories. The population breaks are meant to aid in the grouping of similar monitors and therefore correspond roughly to NAPS population quartiles within each of the urbanization classes. In addition to providing guidance on grouping monitors, the classes also provide insights into emission source strengths, such as domestic heating, as well as inform population exposure assessment. Specific population breaks could have been developed from estimates of the impact of population density-related emissions on ambient air quality levels; however, this was outside the scope of the project and would need to be pollutant specific. Table 15. Categories of residential population within 4km of NAPS stations and the modified urbanization class codes. Urbanization Class Code Neighbourhood (4km) Population1 Large Urban Area LU1 ≥150,000 LU2 80,000-149,999 LU3 50,000-79,999 LU4 ≤49,999 Medium Urban Area MU1 ≥40,000 MU2 30,000-39,999 MU3 25,000-29,999 MU4 ≤24,999 Small Urban Area SU1 ≥12,500 SU2 7,500-12,499 SU3 5,000-7,499 SU4 ≤4,999 Non-Urban NU1 ≥1,500 NU2 500-1,499 NU3 50-499 NU4 ≤49 1 Population categories roughly correspond to quartiles within urbanization classes. Population sizes may not correspond to definitions of urban classes because they are calculated from populations within 4km of NAPS stations and NAPS stations may be located near urbanized area boundaries. Proposed NAPS Monitor Classification March 30th 2012 26 Level 3: Local Land Use Characterization The third level of the monitor classification represents local land use. The primary land use characterization is the land use type covering the greatest area within a 400m radius buffer around each monitor. A radius distance of 400m was selected as this distance represents the middle scale of spatial representativeness (EPA, 1997) and corresponds to the U.S. AQS meta-data, which summarizes land use within a 1/4 mile radius of monitors. In addition to the dominant land use type in the 400m radius buffers, a secondary classification was calculated if another land use category covered greater than 1/3 of the buffer area. The 1/3 cut-off was selected as this represents an amount of land use area that could have significant air pollution effects and also limits the land use classification to two categories. For example, NAPS monitor #70501 has 56% residential land use (R), 35% industrial land use (I), and 9% open land use (O) - the resulting land use classification is R-I. DMTI Inc. (2010) data were used to assess land use for monitors located in areas covered by DMTI landuse (n=211), as unpopulated locations are not covered. DMTI land use has a spatial resolution of 30m and includes residential, industrial, commercial, government and institutional, open, parks, and water classifications. The government and institutional land classification was not included in the final analysis as this category captures a wide range of land use types. For example, NAPS monitor 50128 and 60106 are located in the government and institutional classification; however, one represents an airport location while the other represents a farm location, shown in Figures 3 and 4. Only four stations had government and institutional land use > 50%. Two stations were located at airports and were given commercial classifications, another station was given an agricultural classification (based on the NALC data described next) and the other station was given a residential classification (the second most prevalent land use class within the 4km buffer). Proposed NAPS Monitor Classification March 30th 2012 27 Figure 3. Satellite image of NAPS station 50128 with a 400m buffer and corresponding DMTI government and industrial land use classification. Figure 4. Satellite image of NAPS station 60106 with a 400m buffer and corresponding DMTI government and industrial land use classification. The North American Land Cover (NALC) data, created from 2005 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery at a 250m resolution, was used to Government/Institutional Land Use Government/Institutional Land Use Proposed NAPS Monitor Classification March 30th 2012 28 examine land use for monitors (n=98) not covered by DMTI land use as well as to assess agricultural land use for all monitors. Due to the larger spatial resolution of this land use data, different assignment criteria where developed. All monitors not captured by DMTI land use were given either an open, agricultural or water classification. For urban monitors, agricultural land use was also assessed using the NALC data. If a monitor was classified as open with DMTI data and contained greater than fifty percent agricultural classification from NALC data it was classified as agricultural (the DMTI open classification contains several land use types not related to agriculture). Figure 5 illustrates a NAPS monitor that was classified as open using DMTI data but then classified as agriculture based on both DMTI and NALC data. Table 16 summarizes the primary and secondary land use classes within 400m of NAPS monitors; the majority of land use classifications are residential (42%) and open (37%). Only 41 monitors have more than one land use classification (i.e. two different land use types covering >1/3 of the area within 400m). Table 16. Summary of predominant and secondary land use within 400m of NAPS monitors. Land Use Class Code Predominanta Secondaryb Agricultural* A 35 (11%) 12 (4%) Commercial C 2 (1%) - Industrial I 11 (4%) 5 (2%) Open, O 115 (37%) 8 (3%) Parks P 9 (3%) 1 (0.3%) Residential R 130 (42%) 14 (5%) Water W 7 (2%) 1 (0.3%) * Based on DMTI open and NALC agricultural classifications. a Predominant land use within 400m. b Secondary land use ≥ 1/3 of land use within 400m. Proposed NAPS Monitor Classification March 30th 2012 29 Figure 5. Agricultural classification based on DMTI and NALC land use. Level 4: Local Source Characterization The final level of characterization assesses monitor types through local emission surrogates. While there was consistency in the types of local sources characterized in other country monitor classifications (i.e. traffic, industry, and background (no local source)), the specific criteria used to identify traffic or industrial influenced monitors was not consistent. For example, EUROAIRNET defined a traffic station as "located such that their pollution level is influenced mainly by emissions from a nearby road/street", while the US AQS did not classify traffic influenced versus not influenced monitors, but rather provided the distance to the nearest DMTI Open Classification NALC Agricultural Classification Proposed NAPS Monitor Classification March 30th 2012 30 road and associated road attributes (type, traffic counts, direction). For industrial influenced monitors there were no definitions or criteria given in any reviewed classification. Assessing Monitors Strongly Influenced by Traffic Here we classify monitors strongly influenced by traffic as a monitor located within 100m of a major roadway in an urban area (urbanization codes LU, MU and SU). The 2010 DMTI road network was used with expressways, principal and secondary highways and major road classifications to represent major roads across Canada. A distance of 100m was selected based on a recent critical review conducted by the Health Effects Institute (HEI, 2010) on traffic related air pollution gradients and health effects. The report found that traffic related air pollution gradients are present up to 300-500m from a major roadway; however, the majority of the gradient studies reviewed were conducted on freeways or highways (with traffic volumes over 100,000 AADT) and 500m gradients are unlikely to be observed adjacent to roads with lower traffic volumes. Here, our goal is also to identify a proximity distance that would lead to monitors being strongly influenced by traffic emissions. There is sufficient evidence that documents gradients up to 100m from major roads with traffic volumes (~15,000 AADT) comparable to those seen on major roads in Canada (Zhou et al., 2007; HEI, 2010). Only urban roads were considered to have sufficient traffic volumes to be classified at traffic monitors. Figure 6 illustrates the number of NAPS monitors within 25m to 500m of a major road. Using the distance criteria of 100m, 57 (18%) of NAPS monitors are classified as strongly influenced by traffic. Proposed NAPS Monitor Classification Figure 6. Proximity of NAPS monitors to major roads. The traffic monitor type could also be modified to include a "street canyon" characterization. High pollution levels are often observed in urban increased traffic emissions and reduced natural ventilation outside the scope of this contract to assess street canyons nationallyhowever, this could be done using local knowledge of monitors. using decisions trees will be discussclasses, particularly industrial influenced sitypes. Assessing Monitors Strongly Influenced There is no simple method for estimating the contribution of industrial emissions to measured concentrations using surrogate information combination of approaches was as new quantitative information derived in a GISinformation from: street canyons(Vardoulakis et al. 2003) for all NAPS monitors; Incorporating local knowledge ed later as this is also relevant to other monitor type tes, but also to airport, marine port or other by Industry (Spangl et al. 2007). For this reasonused that capitalized on existing qualitative information as well . The final industrial classification March 30th 2012 31 due to the . It was monitor , a included Proposed NAPS Monitor Classification March 30th 2012 32 • Existing NAPS industrial class codes; • Work done by Tom Dann to characterize VOC and SO2 NAPS stations influenced by industrial emissions (files provided by Dennis Herod); and • Proximity (<400m) to industries reporting >1 tonne of air emissions to the national pollutant release inventory (NPRI) database. The existing NAPS monitoring stations are assigned a land use code to identify dominant land use within a square kilometre of each monitor. Because these land use codes are not explicitly defined, the assignment of codes has been somewhat subjective. Visual examination of the industrial coded NAPS monitors (n=17) with satellite imagery seems to confirm that these monitors may be influenced by industrial emissions to some degree. The degree of influence, however, will depend largely on emissions, meteorology and local topography. Tom Dann provided a file with industrial classifications for all monitors measuring VOC's and SO2. The classification approach was based on measured data and the number of exceedances of a certain threshold at each station. Twelve stations were classified as industrial, eight of which were not classified as industrial in the current NAPS station type classification. Next, NAPS monitors were classified as being strongly influenced by industrial emissions if monitors with located within 400m of a major industrial emissions source, identified from the 2009 National Pollutant Release Inventory (NPRI) database (2010 data is not yet available). The NPRI database contains information on the location of large industries in Canada as well as pollutants released to air (substances and amounts). All locations with at least 1 tonnes of emissions to air were included in the final analysis. The metric of emissions/distance to NAPS station was also explored but there was no clear cut-point to classify stations as industrial influenced or not. It was outside the scope of the project to conduct dispersion modeling to determine whether a NAPS monitor is strongly influenced by industrial Proposed NAPS Monitor Classification emissions. Instead, a proximity measure of 4influenced by these industrial emissions. to the middle scale of spatial representativeness (EPA, 1997). As shown in Figure 7, there is no clear break for monitor siting in terms of proximity to NAPS emission sources.monitors (7%) are classified as strongly influenced by ind400m of a NPRI emitter, if a larger distance threshold was used, such as 4km (neighbourhood scale of representativeness), the majority of NAPS stations (n=211) were classified as industrial monitors. Figure 7. Proximity of NAPS monitors to NPRI emission sources. Of the 23 monitors classified as strongly influenced<400m NPRI criteria, only 6 weremethods. The discrepancy between the three approaches highlights the difficulty in assigning an industrial monitor code as the influence of industrial emissions on NAPS measurements will pollutant specific and dependent upon the location of the emissisize of emissions, and meteorology and 00m was used to identify NAPS monitors likely A distance threshold of 400m was selected to c A total of 23 ustrial emissions using monitors within by industrial emissions using the classified as industrial using the two other classificatioon source(s), composition and topography surround the monitoring station March 30th 2012 33 onform n be . Proposed NAPS Monitor Classification March 30th 2012 34 Monitor Type Classifications Table 17 summarizes the four monitor type classifications. If a monitor was classified as both traffic and point source influenced it was given a code TPS. If a monitor was not largely influenced by traffic or industrial point sources it was classified as a non-specific (background) monitor. The majority of stations (n=210) were classified as non-specific source monitors. Table 17. Summary of monitor type classifications. Local Source Influenced Code NAPS Monitors Non-Specific NS 221 (72%) Traffic T 46 (15%) Point Source PS 31 (10%) Traffic and Point Source TPS 11 (4%) Other monitor type classifications could be added to broaden the types of emission sources captured. For example, airport and marine port monitor types could be added to capture these classes of emission sources. Currently, a number of NAPS monitoring stations (e.g. ID #'s 50128, 100134, 101101, 101004, 101401, and 129103) are located at or near airports. The size, and hence emissions associated with these airports are very different, and similar to industrial emission sources, one surrogate category will poorly reflect emissions and associated impacts on NAPS measurements. Decision trees incorporating quantitative GIS information and local knowledge could be used to enhance the monitor type classifications. The upper level of the decision tree could incorporate GIS-based information as well as monitor-specific measurement data, while the lower levels incorporate qualitative information and local knowledge. This type of information could be used to assess street canyons, industrial influenced monitors (by pollutant type) as well as NAPS monitors influenced by marine, ports and airport emission sources. To ensure uniform application by different provincial NAPS managers, each junction and criteria in the decision tree framework would need to be explicitly defined to ensure uniform application. It was Proposed NAPS Monitor Classification March 30th 2012 35 outside the scope of this contract to create such a framework; however, this could be a useful tool for integrating qualitative information to assess monitor classifications that cannot feasibly be derived in a GIS. Final NAPS Monitor Classification The final NAPS classification for the 309 NAPS monitoring stations is provided in the accompanying excel document. Each classification level (summarized in Table 18 and illustrated for two NAPS monitors in Figure 8) is provided in a separate excel column to allow flexible classifications and sorting by different variables. A column is also provided with the simplified merged classifications (monitor type, urbanization class, and primary local land use); the frequencies of NAPS monitors based on this classification are shown in Table 19. Even this simplified classification with the three scales of influence results in 46 unique classifications, of which a large number have only one or two NAPS monitors. Depending on the specific pollutant of interest and the application of the NAPS data, the final monitor classification may change. The full classification (monitor type, urbanization class, neighborhood population and primary and secondary land use) is also provided in the accompanying excel file. Proposed NAPS Monitor Classification March 30th 2012 36 Table 18. Summary of NAPS monitor classification, including variables, codes and definitions. Monitor Class Variables Code Definition Urbanization Large Urban Area LU Large population centre (population ≥100,000) Medium Urban Area MU Medium population centre (population between 30,000-99,999) Small Urban Area SU Small population centre (population between 1,000-29,999) Non-Urban NU Non-urban area (population ≤999) Neighbourhood Population Population Q1 1 Categories of residential population within 4km. Population categories roughly correspond to population quartiles within each urbanization class. Population Q2 2 Population Q3 3 Population Q4 4 Local Land Use Agricultural A The dominant land use category within a 400m radius area. A secondary land use code is included if greater than 1/3 of the area is also covered by another land use category. Commercial C Industrial I Open O Parks P Residential R Water W Monitor Type Traffic T Monitors within 100m of a major road (defined as all freeways, highways, and arterial and collector roads) and located in an urban region (LU, MU, or SU). Point Source PS Monitors within 400m of a major industrial emissions source (identified from the NPRI database), subjective classification of industrial monitors in the original NAPS monitor meta-data, and industrial classified based on VOC and SO2 measurement data. Traffic and Point Source TPS Traffic and point source influenced monitors. Non-Specific NS Not classified as traffic or industrial influenced (non-specific monitor type). Proposed NAPS Monitor Classification March 30th 2012 37 Figure 8. Illustration of NAPS classification structure. Proposed NAPS Monitor Classification March 30th 2012 38 Table 19. NAPS classification based on monitor type, urbanization class and local land use. NAPS Code NAPS Monitors Percent NS,LU,A 3 1.0 NS,LU,C 2 0.7 NS,LU,I 1 0.3 NS,LU,O 6 1.9 NS,LU,P 3 1.0 NS,LU,R 42 13.6 NS,LU,W 1 0.3 NS,MU,I 2 0.7 NS,MU,P 2 0.7 NS,MU,R 13 4.2 NS,MU,W 4 1.3 NS,NU,A 26 8.4 NS,NU,O 67 21.7 NS,NU,P 1 0.3 NS,NU,R 2 0.7 NS,SU,O 13 4.2 NS,SU,P 2 0.7 NS,SU,R 18 5.8 NS,SU,W 1 0.3 PS,LU,I 1 0.3 PS,LU,O 2 0.7 PS,LU,R 5 1.6 PS,LU,W 1 0.3 PS,MU,I 1 0.3 PS,MU,R 6 1.9 PS,NU,A 1 0.3 PS,NU,I 1 0.3 PS,NU,O 7 2.3 PS,SU,O 3 1.0 PS,SU,R 3 1.0 T,LU,I 3 1.0 T,LU,O 4 1.3 T,LU,R 18 5.8 T,MU,R 6 1.9 T,NU,A 4 1.3 T,NU,O 7 2.3 T,SU,O 3 1.0 T,SU,R 12 3.9 TPS,LU,A 1 0.3 TPS,LU,I 2 0.7 TPS,LU,O 1 0.3 TPS,LU,R 4 1.3 TPS,MU,P 1 0.3 TPS,SU,O 1 0.3 TPS,SU,R 1 0.3 Proposed NAPS Monitor Classification March 30th 2012 39 Visualizing the monitor classification is a useful exercise for examining the strengths and weaknesses of each classification variable. Ten NAPS monitors were randomly selected and are presented in Appendix 2 with the different monitor level characterizations along with satellite imagery. The level 1 (urbanization) and level 2 (neighborhood population) classifications did a very good job of representing urbanization type and population density surrounding NAPS monitors. The classification of local land use (level 3) also corresponded well to the visual interpretation of land use within 400m; however, in some cases, the land use was completely mixed and it was difficult to determine the dominant land use type. The limitations of the land use classification are associated with the representation of complex land use types with one or two land use classes and the lack of emissions data for land use types. For example, NAPS ID #60427 was given a residential land use classification but is also near a small airport. While this land use source has a small area within the 400m buffer, it likely has emissions that are larger than the residential land use (even though the residential land use covers the majority of the surrounding area). This highlights the limitation of using land use types as surrogates for air pollution emissions. On the other hand, the land use type can also be used to guide extrapolation and population exposure assessment using residential land use monitors. Level 4 (monitor type) seemed to be the least robust, especially in terms of representing traffic and industrial emissions. For traffic monitor types, the classification is limited by the use of road classes to represent vehicle emissions, instead of actual traffic counts or emissions estimates. While limiting traffic monitor types to monitors within 100m of a major road in urban areas only, the classification could be further restricted to monitors in large and medium urban areas to increase the specificity of the traffic monitor type classification. For the industrial point source monitor type classification, the NPRI facility location (i.e. latitude and longitude) is typically located at the center of an industrial location and if the site is large a 400m buffer from the centre of the site will not cover neighbouring areas. This was evident for NAPS ID#60427; Proposed NAPS Monitor Classification March 30th 2012 40 however, in this case the local land use classification captured the industrial land use surrounding the monitor. The subjective and measurement based classifications of industrial influenced NAPS monitors seems to correspond well to potential industrial emission sources seen in the satellite imagery; however, a comprehensive evaluation of these industrial point source classification methods was outside the scope of the project. It is important to note that the influence of industrial emissions on NAPS measurements will be pollutant specific and dependent upon the location of the emission source(s), composition and size of emissions, and meteorology and topography surround the monitoring station. Spatial Representativeness of Monitor Classifications While classifying monitor siting characteristics and estimating the spatial representativeness of measurements are related tasks (Spangl et l. 2007), there are important differences that restrict the use of monitor classifications to fully estimate the spatial representativeness of different pollutant measurements. Here the definition of a spatial zone of representativeness is considered constant over time (i.e. it does not include temporal variations) and is therefore related to annual averages and not to short-term value. The distinction between spatial/temporal variability is important. For example, in Seattle, the day-to-day variability in PM2.5 measurements taken at 40 sites was 10 times higher than the spatial variability between sites, but the site mean square was more than twice the error mean square, showing that differences between sites, while modest, are potentially an important contribution to measurement error (Goswami et al. 2002). Spatial representativeness is unique to each pollutant or group of pollutants with similar characteristics. This is one of the major limitations of applying monitor classifications to estimate a zone of representativeness, as the classification is not pollutant specific, due primarily to the need for simplicity in monitor classifications. We have tried to address this issue by using a Proposed NAPS Monitor Classification March 30th 2012 41 hierarchical classification that captures different scales of pollution sources. Monitor classifications typically group primary pollutants more successfully than secondary pollutants, and our classification in no exception. Figures 9-12 illustrate the grouping of NAPS 2009 annual (for NO2, CO and PM2.5) and summer (for O3) measurements by each level of classification for all stations with >200 daily measurements (for annual averages) and >100 measurements between April and September (for O3 summer averages). Monitor type, urbanization class and urbanization size classes grouped NO2 concentrations well (e.g. increasing averages for traffic and industrial sites, large urban areas, and higher neighbourhood population density) but did poorly in grouping O3 concentrations (i.e. slightly reduced averages for traffic sites, decreasing gradients by neighbourhood population density within urbanization classes). Because these pollutants have different scales of spatial variability they are more or less associated with the different levels of the NAPS classification.Proposed NAPS Monitor Classification Figure 9. Grouping of annual NO 2 averages by the four levels of NAPS classifications. March 30th 2012 42 Proposed NAPS Monitor Classification Figure 10. Grouping of summer O 3 averages by the four levels of NAPS classifications. March 30th 2012 43 Proposed NAPS Monitor Classification Figure 11. Grouping of annual PM 2.5 averages by the four levels of NAPS classifications. March 30th 2012 44 Proposed NAPS Monitor Classification Figure 12. Grouping of annual CO averages by the four levels of NAPS classifications. March 30th 2012 45 Proposed NAPS Monitor Classification March 30th 2012 46 Table 20 presents a review of PM2.5, O3 and black carbon (representing traffic emissions) spatial variability by different geographic scales (from Brauer et al., 2010). This table clearly indicates the need for a spatial representative measure to be pollutant specific. Black carbon, often used as an indicator for traffic emissions, has the largest spatial variability, followed by O3 and then PM2.5. Table 20. Summary of PM2.5, O3 and black carbon spatial variability by different geographic scales (Brauer et al., 2010). Geographic Scale Pollutant Variability Local (<1km) PM2.5 >20% (n=1) <20% (n=3) Mean=~19% (Range:5-30%) O3 Mean=~27%; 500 m average spatial dependence Black Carbon >20% (n=5) <20% (n=0) Mean=~310% (Range:50-1000%) Neighbourhood (0-4km) PM2.5 >20% (n=2) <20% (n=3) Mean=~19% (Range:6-60%) O3 >20% (n=9) <20% (n=1) Mean=~39% (Range:4-64%) Black Carbon >20% (n=2) <20% (n=1) Mean=~390% (Range:10-300%) City (5-50km) PM2.5 >20% (n=12) <20% (n=15) Mean=~36% (Range:5-200%) O3 >20% (n=11) <20% (n=4) Mean=~29% (Range:4-58%) Black Carbon >20% (n=13) <20% (n=1) Mean=~218% (Range:27-1300%) Proposed NAPS Monitor Classification March 30th 2012 47 Urban/Suburban O3 >20% (n=3) <20% (n=4) Mean=~21% (Range:3-40%) Urban/Rural O3 >20% (n=3) <20% (n=1) Mean= ~40% (Range:13-65%) The Canadian Wide Standards state that "the goal in siting stations is to match the spatial scale represented by the sample of monitored air with the spatial scale most appropriate for the monitoring objective of the station" (CCME, 2011). The representative measurement scales that were incorporated into this classification system correspond to the widely used spatial scales defined by the EPA (1997) and include: • Micro (Incorporated into the monitor type classification): Concentrations in air volumes associated with area dimensions ranging from several meters up to about 100 meters • Middle (Incorporated into the local land use classification): Concentrations typical of areas up to several city blocks in size with dimensions ranging from about 100 meters to 0.5 kilometer. • Neighbourhood (Incorporated into the neighborhood population classification): Concentrations within some extended area of the city that has relatively uniform land use with dimensions in the 0.5 to 4.0 kilometers range. • Urban (Incorporated into the urbanization classification): Overall, citywide conditions with dimensions on the order of 4 to 50 kilometers. This scale would usually require more than one site for definition. • Regional: Usually a rural area of reasonably homogeneous geography and extends from tens to hundreds of kilometers. Table 21 summarizes the monitor classification levels that capture the associated spatial scales for different pollutants. A √ indicates that the spatial scale for a specific pollutant is Proposed NAPS Monitor Classification March 30th 2012 48 captured by the NAPS classification, while an x indicates that the classification does not capture the spatial scale for that pollutant. Table 21. Spatial scales of specific pollutants (modified from EPA, 2007) and the monitor classification variables associated with each spatial scale. Category of Spatial Scale Scale Class Level SO2 CO O3 NO NO2 PM 10 PM 2.5 PM 1-2.5 Micro m's-0.1km Monitor Type (T) √ √ √ √ √ √ √ √ Middle 0.1-0.5 km's Monitor Type (PS); Local Land use √ √ √ √ √ √ √ Neighbourhood 0.5-4 km's Population Density x √ √ √ √ √ Urban 4 - 50 km's Urbanization Level √ √ √ Regional 1000 km's - x x Global National - x x It is important to note that if the classification level captures the spatial scale for a specific pollutant, it does not necessarily fully capture all of the sources or dynamics of the pollutant at the spatial scale. For example, the urbanization and neighborhood population level can be used to capture some elements of O3 formation and differentiation at the urban scale (i.e. citywide conditions of 4 to 50km); however, much more detail is needed for local topography and emission sources to fully capture the spatial patterns of O3 (as shown previously in Figure10). In some existing monitor classifications, a different classification scheme has also been developed to characterize O3 (e.g. the PAMS O3 monitoring network site classifications (EPA, 1998), due to unique atmospheric photochemical reactions and their associated spatial scales. In addition, the monitor classification does not capture any regional pollutant variation as well neighbourhood spatial variation of industrial pollutants (e.g. SO2) as Proposed NAPS Monitor Classification March 30th 2012 49 only NPRI emissions within 400m were included in the classification; the subjective and measurement based classifications may include some neighborhood scale influence but this was not quantified. The NAPS monitor classification developed here provides the basis for a first estimate of the representativeness of a monitor's measurements (by pollutant type), based on a quantitative assessment of a limited number of parameters influencing pollution levels (in this case local emissions, population distribution, and land use). It is important to highlight again that the scale of representativeness will change depending on the specific pollutant as well as with the micro (monitor type), middle (local land use), neighbourhood (population density) and urban (urbanization level) classifications. Monitor type is the most important classification variable for estimating a spatial representativeness zone; particularly for primary combustion-related pollutants. Monitors classified as largely traffic influenced represent areas parallel (within 100m) of other (similar) major roads. Interpolation of traffic monitors based solely on geography (e.g. inverse distance weighting interpolation methods) should not be conducted. The zones of spatial representativeness for NO2 were previously examined using detailed land use regression (LUR) models of within-city NO2 variation (Brauer et al., 2010). Figure 13a provides an example of the representative areas covered by a NAPS monitor in Victoria according to the LUR model. Assigning traffic monitor representative zones based on 100m buffer areas around similar roadways is a related, though more basic, approach. Figure 13b indicates the 100m buffer regions around major roads in Victoria. From these figures it can be seen that while there is some overlap between the two approaches, there are large differences. Ideally, monitors would have a spatial representative zone defined using detailed measurement or modeling techniques, rather than simple proximity methods. Proposed NAPS Monitor Classification March 30th 2012 50 Figure 13. Spatial representativeness of 2006 annual average NO2 levels (ppb) from NAPS monitor 100314. In Figure (a) representative areas are defined as land use regression NO2 estimates within 5 ppb of the NAPS monitors’ values, while Figure (b) uses proximity (100m) to major roads as representative areas. Industrial monitors should not be extrapolated as the spatial representativeness will depend largely on the type and amount of industrial emissions and local meteorology and topography. Non-specific (background) monitor types can be extrapolated geographically; however, other classifications should also be used to determine the geographic scale of representativeness. For example, industrial or commercial local land use monitors may not be as representative as residential monitors for population exposure assessment. This, however, will also depend on the pollutant of interest. In addition to straight geographic extrapolation, which does not take into account topography, NAPS monitors could be assigned to airsheds, which would also aid in the interpretation and extrapolation of measurements. This is especially true for pollutants with large-scale spatial variation, such as PM2.5 or O3. Monitors falling into the A B Proposed NAPS Monitor Classification March 30th 2012 51 same airshed could be averaged to determine airshed-level representative values, which are useful for regulatory as well as population exposure assessment applications. Figure 14 illustrates an example of watershed boundaries for Canada, which could be modified to represent airsheds and airshed representative areas. Figure 14. Location of NAPS monitors and watersheds that could be modified to represent representative airsheds. Conclusions We have developed a hierarchical monitor classification for the NAPS network that can replace the existing subjective classifications. The classification includes variables derived in a GIS and captures urbanization, neighborhood population, local land use, and monitor type characteristics. The excel file provided with this report contains the final monitor characterization for all 309 NAPS monitoring stations included in the database that was provided by Environment Canada. Information on monitor siting characteristics provides guidance for grouping monitors and for using, interpreting and extrapolating monitoring data for different purposes. Monitor Proposed NAPS Monitor Classification March 30th 2012 52 classifications, however, may be misleading as the monitor siting characteristics of most importance are likely to change with different questions and data uses. In addition, monitor classifications are not pollutant specific, due to the trade-offs between usability and complexity. We have tried to address these issues by creating a hierarchical classification that includes information on different scales of pollution emissions and influences, including urbanization, neighborhood population, local land use and monitor types (i.e. traffic, point source, and non-specific). The spatial scales that were incorporated into this classification system correspond to the widely used urban (4-50km), neighborhood (0.5-5km) and middle (0.1-0.5km) spatial scales defined by the U.S. EPA (2007). These variables provide the basis for a first estimate of the representativeness of a monitor's measurements, based on a quantitative assessment of a limited number of parameters influencing pollution levels. Importantly, while the monitor classification can be used to guide use, interpretation and extrapolation of NAPS measurements, spatial representativeness is unique to each pollutant or group of pollutants with similar characteristics, and is also dependent upon the local context. Ideally, a representative area should be defined for each pollutant and NAPS monitor, either using measurement or modeling approaches. Proposed NAPS Monitor Classification March 30th 2012 53 References AQS. (2010). AQS Data Coding Manual. http://www.epa.gov/ttn/airs/airsaqs/manuals/ AQS%20Data%20Coding%20Manual.pdf [Accessed January 24th 2012]. BLANCHARD, C. L.; CARR, E. L.; COLLINS, J. F. ET AL. (1999). Spatial representativeness and scales of transport during the 1995 Integrated Monitoring Study in California’s San Joaquin Valley. Atmospheric Environment. Vol. 33. pp. 4775–4786. Brauer M., Hystad P., Poplowski K. (2011). Assessing the spatial representativeness of PM₂.₅ and O₃ measurements from the National Air Pollutant Surveillance System. https://circle.ubc.ca/handle/2429/41543 [Accessed March 4th 2012]. Canada-U.S. Air Quality Agreement, 2010. Biennial Progress Report. http://www.ec.gc.ca/air/default.asp?lang=En&n=83930AC3-1 [Accessed March 30th 2012]. CCME (2011). 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Traffic-related air pollution: A critical review of the literature on emissions, exposure, and health effects. Final Version of Special Report No. 17. Boston, Mass.: Health Effects Institute. Available from: http://pubs.healtheffects.org/view.php?id=334 Larssen, S., Sluyter, R., Helmis, C. (1999). Criteria for EUROAIRNET: The EEA air quality monitoring and information network. Technical Report No. 12. European Environment Agency. Ministry of the Environment (2009). Where to Monitor? - Selecting Monitoring Sites. http://www.mfe.govt.nz/publications/air/good-practice-guide-air-quality-2009/html/page8.html [Accessed March 10th 2012]. Ministry for the Environment. (1997). Environmental Performance Indicators: Proposals for Air, Fresh Water and Land. Ministry for the Environment; Wellington. Spangl, W., Schneider, J., Moosmann, L., Nagl, C. (2007). Representativeness and Classification of Air Quality Monitoring Stations. Umweltbundesamt report. Proposed NAPS Monitor Classification March 30th 2012 55 ec.europa.eu/environment/air/quality/legislation/pdf/report_uba.pdf [Accessed March 15th 2012]. Turcotte, M. (2008). Life in Metropolitan Areas: The city/suburb contrast: How can we measure it? http://www.statcan.gc.ca/pub/11-008-x/2008001/article/10459-eng.htm [Accessed January 2nd 2012]. Vardoulakis, S. (2003). Modeling air quality in street canons: a review. Atmospheric Environment. 37(2): 155-182. Zhou, Y., & Levy, J. I. (2007). Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis. BMC Public Health, 7, 89. doi:10.1186/1471-2458-7-89 Proposed NAPS Monitor Classification March 30th 2012 56 Appendices Appendix 1. Maps of urbanization classifications for major urban areas of Canada. Appendix 2. Evaluation of NAPS classification for 10 randomly selected monitors with satellite imagery. Proposed NAPS Monitor Classification March 30th 2012 57 Appendix 1a. Urban classifications for western Canada. Proposed NAPS Monitor Classification March 30th 2012 58 Appendix 1b. Urban classifications for the prairies. Proposed NAPS Monitor Classification March 30th 2012 59 Appendix 1c. Urban classifications for southern Ontario and Quebec. Proposed NAPS Monitor Classification March 30th 2012 60 Appendix 1d. Urban classifications for Atlantic Canada. Proposed NAPS Monitor Classification March 30th 2012 61 NAPS ID # 60427 (Toronto) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS NAPS ID # 100110 (Vancouver) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS Proposed NAPS Monitor Classification March 30th 2012 62 NAPS ID # 90601 (Fort Saskatchewan) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS NAPS ID # 53501 (Saint-Francois) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS Proposed NAPS Monitor Classification March 30th 2012 63 NAPS ID # 103502 (Castlegar) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS NAPS ID # 101101 (Chilliwack) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS Proposed NAPS Monitor Classification March 30th 2012 64 NAPS ID # 102301 (Powell River) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS NAPS ID # 52601 (Varennes) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS Proposed NAPS Monitor Classification March 30th 2012 65 NAPS ID # 51801 (Saint-Joseph) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS NAPS ID # 61702 (Oshawa) Level 1: Urbanization LU MU SU NU Level 2: Neighborhood Population 1 2 3 4 Level 3: Local Land Use A C I O P R W - Level 4: Monitor Type T PS TPS NS
Report
eng
Unreviewed
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Faculty
Graduate
Developing a Monitor Classification System for the National Air Pollution Surveillance System
Text