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Land use regression modelling of NO₂, NO, PM₂.₅ and black carbon in Hong Kong Lee, Martha


Land use regression (LUR) modelling is a common method for estimating pollutant concentrations. This project created two-dimensional LUR models for nitrogen dioxide (NO₂), nitric oxide (NO), fine particulate matter (PM₂.₅), and black carbon (BC) for Hong Kong, a prototypical high-density high-rise city. Two sampling campaigns (April-May and November-January) were carried out in Hong Kong. Measurements of NO₂ and NO (2-3-week averaged) and PM₂.₅ and BC (24-hour averaged) were adjusted for instrument bias and temporal variation, and offered to multiple linear regression models along with 365 potential geospatial predictor variables. Variables were created from a number of geospatial metrics including land use and traffic variables (road length, average annual daily traffic [AADT], traffic loading [AADT * road length]). Measurement averaged across both campaigns were: a) NO₂ (M = 106 μg/m³, SD = 38.5, N = 95), b) NO (M = 147 μg/m³, SD = 88.9, N = 40), c) PM₂.₅ (M = 35 μg/m³, SD = 6.3, N = 64), and BC (M = 10.6 μg/m³, SD = 5.3, N = 76). Thirty-six LUR models were created (4 pollutants * 3 combined and separate sampling campaigns * 3 traffic variable type). The annual (combined values from both campaigns) road length models were selected as preferred models based on data reliability and overall model fit. Road length, car park density, and land use types were commonly selected predictors in the final preferred models. The preferred models had the following parameters: a) NO₂ (R² = 0.46, RMSE = 28 μg/m³) b) NO (R² = 0.50, RMSE = 62 μg/m³), c) PM₂.₅ (R² = 0.59; RMSE = 4 μg/m³), and d) BC (R² = 0.50, RMSE = 4 μg/m³). NO₂ predictions were strongly influenced by traffic and higher around Kowloon and northern Hong Kong Island. PM₂.₅ predictions had a strong northwest (high) to southeast (low) gradient. BC had a similar gradient and high predictions around the port. This matched with existing literature of spatial variation and sources in Hong Kong. Spatial patterns varied by pollutant. The success of this modelling suggests LUR modelling is appropriate in high-density high-rise cities.

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