Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities and the implications for epidemiological research

Abstract. Exposure models for some criteria air pollutants have been intensively developed in past research; multi-air-pollutant exposure models, especially for particulate chemical species, have been however overlooked in Asia. Lack of an integrated model framework to calculate multi-air-pollutant exposure hinders the combined exposure assessment and the corresponding health assessment. This work applied the land-use regression (LUR) approach to develop an integrated model framework to estimate annual-average exposures of four major PM₁₀ chemical species as well as four criteria air pollutants of PM₁₀, PM_2.5, NO₂, and O₃ in a typical high-rise and high-density Asian city (Hong Kong, China). Our integrated multi-air-pollutant exposure model framework is capable of explaining 91–97 % of the variability of measured air pollutant concentration, with the leave-one-out cross-validation R² values ranging from 0.73 to 0.93. Using the model framework, the spatial distribution of the concentration of various air pollutants at a spatial resolution of 500 m was generated. The LUR model-derived spatial distribution maps revealed weak to moderate spatial correlations between the PM₁₀ chemical species and the criteria air pollutants, which may help to distinguish their independent chronic health effects. In addition, further improvements in the development of air pollution exposure models are discussed. This study proposes an integrated model framework for estimating multi-air-pollutant exposure in high-density and high-rise urban areas, serving an important tool for combined exposure assessment and the corresponding epidemiological studies.