Grid-based spatiotemporal modeling of ambient ozone to assess human exposure using environmental big data

Abstract

Ozone (O3) pollution in China is increasing. It is the primary pollutant in summer and ambient O3 can lead to serious health problems for the public. Therefore, characterizing the spatiotemporal distribution of O3 is required for better environmental management and human exposure assessment. Statistical models, especially those based on machine learning, can be more convenient to use than chemical transport models and have shown improved accuracy. However, the quality of data affects model precision especially at fine spatiotemporal scales. Web-based environmental data can improve the spatial and temporal resolution of modeling data. This study applied high spatiotemporal resolution source information and emission inventories based on point of interest and real-time traffic data in a fine scale grid network to predict the O3 concentrations and assess the human exposure within Chengdu, China. The results showed that the web-based environmental data could be combined with statistical models such as random forest in air quality modeling. The model precision was high, especially at finer spatiotemporal scales. The R2 of the hourly and daily maximum 8-h mean concentrations of O3 models built in this study were 0.83 and 0.91 for sample-based cross-validation, and 0.79 and 0.90 for site-based cross-validation, respectively. Meteorological variables had the greatest impact on O3 concentrations especially sea-level pressure, temperature, vapor pressure, and humidity. People within the research area had a relatively high exposure level to pollutants over a longer time scale in the summer and spring. Findings from this work provide a good reference for related research on modeling air quality, and human health risk assessment using environmental big data.