The application of land use regression model to investigate spatiotemporal variations of PM_2.5 in Guangzhou, China: Implications for the public health benefits of PM_2.5 reduction

Understanding the intra-city variation of PM_2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM_2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R², adjust R² and 10-fold cross validation R² for the annual PM_2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM_2.5 and controlling vehicle emissions would be an effective way to reduce PM_2.5. The predicted PM_2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM_2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM_2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM_2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM_2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221-2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257-2524) (willingness to pay approach) if the annual PM_2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15 μg/m³. Our results are expected to provide valuable information for further air pollution control strategies in China.