A method to develop the surrogate map of emissions in China

As the world's population continues to grow and urbanization accelerates, emissions have become a significant contributor to air pollution and climate change. Air quality simulation is a useful tool for analyzing the impact of emissions and meteorological data on human health. In this study, we used the Weather Research and Forecasting model - Sparse Matrix Operator Kernel Emissions - Models3 Community Multiscale Air Quality Modeling System (WRF-SMOKE-CMAQ) model to explore the future air quality of Guangdong and Hong Kong. To analyze the spatial distribution of emissions, we created surrogate maps based on cultivated land, power plants, factories, roads, and population data. We obtained five surrogate maps to estimate the spatial distribution of emissions for the agriculture, power, industry, transportation, and residential sectors. The developed surrogate maps were utilized for spatial distribution from Domain 1 to Domain 3, achieving a resolution of 3 km. By using this mapping method, we improved the PM2.5 index of agreement compared to 19 air quality monitoring stations in Hong Kong. By combining anthropogenic emissions and marine emissions, along with downscaling meteorological data to drive the air quality model, this study provides a valuable reference for policymakers and individuals to guide actions towards air quality. Overall, our study highlights the importance of analyzing emissions and their spatial distribution to better understand their impact on air quality.