Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: methods and assessment

TL;DR

This study develops a national-scale neural network model to estimate PM2.5 pollution in China, demonstrating improved accuracy over traditional models and providing detailed spatial distribution maps for health and policy applications.

Contribution

The paper introduces a generalized regression neural network model for PM2.5 estimation at national scale in China, with comprehensive evaluation and a novel pixel-based mapping strategy.

Findings

GRNN outperforms traditional models in accuracy

The model achieves high correlation with station measurements

PM2.5 distribution maps align well with observed data

Abstract

Fine particulate matter (PM2.5) is associated with adverse human health effects, and China is currently suffering from serious PM2.5 pollution. To obtain spatially continuous ground-level PM2.5 concentrations, several models established by point-surface fusion of ground station and satellite observations have been developed. However, how well do these models perform at national scale in China? Is there space to improve the estimation accuracy of PM2.5 concentration? The contribution of this study is threefold. Firstly, taking advantage of the newly established national monitoring network, we develop a national-scale generalized regression neural network (GRNN) model to estimate PM2.5 concentrations. Secondly, different assessment experiments are undertaken in time and space, to comprehensively evaluate and compare the performance of the widely used models. Finally, to map the yearly and seasonal mean distribution of PM2.5 concentrations in China, a pixel-based merging strategy is proposed. The results indicate that the conventional models (linear regression, multiple linear regression, and semi-empirical model) do not perform well at national scale, with cross-validation R values of 0.488~0.552 and RMSEs of 30.80~31.51 {\mu}g/m3, respectively. In contrast, the more advanced models (geographically weighted regression, back-propagation neural network, and GRNN) have great advantages in PM2.5 estimation, with R values ranging from 0.610 to 0.816 and RMSEs from 20.93 to 28.68 {\mu}g/m3, respectively. In particular, the proposed GRNN model obtains the best performance. Furthermore, the mapped PM2.5 distribution retrieved from 3-km MODIS aerosol optical depth (AOD) products, agrees quite well with the station measurements. The results also show that our study has the capacity to provide reasonable information for the global monitoring of PM2.5 pollution in China.