Background

Few studies have estimated historical exposures to PM₁₀ at a national scale using satellite-based aerosol optical depth (AOD), and long-term trends have not been investigated.

Objectives

In this study, daily concentrations of PM₁₀ over China during the past 12 years were estimated with the most recent ground monitoring data and a novel statistical model.

Methods

Daily measurements of PM₁₀ during 2014–2016 were collected from 1479 sites in China. Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data were downloaded and merged. A random forest model (non-parametric machine learning algorithms) and two traditional regression models between PM₁₀ and AOD were developed and their predictive abilities compared. The model was applied to estimate daily concentrations of PM₁₀ across China during 2005–2016 at 0.1⁰ (≈10 km).

Results

Cross-validation showed our random forests model explained 78% of daily variability of PM₁₀ [root mean squared prediction error (RMSE) = 31.5 μg/m³]. When aggregated into monthly and annual averages, the models captured 82% (RMSE = 19.3 μg/m³) and 81% (RMSE = 14.4 μg/m³) of the variability. The random forests model showed much higher predictive ability and lower bias than the two regression models. Based on the predictions, around one-third of China experienced with PM₁₀ pollution exceeding Grade Ⅱ National Ambient Air Quality Standard (>70 μg/m³) in China during the past 12 years. The highest levels of estimated PM₁₀ were present in the Taklamakan Desert of Xinjiang, Beijing-Tianjin metropolitan region, while the lowest were observed in Tibet, Yunnan and Hainan. Overall, the PM₁₀ level in China peaked in 2008 and has declined since 2009.

Conclusions

This is the first study to estimate the long-term exposure to PM₁₀ pollution historically using satellite-based AOD data in China. The results could be applied to investigate the long-term health effects of PM₁₀ in China.