Estimating spatiotemporal distribution of PM concentrations in China with satellite remote sensing, meteorology, and land use information.

Background: PM might be more hazardous than PM (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM concentrations and its health effects are limited due to a lack of PM monitoring data.

Objectives: To estimate spatial and temporal variations of PM concentrations in China during 2005-2014 using satellite remote sensing, meteorology, and land use information.

Methods: Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability.

Results: The results of 10-fold cross-validation showed R and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m, respectively. For seasonal prediction, the R and RMSE were 77% and 11.4 μg/m, respectively. The predicted annual mean concentration of PM across China was 26.9 μg/m. The PM level was highest in winter while lowest in summer. Generally, the PM levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM levels increased substantially in the South-Western Hebei and Beijing-Tianjin region.

Conclusions: GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM. Ambient PM reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM.