Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information

Background: PM1 might be more hazardous than PM2.5 (particulate matter with an aerodynamic diameter <= 1 mu m and <= 2.5 mu m, respectively). However, studies on PM1 concentrations and its health effects are limited due to a lack of PM1 monitoring data. Objectives: To estimate spatial and temporal variations of PM1 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 PM1 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-2 and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 mu g/m(3), respectively. For seasonal prediction, the R-2 and RMSE were 77% and 11.4.Lg/m(3), respectively. The predicted annual mean concentration of PM1 across China was 26.9 mu g/m(3). The PM1 level was highest in winter while lowest in summer. Generally, the PM1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM1 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-evel PM1. Ambient PM1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM1. (C) 2017 Elsevier Ltd. All rights reserved.