Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model

Abstract

Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R2), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m−3 and 8.25 (8.78) μg m−3, respectively. The monthly, seasonal, and annual mean PM2.5 were also successfully captured (CV-R2 = 0.91–0.92, RMSE = 4.35–6.72 μg m−3). Then, the spatial characteristics of PM2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m−3). In addition, 19 provinces or administrative regions have annual PM2.5 concentrations >35 μg m−3, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m−3), Tianjin (~49.65 μg m−3), and Henan Province (~48.60 μg m−3). Our estimated surface PM2.5 concentrations are accurate, which could benefit further research on air pollution in China.

Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region

“… where denotes the simulated near-surface O 3 concentration in grid i on day j at hour h , denotes the WRF-Chem simulated near-surface O 3 concentration (WRFO 3 ) in grid i on day j at hour h and denotes the temporal information in grid i on day j at hour h . In this study, a temporal weighted matrix was established according to the method described by Xue et al, which includes the day of the year (DOY), time distance of one day to spring, summer, autumn and winter, and local time (LT) [ 54 ]. In addition to temporal information, VIDO, CVL and meteorological parameters, including TEM, RAD, WS, WD, BLH, SP, EVA, and RH, in grid i on day j at hour h were selected as explanatory variables for model construction.…”

Optimal Planning of Air Quality-Monitoring Sites for Better Depiction of PM2.5 Pollution across China

“…China’s rapid and energy-intensive development over recent decades has caused a series of environmental issues, among which PM 2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) pollution received worldwide attention. − It has been well recognized that both acute and chronic exposures to high PM 2.5 concentrations are associated with multiple health issues, including heart disease, lung cancer, respiratory infection, etc. − Monitoring air pollution is critical to understanding the formation mechanism of PM 2.5 , to supporting air quality management, and to reducing human exposure. , Since 2013, Chinese government has established a ground-based monitoring network across China to measure hourly concentrations of six air pollutants, including PM 2.5 . These monitoring stations were empirically established, mainly concentrated in urban areas. , Activities in rural or suburb regions including agricultural burning also exert influences on air quality, weather, and climate, yet are commonly missed by the current monitoring network.…”

“…Due to existences of clouds and occurrences of snow/extreme haze, AOD-retrieving algorithms usually fail to offer valid values . Moreover, a large range of missing and abnormal data could happen when some satellites stay in service beyond their design life . These issues could be addressed with advances in machine learning and growing volume of observations, and a wide range of algorithms or satellite retrievals (low-Earth orbit, geostationary, etc.)…”

“… 11 These monitoring stations were empirically established, mainly concentrated in urban areas. 12 , 13 Activities in rural or suburb regions including agricultural burning also exert influences on air quality, weather, and climate, yet are commonly missed by the current monitoring network.…”

“… 17 Moreover, a large range of missing and abnormal data could happen when some satellites stay in service beyond their design life. 13 These issues could be addressed with advances in machine learning and growing volume of observations, and a wide range of algorithms or satellite retrievals (low-Earth orbit, geostationary, etc.) were explored previously.…”