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

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.

Determination of the complex index of refraction and size distribution of aerosols from polar nephelometer measurements

The polar nephelometer of Tohoku University has been improved so that it can be used to measure the scattering matrix element D21. Polar nephelometer observations were conducted at Tohoku University in the suburbs of Sendai, Japan, from 12 October to 10 December 1988. A method presented in an earlier publication [Appl. Opt. 36, 7992 (1997)] is applied to determine the complex index of refraction and size distribution of aerosols simultaneously. It is shown that the accuracy of the determined quantities is reasonable.

Simultaneous determination of water-leaving reflectance and aerosol optical thickness from Coastal Zone Color Scanner measurements

We present an iterative algorithm for the simultaneous determination of water-leaving reflectances and aerosol optical thickness from the total outgoing radiances measured in the Coastal Zone Color Scanner (CZCS) visible and near-infrared channels. Numerical experiments were carried out to investigate the feasibility of the algorithm. The results show that the errors in determined water-leaving reflectance at 0.443 microm are approximately 10% in most cases. The errors in determined water-leaving reflectance at 0.55 microm are in the 4.05-7.2% range. The errors in the simultaneously determined aerosol optical thickness for all CZCS visible and near-infrared channels are less than approximately 10%.

Extraction of bimodal aerosol-size distribution radii from spectral and angular slope (Angstrom) coefficients

A methodology is defined whereby the effective aerosol bimodal radii of the fine (accumulation) mode and the coarse (supermicron) mode can be directly and simply extracted from slope (Angstrom) coefficients derived from ground-based solar spectral transmission data and aureole scattering data.