Abstract
Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours.
Highlights
Ambient fine particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) are associated with adverse human health effects; they are regarded worldwide as a public health threat [1,2]
Evaluation methods were applied as follows: (1) accuracy, which refers to the average difference between two datasets; (2) precision, which is the standard deviation of the difference; (3) uncertainty, which refers to root mean square deviation; (4) correlation coefficient (R), which refers to the correlation and dependence of the statistical relationships between two datasets; and (5) percentage of Himawari-8
The primary estimation of hourly PM2.5 concentrations at daytime in the BTH region was executed with a proposed improved linear mixed-effect (LME) model using ground-based
Summary
Ambient fine particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) are associated with adverse human health effects; they are regarded worldwide as a public health threat [1,2]. In addition to the AOD, predictors, such as aerosol types, meteorological factors, and land use information, have been incorporated into models to improve model performance [13,14,15] Advanced statistical methods, such as generalized linear regression models [12], mixed effects models [16], generalized additive models [9], geographically-weighted regression [2], and semi-empirical models [7], have been employed to represent the relationships between the ground-level PM2.5 concentration and various predictors. A similar study developed linear mixed-effect (LME) models to integrate MODIS AODs, meteorological parameters, and satellite-derived tropospheric NO2 column density to estimate daily PM2.5 concentrations over the BTH region, in which model accuracy was calculated at R2 = 0.77 with a mean error of 22.4% [18]. The spatial distributions of hourly PM2.5 concentrations were derived from the improved LME model
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