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High spatiotemporal resolution fine particulate matter (PM 2.5 ) simulations can provide important exposure data for the assessment of long-term and short-term health effects. Satellite-based aerosol optical depth (AOD) data, meteorological data, and topographic data have become key variables for PM 2.5 estimation. In this study, a random forest model was developed and used to estimate the highest resolution (0.01° × 0.01°) daily PM 2.5 concentrations in the Beijing-Tianjin-Hebei region. Our model had a suitable performance (cv-R 2  = 0.83 and test-R 2  = 0.86). The regional test-R 2 value in southern Beijing-Tianjin-Hebei was higher than that in northern Beijing-Tianjin-Hebei. The model performance was excellent at medium to high PM 2.5 concentrations. Our study considered meteorological lag effects and found that the boundary layer height of the one-day lag had the most important contribution to the model. AOD and elevation factors were also important factors in the modeling process. High spatiotemporal resolution PM 2.5 concentrations in 2010-2016 were estimated using a random forest model, which was based on PM 2.5 measurements from 2013 to 2016., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A statistical model was developed using satellite remote sensing data and meteorological parameters to evaluate the effectiveness of air pollution control measures during the 2008 Beijing Olympic Games. Custom satellite retrievals under hazy conditions were included in the modeling dataset to represent the air pollution levels more accurately. This model explained 70% of the PM(2.5) variability during the modeling period from June to October 2008. Using this tool, we estimate that the aggressive emission reduction measures alone effectively lowered PM(2.5) levels by 20-24 μg/m(3) or 27-33% on average during the Games period, which is substantially greater than those reported previously. Since parameters required to develop this model are readily available in most cities of the world, it can be quickly applied after other major events to evaluate air pollution control policy., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The health risks of fine particulate matter (PM 2.5) and ambient ozone (O 3) have been widely recognized in recent years. An accurate estimate of PM 2.5 and O 3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance and estimate daily average PM 2.5 concentration and O 3 daily maximum of 8 h average concentration (O 3 -8 hmax) of China in 2005–2017 at a spatial resolution of 1 km × 1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on 10-fold cross-validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly estimations of PM 2.5 from test datasets gave average model-fitting R2 values of 0.85, 0.88 and 0.90, respectively; these R2 values were 0.77, 0.77 and 0.69 for O 3 -8 hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM 2.5 and O 3 -8 hmax estimations. During 2005–2017, PM 2.5 concentration exhibited an overall downward trend, while ambient O 3 concentration experienced an upward trend. Whilst the spatial patterns of PM 2.5 and O 3 -8 hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristics. The dataset is accessible to the public at 10.5281/zenodo.4009308 (Ma et al., 2021a), and the shared dataset of Chinese Environmental Public Health Tracking (CEPHT, 2022) is available at https://cepht.niehs.cn:8282/developSDS3.html. [ABSTRACT FROM AUTHOR]

In the autumn and winter, aerosol is the important atmospheric pollutant over the Beijing–Tianjin–Hebei region. For monitoring aerosol in the autumn and winter, the lack of vegetation and the aging of MODIS sensor are two problems that needed to be solved. In this paper, after analyzing the characteristics of aerosol radiance in the red and shortwave infrared ($2.12~\mu \text{m}$) bands of MODIS, we develop a new algorithm for terrestrial aerosol with the assumption that the reflectance ratio between the red and $2.12~\mu \text{m}$ bands is invariant. With MODIS data over the Beijing–Tianjin–Hebei region from September 2016 to February 2017, the algorithm is applied to aerosol retrieval. The retrieved aerosol optical depth images show that our algorithm can retrieve aerosol over sparse vegetation, and the validation with the AERONET/PHOTONS Beijing site shows that the correlation is greater than 0.9% and 77% of the retrievals fall within the expected error. An error analysis shows that a 2% error in the proportion of the soot component can lead to 15% retrieval error, and over more than 60% of the surface area, the error from the changes in the ratio between the red and $2.12~\mu \text{m}$ bands can lead to retrieved errors less than 0.1. [ABSTRACT FROM AUTHOR]

The Chengdu–Chongqing Economic Zone (CCEZ), which is located in southwestern China, is the fourth largest economic zone in China. The rapid economic development of this area has resulted in many environmental problems, including extremely high concentrations of nitrogen dioxide (NO2) and fine particulate matter (PM2.5). However, current ground observations lack spatial and temporal coverage. In this study, satellite remote sensing techniques were used to analyze the variation in NO2 and PM2.5 from 2005 to 2015 in the CCEZ. The Ozone Monitoring Instrument (OMI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product were used to retrieve tropospheric NO2 vertical columns and estimate ground-level PM2.5 concentrations, respectively. Geographically, high NO2 concentrations were mainly located in the northwest of Chengdu and southeast of Chongqing. However, high PM2.5 concentrations were mainly located in the center areas of the basin. The seasonal average NO2 and PM2.5 concentrations were both highest in winter and lowest in summer. The seasonal average NO2 and PM2.5 were as high as 749.33 × 1013 molecules·cm−2 and 132.39 µg·m−3 in winter 2010, respectively. Over 11 years, the annual average NO2 and PM2.5 values in the CCEZ increased initially and then decreased, with 2011 as the inflection point. In 2007, the concentration of NO2 reached its lowest value since 2005, which was 230.15 × 1013 molecules·cm−2, and in 2015, the concentration of PM2.5 reached its lowest value since 2005, which was 26.43 µg·m−3. Our study demonstrates the potential use of satellite remote sensing to compensate for the lack of ground-observed data when quantitatively analyzing the spatial–temporal variations in regional air quality. [ABSTRACT FROM AUTHOR]

Abstract: A statistical model was developed using satellite remote sensing data and meteorological parameters to evaluate the effectiveness of air pollution control measures during the 2008 Beijing Olympic Games. Custom satellite retrievals under hazy conditions were included in the modeling dataset to represent the air pollution levels more accurately. This model explained 70% of the PM2.5 variability during the modeling period from June to October 2008. Using this tool, we estimate that the aggressive emission reduction measures alone effectively lowered PM2.5 levels by 20–24μg/m3 or 27–33% on average during the Games period, which is substantially greater than those reported previously. Since parameters required to develop this model are readily available in most cities of the world, it can be quickly applied after other major events to evaluate air pollution control policy. [Copyright &y& Elsevier]