Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing

2014 ◽  
Vol 48 (13) ◽  
pp. 7436-7444 ◽  
Author(s):  
Zongwei Ma ◽  
Xuefei Hu ◽  
Lei Huang ◽  
Jun Bi ◽  
Yang Liu
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Ground Level

Deep Learning Architecture for Estimating Hourly Ground-Level PM2.5 Using Satellite Remote Sensing

2019 ◽  
Vol 16 (9) ◽  
pp. 1343-1347 ◽  
Author(s):  
Yibo Sun ◽  
Qiaolin Zeng ◽  
Bing Geng ◽  
Xinwen Lin ◽  
Bilige Sude ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Satellite Remote Sensing ◽  
Ground Level

scholarly journals Evaluating the potential of remote sensing imagery in mapping ground-level fine particulate matter (PM2.5) for the Vaal Triangle Priority Area

2020 ◽  
Vol 30 (1) ◽  
Author(s):  
Luckson Muyemeki ◽  
Roelof Burger ◽  
Stuart J. Piketh
Keyword(s):  
South Africa ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Temporal Trends ◽  
Ground Level ◽  
Monitoring Networks ◽  
Priority Area ◽  
Satellite Retrievals ◽  
Vaal Triangle

The quality of air breathed in South Africa is of great concern, especially in industrialised regions where PM2.5 concentrations are high. Long term exposure to PM2.5 is associated with serious adverse health impacts. Traditionally, PM2.5 is monitored by a network of ground-based instruments. However, the coverage of monitoring networks in South Africa is not dense enough to fully capture the spatial variability of PM2.5 concentrations. This study explored whether satellite remote sensing could offer a viable alternative to ground-based monitoring. Using an eight-year record (2009 to 2016) of satellite retrievals (MODIS, MISR and SeaWIFS) for PM2.5 concentrations, spatial variations and temporal trends for PM2.5 are evaluated for the Vaal Triangle Airshed Priority Area (VTAPA). Results are compared to corresponding measurements from the VTAPA surface monitoring stations. High PM2.5 concentrations were clustered around the centre and towards the south-west of the VTAPA over the highly industrialised cities of Vanderbijlpark and Sasolburg. Satellite retrievals tended to overestimate PM2.5 concentrations. Overall, there was a poor spatial agreement between satellite-retrieved PM2.5 estimates and ground-level PM2.5 measurements. Root mean square error values ranged from 6 to 11 µg/m3 and from -0.89 to 0.32 for the correlation coefficient. For satellite remote sensing to be effectively exploited for air quality assessments in the VTAPA and elsewhere, further research to improve the precision and accuracy of satellite-retrieved PM2.5 is required.

scholarly journals REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM<sub>2.5</sub> USING SATELLITE REMOTE SENSING

2018 ◽  
Vol IV-3 ◽  
pp. 143-147
Author(s):  
Tongwen Li ◽  
Chengyue Zhang ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Real Time ◽  
Satellite Remote Sensing ◽  
Ground Level ◽  
Fusion Model ◽  
The Real ◽  
Data Gaps ◽  
Spatio Temporal ◽  
Polar Orbiting Satellite

Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM<sub>2.5</sub>. However, the satellite-based monitoring of ground-level PM<sub>2.5</sub> is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM<sub>2.5</sub> in real time. Second, many data gaps exist in satellitederived PM<sub>2.5</sub> due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM<sub>2.5</sub> in a deep learning architecture. On this basis, the satellite-derived PM<sub>2.5</sub> in conjunction with ground PM<sub>2.5</sub> measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM<sub>2.5</sub> distributions. The results demonstrate that Harawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.80, RMSE&amp;thinsp;=&amp;thinsp;17.49&amp;thinsp;&amp;mu;g/m<sup>3</sup>) for the estimation of PM<sub>2.5</sub>. The missing data in satellite-derive PM<sub>2.5</sub> are accurately recovered, with R<sup>2</sup> between recoveries and ground measurements of 0.75. Overall, this study has inherently provided an effective strategy for the realtime and seamless monitoring of ground-level PM<sub>2.5</sub>.

scholarly journals Estimating Ground-Level PM2.5in the Eastern United States Using Satellite Remote Sensing

2005 ◽  
Vol 39 (9) ◽  
pp. 3269-3278 ◽  
Author(s):  
Yang Liu ◽  
Jeremy A. Sarnat ◽  
Vasu Kilaru ◽  
Daniel J. Jacob ◽  
Petros Koutrakis
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Ground Level ◽  
Eastern United States

scholarly journals Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3950 ◽  
Author(s):  
Kun Cai ◽  
Qiushuang Zhang ◽  
Shenshen Li ◽  
Yujing Li ◽  
Wei Ge
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Temporal Variations ◽  
Ozone Monitoring Instrument ◽  
Economic Zone ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Concentrations ◽  
Remote Sensing Techniques

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.

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