Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

Sun, Yuxin; Xue, Yong; Jiang, Xingxing; Jin, Chunlin; Wu, Shuhui; Zhou, Xingxing

doi:10.3390/rs13214276

Cited by 11 publications

(3 citation statements)

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“…The EVI (enhanced vegetation index) can reflect the extent of capturing particulate matter on plant leaves [65], and NDVI can also be considered in the IGTWR model [66]. Because the study was conducted during the summer months, EVI and NDVI were not considered in the final model.…”

Section: Igtwrmentioning

confidence: 99%

Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution

Wu,

Sun,

Bai

et al. 2024

Remote Sensing

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Due to the advantage of high spatial coverage, using satellite-retrieved aerosol optical depth (AOD) data to estimate PM2.5 and PM10 mass concentrations is a current research priority. Statistical models are the common method of PM estimation currently, which do not require the knowledge of complex chemical and physical interactions. However, the statistical models rely on station data, which results in less accurate PM estimation concentrations in areas where station data are missing. Hence, a new hybrid model, with low dependency on on-site data, was proposed for PM2.5 and PM10 mass concentration estimation. The Gaofen-1 satellite and MODIS data were employed to estimate PM2.5 and PM10 concentrations with 100 m spatial resolution in Beijing, China. Then, the estimated PM2.5/10 mass concentration data in 2020 were employed to conduct a spatio-temporal analysis for the investigation of the particulate matter characteristic in Beijing. The estimation result of PM2.5 was validated by the ground stations with R2 ranging from 0.91 to 0.98 and the root mean square error (RMSE) ranging from 4.51 μg/m3 to 17.04 μg/m3, and that for PM10 was validated by the ground stations with R2 ranging from 0.85 to 0.98 and the RMSE ranging from 6.98 µg/m3 to 29.00 µg/m3. The results showed that the hybrid model has a good performance in PM2.5/10 estimation and can improve the coverage of the results without sacrificing the effectiveness of the model, providing more detailed spatial information for urban-scale studies.

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Section: Igtwrmentioning

confidence: 99%

Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution

Wu,

Sun,

Bai

et al. 2024

Remote Sensing

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“…Previous studies have typically relied on moderate-resolution imaging spectroradiometer (MODIS) AOD data, including the MODIS Terra and Aqua Collection and the MODIS multi-angle implementation of atmospheric correction (MAIAC) AOD, to generate daily PM 2.5 distributions [7][8][9][10]. Furthermore, hourly concentrations of PM 2.5 were calculated using the hourly AOD products derived from various sources, such as the 8th Himawari geostationary weather satellites (Himawari-8), the geostationary ocean color imager (GOCI), and Fengyun (FY-4A) AOD data [11][12][13]. Although these studies have significantly assisted in assessing PM 2.5 exposure risk, some technical and data limitations may have led to omitting data gaps in AOD data.…”

Section: Introductionmentioning

confidence: 99%

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Chu,

Zhang,

2023

Remote Sensing

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Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM2.5, thereby impeding accurate exposure risk assessment. Here, this study presents a novel and flexible framework that couples stacking and flexible spatiotemporal data fusion (FSDAF) approaches. By integrating multiple models and data sources, this framework aims to generate hourly (24-h) gap-free PM2.5 estimates for the Beijing–Tianjin–Hebei (BTH) region in 2018. This study effectively reconstructed data at least three times more effectively than the original AOD-derived PM2.5, achieving the Pearson coefficient (r), the coefficient determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) values of 0.91, 0.84, 19.38 µg/m3, and 12.17 µg/m3, respectively, based on entire samples. Such strong predictive performance was also exhibited in spatial-based (r: 0.92–0.93, R2: 0.85–0.87, RMSE: 18.13 µg/m3–20.18 µg/m3, and MAE: 11.21 µg/m3–12.52 µg/m3) and temporal-based (r: 0.91–0.98, R2: 0.82–0.96, RMSE: 3.8 µg/m3–21.89 µg/m3, and MAE: 2.71 µg/m3–14.00 µg/m3) validations, indicating the robustness of this framework. Additionally, this framework enables the assessment of annual and seasonal PM2.5 concentrations and distributions, revealing that higher levels are experienced in the southern region, while lower levels prevail in the northern part. Winter exhibits the most severe levels, followed by spring and autumn, with comparatively lower levels in summer. Notably, the proposed framework effectively mitigates bias in calculating population-weighted exposure risk by filling data gaps with calculated values of 51.04 µg/m3, 54.17 µg/m3, 56.24 µg/m3, and 55.00 µg/m3 in Beijing, Tianjin, Hebei, and the BTH region, respectively.

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“…Therefore, satellite remote sensing data has been applied by numerous researchers to study the spatiotemporal distribution of atmospheric pollutant concentrations [19][20][21][22][23][24]. In addition, after repeated experiments, it has been proven that aerosol optical depth (AOD) has a high correlation with the concentration of particulate matter near the ground, and therefore it has been widely used in the analysis and research of atmospheric particulate matter concentration [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Studying the Regional Transmission of Air Pollution Based on Spatiotemporal Multivariable Data

Lu,

Xue,

et al. 2023

Atmosphere

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Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a “Time-Longitude-Latitude” three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities.

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Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

Cited by 11 publications

References 50 publications

Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution

Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Studying the Regional Transmission of Air Pollution Based on Spatiotemporal Multivariable Data

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