Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms

Chen, Bin; Song, Zhihao; Pan, Feng; Huang, Yongjun

doi:10.1016/j.scitotenv.2021.150338

Cited by 37 publications

(19 citation statements)

References 89 publications

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“…A 10‐fold cross‐validation method was used to test the model performance (Rodriguez et al., 2010). The parameters used to describe the model performance included the determination coefficient ( R 2 ), root mean square error (RMSE), and mean absolute error (MAE; Chen, Song, Pan, & Huang, 2022; Chen, Song, Shi, & Li, 2022). The expected error (EE, Equation ) was used to evaluate the accuracy of the TOAR‐PM 10 model.…”

Section: Methodsmentioning

confidence: 99%

“…Wang et al, 2020), Visible Infrared Imaging Radiometer Suite (VIIRS; Wu et al, 2016), MISR (X. Meng et al, 2018), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP; Chen, Song, Pan, & Huang, 2022).…”

Section: Estimation Of Atmospheric Pm 10 Concentration In Chinamentioning

confidence: 99%

“…(2021) used the space‐time extremely randomized trees (STET) to generate PM10 data in China from 2015 to 2019, with a spatial resolution of 1 km, and pointed out that PM 10 showed a significant downward trend. In addition, researchers have shown the relationship between AOD and particulate matter of polar orbiting satellites in China, such as MODIS (X. Wang et al., 2020), Visible Infrared Imaging Radiometer Suite (VIIRS; Wu et al., 2016), MISR (X. Meng et al., 2018), and Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP; Chen, Song, Pan, & Huang, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Section: Estimation Of Atmospheric Pm 10 Concentration In Chinamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

et al. 2022

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“…Since 2013, after implementing the "Ambient Air Quality Standards" (GB3095-2012), the Ministry of Environmental Protection has published air quality data including PM 2.5 and PM 10 in more than 90 cities (about 1,600 observation sites showed in Figure 10) on its official website. The datasets can provide a comprehensive analysis of the characteristics and temporal trends of air pollution in China (Fan et al, 2020;Song et al, 2021;Chen et al, 2022).…”

Section: The Air Quality Datamentioning

confidence: 99%

Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19

Chen

Huang

et al. 2021

Front. Environ. Sci.

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Asian dust can be transported at least one full circuit around the globe. During the transportation, dust can interact with local air-borne dust and pollutants, and has a profound impact on the environment. A novel coronavirus (COVID-19) has been affecting human activities worldwide since early 2020. The Chinese government has implemented emergency control measures. Since April 2020, control measures to reduce anthropogenic emissions have been gradually reduced. The optical properties of aerosols during the dust transport were affected by meteorological conditions, local environmental conditions and human activities. Therefore, two dust weather processes in March 2018 and March 2020 were screened under similar meteorological fields and transportation paths, which were mainly affected by human activities. Based on lidar data, in East China, compared with 2018, the average aerosol optical depth (AOD) of all types of aerosols at 0–4 km in 2020 decreased by 55.48%, while the AOD of dust aerosols decreased by 43.59%. The average particle depolarization ratio and color ratio decreased by 40.33 and 10.56% respectively. Due to the reduction of anthropogenic emissions in China (detected by lidar), the concentration of surface PM2.5 decreased by 57.47%. This indicated that due to the decrease in human activities caused by COVID-19 control measures, the optical properties of aerosols were significantly reduced during dust weather process in eastern China. However, in the Pacific region, compared with 2018, the AOD values of 0–1 km layer and 1–6 km layer in 2020 increased by 56.4% and decreased by 29.2% respectively. The difference between the two contributions of dust aerosols was very small. Meanwhile, compared with 2018, China’s near surface pollutants decreased significantly in 2020, indicating that the near surface AOD of the Pacific in 2020 was mainly contributed by local pollutants. This study was of great significance to the study of long-range and cross regional transport of pollutants.

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“…Machine LearningMachine learning (ML) has been widely used in remote sensing fields recently(Li et al, 2021;Smith et al, 2021;Chen et al, 2022b;Song et al, 2022;Chen et al, 2022a;Gui et al, 2020). The K-nearest neighbor (KNN) algorithm(Zhang and Zhou, 2007;Luo et al, 2022) is one of the most basic and simple ML algorithms.…”

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confidence: 99%

Mapping of Pollution Distribution for Electric Power System Based on Satellite Remote Sensing

Pei

et al. 2022

Front. Environ. Sci.

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In recent years, the frequent fouling accidents have posed a serious threat to people’s life and property safety. Owing to the wide distribution of pollution sources and variable meteorological factors, it is a very time-consuming and labor-intensive task to map the pollution distribution using traditional methods. In this work, a study on the mapping of pollution distribution based on satellite remote sensing is carried out in Yunnan Province, China, as an example. Several machine learning methods (e.g. K-nearest neighbor, support vector machine) are used to analyze the effects of conditions such as multiple air pollution and meteorological data on pollution distribution map levels. The results indicate that the ensemble learning model has the highest accuracy of 72.32% in this application. The new pollution distribution map using this classifier has 5,506 more pixels in the most severe pollution level than the traditional map. Last, the remote sensing-based map and the manual measurement-based map were combined with corresponding experience weight to obtain a weighted pollution distribution map.

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Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms

Cited by 37 publications

References 89 publications

Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19

Mapping of Pollution Distribution for Electric Power System Based on Satellite Remote Sensing

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Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms

Cited by 37 publications

References 89 publications

Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19

Mapping of Pollution Distribution for Electric Power System Based on Satellite Remote Sensing

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Product

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Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A

Estimation of Atmospheric PM₁₀ Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China’s New Generation Geostationary Meteorological Satellite, FY‐4A