Predicting spatiotemporal variations of PM2.5 concentrations during spring festival for county-level cities in China using VIIRS-DNB data

Erkin, Nurmemet; Simayi, Maimaiti; Ablat, Xarapat; Yahefu, Palida; Maimaiti, Bumairiyemu

doi:10.1016/j.atmosenv.2022.119484

Cited by 7 publications

(2 citation statements)

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“…Estimating PM 2.5 concentrations using Aerosol optical depth (AOD) as a remote sensing satellite derivative can be used to fill the gap of spatial and temporal data gaps left by ground stations [6] . Various remote sensing satellite sensors, such as Moderate Resolution Imaging Spectrometer (MODIS) [7], [8] The Visible-infrared Imaging Radiometer Suite (VIIRS) [9], [10], the Advanced Himawari Imager [11], [12] the Advanced Geosynchronous Radiation Image (AGRI) [13], [14] have been applied to estimate PM 2.5 concentrations.…”

Section: Introductionmentioning

confidence: 99%

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PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review

Unik,

Sitanggang,

Syaufina

et al. 2023

IJACSA

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Most researchers are beginning to appreciate the use of remote sensing satellites to assess PM 2.5 levels and use machine learning algorithms to automate the collection, make sense of remote sensing data, and extract previously unseen data patterns. This study reviews delicate particulate matter (PM 2.5 ) predictions from satellite aerosol optical depth (AOD) and machine learning. Specifically, we review the characteristics and gap-filling methods of satellite-based AOD products, sources and components of PM 2.5 , observable AOD products, data mining, and the application of machine learning algorithms in publications of the past two years. The study also included functional considerations and recommendations in covariate selection, addressing the spatiotemporal heterogeneity of the PM 2.5 -AOD relationship, and the use of cross-validation, to aid in determining the final model. A total of 79 articles were included out of 112 retrieved records consisting of articles published in 2022 totaling 43 articles, as of 2023 (until February) totaling 19 articles, and other years totaling 18 articles. Finally, the latest method works well for monthly PM 2.5 estimates, while daily PM 2.5 and hourly PM 2.5 can also be achieved. This is due to the increased availability and computing power of large datasets and increased awareness of the potential benefits of predictors working together to achieve higher estimation accuracy. Some key findings are also presented in the conclusion section of this article. Keywords-AOD; machine learning; PM 2.5; remote sensing; pollutant Eligibility: Eligibility was determined by reading the main findings, use of data, results, and discussion. The authors considered journal articles and books published by reputable publishers as high-quality research and included them in summary. The authors used "Scimago Journal & Country Rank" to check the rankings of the included articles. Inclusion: The research then lists literature articles that correspond to the main topic.Our initial search yielded 112 articles. After passing the initial screening to eligibility assessment, this study used 61 primary and 18 other articles.

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Section: Introductionmentioning

confidence: 99%

“…Land variables, such as NDVI. Variables related to population) such as population density, road network density, height, and the number of buildings, and others, including data on PM 2.5 , carbon monoxide (CO), ozon (O 3 ), nitrogen oxides (NO), nitrogen dioxide ( NO2), and sulfur dioxide (SO 2 ) [10], [11], [14], [26], [30].…”

Section: Introductionmentioning

confidence: 99%

PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review

Unik,

Sitanggang,

Syaufina

et al. 2023

IJACSA

View full text Add to dashboard Cite

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Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban

Liu,

Zheng,

Bao

et al. 2024

Environmental Pollution

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Spatiotemporal characteristics of PM2.5 concentrations and responses to land-use change in Urumqi, China

Rong,

Erkin,

et al. 2024

J. Appl. Rem. Sens.

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The acceleration of urbanization has increasingly exacerbated air pollution in Northwest China. However, existing studies have relatively few analyses of PM 2.5 concentrations in response to land-use changes. This study quantitatively evaluated the impact of land-use changes on PM 2.5 concentrations in Urumqi (2014 to 2023) using remote sensing techniques and machine learning methods. The MCD19-A2 aerosol optical depth (AOD) product, with gaps filled using a singular spectrum analysis algorithm (99.63% AOD coverage), was used to predict PM 2.5 concentrations based on the light gradient boosting machine method (10-CV R 2 ¼ 0.93, root mean square error ¼ 17.98 μg∕m 3 ). The spatial correlation between land-use changes and PM 2.5 concentrations showed that PM 2.5 concentrations were highest in central urban areas but decreased by an average of 27.41 μg∕m 3 over the decade. Land-use type transitions (barren-grassland, grassland-barren, and grassland-cropland) were significantly negatively correlated with PM 2.5 , indicating these changes reduced aerosol concentrations during the research period in Urumqi. The reaction of dynamic PM 2.5 to land-use and land-cover changes showed a local overlap but was not entirely consistent, as reflected by the geographically weighted regression model. Geodetector quantified the contribution of land-use change to PM 2.5 reduction, particularly barren-grassland conversion, which notably reduced PM 2.5 (contribution coefficient = 0.161), highlighting the importance of protecting vegetated areas for PM 2.5 control in Urumqi. These findings clarify the impact of land-use change on PM 2.5 , supporting improvements in land management and atmospheric control strategies for sustainable development in Urumqi.

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Predicting spatiotemporal variations of PM2.5 concentrations during spring festival for county-level cities in China using VIIRS-DNB data

Cited by 7 publications

References 85 publications

PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review

PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review

Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban

Spatiotemporal characteristics of PM2.5 concentrations and responses to land-use change in Urumqi, China

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