Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China

Song, Zhihao; Chen, Bin; Huang, Jianping

doi:10.1016/j.envpol.2022.118826

Cited by 22 publications

(6 citation statements)

References 72 publications

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“…Details of 16 bands information of the Advanced Himawari Imager (AHI) instrument on Himawari-8 satellite is shown in Table 1. However, the observation range of Himawari-8 is limited to 80 °E −200 °E and 60 °S-60 °N, so no valid data can be obtained in China's Tibet, Xinjiang and western Sichuan (Song et al, 2022a). The Himawari-8 TOAR data used in this paper have a temporal resolution of 1 h and a spatial resolution of 5 km.…”

Section: Himawari-8 Toarmentioning

confidence: 99%

“…Its TOAR data covers a wide area of eastern China with high temporal resolution, including 16 bands ranging from visible to near-infrared light. Therefore, the Himawari-8 TOAR has great advantages in building a high spatial and temporal resolution estimation model of ground-level pollutant concentration, which has been widely used in many related studies (Zang et al, 2018;Wei et al, 2021;Xu et al, 2021;Song et al, 2022a;Chen et al, 2022c). However, to the best of our knowledge, the Himawari-8 TOAR has not been applied to ground-level SO 2 concentration estimation so far.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

et al. 2023

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Sulfur dioxide (SO2) is one of the main pollutants in China’s atmosphere, but the spatial distribution of ground-based SO2 monitors is too sparse to provide a complete coverage. Therefore, obtaining a high spatial resolution of SO2 concentration is of great significance for SO2 pollution control. In this study, based on the LightGBM machine learning model, combined with the top-of-atmosphere radiation (TOAR) of Himawari-8 and additional data such as meteorological factors and geographic information, a high temporal and spatial resolution TOAR-SO2 estimation model in eastern China (97–136°E, 15–54°N) is established. TOAR and meteorological factors are the two variables that contribute the most to the model, and both of their feature importance values exceed 30%. The TOAR-SO2 model has great performance in estimating ground-level SO2 concentrations with 10-fold cross validation R2 (RMSE) of 0.70 (16.26 μg/m3), 0.75 (12.51 μg/m3), 0.96 (2.75 μg/m3), 0.97 (2.16 μg/m3), and 0.97 (1.71 μg/m3) when estimating hourly, daily, monthly, seasonal, and annual average SO2. Taking North China as main study area, the annual average SO2 is estimated. The concentration of SO2 in North China showed a downward trend since 2016 and decreased to 15.19 μg/m3 in 2020. The good agreement between ground measured and model estimated SO2 concentrations highlights the capability and advantage of using the model to monitor spatiotemporal variations of SO2 in Eastern China.

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Section: Himawari-8 Toarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

et al. 2023

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“…Launched in 2014, it has the incomparable advantages of 10 min time resolution and 500 m spatial resolution simultaneously, which ensure abundant reliable data are detected and used in distinguished meteorological models. Hourly AOD data could be derived from Himawari-8 and subsequently ensure multiple relevant research studies in China [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

PD-LL-Transformer: An Hourly PM2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China

Zou,

Huang,

et al. 2024

Remote Sensing

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As the urgency of PM2.5 prediction becomes increasingly ingrained in public awareness, deep-learning methods have been widely used in forecasting concentration trends of PM2.5 and other atmospheric pollutants. Traditional time-series forecasting models, like long short-term memory (LSTM) and temporal convolutional network (TCN), were found to be efficient in atmospheric pollutant estimation, but either the model accuracy was not high enough or the models encountered certain challenges due to their own structure or some specific application scenarios. This study proposed a high-accuracy, hourly PM2.5 forecasting model, poly-dimensional local-LSTM Transformer, namely PD-LL-Transformer, by deep-learning methods, based on air pollutant data and meteorological data, and aerosol optical depth (AOD) data retrieved from the Himawari-8 satellite. This research was based on the Yangtze River Delta Urban Agglomeration (YRDUA), China for 2020–2022. The PD-LL-Transformer had three parts: a poly-dimensional embedding layer, which integrated the advantages of allocating and embedding multi-variate features in a more refined manner and combined the superiority of different temporal processing methods; a local-LSTM block, which combined the advantages of LSTM and TCN; and a Transformer encoder block. Over the test set (the whole year of 2022), the model’s R2 was 0.8929, mean absolute error (MAE) was 4.4523 µg/m3, and root mean squared error (RMSE) was 7.2683 µg/m3, showing great accuracy for PM2.5 prediction. The model surpassed other existing models upon the same tasks and similar datasets, with the help of which a PM2.5 forecasting tool with better performance and applicability could be established.

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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%

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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Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China

Cited by 22 publications

References 72 publications

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

PD-LL-Transformer: An Hourly PM2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China

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

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