The Ground-Level Particulate Matter Concentration Estimation Based on the New Generation of FengYun Geostationary Meteorological Satellite

Tian, Lin; Chen, Lin; Zhang, Peng; Hu, Bo; Gao, Yang; Si, Ying

doi:10.3390/rs15051459

Cited by 6 publications

(1 citation statement)

References 49 publications

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“…Himawari-8 takes pictures at a rate of every 10 min and updates regional pictures at a rate of every 2.5 min, indicating that the satellite provides real-time meteorological data at a high spatial and temporal resolution. Hence, the H8 would help to monitor rapid changes in the weather phenomena (Han et al, 2023). The channels of H8 are shown in Supplementary Table S1.…”

Section: H8 Datamentioning

confidence: 99%

Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin

Shi,

Han,

Wang

2024

Front. Environ. Sci.

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Lake Surface Water Temperature (LSWT) is essential for understanding and regulating various processes in lake ecosystems. Remote sensing for large-scale aquatic monitoring offers valuable insights, but its limitations call for a dynamic LSWT monitoring model. This study developed multiple machine learning models for LSWT retrieval of four representative freshwater lakes in the Yangtze River Basin using Himawari-8 (H8) remote sensing imagery and in-situ data. Based on the in situ monitoring dataset in Lake Chaohu, the dynamic LSWT retrieval models were effectively configured and validated to perform H8-based remote sensing inversion. The test results showed that six models provided satisfactory LSWT retrievals, with the Back Propagation (BP) neural network model achieving the highest accuracy with an R-squared (R2) value of 0.907, a Root Mean Square Error (RMSE) of 2.52°C, and a Mean Absolute Error (MAE) of 1.68°C. Furthermore, this model exhibited universality, performing well in other lakes within the Yangtze River Basin, including Taihu, Datonghu and Dongtinghu. The ability to derive robust LSWT estimates confirms the feasibility of real-time LSWT retrieval using synchronous satellites, offering a more efficient and accurate approach for LSWT monitoring in the Yangtze River Basin. Thus, this proposed model would serve as a valuable tool to support the implementation of more informed policies for aquatic environmental conservation and sustainable water resource management, addressing challenges such as climate change, water pollution, and ecosystem restoration.

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Section: H8 Datamentioning

confidence: 99%

Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin

Shi,

Han,

Wang

2024

Front. Environ. Sci.

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Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach

Aman,

Panyametheekul,

Sudhibrabha

et al. 2024

Environ Sci Pollut Res

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In this study, a range of machine learning (ML) models including random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting, cat boosting, and a stacked ensemble model, were employed to predict visibility at Bangkok airport. Furthermore, the impact of in uential factors was examined using the Shapley method, an interpretable ML technique inspired by the game theory-based approach. Air pollutant data from seven Pollution Control Department monitoring stations, visibility, and meteorological data from the Thai Meteorological Department's Weather station at Bangkok Airport, ERA5_LAND, and ERA5 datasets, and time-related dummy variables were considered. Daytime visibility ((here, 8-17 local time) was screened for rainfall, and ML models were developed for visibility prediction during the dry season (November -April). The light gradient boosting model is identi ed as the most effective individual ML model with superior performance in three out of four evaluation metrics (i.e., highest ρ, zero MB, second lowest ME, and lowest RMSE). However, the SEM outperformed all the individual models in visibility prediction at both hourly and daily time scales. The seasonal mean and standard deviation of normalized meteorological visibility are lower than those of the original visibility, indicating more in uence of meteorology than emission reduction on visibility improvement. The Shapley analysis identi ed RH, PM 2.5 , PM 10 , day of the season year, and O 3 as the ve most important variables. At low relative humidity (RH), there is no notable impact on visibility. Nevertheless, beyond this threshold, negative correlation between RH and visibility. An inverse correlation between visibility and both PM 2.5 and PM 10 was identi ed. Visibility is negatively correlated with O 3 at lower to moderate concentrations, with diminishing impact at very high concentrations. The day of the season year (i.e., Julian day) (JD) exhibits an initial negative and later positive association with visibility, suggesting a periodic effect. The dependence of the Shapley values of PM 2.5 and PM 10 on RH, and the equal step size method to understand RH effects, suggest the effect of hygroscopic growth of aerosol on visibility. Findings from this research suggest the feasibility of employing machine learning techniques for predicting visibility and comprehending the factors in uencing its uctuations. Based on the above ndings, certain policy-related implications, and future work have been suggested.

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The future of urban air quality management in Thailand: the cutting-edge platform for monitoring and management of public health and sustainable development

Sukawattanavijit,

Kruasilp,

Kaewmesri

et al. 2023

Remote Sensing Technologies and Applications in Urban Environments VIII

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Thailand has experienced rapid urbanization and industrialization, resulting in increased air pollution. Urban air pollution is a major environmental issue contributing to respiratory diseases. The purpose of this study was to develop an air pollution platform, namely "Life Dee", for near real-time monitoring of the fine particulate matter (PM2.5) in Chonburi province at microclimate conditions. This was achieved using a combination of remote sensing data, ground based stations, and microclimate modeling. The Weather Research and Forecasting with Chemistry (WRF-CHEM) model was used to simulate PM2.5 concentrations with 1 km × 1 km spatial resolution. A High Definition (HD) map was created using ArcGIS CityEngine, which utilizing for visualization of PM2.5 concentrations in urban areas. The user-friendly platform was developed to make the data accessible to the public via mobile applications. This platform can serve as a prototype model for other urban areas dealing with similar air pollution challenges. Users can utilize the platform to monitor air quality and receive information on appropriate action plans to protect their health. In addition, the platform provides valuable information to government agencies, allowing them to take proactive measures to mitigate air pollution and improve quality of life for citizens.

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The Ground-Level Particulate Matter Concentration Estimation Based on the New Generation of FengYun Geostationary Meteorological Satellite

Cited by 6 publications

References 49 publications

Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin

Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin

Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach

The future of urban air quality management in Thailand: the cutting-edge platform for monitoring and management of public health and sustainable development

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