Spatiotemporal estimation of hourly PM2.5 using AOD derived from geostationary satellite Fengyun-4A and machine learning models for Greater Bangkok
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Cited by 3 publications
References 46 publications
Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach
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.
Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach
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.
Spatiotemporal analysis of PM2.5 estimated using machine learning over Greater Bangkok: Variability, trends, and persistence
The estimation of surface PM2.5 over Greater Bangkok (GBK) was done using six individual machine learning models (random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting, and cat boosting), and a stacked ensemble model (SEM) during the dry season (November–April) for 2018–2022. The predictor variables include aerosol optical depth (AOD) from the Himawari-8 satellite, a set of meteorological variables from ERA5_LAND and ERA5 reanalysis datasets, fire hotspots count and NDVI from MODIS, population density from WorldPop database, and the terrain elevation from USGS. Surface PM2.5 was collected for 37 air quality monitoring stations from the Pollution Control Department and Bangkok Meteorological Administration. A good agreement was found between Satellite AOD and AERONET AOD from two AERONET sites in GBK. Among individual models, light gradient boosting showed the best performance in estimating surface PM2.5 on both hourly and daily scales. The SEM outperformed all the individual models and hence was used for the estimation of PM2.5 for each grid in GBK for each hour. A higher risk of PM2.5 pollution in winter (November–February) as compared to summer (March–April) with a higher intensity in Bangkok province was evident from the spatiotemporal maps for both PM2.5 and its exposure intensity. The increasing trend in PM2.5 was reported over more than half of the area in GBK in winter and one-fifth of areas in summer. PM2.5 showed higher variability in winter as compared to summer which can be attributed to the episodical increase in PM2.5 concentration due to changes in meteorological condition suppressing dilution of PM2.5. The persistence analysis using the Hurst exponent suggested an overall higher persistence in PM2.5 during winter as compared to summer but opposite behaviors in nearby coastal regions. The results suggest the potential of using satellite data in combination with ML techniques to advance air quality monitoring from space over the data-scare regions in developing countries. A derived PM2.5 dataset and results of the study could support the formulation of effective air quality management strategies in GBK.
Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach
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 influential 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 identified 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 influence of meteorology than emission reduction on visibility improvement. The Shapley analysis identified RH, PM2.5, PM10, day of the season year, and O3 as the five 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 PM2.5 and PM10 was identified. Visibility is negatively correlated with O3 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 PM2.5 and PM10 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 influencing its fluctuations. Based on the above findings, certain policy–related implications, and future work have been suggested.
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