A Study of Urban Haze and Its Association with Cold Surge and Sea Breeze for Greater Bangkok

Aman, Nishit; Manomaiphiboon, Kasemsan; Pala-En, Natchanok; Devkota, Bikash; Inerb, Muanfun; Kokkaew, Eakkachai

doi:10.3390/ijerph20043482

Cited by 6 publications

(9 citation statements)

References 51 publications

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“…Due to this direct linkage with air pollution, it is used as a surrogate for particulate matter (PM) for long-term air quality studies when long-term air quality data is not available (Aman et al 2019; Singh et al 2017). In addition to air pollutants, it is also dependent on various meteorological variables either directly or indirectly (Aman et al 2019(Aman et al , 2023Singh et al 2017;Majewski et al 2022). As most of the aerosols are hygroscopic, an increase in humidity can lead to an increase in aerosol size, and this increases light scattering.…”

Section: Introductionmentioning

confidence: 99%

“…This is linked to emissions from both industrial and vehicular sources (ChooChuay et al 2020; Narita et al 2019), as well as biomass burning in the area (ChooChuay et al 2020; Narita et al 2019;Phairuang et al 2019). Additionally, stagnant meteorological conditions induced by cold surges and sea breezes in GBK contribute to the persistence of this pollution (Aman et al 2020(Aman et al , 2023.…”

Section: Introductionmentioning

confidence: 99%

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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

Preprint

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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 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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Preprint

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“…ML methods have been proven to efficiently combine information on PM 2.5 , AOD and other spatialtemporal varying predictors. Several studies have compared the performance of different ML methods for PM 2.5 predictions, such as simple decision trees, random forests, support vector machines or Gradient Boosting, and a majority of them found random forest (RF) to perform the best [30][31][32][33][34][35]. The RF models allow for the consideration of numerous parameters for very accurate PM 2.5 predictions and provide importance measures to assess the influence of the respective parameters on the model's accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies using RF for AOD-based PM 2.5 estimations were performed for Asia, including different parts of China [28,[35][36][37][38], Thailand [34,39] and Iran [40]; fewer focused on the United States [41][42][43] and Europe, including Italy [44] and Great Britain [45].…”

Section: Introductionmentioning

confidence: 99%

Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach

2023

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The latest epidemiological studies have revealed that the adverse health effects of PM2.5 have impacts beyond respiratory and cardio-vascular diseases and also affect the development of the brain and metabolic diseases. The need for accurate and spatio-temporally resolved PM2.5 data has thus been substantiated. While the selective information provided by station measurements is mostly insufficient for area-wide monitoring, satellite data have been increasingly applied to comprehensively monitor PM2.5 distributions. Although the accuracy and reliability of satellite-based PM2.5 estimations have increased, most studies still rely on a single sensor. However, several datasets have become available in the meantime, which raises the need for a systematic analysis. This study presents the first systematic evaluation of four satellite-based AOD datasets obtained from different sensors and retrieval methodologies to derive ground-level PM2.5 concentrations. We apply a random forest approach and analyze the effect of the resolution and coverage of the satellite data and the impact of proxy data on the performance. We examine AOD data from the Moderate resolution Imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites, including Dark Target (DT) algorithm products and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. Additionally, we explore more recent datasets from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3a and from the Tropospheric Monitoring Instrument (TROPOMI) operating on the Sentinel-5 precursor (S5p). The method is demonstrated for Germany and the year 2018, where a dense in situ measurement network and relevant proxy data are available. Overall, the model performance is satisfactory for all four datasets with cross-validated R2 values ranging from 0.68 to 0.77 and excellent for MODIS AOD reaching correlations of almost 0.9. We find a strong dependency of the model performance on the coverage and resolution of the AOD training data. Feature importance rankings show that AOD has less weight compared to proxy data for SLSTR and TROPOMI.

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“…al. 2019;Phairuang et al 2019;Aman et al 2022Aman et al , 2023. Given that PM 2.5 is not found to be a concern during the wet season, this study focuses on the dry season only.…”

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confidence: 99%

Spatiotemporal analysis of PM2.5 estimated using machine learning over Greater Bangkok: Variability, trends, and persistence

Aman,

Panyametheekul,

Pawarmart

et al. 2024

Preprint

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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.

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A Study of Urban Haze and Its Association with Cold Surge and Sea Breeze for Greater Bangkok

Cited by 6 publications

References 51 publications

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

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

Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach

Spatiotemporal analysis of PM2.5 estimated using machine learning over Greater Bangkok: Variability, trends, and persistence

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