Visibility, aerosol optical depth, and low-visibility events in Bangkok during the dry season and associated local weather and synoptic patterns

Aman, Nishit; Manomaiphiboon, Kasemsan; Suwattiga, Panwadee; Assareh, Nosha; Limpaseni, Wongpun; Suwanathada, Patcharawadee; Soonsin, V.; Wang, Yangjun

doi:10.1007/s10661-022-09880-2

Cited by 9 publications

(9 citation statements)

References 47 publications

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“…Dejchanchaiwong et al (2020) [ 19 ] found fires in Thailand and Cambodia to be a contributor to a haze episode in Bangkok. Aman et al 2022 [ 20 ] analyzed two low-visibility events in Bangkok, both of which were partly affected by biomass burning.…”

Section: Discussionmentioning

confidence: 99%

“…Haze pollution is frequently experienced in GBK during the dry season when the daily PM 2.5 exceeds the daily National Ambient Air Quality Standard (NAAQS) of 50 µg m −3 several times per year as reported by the Pollution Control Department (PCD) [ 13 ]. This haze pollution is related to the emission from on-road vehicles [ 14 , 15 , 16 , 17 ], biomass burning [ 16 , 17 , 18 , 19 , 20 ], and unfavorable meteorological conditions induced by cold surges in Greater Bangkok [ 7 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

Aman

Manomaiphiboon

Pala-En³

et al. 2023

IJERPH

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This study deals with haze characteristics under the influence of the cold surge and sea breeze for Greater Bangkok (GBK) in 2017–2022, including haze intensity and duration, meteorological classification for haze, and the potential effects of secondary aerosols and biomass burning. A total of 38 haze episodes and 159 haze days were identified. The episode duration varies from a single day to up to 14 days, suggesting different pathways of its formation and evolution. Short-duration episodes of 1–2 days are the most frequent with 18 episodes, and the frequency of haze episodes decreases as the haze duration increases. The increase in complexity in the formation of relatively longer episodes is suggested by a relatively higher coefficient of variation for PM2.5. Four meteorology-based types of haze episodes were classified. Type I is caused by the arrival of the cold surge in GBK, which leads to the development of stagnant conditions favorable for haze formation. Type II is induced by sea breeze, which leads to the accumulation of air pollutants due to its local recirculation and development of the thermal internal boundary layer. Type III consists of the haze episodes caused by the synergetic effect of the cold surge and sea breeze while Type IV consists of short haze episodes that are not affected by either the cold surge or sea breeze. Type II is the most frequent (15 episodes), while Type III is the most persistent and most polluted haze type. The spread of haze or region of relatively higher aerosol optical depth outside GBK in Type III is potentially due to advection and dispersion, while that in Type IV is due to short 1-day episodes potentially affected by biomass burning. Due to cold surge, the coolest and driest weather condition is found under Type I, while Type II has the most humid condition and highest recirculation factor due to the highest average sea breeze duration and penetration. The precursor ratio method suggests the potential effect of secondary aerosols on 34% of the total haze episodes. Additionally, biomass burning is found to potentially affect half of the total episodes as suggested by the examination of back trajectories and fire hotspots. Based on these results, some policy implications and future work are also suggested.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Aman

Manomaiphiboon

Pala-En³

et al. 2023

IJERPH

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“…The highest excess risk of mortality from exposure to PM 2.5 in Bangkok was 2.61%, as calculated using Equation (5). This shows that the percentage increase in respiratory disease-related deaths was higher than that found in a study involving 272 cities in China, which reported an ER of 0.29% (95% CI, 0.17-0.42%) [30], and in a study conducted in New England (USA), with an ER of 0.41% (95% CI, 0.16-0.65%) [44].…”

Section: Excess Risk Of Mortalitymentioning

confidence: 99%

“…According to the World Health Organization (WHO), the greatest burden of outdoor air pollution, with 91% of the 4.2 million premature deaths, is in low-and middle-income countries in the western Pacific and southeast Asia regions [2]. In the case of Bangkok, Thailand, its air pollution situation is frequently severe during the dry season, when the atmospheric conditions are stable and the winds are light [3][4][5]. Health impacts due to exposure to air pollution in the city remain a key public health concern [6].…”

Section: Introductionmentioning

confidence: 99%

Establishment of a City-Based Index to Communicate Air Pollution-Related Health Risks to the Public in Bangkok, Thailand

2022

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An Air Quality Health Index (AQHI), a health risk-based air pollution index, was constructed to communicate to the public their health risks due to exposure to air pollution in Bangkok, Thailand. This AQHI was built by analyzing the association between total excess respiratory disease-related deaths and individual air pollutants, using a time-series analysis of daily data from 2010 to 2019. We used Poisson regression in a generalized additive model, with natural cubic smooth splines to analyze the data and controls for other common variables (time, temperature, relative humidity, day of the week, and public holidays). The regression coefficients of these variables were then employed to establish the suitability of this AQHI for Bangkok. The results indicated that a 10-unit increase in particulate matter (PM10), fine particulate matter (PM2.5), ozone (O3), or nitrogen dioxide (NO2) was statistically associated with increased respiratory disease mortality. The coefficients of these four pollutants were then adopted in the construction of an AQHI for Bangkok. Compared with the currently used Air Quality Index (AQI), the AQHI was a more effective indicator in communicating multiple air pollution-related health risks to the public in Bangkok.

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“…Lee et al (2017) used the WRF-Chem model to investigate the effect of biomass-burning aerosol on low-visibility events in Bangkok and other cities in Southeast Asia. Aman et al (2022) reported two visibility events in the winter of 2014 and 2015 affected by the synergetic effect of particulate pollution and meteorology. Recently, visibility-related studies across the world have started using machine learning (ML) models for visibility prediction using particulate matter and meteorological variables as input data (Kim et al 2022a; Kim et al 2022b; Penov and Guerova 2023) and the importance of different features is identi ed by feature importance plots (Kim et al 2022a; Penov and Guerova 2023).…”

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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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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Visibility, aerosol optical depth, and low-visibility events in Bangkok during the dry season and associated local weather and synoptic patterns

Cited by 9 publications

References 47 publications

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

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

Establishment of a City-Based Index to Communicate Air Pollution-Related Health Risks to the Public in Bangkok, Thailand

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

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