Improved Hourly and long-term PM2.5 Prediction Modeling Based on MODIS in Bangkok

Kumharn, Wilawan; Sudhibrabha, Sumridh; Hanprasert, Kesrin; Janjai, Serm; Masiri, Itsara; Buntoung, Sumaman; Pattarapanitchai, S.; Wattan, R.; Pilahome, Oradee; Nissawan, Waichaya; Jankondee, Yuttapichai

doi:10.1016/j.rsase.2022.100864

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…This difference between study domains is in line with the technological reality of trucks in Thailand: mostly EURO2 or at best EURO4. Our PM 2.5 model for the Bangkok CBD can provide new insights into PM 2.5 models including previously developed models that used different techniques and tools (Ketjumpol and Sakworawich, 2020;Chalermpong et al, 2021;Kumharn et al, 2022;Peng-In et al, 2022;Thongthammachart et al, 2023). The variability in emissions rates from time-varying traffic data may provide an indepth tool to investigate the particular incidence of extreme health risks posed by PM 2.5 .…”

Section: Discussionmentioning

confidence: 99%

Application of a PM2.5 dispersion model in the Bangkok central business district for air quality management

Ratanavalachai,

Trivitayanurak

2023

Front. Environ. Sci.

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Model simulations are conducted for fine particles diameter less than 2.5 microns (PM2.5) in the Chulalongkorn University area in the central business district of Bangkok, Thailand, where PM2.5 originating from road traffic is a recurring problem. For input to the American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD), an hourly continuous vehicle type classified emissions inventory is developed based on local traffic observations and published emissions factors. The simulation accounts for advected-in PM2.5 by hourly measurements from upwind stations. The result reveals a hotspot location near a nearby expressway with PM2.5 concentration peaking at 1-h and 24-h averages of 344 and 130 μg m−3, respectively. Source contribution analysis of the annual average PM2.5 at this hotspot suggests that the expressway contributes approximately 32% of the total PM2.5. Meanwhile, at receptor points farther from the expressway, ground-level urban roads contribute only 17.5% roadside PM2.5 concentrations, the remainder coming from outside the modeled area. A different source contribution breakdown by vehicle type suggests that heavy-duty vehicles contribute up to 21% of annual average PM2.5 at a location near the expressway. At a roadside receptor point farther from the expressway, the top contributors are light-duty (9%) and heavy-duty vehicles (6%). Advected-in PM2.5 dominates the overall PM2.5 concentrations, accounting for 64%–99% depending on the receptor point. The model performance for 24-h average prediction is acceptable. A scenario study is also performed to compare the potential effectiveness of two PM2.5 abatement measures.

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Section: Discussionmentioning

confidence: 99%

Application of a PM2.5 dispersion model in the Bangkok central business district for air quality management

Ratanavalachai,

Trivitayanurak

2023

Front. Environ. Sci.

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State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods

Gianquintieri,

Oxoli,

Caiani

et al. 2024

Environ Dev Sustain

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Air pollution is the one of the most significant environmental risks to health worldwide. An accurate assessment of population exposure would require a continuous distribution of measuring ground-stations, which is not feasible. Therefore, significant efforts are spent in implementing air-quality models. However, a complex scenario emerges, with the spread of many different solutions, and a consequent struggle in comparison, evaluation and replication, hindering the definition of the state-of-art. Accordingly, aim of this scoping review was to analyze the latest scientific research on air-quality modelling, focusing on particulate matter, identifying the most widespread solutions and trying to compare them. The review was mainly focused, but not limited to, machine learning applications. An initial set of 940 results published in 2022 were returned by search engines, 142 of which resulted significant and were analyzed. Three main modelling scopes were identified: correlation analysis, interpolation and forecast. Most of the studies were relevant to east and south-east Asia. The majority of models were multivariate, including (besides ground stations) meteorological information, satellite data, land use and/or topography, and more. 232 different algorithms were tested across studies (either as single-blocks or within ensemble architectures), of which only 60 were tested more than once. A performance comparison showed stronger evidence towards the use of Random Forest modelling, in particular when included in ensemble architectures. However, it must be noticed that results varied significantly according to the experimental set-up, indicating that no overall best solution can be identified, and a case-specific assessment is necessary.

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Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2.5 concentration long-term prediction

Zheng,

Tian,

et al. 2024

Expert Systems with Applications

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Improved Hourly and long-term PM2.5 Prediction Modeling Based on MODIS in Bangkok

Cited by 4 publications

References 45 publications

Application of a PM2.5 dispersion model in the Bangkok central business district for air quality management

Application of a PM2.5 dispersion model in the Bangkok central business district for air quality management

State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods

Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2.5 concentration long-term prediction

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