Key determinants of particulate matter 2.5 concentrations in urban environments with scenario analysis

Chun, Bumseok; Choi, Kwangyul; Pan, Qingqing

doi:10.1177/23998083221078306

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…For example, population agglomeration and open space may substantially impact particulate matter. Because the heavier traffic volume resulting from the population agglomeration increases PM 2.5 in the atmosphere, the more the open space exists, the better air ventilation (Chun et al, 2022; Song et al, 2021). Additionally, this study has investigated the impact of urban form factors on the particulate matter concentration within a 1 km buffer from each station, but the buffer distance cannot be universalized to all situations.…”

Section: Resultsmentioning

confidence: 99%

Deep learning-based investigation of the impact of urban form on the particulate matter concentration on a neighborhood scale

Kim

Gim

2022

Environment and Planning B: Urban Analytics and City Science

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Despite the importance of urban forms to the dispersion of particulate matter (PM), only a few studies exist on the relationship between them due to the limitations of the data and methodology. Thus, this study used a deep learning-based investigation of the impact of urban form on PM2.5 concentration. Autoencoder, long short-term memory (LSTM), and the random forest model were used to analyze their relationship. The random forest model showed that urban form variables predict PM2.5 concentration with a 95.66% accuracy, confirming that urban form characteristics significantly impact PM2.5 concentrations. Among the urban form variables, floor area ratio turned out to be the most important, suggesting the need for more detailed efforts to reduce PM2.5 in locations with high floor areas. The effect on PM2.5 prediction accuracy was evaluated with root mean square error. It was difficult to accurately predict PM2.5 in areas with large building coverage areas and low-rise residential areas. This study improved the accuracy of results on the influence of urban form by using PM2.5 non-aggregated data measured hourly over 4 years, which expanded the applicability of deep learning-based urban analysis.

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Section: Resultsmentioning

confidence: 99%

Deep learning-based investigation of the impact of urban form on the particulate matter concentration on a neighborhood scale

Kim

Gim

2022

Environment and Planning B: Urban Analytics and City Science

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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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Key determinants of particulate matter 2.5 concentrations in urban environments with scenario analysis

Cited by 2 publications

References 39 publications

Deep learning-based investigation of the impact of urban form on the particulate matter concentration on a neighborhood scale

Deep learning-based investigation of the impact of urban form on the particulate matter concentration on a neighborhood scale

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

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