Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality
A pivotal element for metropolitan planning and an essential component describing the urban design is block typology, affecting the pollution concentration. Consequently, this research examines the influence of various urban block typologies on urban pollutant distribution. Four typologies are simul...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Sustainable Food Systems |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsufs.2022.898549/full |
| _version_ | 1797222355270893568 |
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| author | Milad Karimian Shamsabadi Mansour Yeganeh Mansour Yeganeh Elham Pourmahabadian |
| author_facet | Milad Karimian Shamsabadi Mansour Yeganeh Mansour Yeganeh Elham Pourmahabadian |
| author_sort | Milad Karimian Shamsabadi |
| collection | DOAJ |
| description | A pivotal element for metropolitan planning and an essential component describing the urban design is block typology, affecting the pollution concentration. Consequently, this research examines the influence of various urban block typologies on urban pollutant distribution. Four typologies are simulated by ENVI-MET software. These typologies are cubic-shaped, L-shaped, C-shaped, and linear-shaped models. Urban air quality was assessed using relative humidity, temperature, and pollution PM2.5 concentration. The performance of typologies in terms of temperature, relative humidity, and reduction of air permeability is strongly dependent on the blocks' orientation, the block shape's rotation concerning the horizontal and vertical extensions, the height of the blocks, and the type of typology. According to these parameters, the performance is different in each of these studied typologies. Regression models propose a more reliable prediction of PM2.5 when the independent variables are temperature, relative humidity, and height of buildings, among various block typologies. Hence, this article suggests a machine learning approach, and the model evaluation shows that the Polynomial Linear Regression (PLR) model is excellent for measuring air pollution and temperature. |
| first_indexed | 2024-04-24T13:20:01Z |
| format | Article |
| id | doaj.art-0de37a15942f4db886dd0a93133d699d |
| institution | Directory Open Access Journal |
| issn | 2571-581X |
| language | English |
| last_indexed | 2024-04-24T13:20:01Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainable Food Systems |
| spelling | doaj.art-0de37a15942f4db886dd0a93133d699d2024-04-04T15:56:29ZengFrontiers Media S.A.Frontiers in Sustainable Food Systems2571-581X2022-09-01610.3389/fsufs.2022.898549898549Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air qualityMilad Karimian Shamsabadi0Mansour Yeganeh1Mansour Yeganeh2Elham Pourmahabadian3Department of Architecture, Shahrekord Branch, Islamic Azad University, Shahrekord, IranDepartment of Architecture, Shahrekord Branch, Islamic Azad University, Shahrekord, IranDepartment of Architecture, Digital Architecture and Artificial Intelligence Lab, Tarbiat Modares University, Tehran, IranDepartment of Architecture and Urban Studies, Central Tehran Branch, Islamic Azad University, Tehran, IranA pivotal element for metropolitan planning and an essential component describing the urban design is block typology, affecting the pollution concentration. Consequently, this research examines the influence of various urban block typologies on urban pollutant distribution. Four typologies are simulated by ENVI-MET software. These typologies are cubic-shaped, L-shaped, C-shaped, and linear-shaped models. Urban air quality was assessed using relative humidity, temperature, and pollution PM2.5 concentration. The performance of typologies in terms of temperature, relative humidity, and reduction of air permeability is strongly dependent on the blocks' orientation, the block shape's rotation concerning the horizontal and vertical extensions, the height of the blocks, and the type of typology. According to these parameters, the performance is different in each of these studied typologies. Regression models propose a more reliable prediction of PM2.5 when the independent variables are temperature, relative humidity, and height of buildings, among various block typologies. Hence, this article suggests a machine learning approach, and the model evaluation shows that the Polynomial Linear Regression (PLR) model is excellent for measuring air pollution and temperature.https://www.frontiersin.org/articles/10.3389/fsufs.2022.898549/fullblock typologypollution concentrationregression modelsmachine learningbuilding |
| spellingShingle | Milad Karimian Shamsabadi Mansour Yeganeh Mansour Yeganeh Elham Pourmahabadian Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality Frontiers in Sustainable Food Systems block typology pollution concentration regression models machine learning building |
| title | Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality |
| title_full | Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality |
| title_fullStr | Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality |
| title_full_unstemmed | Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality |
| title_short | Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality |
| title_sort | urban buildings configuration and pollutant dispersion of pm 2 5 particulate to enhance air quality |
| topic | block typology pollution concentration regression models machine learning building |
| url | https://www.frontiersin.org/articles/10.3389/fsufs.2022.898549/full |
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