Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran
Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environment...
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Veröffentlicht in: | Sustainability 2022-07, Vol.14 (13), p.8027 |
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description | Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data. |
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Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14138027</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Air monitoring ; Air pollution ; Air quality ; Air temperature ; Artificial intelligence ; Deep learning ; Economic statistics ; Emission analysis ; Emissions ; Environmental health ; Global economy ; Humidity ; Learning algorithms ; Machine learning ; Model accuracy ; Nitrogen dioxide ; Outdoor air quality ; Particulate emissions ; Particulate matter ; Pollutants ; Pollution monitoring ; Public health ; Quality management ; Relative humidity ; Root-mean-square errors ; Sulfur dioxide ; Sustainability ; Vegetation ; Wind ; Wind direction ; Wind speed</subject><ispartof>Sustainability, 2022-07, Vol.14 (13), p.8027</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-50c6166281ebc6f1900c2299e4fdd3cd6d16ab1b69879cbe636df7f0bdd8fe443</citedby><cites>FETCH-LOGICAL-c295t-50c6166281ebc6f1900c2299e4fdd3cd6d16ab1b69879cbe636df7f0bdd8fe443</cites><orcidid>0000-0002-5775-9654 ; 0000-0002-4210-4348 ; 0000-0001-5664-6618</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Rad, Abdullah Kaviani</creatorcontrib><creatorcontrib>Shamshiri, Redmond R.</creatorcontrib><creatorcontrib>Naghipour, Armin</creatorcontrib><creatorcontrib>Razmi, Seraj-Odeen</creatorcontrib><creatorcontrib>Shariati, Mohsen</creatorcontrib><creatorcontrib>Golkar, Foroogh</creatorcontrib><creatorcontrib>Balasundram, Siva K.</creatorcontrib><title>Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran</title><title>Sustainability</title><description>Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. 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Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.</description><subject>Air monitoring</subject><subject>Air pollution</subject><subject>Air quality</subject><subject>Air temperature</subject><subject>Artificial intelligence</subject><subject>Deep learning</subject><subject>Economic statistics</subject><subject>Emission analysis</subject><subject>Emissions</subject><subject>Environmental health</subject><subject>Global economy</subject><subject>Humidity</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Model accuracy</subject><subject>Nitrogen dioxide</subject><subject>Outdoor air quality</subject><subject>Particulate emissions</subject><subject>Particulate matter</subject><subject>Pollutants</subject><subject>Pollution monitoring</subject><subject>Public health</subject><subject>Quality management</subject><subject>Relative humidity</subject><subject>Root-mean-square errors</subject><subject>Sulfur dioxide</subject><subject>Sustainability</subject><subject>Vegetation</subject><subject>Wind</subject><subject>Wind direction</subject><subject>Wind speed</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpNUF9LwzAcDKLgmHvxEwR8E6r506XN45hTBxP3MJ9LmvziMtp0Jqmyb2_nBL2Xu4PjDg6ha0ruOJfkPvY0p7wkrDhDI0YKmlEyJef_9CWaxLgjAzinkooROrwovXUe8ApU8M6_Y9sF_AAJQut-_NIPWunkOh9xDekLwOOZC3jdNU2flE8RK2_wwn-60PkWfFINXqug2mNLxM7jzTYA4LlLDiLuLF4G5a_QhVVNhMkvj9Hb42Izf85Wr0_L-WyVaSanKZsSLagQrKRQa2GpJEQzJiXk1hiujTBUqJrWQpaF1DUILowtLKmNKS3kOR-jm1PvPnQfPcRU7bo--GGyYqIUjDDJ6JC6PaV06GIMYKt9cK0Kh4qS6vhu9fcu_wbkBG4S</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Rad, Abdullah Kaviani</creator><creator>Shamshiri, Redmond R.</creator><creator>Naghipour, Armin</creator><creator>Razmi, Seraj-Odeen</creator><creator>Shariati, Mohsen</creator><creator>Golkar, Foroogh</creator><creator>Balasundram, Siva K.</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-5775-9654</orcidid><orcidid>https://orcid.org/0000-0002-4210-4348</orcidid><orcidid>https://orcid.org/0000-0001-5664-6618</orcidid></search><sort><creationdate>20220701</creationdate><title>Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran</title><author>Rad, Abdullah Kaviani ; Shamshiri, Redmond R. ; Naghipour, Armin ; Razmi, Seraj-Odeen ; Shariati, Mohsen ; Golkar, Foroogh ; Balasundram, Siva K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-50c6166281ebc6f1900c2299e4fdd3cd6d16ab1b69879cbe636df7f0bdd8fe443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Air monitoring</topic><topic>Air pollution</topic><topic>Air quality</topic><topic>Air temperature</topic><topic>Artificial intelligence</topic><topic>Deep learning</topic><topic>Economic statistics</topic><topic>Emission analysis</topic><topic>Emissions</topic><topic>Environmental health</topic><topic>Global economy</topic><topic>Humidity</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Model accuracy</topic><topic>Nitrogen dioxide</topic><topic>Outdoor air quality</topic><topic>Particulate emissions</topic><topic>Particulate matter</topic><topic>Pollutants</topic><topic>Pollution monitoring</topic><topic>Public health</topic><topic>Quality management</topic><topic>Relative humidity</topic><topic>Root-mean-square errors</topic><topic>Sulfur dioxide</topic><topic>Sustainability</topic><topic>Vegetation</topic><topic>Wind</topic><topic>Wind direction</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rad, Abdullah Kaviani</creatorcontrib><creatorcontrib>Shamshiri, Redmond R.</creatorcontrib><creatorcontrib>Naghipour, Armin</creatorcontrib><creatorcontrib>Razmi, Seraj-Odeen</creatorcontrib><creatorcontrib>Shariati, Mohsen</creatorcontrib><creatorcontrib>Golkar, Foroogh</creatorcontrib><creatorcontrib>Balasundram, Siva K.</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rad, Abdullah Kaviani</au><au>Shamshiri, Redmond R.</au><au>Naghipour, Armin</au><au>Razmi, Seraj-Odeen</au><au>Shariati, Mohsen</au><au>Golkar, Foroogh</au><au>Balasundram, Siva K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran</atitle><jtitle>Sustainability</jtitle><date>2022-07-01</date><risdate>2022</risdate><volume>14</volume><issue>13</issue><spage>8027</spage><pages>8027-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. 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subjects | Air monitoring Air pollution Air quality Air temperature Artificial intelligence Deep learning Economic statistics Emission analysis Emissions Environmental health Global economy Humidity Learning algorithms Machine learning Model accuracy Nitrogen dioxide Outdoor air quality Particulate emissions Particulate matter Pollutants Pollution monitoring Public health Quality management Relative humidity Root-mean-square errors Sulfur dioxide Sustainability Vegetation Wind Wind direction Wind speed |
title | Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran |
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