Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia
Air pollution has serious environmental and human health-related consequences; however, little work seems to be undertaken to address the harms in Middle Eastern countries, including Saudi Arabia. We installed a continuous air quality monitoring station in Jeddah, Saudi Arabia and monitored several...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/10/7/376 |
| _version_ | 1797433042386550784 |
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| author | Mohammad Rehan Said Munir |
| author_facet | Mohammad Rehan Said Munir |
| author_sort | Mohammad Rehan |
| collection | DOAJ |
| description | Air pollution has serious environmental and human health-related consequences; however, little work seems to be undertaken to address the harms in Middle Eastern countries, including Saudi Arabia. We installed a continuous air quality monitoring station in Jeddah, Saudi Arabia and monitored several air pollutants and meteorological parameters over a 2-year period (2018–2019). Here, we developed two supervised machine learning models, known as quantile regression models, to analyze the whole distribution of the modeled pollutants, not only the mean values. Two pollutants, namely NO<sub>2</sub> and O<sub>3</sub>, were modeled by dividing their concentrations into several quantiles (0.05, 0.25, 0.50, 0.75, and 0.95) and the effect of several pollutants and meteorological variables was analyzed on each quantile. The effect of the explanatory variables changed at different segments of the distribution of NO<sub>2</sub> and O<sub>3</sub> concentrations. For instance, for the modeling of O<sub>3</sub>, the coefficients of wind speed at quantiles 0.05, 0.25, 0.5, 0.75, and 0.95 were 1.40, 2.15, 2.34, 2.31, and 1.56, respectively. Correlation coefficients of 0.91 and 0.92 and RMSE values of 14.41 and 8.96, which are calculated for the cross-validated models of NO<sub>2</sub> and O<sub>3</sub>, showed an acceptable model performance. Quantile analysis aids in better understanding the behavior of air pollution and how it interacts with the influencing factors. |
| first_indexed | 2024-03-09T10:11:31Z |
| format | Article |
| id | doaj.art-6718de860ebb491f85ac1a9245c6255a |
| institution | Directory Open Access Journal |
| issn | 2305-6304 |
| language | English |
| last_indexed | 2024-03-09T10:11:31Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj.art-6718de860ebb491f85ac1a9245c6255a2023-12-01T22:45:13ZengMDPI AGToxics2305-63042022-07-0110737610.3390/toxics10070376Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi ArabiaMohammad Rehan0Said Munir1Center of Excellence in Environmental Studies (CEES), King Abdulaziz University, Jeddah 21589, Saudi ArabiaInstitute for Transport Studies, Faculty of Environment, University of Leeds, Leeds LS2 9JT, UKAir pollution has serious environmental and human health-related consequences; however, little work seems to be undertaken to address the harms in Middle Eastern countries, including Saudi Arabia. We installed a continuous air quality monitoring station in Jeddah, Saudi Arabia and monitored several air pollutants and meteorological parameters over a 2-year period (2018–2019). Here, we developed two supervised machine learning models, known as quantile regression models, to analyze the whole distribution of the modeled pollutants, not only the mean values. Two pollutants, namely NO<sub>2</sub> and O<sub>3</sub>, were modeled by dividing their concentrations into several quantiles (0.05, 0.25, 0.50, 0.75, and 0.95) and the effect of several pollutants and meteorological variables was analyzed on each quantile. The effect of the explanatory variables changed at different segments of the distribution of NO<sub>2</sub> and O<sub>3</sub> concentrations. For instance, for the modeling of O<sub>3</sub>, the coefficients of wind speed at quantiles 0.05, 0.25, 0.5, 0.75, and 0.95 were 1.40, 2.15, 2.34, 2.31, and 1.56, respectively. Correlation coefficients of 0.91 and 0.92 and RMSE values of 14.41 and 8.96, which are calculated for the cross-validated models of NO<sub>2</sub> and O<sub>3</sub>, showed an acceptable model performance. Quantile analysis aids in better understanding the behavior of air pollution and how it interacts with the influencing factors.https://www.mdpi.com/2305-6304/10/7/376extreme value analysisquantile regressionair pollutionozonenitrogen oxidessupervised machine learning |
| spellingShingle | Mohammad Rehan Said Munir Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia Toxics extreme value analysis quantile regression air pollution ozone nitrogen oxides supervised machine learning |
| title | Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia |
| title_full | Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia |
| title_fullStr | Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia |
| title_full_unstemmed | Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia |
| title_short | Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia |
| title_sort | analysis and modeling of air pollution in extreme meteorological conditions a case study of jeddah the kingdom of saudi arabia |
| topic | extreme value analysis quantile regression air pollution ozone nitrogen oxides supervised machine learning |
| url | https://www.mdpi.com/2305-6304/10/7/376 |
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