Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants
The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile v...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-02-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2020.1712064 |
| _version_ | 1797679052649136128 |
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| author | Omar F. Althuwaynee Abdul‐Lateef Balogun Wesam Al Madhoun |
| author_facet | Omar F. Althuwaynee Abdul‐Lateef Balogun Wesam Al Madhoun |
| author_sort | Omar F. Althuwaynee |
| collection | DOAJ |
| description | The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NOx during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO2, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O3 and NOx) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while traffic emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects. |
| first_indexed | 2024-03-11T23:08:48Z |
| format | Article |
| id | doaj.art-d4f8b275c44d4b5bbae98d33c78927d6 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:48Z |
| publishDate | 2020-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-d4f8b275c44d4b5bbae98d33c78927d62023-09-21T12:34:16ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262020-02-0157220722610.1080/15481603.2020.17120641712064Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutantsOmar F. Althuwaynee0Abdul‐Lateef Balogun1Wesam Al Madhoun2Sejong UniversityUniversiti Teknologi PETRONAS (UTP)Universiti Teknologi PETRONAS (UTP)The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NOx during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO2, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O3 and NOx) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while traffic emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.http://dx.doi.org/10.1080/15481603.2020.1712064air qualitymachine learningpm10r programmingsustainability |
| spellingShingle | Omar F. Althuwaynee Abdul‐Lateef Balogun Wesam Al Madhoun Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants GIScience & Remote Sensing air quality machine learning pm10 r programming sustainability |
| title | Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants |
| title_full | Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants |
| title_fullStr | Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants |
| title_full_unstemmed | Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants |
| title_short | Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants |
| title_sort | air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function evaluating inter correlation clusters of pm10 and other air pollutants |
| topic | air quality machine learning pm10 r programming sustainability |
| url | http://dx.doi.org/10.1080/15481603.2020.1712064 |
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