eprintid: 13461
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/34/61
datestamp: 2023-11-10 03:28:01
lastmod: 2023-11-10 03:28:01
status_changed: 2023-11-10 01:51:14
type: article
metadata_visibility: show
creators_name: Althuwaynee, O.F.
creators_name: Balogun, A.
creators_name: Al Madhoun, W.
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
ispublished: pub
note: cited By 30
abstract: 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 tra�c 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. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
date: 2020
publisher: Taylor and Francis Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078617682&doi=10.1080%2f15481603.2020.1712064&partnerID=40&md5=edf6932572602ffe371660517a06b4a8
id_number: 10.1080/15481603.2020.1712064
full_text_status: none
publication: GIScience and Remote Sensing
volume: 57
number: 2
pagerange: 207-226
refereed: TRUE
issn: 15481603
citation:   Althuwaynee, O.F. and Balogun, A. and Al Madhoun, W.  (2020) 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 and Remote Sensing, 57 (2).  pp. 207-226.  ISSN 15481603