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首页> 外文期刊>GIScience & remote sensing >Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants
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Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

机译：使用决策树算法和二元概率簇极函数的空气污染危害评估：评估PM10与其他空气污染物的相互关联簇

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摘要

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 (O-3 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 trac 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.
机译：环境空气污染物的自动分类是空气污染危害评估和生活质量研究的重要任务。在当前的研究中，机器学习（ML）算法用于识别PM10百分数值的主要空气污染指数（API）与其他主要空气污染物之间的相互关系，以便在周围环境监测数据中检测出重要污染物的簇学习区。由于其战略位置，选择了两个空气质量站CA0016和CA0054进行此项研究。在R编程环境中采用决策树（DT）算法的非线性RPart和树模型进行分类分析。使用DT算法的随机森林（RF）评估了污染物对PM10发生的重要性，K表示极性簇功能已识别并归类为相似特征，并且还在工业区周围的环境监测数据中检测到了重要簇。结果表明，簇数的增加并未显着改变结果。 PM10通常显示出趋势的降低，特别是在西南方向，并且观察到所有方向上污染物浓度的总体最小降低（小于1）。白天观察到CO和NOx的行为有波动，而NOx显示相对稳定。结果还表明，PM10（目标污染物）与CO，SO2之间存在直接和正线性关系，这表明这些污染物源自相同的来源。在PM10与其他气体（O-3和NOx）之间观察到半线性关系，而湿度与PM10呈负线性关系。我们得出的结论是，两个站台的大多数主要污染物都朝着工业区显示出积极的趋势，而TRACT排放占该站的CO和NOx排放总量（CA0016）。还讨论了从这些结果中得出的信息块在减少空气污染和确保城市可持续性方面的潜在应用。预期这项研究的结果将为决策者提供有价值的信息，以实施能够减轻空气污染影响的可行策略。

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  • 来源
    《GIScience & remote sensing》 |2020年第2期|207-226|共20页
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  • 作者单位

    Sejong Univ Dept Energy & Mineral Resources Engn Seoul South Korea;

    UTP Dept Civil & Environm Engn GAM Res Grp Seri Iskandar Perak Malaysia;

    UTP Dept Civil & Environm Engn Seri Iskandar Perak Malaysia;

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  • 正文语种 eng
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  • 关键词

    Air quality; machine learning; PM10; R programming; sustainability;

    机译：空气质量;机器学习PM10;R编程;可持续性;

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