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 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.

中文翻译：

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使用决策树算法和双变量概率簇极函数进行空气污染危害评估：评估 PM10 和其他空气污染物的相互关联簇

摘要 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中的一项重要任务。在当前的研究中，机器学习 (ML) 算法用于识别 PM10 百分位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联，以检测周围环境监测数据中的重要污染物集群。研究区。两个空气质量站 CA0016 和 CA0054 因其战略位置而被选择用于本研究。在R编程环境中采用非线性RPart和决策树(DT)算法的树模型进行分类分析。使用DT算法的随机森林（RF）和K均值极地聚类函数对相似特征进行了识别和分组，并在工业区周围的环境监测数据中检测到了重要聚类，从而评估了污染物对PM10发生的各自重要性。结果显示集群数量的增加没有显着改变结果。PM10 总体呈下降趋势，特别是在 SW 方向，并且观察到污染物浓度在所有方向上的总体下降幅度最小（小于 1）。白天CO和NOx的行为有波动，而NOx表现出相对稳定。结果还表明 PM10（目标污染物）与 CO、SO2 之间存在直接且正线性关系，这表明这些污染物来自相同的来源。在 PM10 和其他（O3 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，两个站点的大部分主要污染物都显示出向工业区的积极趋势，而交通排放在该站点 (CA0016) 中的 CO 和 NOx 占主导地位。还讨论了从这些结果中获得的信息在减少空气污染和确保城市可持续性方面的潜在应用。这项研究的结果有望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。我们得出的结论是，两个站点的大部分主要污染物都显示出向工业区的积极趋势，而交通排放在该站点 (CA0016) 的 CO 和 NOx 中占主导地位。还讨论了从这些结果中获得的信息在减少空气污染和确保城市可持续性方面的潜在应用。这项研究的结果有望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。我们得出的结论是，两个站点的大部分主要污染物都显示出向工业区的积极趋势，而交通排放在该站点 (CA0016) 的 CO 和 NOx 中占主导地位。还讨论了从这些结果中获得的信息在减少空气污染和确保城市可持续性方面的潜在应用。这项研究的结果有望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。