Detection of anomalous nitrogen dioxide (NO2) concentration in urban air of India using proximity and clustering methods

• Published in: Journal of the Air & Waste Management Association (1995) 2019-07, Vol.69 (7), p.805-822
• Main Authors: Aggarwal, Apeksha, Toshniwal, Durga
• Format: Article
• Language: English
• Subjects: Air Pollutants - analysis; Air pollution; Air Pollution - analysis; Air quality; Alarms; Anomalies; Cities; Cluster Analysis; Clustering; Comparative analysis; Datasets; Decision making; Economic conditions; Environmental Monitoring - methods; Environmental Monitoring - statistics & numerical data; False alarms; Health risks; India; Nitrogen dioxide; Nitrogen Dioxide - analysis; Normal distribution; Pollutants; Residential areas; Sensitivity analysis; Statistical analysis
• ISSN: 1096-2247; 2162-2906
• DOI: 10.1080/10962247.2019.1577314

Owing to accurate future air quality estimates, need for detecting the anomalously high increase in concentration of pollutants cannot be adjourned. Plentiful approaches were proposed in the past to substantially determine the abnormal conditions, but most of the statistical approaches were computationally expensive and ignored the false alarm ratios. Thus, a hybrid of proximity- and clustering-based anomaly detection approaches to identify anomalies in the air quality data is suggested in this work. The Gaussian distribution property of the real-world data set is utilized further to segregate out anomalies. The results depicted twofold advantages of our approach, by efficient extraction of anomalies and with increased accuracy by reducing the number of false alarms. Specifically, the presence of NO 2 concentration in air is investigated in this work, considering its constant increase over decades as well as its inevitable health risks. Furthermore, spatiotemporal segments with anomalously high NO 2 concentrations for 14 residential, industrial, and commercial areas of five cities in India are extracted. To validate the results, a comparative analysis with existing approaches of anomaly detection and with two benchmark data sets is performed. Results showed that our method outperformed the existing methods of anomaly detection, when evaluated over metrics such as sensitivity, miss rate, and false alarms. Further, a detailed analysis of extracted anomalies and a detailed discussion about the factors responsible for such anomalies are presented in this work. This study is helpful in educating government and people about spatiotemporal, geographical, and economic conditions responsible for anomalously high NO 2 concentrations in air. Implications: Using our methodology, days with extremely high concentration of any pollutant in air, at any particular location, can be extracted. The reasons for such extremely high pollutant concentration on particular days of a year can be studied and preventive measures can be taken by the government. Thus, by identification of causes of anomalies, future similar events can be avoided. This would also help in people's decision making in case such events occur in the future.