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Title: Detection of anomalous nitrogen dioxide (NO2) concentration in urban air of India using proximity and clustering methods
Authors: Aggarwal A.
Toshniwal, Durga
Published in: Journal of the Air and Waste Management Association
Abstract: 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 NO2 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 NO2 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 NO2 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. © 2019, © 2019 A&WMA.
Citation: Journal of the Air and Waste Management Association, 69(7): 805-822
Issue Date: 2019
Publisher: Taylor and Francis Inc.
ISSN: 10962247
Author Scopus IDs: 57202949741
Author Affiliations: Aggarwal, A., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Toshniwal, D., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Funding Details: The authors thank the China Section of the Air & Waste Management Association for the generous scholarship they received to cover the cost of page charges, and to make the publication of this paper possible.
Corresponding Author: Aggarwal, A.; Department of Computer Science and Engineering, India; email:
Appears in Collections:Journal Publications [CS]

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