Anomaly detection and assessment of PM10 functional data at several locations in the Klang Valley, Malaysia

• 出版年: Atmospheric Pollution Research
• 第一著者: 2-s2.0-84923190837
• フォーマット: 論文
• 言語: English
• 出版事項: Dokuz Eylul Universitesi 2015
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923190837&doi=10.5094%2fAPR.2015.040&partnerID=40&md5=1d98824e7710cc81e59246a98c609ca0

In environmental data sets, the occurrence of a high concentration of an unusual pollutant, more formally known as an anomaly, may indicate air quality problems. Thus, a critical understanding of the behavior of anomalies is increasingly becoming very important for air pollution investigations. This study was conducted to detect anomalies in daily PM10 functional data, to investigate the patterns of behavior as well as to identify possible factors that determine PM10 anomalies at three selected air quality monitoring stations (Klang, Kuala Selangor and Petaling Jaya) in the Klang Valley, Malaysia. The statistical method employed to detect these anomalies consisted of a combination of the robust projection pursuit and the robust Mahalanobis distance methods using air quality data recorded from 2005 to 2010. Analysis of obtained anomalous PM10 profiles showed that data recorded during El Nino years (2005, 2006 and 2009) contained the highest frequency of anomalies. More frequent anomalies appeared during the southwest (SW) monsoon which occurs in the months of July and August as well as during the northeast (NE) monsoon in February. A lesser number of anomalies were also observed during weekends compared to weekdays. The weekend and monsoonal effect phenomena were shown to be significantly existent at all stations while wind speed was positively associated with extreme PM10 anomalies at the Klang and Petaling Jaya stations. In conclusion, anomalies detection was found useful for air pollution investigation in this study. The findings of this study imply that the location and background of a station, as well as wind speed, seasonal (monsoon) and weekdays-weekend variations play important role in influencing PM10 anomalies. © Author(s) 2015. This work is distributed under the Creative Commons Attribution 3.0 License.