Article in Press  PDF(5.22 MB)
Supplementary MaterialPDF (575 KB)
Extreme Events of Reactive Ambient Air Pollutants and their Distribution Pattern at Urban Hotspots
Sunil Gulia1, S.M. Shiva Nagendra2, Mukesh Khare1
1 Civil Engineering Department, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
2 Civil Engineering Department, Indian Institute of Technology Madras, Chennai, India
- Hourly PM2.5 and NO2 are analyzed at urban hotspots in Delhi and Chennai cities.
- PM2.5 and NO2 are found higher in winter compared to summer seasons.
- NO2 distribution is more affected by seasonal variations compared to PM2.5.
- Statistical distribution model predict exceedances of NO2 and PM2.5 satisfactorily.
The occurrence of extreme events of air pollutant concentrations at urban hotspots is a routine phenomenon, particularly during the winter season. However, extreme events of reactive air pollutants are more frequent during the summer season. The assessment of air pollution extreme events will provide a platform to formulate an effective and efficient hotspot urban air quality management plan. The statistical distribution model (SDM) is widely used to describe the average as well as extreme air pollutant concentration in a more organized and efficient manner. In the present study, the best fit SDM has been evaluated for hourly average PM2.5 and NO2 concentrations at one of the busiest traffic intersections in Delhi city (air pollution hotspot 1: APH-1) and for PM2.5 at one of the heavily trafficked road corridors in Chennai city (air pollution hotspot 2: APH -2). The SDMs were developed for different seasons to evaluate the impacts of climatic conditions on the air pollution events. Results indicate that NO2 concentrations were best fitted with lognormal and log logistic distribution models respectively, for winter and summer seasons at APH-1. However, lognormal distribution was best fitted to PM2.5 concentration of winter and summer seasons at both APHs.
Extreme pollutant concentrations; Urban hotspot; Statistical distribution model; Goodness of fit test; Location and Scale parameters.