Abstract
The source and origin of the ambient PM2.5 concentration at a traffic site in Delhi was studied using the persistence analysis and nonparametric wind regression (NWR) technique. The analysis was performed for the original PM2.5 data observed during 2007–2009, by removing seasonal and trend patterns (PM2.5-AR1), and for the exceedance time series. Detrended fluctuation analysis showed the strong persistence in the original and exceeded PM2.5 time series. This behavior was linked with the self-organized criticality of the process generating PM2.5 concentrations. NWR analysis was carried out to infer the sources of PM2.5 concentrations in the area. Power plants and medium scale industries along with the local transport emissions were found to be responsible for PM2.5 emissions at the site. Analysis of seasonal variations showed significant contributions from medium scale industries and power plants in winter, and dust storms and industrial contribution in summer. An analysis of the results obtained during calm conditions suggests the dominance of local transport emissions along with the above sources of PM2.5 concentrations at the site.
Highlights
Fine particulate matter poses a serious health risk to individuals exposed
Detrended fluctuation analysis suggested the presence of strong persistence in original and exceeded PM2.5 time series
The presence of persistence is linked with the self-organized criticality of the process generating the time series of PM2.5 concentrations, which suggests the uniformity in the generation mechanism of the concentrations over time
Summary
Fine particulate matter poses a serious health risk to individuals exposed. These particles penetrate into the lungs and can cause respiratory illness and lung related diseases (Dockery et al, 1993). The source and origin of the ambient PM2.5 concentration at a traffic site in Delhi was studied using the persistence analysis and nonparametric wind regression (NWR) technique. Detrended fluctuation analysis showed the strong persistence in the original and exceeded PM2.5 time series.
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