Composition and source apportionment of PM1 at urban site Kanpur in India using PMF coupled with CBPF

Abstract

This study addresses the three major questions: (1) what are the emission sources of PM1 which are affecting the study area; (2) where do these emission sources come from; and (3) is there any temporal variation in the emission sources. To address these issues, two advanced statistical methods are described in this paper. Identification of emission sources was performed by EPA PMF (v 5.0) and to understand the temporal variability, sampling was done for three winter seasons 2008–09, 2009–10 and 2011–12 within Kanpur city. To identify the possible source directions, Conditional Bivariate Probability function (CBPF) was used. The average PM1 concentration was higher in 2008–09 followed by 2011–12 and 2009–10 winter seasons. 2008–09 winter showed sources such as secondary sources mixed with power plant emission (42.8%), industrial emission (32.3%), coal combustion, brick kilns and vehicular emission (13.2%) and residual oil combustion and road dust (11.7%). The major contributors during winter season 2009–10 were secondary sources (33.1%), biomass burning (23.3%), heavy oil combustion (13%), vehicular emission mixed with crustal dust (11.3%), leather tanning industries (10.3%), industrial emission (4%), coal combustion and brick kilns (3.4%) and solid waste burning and incineration (1.5%) compared to secondary sources mixed with biomass burning (42.3%), industrial emission and crustal dust (35.1%) and vehicular emission and brick kilns (22.6%) during 2011–12 winter season. PMF model revealed that secondary sources were the main contributors for all the three winter seasons followed by biomass burning and power plant emission. The results of CBPF analysis agreed well with the locations of known local point sources., e.g. in the case of industrial emissions, the maximum probability was in the direction between NES direction where almost all the major industries are located in and around Kanpur while in the opposite direction the probability of biomass burning was high due to a rural area in NWS direction.