Chemical Profiling of Exhaust Particulate Matter from Indian In-Service Vehicles 2021-26-0192

Particulate matter is one of the major pollutant responsible for deteriorating air quality, particularly in urban centers. Information on contributing sources with the share from different sources is a first and one of the important steps in controlling pollution. Diverse sources, anthropogenic as well as natural, like industries, transport, domestic burning, construction, wind-blown dust, road dust contribute to particulate matter pollution. Receptor modeling is a scientific method which is utilized for assessment of the contribution of various sources based on chemical characteristics of particulate matter sources and ambient air particulate matter. Representative data of fractions of various chemical species in the particulate matter from the different sources i.e. source fingerprint is an essential input for the receptor modeling approach.

A comprehensive national database on the source profiles of particulate matter collected from in-service vehicles of different technologies, fuel types and vintages is presented. Particulate matter from vehicular exhaust emissions was collected through mass emission tests on chassis dynamometer for 2-wheelers, 3-wheelers, passenger cars, LCV and HCV using respective standard driving cycles. In-service vehicles of vintages from 2005-10, 2010-15 and post-2015 were selected. The collected particulate matter was then chemically speciated into organic carbon, elemental carbon, ionic species and elements. More than 150 number mass emission tests were conducted for collection of exhaust PM samples on Teflon and quartz filter paper for subsequent analysis to generate the extensive database of source profiles. The distinctive chemical characteristics of gasoline, CNG and diesel vehicle emissions were reflected in the respective source profiles. Diesel particulate matter was found to be dominated by elemental carbon fractions, whereas gasoline particulates exhibited a higher proportion of organic carbon. The source profiles generated under this study can be effectively utilized as an input for particulate matter source identification using receptor modeling.