Seasonality in size-segregated ionic composition of ambient particulate pollutants over the Indo-Gangetic Plain:...

Size-segregated particulate pollutants (PM_<0.95, PM_0.95-1.5, PM_1.5-3.0, PM_3.0-7.2 and PM_>7.2) were collected over Patiala (30.33°N, 76.40°E; 250 m amsl), a semi-urban city located in northwestern Indo-Gangetic Plain (IGP), during October, 2012 to September, 2013. Mass concentration of total suspended particulates (TSP), derived by summation of particulate (aerosol) mass in different size range, varied from 88 to 387 μg m^-3 with highest mass concentration (∼55% of total mass) in submicron size (PM_<0.95) during the entire study period, which broadly reflects relative higher contribution of various anthropogenic sources (emissions from biomass and bio-fuel burning, vehicles, thermal power plants, etc) to ambient particles. Concentration of SO₄^2-, NO₃^-, NH₄⁺, K⁺ and Ca²⁺ exhibited large variability ranging from 0.52 to 40, 0.20 to 19, 0.14 to 12, 0.06 to 5.3 and 0.08 to 5.6 μg m^-3, respectively, in different size ranges with varying size distribution for most of the species, except NH₄⁺. A strong linear correlation (r = 0.97) between (SO₄^2- + NO₃^-) and (K⁺ + NH₄⁺) concentrations has been observed in submicron particles collected in different seasons, suggesting the formation of secondary inorganic salts. However, relatively poor correlation is observed in higher size ranges where significant correlation between (SO₄^2- + NO₃^-) and (Ca²⁺ + Mg²⁺) has been observed. These observations indicate the acid neutralization by dust in coarser modes of particles. Chemical composition of submicron particulates (PM_<0.95) in different seasons as well as for whole year was used to identify PM sources through the application of Positive Matrix Factorization (PMF, version 5.0) model. Based on annual data, PMF analyses suggests that six source factors namely biomass burning emission (24%), vehicular emission (22%), secondary organic aerosols (20%), power plant emission (13%), secondary inorganic aerosols (12%) and mineral dust (9%) contribute to PM_<0.95 loading over the study region. Such studies are important in dispersion modeling, health impact assessment, and planning of pollution mitigation strategies.