Chemical composition and source-apportionment of sub-micron particles during wintertime over Northern India: New insights on influence of fog-processing

A comprehensive study was carried out from central part of Indo-Gangetic Plain (IGP; at Kanpur) to understand abundance, temporal variability, processes (secondary formation and fog-processing) and source-apportionment of PM1-bound species (PM1: particulate matter of aerodynamic diameter <= 1.0 mu m) during wintertime. A total of 50 PM1 samples were collected of which 33 samples represent submicron aerosol characteristics under non-foggy condition whereas 17 samples represent characteristics under thick foggy condition. PM1 mass concentration during non-foggy episodes varied from 24-393 (Avg.: 247) mu g m(-3), whereas during foggy condition it ranged from 42-243 (Avg.: 107) mu g m(-3). With respect to non-foggy condition, the foggy conditions were associated with higher contribution of PM1-bound organic matter (OM, by 23%). However, lower fractional contribution of SO42-, NO3- and NH4+ during foggy conditions is attributable to wet-scavenging owing to their high affinity to water. Significant influence of fog-processing on organic aerosols composition is also reflected by co-enhancement in OC/EC and WSOC/OC ratio during foggy condition. A reduction by 5% in mineral dust fraction under foggy condition is associated with a parallel decrease in PM1 mass concentration. However, mass fraction of elemental carbon (EC) looks quite similar (approximate to 3% of PM1) but the mass absorption efficiency (MAE) of EC is higher by 30% during foggy episodes. Thus, it is evident from this study that fog processing leads to quite significant enhancement in OM (23%) contribution (and MAE of EC) with nearly equal and parallel decrease in SO42-, NO3- and NH4+ and mineral dust fractions (totaling to 24%). Characteristic features of mineral dust remain similar under foggy and non-foggy conditions; inferred from similar ratios of Fe/AI (approximate to 0.3), Ca/Al (0.35) and Mg/Al (0.22). Positive matrix factorization (PMF) resolves seven sources: biomass burning (19.4%), coal combustion (1.1%), vehicular emission (3%), industrial activities (6.1%), leather tanneries (4%), secondary transformations (46.2%) and mineral dust (20.2%). (C) 2017 Elsevier Ltd. All rights reserved.