American Association for Aerosol Research - Abstract Submission

AAAR 37th Annual Conference
October 14 - October 18, 2019
Oregon Convention Center
Portland, Oregon, USA

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Extensive Source Apportionment of PM2.5 Organic Aerosols in New Delhi

ANNA K. TOBLER, Deepika Bhattu, Francesco Canonaco, Sachchida N. Tripathi, Suresh Tiwari, Jay G. Slowik, Urs Baltensperger, Andre S.H. Prévôt, Paul Scherrer Institute

     Abstract Number: 103
     Working Group: Source Apportionment

Abstract
Because atmospheric aerosols are recognized to have adverse effects on climate, visibility and human health, the identification and source apportionment on those particles is of great importance. In 2018, New Delhi was ranked the most polluted capital in the world regarding PM2.5 mass concentrations. To study the long-term PM2.5 mass concentration and its chemical composition evolution in New Delhi, an Aerodyne Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) was installed at the Indian Institute of Tropical Meteorology (IITM) for over a year.

In this study, we present the high time-resolved, detailed chemical composition of non-refractory PM2.5 in New Delhi. In the winter season, total non-refractory peak concentrations over 400 µg/m3 and chloride concentrations exceeding 100 µg/m3 were routinely observed, whereas the concentrations in summer are much lower. The complex organic mass spectra were further analyzed with positive matrix factorization (PMF) within the Source Finder (SoFi) software. Two primary factors, hydrocarbon-like organic aerosol (HOA) and solid fuel combustion organic aerosol (SFC-OA), were resolved besides the more oxygenated organic aerosol factors. The PMF results suggest that chloride could play an important role in the oxidation processes. To overcome the limitation of the assumption of constant sources within the PMF algorithm, a small and rolling PMF window is moved over the dataset to allow the factor profiles to vary over time. In addition, to estimate the statistical uncertainty of the PMF solution, PMF runs were resampled using the bootstrap algorithm.