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Transportation emissions of aromatic hydrocarbons such as xylenes evolve in the atmosphere due to radical oxidation and can form a variety of products, including secondary organic aerosol (SOA). 

Abstract. Present methodologies for source apportionment assumefixed source profiles. Since meteorology and human activity patterns changeseasonally and diurnally, application of source apportionment techniques toshorter rather than longer time periods generates more representative massspectra. Here, we present a new method to conduct source apportionmentresolved by time of day using the underlying approach of positive matrixfactorization (PMF). We call this approach “time-of-day PMF” andstatistically demonstrate the improvements in this approach over traditionalPMF. We report on source apportionment conducted on four example timeperiods in two seasons (winter and monsoon seasons of 2017), using organic aerosolmeasurements from an aerosol chemical speciation monitor (ACSM). We deploythe EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMFsolver. Compared to the traditional seasonal PMF approach, we extract alarger number of factors as well as PMF factors that represent the expectedsources of primary organic aerosol using time-of-day PMF. By capturingdiurnal time series patterns of sources at a low computational cost,time-of-day PMF can utilize large datasets collected using long-termmonitoring and improve the characterization of sources of organic aerosolcompared to traditional PMF approaches that do not resolve by time of day. 

Abstract. Delhi, India, experiences extremely high concentrations ofprimary organic aerosol (POA). Few prior source apportionment studies onDelhi have captured the influence of biomass burning organic aerosol (BBOA) and cooking organic aerosol(COA) on POA. In a companion paper, we develop a new method to conductsource apportionment resolved by time of day using the underlying approachof positive matrix factorization (PMF). We call this approach “time-of-dayPMF” and statistically demonstrate the improvements of this approach overtraditional PMF. Here, we quantify the contributions of BBOA, COA, andhydrocarbon-like organic aerosol (HOA) by applying positive matrixfactorization (PMF) resolved by time of day on two seasons (winter andmonsoon seasons of 2017) using organic aerosol measurements from an aerosol chemicalspeciation monitor (ACSM). We deploy the EPA PMF tool with the underlyingMultilinear Engine (ME-2) as the PMF solver. We also conduct detaileduncertainty analysis for statistical validation of our results. HOA is a major constituent of POA in both winter and the monsoon. In addition toHOA, COA is found to be a major constituent of POA in the monsoon, and BBOA isfound to be a major constituent of POA in the winter. Neither COA nor thedifferent types of BBOA were resolved in the seasonal (not time-resolved)analysis. The COA mass spectra (MS) profiles are consistent with massspectral profiles from Delhi and around the world, particularly resemblingMS of heated cooking oils with a high m/z 41. The BBOA MS have a very prominentm/z 29 in addition to the characteristic peak at m/z 60, consistent with previousMS observed in Delhi and from wood burning sources. In addition toseparating the POA, our technique also captures changes in MS profiles withthe time of day, a unique feature among source apportionment approachesavailable. In addition to the primary factors, we separate two to three oxygenated organicaerosol (OOA)components. When all factors are recombined to total POA and OOA, ourresults are consistent with seasonal PMF analysis conducted using EPA PMF.Results from this work can be used to better design policies that targetrelevant primary sources of organic aerosols in Delhi.