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.
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Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution
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.
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- Award ID(s):
- 1653625
- NSF-PAR ID:
- 10376952
- Date Published:
- Journal Name:
- Atmospheric Measurement Techniques
- Volume:
- 15
- Issue:
- 20
- ISSN:
- 1867-8548
- Page Range / eLocation ID:
- 6051 to 6074
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract. Delhi, India, is the second most populated city in the world and routinely experiences some of the highest particulate matter concentrations of any megacity on the planet, posing acute challenges to public health (World Health Organization, 2018). However, the current understanding of the sources and dynamics of PM pollution in Delhi is limited. Measurements at the Delhi Aerosol Supersite (DAS) provide long-term chemical characterization of ambient submicron aerosol in Delhi, with near-continuous online measurements of aerosol composition. Here we report on source apportionment based on positive matrix factorization (PMF), conducted on 15 months of highly time-resolved speciated submicron non-refractory PM1 (NR-PM1) between January 2017 and March 2018. We report on seasonal variability across four seasons of 2017 and interannual variability using data from the two winters and springs of 2017 and 2018. We show that a modified tracer-based organic component analysis provides an opportunity for a real-time source apportionment approach for organics in Delhi. Phase equilibrium modeling of aerosols using the extended aerosol inorganics model (E-AIM) predicts equilibrium gas-phase concentrations and allows evaluation of the importance of the ventilation coefficient (VC) and temperature in controlling primary and secondary organic aerosol. We also find that primary aerosol dominates severe air pollution episodes, and secondary aerosol dominates seasonal averages.more » « less
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null (Ed.)Abstract. We present a rapid method for apportioning the sources of atmospheric organic aerosol composition measured by gas chromatography–mass spectrometry methods. Here, we specifically apply this new analysis method to data acquired on a thermal desorption aerosol gas chromatograph (TAG) system. Gas chromatograms are divided by retention time into evenly spaced bins, within which the mass spectra are summed. A previous chromatogram binning method was introduced for the purpose of chromatogram structure deconvolution (e.g., major compound classes) (Zhang et al., 2014). Here we extend the method development for the specific purpose of determining aerosol samples' sources. Chromatogram bins are arranged into an input data matrix for positive matrix factorization (PMF), where the sample number is the row dimension and the mass-spectra-resolved eluting time intervals (bins) are the column dimension. Then two-dimensional PMF can effectively do three-dimensional factorization on the three-dimensional TAG mass spectra data. The retention time shift of the chromatogram is corrected by applying the median values of the different peaks' shifts. Bin width affects chemical resolution but does not affect PMF retrieval of the sources' time variations for low-factor solutions. A bin width smaller than the maximum retention shift among all samples requires retention time shift correction. A six-factor PMF comparison among aerosol mass spectrometry (AMS), TAG binning, and conventional TAG compound integration methods shows that the TAG binning method performs similarly to the integration method. However, the new binning method incorporates the entirety of the data set and requires significantly less pre-processing of the data than conventional single compound identification and integration. In addition, while a fraction of the most oxygenated aerosol does not elute through an underivatized TAG analysis, the TAG binning method does have the ability to achieve molecular level resolution on other bulk aerosol components commonly observed by the AMS.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/amt-15-6051-2022
