Search for: All records

Abstract Ambient fine particulate matter (PM_2.5) is the world’s leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM_2.5exposure. Here we interpret satellite-derived PM_2.5estimates over 1998-2019 and find a reversal of previous growth in global PM_2.5air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM_2.5exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m³) to a peak in 2011 (38.9 μg/m³) and decreased steadily afterwards (34.7 μg/m³in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM_2.5-attributable mortality and increasing health benefits per µg/m³marginal reduction in exposure, implying increasing urgency and benefits of PM_2.5mitigation with aging population and cleaner air. 

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. 

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, 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.