Contribution of fossil and biomass-derived secondary organic carbon to winter water-soluble organic aerosols in Delhi, India.

Delhi, among the world's most polluted megacities, is a hotspot of particulate matter emissions, with high contribution from organic aerosol (OA), affecting health and climate in the entire northern India. While the primary organic aerosol (POA) sources can be effectively identified, an incomplete source apportionment of secondary organic aerosol (SOA) causes significant ambiguity in the management of air quality and the assessment of climate change. Present study uses positive matrix factorization analysis on the water-soluble organic aerosol (WSOA) data from the offline-aerosol mass spectrometry (AMS). It revealed POA as the dominant source of WSOA, with biomass-burning OA (31-34 %) and solid fuel combustion OA (∼21 %) being two major contributors. Here we use water-solubility fingerprints to track the SOA precursors, such as oxalates or organic nitrates, instead of identifying them based on their O:C ratio. Non-fossil precursors dominate in more oxidized oxygenated organic carbon (MO-OOC) (∼90 %), a proxy for aged secondary organic carbon (SOC), by coupling offline-AMS with 14C measurements. On the contrary, the oxidation of fossil fuel emissions produces a large quantity of fresh fossil SOC, which accounts for ∼75 % of less oxidized oxygenated organic carbon (LO-OOC). Our study reveals that apart from major POA contributions, large fractions of fossil (10-14 %) and biomass-derived SOA (23-30 %) contribute significantly to the total WSOA load, having impact on climate and air quality of the Delhi megacity. Our study reveals that large-scale unregulated biomass burning was not only found to dominate in POA but was also observed to be a significant contributor to SOA with implications on human health, highlighting the need for effective control strategies.

Molecular distribution, sources and potential health risks of fine particulate-bound polycyclic aromatic hydrocarbons during high pollution episodes in a subtropical urban city.

Abundance of fine particulate-bound 16 priority polycyclic aromatic hydrocarbons (PAHs) was investigated to ascertain its sources and potential carcinogenic health risks in Varanasi, India. The city represents a typical urban settlement of South Asia having particulate exposure manyfold higher than standard with reports of pollution induced mortalities and morbidities. Fine particulates (PM2.5) were monitored from October 2019 to May 2020, with 32% of monitoring days accounting ≥100 µgm-3 of PM2.5 concentration, frequently from November to January (99% of monitoring days). The concentration of 16 priority PAHs varied from 24.1 to 44.6 ngm-3 (mean: 33.1 ± 3.2 ngm-3) without much seasonal deviations. Both low (LMW, 56%) and high molecular weight (HMW, 44%) PAHs were abundant, with Fluoranthene (3.9 ± 0.4ngm-3) and Fluorene (3.5 ± 0.3ngm-3) emerged as most dominating PAHs. Concentration of Benzo(a)pyrene (B(a)P, 0.5 ± 0.1ngm-3) was lower than the national standard as it contributed 13% of total PAHs mass. Diagnostic ratios of PAH isomers indicate predominance of pyrogenic sources including emissions from biomass burning, and both from diesel and petrol-driven vehicles. Source apportionment using receptor model revealed similar observation of major PAHs contribution from biomass burning and fuel combustion (54% of source contribution) followed by coal combustion for residential heating and cooking purposes (44%). Potential toxicity of B[a]P equivalence ranged from 0.003 to 1.365 with cumulative toxicity of 2.13ngm-3. Among the PAH species, dibenzo[h]anthracene contributed maximum toxicity followed by B[a]P, together accounting 86% of PAH induced carcinogenicity. Incremental risk of developing cancer through lifetime exposure (ILCR) of PAHs was higher in children (3.3 × 10-4) with 56% contribution from LMW PAHs, primarily through ingestion and dermal contact. Adults in contrast, were more exposed to inhale airborne PAHs with cumulative ILCR of 2.2 × 10-4. However, ILCR to PM2.5 exposure is probably underestimated considering unaccounted metal abundance thus, require source-specific control measures.

Insights into size-segregated particulate chemistry and sources in urban environment over central Indo-Gangetic Plain.

Size-segregated airborne fine (PM2.1) and coarse (PM>2.1) particulates were measured in an urban environment over central Indo-Gangetic plain in between 2015 and 2018 to get insights into its nature, chemistry and sources. Mean (±1σ) concentration of PM2.1 was 98 (±76) µgm-3 with a seasonal high during winter (DJF, 162 ± 71 µgm-3) compared to pre-monsoon specific high in PM>2.1 (MAMJ, 177 ± 84 µgm-3) with an annual mean of 170 (±69) µgm-3. PM2.1 was secondary in nature with abundant secondary inorganic aerosols (20% of particulate mass) and water-soluble organic carbon (19%) against metal enriched (25%) PM>2.1, having robust signature of resuspensions from Earth's crust and road dust. Ammonium-based neutralization of particulate acidity was essentially in PM2.1 with an indication of predominant H2SO4 neutralization in bisulfate form compared to Ca2+ and Mg2+-based neutralization in PM>2.1. Molecular distribution of n-alkanes homologues (C17-C35) showed Cmax at C23 (PM2.1) and C18 (PM>2.1) with weak dominance of odd-numbered n-alkanes. Carbon preference index of n-alkanes was close to unity (PM2.1: 1.4 ± 0.3; PM>2.1: 1.3 ± 0.4). Fatty acids (C12-C26) were characterized with predominance of even carbon with Cmax at n-hexadecanoic acid (C16:0). Low to high molecular weight fatty acid ratio ranged from 2.0 (PM>2.1) to 5.6 (PM2.1) with vital signature of anthropogenic emissions. Levoglucosan was abundant in PM2.1 (758 ± 481 ngm-3) with a high ratio (11.6) against galactosan, emphasizing robust contribution from burning of hardwood and agricultural residues. Receptor model resolves secondary aerosols and biomass burning emissions (45%) as the most influential sources of PM2.1 whereas, crustal (29%) and secondary aerosols (29%) were found responsible for PM>2.1; with significant variations among the seasons.

Source apportionment and health risk assessment of airborne particulates over central Indo-Gangetic Plain.

Sources of airborne particulates (PM10) were investigated in two contrasting sites over central Indo-Gangetic Plain (IGP), one representing a rural background (Mirzapur) and another as an urban pollution hotspot (Varanasi). Very high PM10 concentration was noted both in Varanasi (178 ± 105 µgm-3; N:435) and Mirzapur (131 ± 56 µgm-3; N:169) with 72% and 62% of monitoring days exceeded the national air quality standard, respectively. Particulate-bound elements contribute significant proportion of PM10 mass (15%-18%), with highest contribution from Ca (7%-10%) and Fe (2%-3%). Besides, presence of Zn (1%-3%), K (1%-2%) and Na (1%-2%) was also noted. Water-soluble ionic species contributed 15%-19% of particulate mass, primarily by the secondary inorganic aerosols (SIA). Among the SIA, sulphate (5%-7%) and nitrate (4%) were prominent, contributing 59%-62% of the total ionic load, especially in winter. Particulate-bound metallic species and ions were selectively used as signatory molecules and source apportionment of PM10 was done by multivariate factor analysis. UNMIX was able to extract particulate sources in both the locations and crustal resuspensions (dust/-soil) were identified as the dominant source contributing 57%-63% of PM10 mass. Secondary aerosols were the second important source (17%-23%), followed by emissions from biomass/-refuse burning (10-19%). Transport of airborne particulates from upper IGP by prevailing westerly were identified as the important contributor of particulates, especially during high particulate loading days. Health risks associated to particulate-bound toxic metal exposure were also assessed. Non-carcinogenic health risk was within the permissible limit while there is possibility of elevated risk for PM10-bound Cr and Cd, if adequate control measures are not in place.

Airborne particulate in Varanasi over middle Indo-Gangetic Plain: variation in particulate types and meteorological influences.

The variation in particulate mass and particulate types (PM2.5 and PM10) with respect to local/regional meteorology was analyzed from January to December 2014 (n = 104) for an urban location over the middle Indo-Gangetic Plain (IGP). Both coarser (mean ± SD; PM10 161.3 ± 110.4 µg m-3, n = 104) and finer particulates (PM2.5 81.78 ± 66.4 µg m-3) revealed enormous mass loading with distinct seasonal effects (range: PM10 12-535 µg m-3; PM2.5 8-362 µg m-3). Further, 56% (for PM2.5) to 81% (for PM10) of monitoring events revealed non-attainment national air quality standard especially during winter months. Particulate types (in terms of PM2.5/PM10 0.49 ± 0.19) also exhibited temporal variations with high PM2.5 loading particularly during winter (0.62) compared to summer months (0.38). Local meteorology has clear distinguishing trends in terms of dry summer (March to June), wet winter (December to February), and monsoon (July to September). Among all the meteorological variables (average temperature, rainfall, relative humidity (RH), wind speed (WS)), temperature was found to be inversely related with particulate loading (rPM10 -0.79; rPM2.5 -0.87) while RH only resulted a significant association with PM2.5 during summer (rPM10 0.07; rPM2.5 0.55) and with PM10 during winter (rPM10 0.53; rPM2.5 0.24). Temperature, atmospheric boundary layer (ABL), and RH were cumulatively recognized as the dominant factors regulating particulate concentration as days with high particulate loading (PM2.5 >150 µg m-3; PM10 >260 µg m-3) appeared to have lower ABL (mean 660 m), minimum temperature (<22.6 °C), and high RH (∼79%). The diurnal variations of particulate ratio were mostly insignificant except minor increases during night having a high wintertime ratio (0.58 ± 0.07) over monsoon (0.34 ± 0.05) and summer (0.30 ± 0.07). Across the region, atmospheric visibility appeared to be inversely associated with particulate (rPM2.5 -0.84; rPM10 -0.79) for all humid conditions, while at RH ≥80%, RH appeared as the most dominant factor in regulating visibility compared to particulate loading. The Lagrangian particle dispersion model was further used to identify possible regions contributing particulate loading through regional/transboundary movement.

Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model.

Fine particulates (PM2.5) constitute dominant proportion of airborne particulates and have been often associated with human health disorders, changes in regional climate, hydrological cycle and more recently to food security. Intrinsic properties of particulates are direct function of sources. This initiates the necessity of conducting a comprehensive review on PM2.5 sources over South Asia which in turn may be valuable to develop strategies for emission control. Particulate source apportionment (SA) through receptor models is one of the existing tool to quantify contribution of particulate sources. Review of 51 SA studies were performed of which 48 (94%) were appeared within a span of 2007-2016. Almost half of SA studies (55%) were found concentrated over few typical urban stations (Delhi, Dhaka, Mumbai, Agra and Lahore). Due to lack of local particulate source profile and emission inventory, positive matrix factorization and principal component analysis (62% of studies) were the primary choices, followed by chemical mass balance (CMB, 18%). Metallic species were most regularly used as source tracers while use of organic molecular markers and gas-to-particle conversion were minimum. Among all the SA sites, vehicular emissions (mean ± sd: 37 ± 20%) emerged as most dominating PM2.5 source followed by industrial emissions (23 ± 16%), secondary aerosols (22 ± 12%) and natural sources (20 ± 15%). Vehicular emissions (39 ± 24%) also identified as dominating source for highly polluted sites (PM2.5>100 µgm-3, n = 15) while site specific influence of either or in combination of industrial, secondary aerosols and natural sources were recognized. Source specific trends were considerably varied in terms of region and seasonality. Both natural and industrial sources were most influential over Pakistan and Afghanistan while over Indo-Gangetic plain, vehicular, natural and industrial emissions appeared dominant. Influence of vehicular emission was found single dominating source over southern part while over Bangladesh, both vehicular, biomass burning and industrial sources were significant.

Temporal variability of MODIS aerosol optical depth and chemical characterization of airborne particulates in Varanasi, India.

Temporal variation of airborne particulate mass concentration was measured in terms of toxic organics, metals and water-soluble ionic components to identify compositional variation of particulates in Varanasi. Information-related fine particulate mass loading and its compositional variation in middle Indo-Gangetic plain were unique and pioneering as no such scientific literature was available. One-year ground monitoring data was further compared to Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 retrieved aerosol optical depth (AOD) to identify trends in seasonal variation. Observed AOD exhibits spatiotemporal heterogeneity during the entire monitoring period reflecting monsoonal low and summer and winter high. Ground-level particulate mass loading was measured, and annual mean concentration of PM2.5 (100.0 ± 29.6 µg/m(3)) and PM10 (176.1 ± 85.0 µg/m(3)) was found to exceed the annual permissible limit (PM10: 80 %; PM2.5: 84 %) and pose a risk of developing cardiovascular and respiratory diseases. Average PM2.5/PM10 ratio of 0.59 ± 0.18 also indicates contribution of finer particulates to major variability of PM10. Particulate sample was further processed for trace metals, viz. Ca, Fe, Zn, Cu, Pb, Co, Mn, Ni, Cr, Na, K and Cd. Metals originated mostly from soil/earth crust, road dust and re-suspended dust, viz. Ca, Fe, Na and Mg were found to constitute major fractions of particulates (PM2.5: 4.6 %; PM10: 9.7 %). Water-soluble ionic constituents accounted for approximately 27 % (PM10: 26.9 %; PM2.5: 27.5 %) of the particulate mass loading, while sulphate (8.0-9.5 %) was found as most dominant species followed by ammonium (6.0-8.2 %) and nitrate (5.5-7.0 %). The concentration of toxic organics representing both aliphatic and aromatic organics was determined by organic solvent extraction process. Annual mean toxic organic concentration was found to be 27.5 ± 12.3 µg/m(3) (n = 104) which constitutes significant proportion of (PM2.5, 17-19 %; PM10, 11-20 %) particulate mass loading with certain exceptions up to 50 %. Conclusively, compositional variation of both PM2.5 and PM10 was compared to understand association of specific sources with different fractions of particulates.