Local incomplete combustion emissions define the PM2.5 oxidative potential in Northern India.

The oxidative potential (OP) of particulate matter (PM) is a major driver of PM-associated health effects. In India, the emission sources defining PM-OP, and their local/regional nature, are yet to be established. Here, to address this gap we determine the geographical origin, sources of PM, and its OP at five Indo-Gangetic Plain sites inside and outside Delhi. Our findings reveal that although uniformly high PM concentrations are recorded across the entire region, local emission sources and formation processes dominate PM pollution. Specifically, ammonium chloride, and organic aerosols (OA) from traffic exhaust, residential heating, and oxidation of unsaturated vapors from fossil fuels are the dominant PM sources inside Delhi. Ammonium sulfate and nitrate, and secondary OA from biomass burning vapors, are produced outside Delhi. Nevertheless, PM-OP is overwhelmingly driven by OA from incomplete combustion of biomass and fossil fuels, including traffic. These findings suggest that addressing local inefficient combustion processes can effectively mitigate PM health exposure in northern India.

Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter.

National Capital Region (NCR) encompassing New Delhi is one of the most polluted urban metropolitan areas in the world. Real-time chemical characterization of fine particulate matter (PM1 and PM2.5) was carried out using three aerosol mass spectrometers, two aethalometers, and one single particle soot photometer (SP2) at two sites in Delhi (urban) and one site located ~40 km downwind of Delhi, during January-March 2018. The campaign mean PM2.5 (NR-PM2.5 + BC) concentrations at the two urban sites were 153.8 ± 109.4 µg.m-3 and 127.8 ± 83.2 µg.m-3, respectively, whereas PM1 (NR-PM1 + BC) was 72.3 ± 44.0 µg.m-3 at the downwind site. PM2.5 particles were composed mostly of organics (43-44)% followed by chloride (14-17)%, ammonium (9-11)%, nitrate (9%), sulfate (8-10)%, and black carbon (11-16)%, whereas PM1 particles were composed of 47% organics, 13% sulfate as well as ammonium, 11% nitrate as well as chloride, and 5% black carbon. Organic aerosol (OA) source apportionment was done using positive matrix factorization (PMF), solved using an advanced multi-linear engine (ME-2) model. Highly mass-resolved OA mass spectra at one urban and downwind site were factorized into three primary organic aerosol (POA) factors including one traffic-related and two solid-fuel combustion (SFC), and three oxidized OA (OOA) factors. Whereas unit mass resolution OA at the other urban site was factorized into two POA factors related to traffic and SFC, and one OOA factor. OOA constituted a majority of the total OA mass (45-55)% with maximum contribution during afternoon hours ~(70-80)%. Significant differences in the absolute OOA concentration between the two urban sites indicated the influence of local emissions on the oxidized OA formation. Similar PM chemical composition, diurnal and temporal variations at the three sites suggest similar type of sources affecting the particulate pollution in Delhi and adjoining cities, but variability in mass concentration suggest more local influence than regional.

Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi, India.

Delhi is one of the most polluted cities worldwide and a comprehensive understanding and deeper insight into the air pollution and its sources is of high importance. We report 5 months of highly time-resolved measurements of non-refractory PM2.5 and black carbon (BC). Additionally, source apportionment based on positive matrix factorization (PMF) of the organic aerosol (OA) fraction is presented. The highest pollution levels are observed during winter in December/January. During that time, also uniquely high chloride concentrations are measured, which are sometimes even the most dominant NR-species in the morning hours. With increasing temperature, the total PM2.5 concentration decreases steadily, whereas the chloride concentrations decrease sharply. The concentrations measured in May are roughly 6 times lower than in December/January. PMF analysis resolves two primary factors, namely hydrocarbon-like (traffic-related) OA (HOA) and solid fuel combustion OA (SFC-OA), and one or two secondary factors depending on the season. The uncertainties of the PMF analysis are assessed by combining the random a-value approach and the bootstrap resampling technique of the PMF input. The uncertainties for the resolved factors range from ±18% to ±19% for HOA, ±7% to ±19% for SFC-OA and ±6 % to ±11% for the OOAs. The average correlation of HOA with equivalent black carbon from traffic (eBCtr) is R2 = 0.40, while SFC-OA has a correlation of R2 = 0.78 with equivalent black carbon from solid fuel combustion (eBCsf). Anthracene (m/z 178) and pyrene (m/z 202) (PAHs) are mostly explained by SFC-OA and follow its diurnal trend (R2 = 0.98 and R2 = 0.97). The secondary oxygenated aerosols are dominant during daytime. The average contribution during the afternoon hours (1 pm-5 pm) is 59% to the total OA mass, with contributions up to 96% in May. In contrast, the primary sources are more important during nighttime: the mean nightly contribution (22 pm-3 am) to the total OA mass is 48%, with contributions up to 88% during some episodes in April.

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region.

In the present study, we assessed for the first time the performance of our custom-designed low-cost Particulate Matter (PM) monitoring devices (Atmos) in measuring PM10 concentrations. We examined the ambient PM10 levels during an intense measurement campaign at two sites in the Delhi National Capital Region (NCR), India. In this study, we validated the un-calibrated Atmos for measuring ambient PM10 concentrations at highly polluted monitoring sites. PM10 concentration from Atmos, containing laser scattering-based Plantower PM sensor, was comparable with that measured from research-grade scanning mobility particle sizers (SMPS) in combination with optical particle sizers (OPS) and aerodynamic particle sizers (APS). The un-calibrated sensors often provided accurate PM10 measurements, particularly in capturing real-time hourly concentrations variations. Quantile-Quantile plots (QQ-plots) for data collected during the selected deployment period showed positively skewed PM10 datasets. Strong Spearman's rank-order correlations (rs = 0.64-0.83) between the studied instruments indicated the utility of low-cost Plantower PM sensors in measuring PM10 in the real-world context. Additionally, the heat map for weekly datasets demonstrated high R2 values, establishing the efficacy of PM sensor in PM10 measurement in highly polluted environmental conditions.