Carbonaceous Species of PM2.5 in Megacity Delhi, India During 2012-2016

Examining trends and variability of PM2.5-associated organic and elemental carbon in the megacity of Beijing, China: Insight from decadal continuous in-situ hourly observations

Organic carbon (OC) and elemental carbon (EC) in fine particulate matter (PM2.5) play pivotal roles in impacting human health, air quality, and climate change dynamics. Long-term monitoring datasets of OC and EC in PM2.5 are indispensable for comprehending their temporal variations, spatial distribution, evolutionary patterns, and trends, as well as for assessing the effectiveness of clean air action plans. This study presents and scrutinizes a comprehensive 10-year hourly dataset of PM2.5-bound OC and EC in the megacity of Beijing, China, spanning from 2013 to 2022. Throughout the entire study period, the average concentrations of OC and EC were recorded at 8.8 ± 8.7 and 2.5 ± 3.0 μg/m3, respectively. Employing the seasonal and trend decomposition methodology, specifically the locally estimated scatter plot smoothing method combined with generalized least squares with the autoregressive moving average method, the study observed a significant decline in OC and EC concentrations, reducing by 5.8 % yr-1 and 9.9 % yr-1 at rates of 0.8 and 0.4 μg/m3 yr-1, respectively. These declining trends were consistently verified using Theil-Sen method. Notably, the winter months exhibited the most substantial declining trends, with rates of 9.3 % yr-1 for OC and 10.9 % yr-1 for EC, aligning with the positive impact of the implemented clean air action plan. Weekend spikes in OC and EC levels were attributed to factors such as traffic regulations and residential emissions. Diurnal variations showcased higher concentrations during nighttime and lower levels during daytime. Although meteorological factors demonstrated an overall positive impact with average reduction in OC and EC concentrations by 8.3 % and 8.7 %, clean air action plans including the Air Pollution Prevention and Control Action Plan (2013-2017) and the Three-Year Action Plan to Win the Blue Sky War (2018-2020) have more contributions in reducing the OC and EC concentrations with mass drop rates of 87.1 % and 89.2 % and 76.7 % and 96.7 %, respectively. Utilizing the non-parametric wind regression method, significant concentration hotspots were identified at wind speeds of ≤2 m/s, with diffuse signals recorded in the southwestern wind sectors at wind speeds of approximately 4-5 m/s. Interannual disparities in potential source regions of OC and EC were evident, with high potential source areas observed in the southern and northwestern provinces of Beijing from 2013 to 2018. In contrast, during 2019-2022, potential source areas with relatively high values of potential source contribution function were predominantly situated in the southern regions of Beijing. This analysis, grounded in observational data, provides insights into the decadal changes in the major atmospheric composition of PM2.5 and facilitates the evaluation of the efficacy of control policies, particularly relevant for developing countries.

Decoding temporal patterns and trends of PM10 pollution over Delhi: a multi-year analysis (2015-2022)

The current study delved into an extensive analysis of multi-year observations on PM10 to have trends at various time scales in Delhi, India. High-resolution ground observations from all 37 monitoring stations from 2015 to 2022 were used. This study used non-parametric generalized additive model (GAM) based smooth-trend and Theil-Sen slope estimator techniques to analyze temporal trends and variations. The long-term PM10 concentration, both in its ambient and de-seasonalized forms, exhibited a statistically significant decreasing trend. An average decrease of - 7.57 [95% confidence interval (CI) - 16.51, 0.18] µg m-3 year-1 for ambient PM10 and - 8.45 [95% CI - 11.96, - 5.58] µg m-3 year-1 for de-seasonalized PM10 mass concentration was observed. Breaking it down into seasons, we observed significant declines in PM10 concentrations during monsoon (- 10.71 µg m-3 year-1, p < 0.1) and post-monsoon (- 7.49 µg m-3 year-1, p < 0.001). On the other hand, summer and winter displayed statistically insignificant declining trends of - 5.32 µg m-3 year-1 and - 6.06 µg m-3 year-1, respectively. Remarkably, all months except March displayed declining PM10 concentrations, suggesting a gradual reduction in particle pollution across the city. Further analysis of PM10 across various wind sectors revealed a consistent decreasing trend in all wind directions. The most substantial decrease was observed from the northwest (- 10.24 µg m-3 year-1), while the minimum reduction occurred from the east (- 5.67 µg m-3 year-1). Throughout the 8-year study period, the daily average PM10 concentration remained at 228 ± 124 µg m-3, ranging from 33 to 819 µg m-3. Seasonal variations were apparent, with concentrations during winter, summer, monsoon, and post-monsoon seasons averaging 279 ± 133, 224 ± 117, 135 ± 95, and 323 ± 142 µg m-3, respectively. November had the highest and August had the lowest concentration. Weekend PM10 concentration is slightly lower than weekdays. These findings emphasize the need for more stringent government action plans.

Chemical characteristics, morphology and source apportionment of PM10 over National Capital Region (NCR) of India

The present study frames the physico-chemical characteristics and the source apportionment of PM10 over National Capital Region (NCR) of India using the receptor model's Positive Matrix Factorization (PMF) and Principal Momponent Mnalysis/Absolute Principal Component Score-Multilinear Regression (PCA/APCS-MLR). The annual average mass concentration of PM10 over the urban site of Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81 µg m-3, respectively. Carbonaceous species (organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)), elemental constituents (Al, Ti, Na, Mg, Cr, Mn, Fe, Cu, Zn, Br, Ba, Mo Pb) and water-soluble ionic components (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) of PM10 were entrenched to the receptor models to comprehend the possible sources of PM10. The PMF assorted sources over Faridabad were soil dust (SD 15%), industrial emission (IE 14%), vehicular emission (VE 19%), secondary aerosol (SA 23%) and sodium magnesium salt (SMS 17%). For IGDTUW-Delhi, the sources were SD (16%), VE (19%), SMS (18%), IE (11%), SA (27%) and VE + IE (9%). Emission sources like SD (24%), IE (8%), SMS (20%), VE + IE (12%), VE (15%) and SA + BB (21%) were extracted over CSIR-NPL, New Delhi, which are quite obvious towards the sites. PCA/APCS-MLR quantified the similar sources with varied percentage contribution. Additionally, catalogue the Conditional Bivariate Probability Function (CBPF) for directionality of the local source regions and morphology as spherical, flocculent and irregular were imaged using a Field Emission-Scanning Electron Microscope (FE-SEM).

Environmental impacts of Indian coal thermal power plants and associated human health risk to the nearby residential communities: A potential review

Worldwide, harmful emissions from coal power plants cause many illnesses contribute to premature deaths burden. Despite its high impact on human health and being a major source of toxic pollutants, coal has been considered a component of global energy for decades. Hence, this work was envisaged to understand the rising environmental and multiple health issues from coal power plants. Studies on the adverse impacts of coal power plants on the environment, including soil, surface water, groundwater and air, were critically evaluated. The health risk from exposure to different pollutants and toxic metals released from the power plant was also demonstrated. The study also highlighted the government initiatives and policies regarding coal power operation and generation. Lastly, the study focused on guiding coal power plant owners and policymakers in identifying the essential cues for the risk assessment and management. The current study found an association between environmental and human health risks due to power generation, which needs intervention from the scientific and medical fields to jointly address public concerns. It is also suggested that future research should concentrate on exposure assessment techniques by integrating source-identification and geographic information systems to assess the health effects of different contaminants from power plants and to mitigate their adverse impact.

Health risk assessment of aerosol particles (PM2.5 and PM10) during winter crop at the agricultural site of Delhi, India

For the last few decades, air pollution in developing country like India is increasing, and it is a matter of huge concern due to its associated human health impacts. In this region, the burgeoning population, escalating urbanization and industrialization, has been cited as the major reason for such a high air pollution. The present study was carried out for health risk assessment of aerosol particles (PM10 and PM2.5) and its associated heavy metals of an agriculture farm site at Indian Agricultural Research Institute (IARI) considered to be green urban area in Delhi, India. The concentrations of both PM10 and PM2.5 varied significantly from 136 to 177 µg/m3 and 56 to 162 µg/m3, respectively at the site. In the present case, the highest PM10 and PM2.5 levels were reported in January, followed by December. The levels of ambient PM10 and PM2.5 are influenced by wind prevailing meteorology. These levels of PM10 and PM2.5 are more than the permissible limits of WHO guidelines of 15 and 5 µg/m3, respectively, thereby leading to high aerosol loadings specifically in winters. The PM concentration of the atmosphere was found to be negatively correlated with temperature during the sampling period. The concentrations of surface ozone O3 and NOx in the present study were observed to be high in February and March, respectively. The increasing air pollution in the city of Delhi poses a great risk to the human health, as the particulate matter loaded with heavy metals can enter humans via different pathways, viz., ingestion, inhalation, and absorption through skin. The mean hazard index for metals (Zn, Pb, Cd, As, Cr, and Ni) was observed within the acceptable limit (HI < 1), thereby indicating negligible non-carcinogenic effects to residing population. The carcinogenic risk assessment was conducted for Cd, Pb, and As only, as the concentrations for other metals were found to be quite low. The carcinogenic risk values were also within the limits of USEPA standards, indicating no carcinogenic risks to the health of children and adults residing near the site. This information about the PM pollution at the agricultural site and health risk assessment will serve as a baseline data in assessment of human health impacts due to air pollution at the local scale and can be used for development of mitigation strategies for tackling air pollution.

Elemental Composition and Sources of Fine Particulate Matter (PM2.5) in Delhi, India

In this study we have analysed the elemental composition of fine particulate matter (PM_2.5) to examine the seasonal changes and sources of the elements in Delhi, India from January, 2017 to December, 2021. During the entire sampling period, 19 elements (Al, Fe, Ti, Cu, Zn, Cr, Ni, As, Mo, Cl, P, S, K, Pb, Na, Mg, Ca, Mn, and Br) of PM_2.5 were identified by Wavelength Dispersive X-ray Fluorescence Spectrometer. The higher annual mean concentrations of S (2.29 µg m^-3), Cl (2.26 µg m^-3), K (2.05 µg m^-3), Ca (0.96 µg m^-3) and Fe (0.93 µg m^-3) were recorded during post-monsoon season followed by Zn > Pb > Al > Na > Cu > Ti > As > Cr > Mo > Br > Mg > Ni > Mn > and P. The annual mean concentrations of elemental composition of PM_2.5 accounted for 10% of PM_2.5 (pooled estimate of 5 year). Principal Component Analysis (PCA) identified the five main sources [crustal/soil/road dust, combustion (BB + FFC), vehicular emissions (VE), industrial emissions (IE) and mixed source (Ti, Cr and Mo rich-source)] of PM_2.5 in Delhi, India.

Long-Term Variation in Carbonaceous Components of PM2.5 from 2012 to 2021 in Delhi

Carbonaceous species [organic carbon (OC), elemental carbon (EC), elemental matter (EM), primary organic carbon (POC), secondary organic carbon (SOC), total carbon (TC), and total carbonaceous matter (TCM)] of PM_2.5 were analyzed to study the seasonal variability and long-term trend of carbonaceous aerosols (CAs) in megacity Delhi, India from January, 2012 to April, 2021. The average concentrations (± standard deviation) of PM_2.5, OC, EC, TC, EM, TCM, POC and SOC were 127 ± 77, 15.7 ± 11.6, 7.4 ± 5.1, 23.1 ± 16.5, 8.2 ± 5.6, 33.3 ± 23.9, 9.3 ± 6.3 and 6.5 ± 5.3 µg m^-3, respectively during the sampling period (10-year average). The average CAs accounted for 26% of PM_2.5 concentration during the entire sampling period. In addition, the seasonal variations in PM_2.5, OC, EC, POC, SOC, and TCM levels were recorded with maxima in post-monsoon and minima in monsoon seasons. The linear relationship of OC and EC, OC/EC and EC/TC ratios suggested that the vehicular emissions (VE), fossil fuel combustion (FFC) and biomass burning (BB) are the major sources of CAs at megacity Delhi, India.

Gridded distribution of total suspended particulate matter (TSP) and their chemical characterization over Delhi during winter

In the present study, total suspended particulate matter (TSP) samples were collected at 47 different sites (47 grids of 5 × 5 km² area) of Delhi during winter (January-February 2019) in campaign mode. To understand the spatial variation of sources, TSP samples were analyzed for chemical compositions including carbonaceous species [organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC)], water-soluble total nitrogen (WSTN), water-soluble inorganic nitrogen (WSIN), polycyclic aromatic hydrocarbons (16 PAHs), water-soluble inorganic species (WSIS) (F^-, Cl^-, SO₄^2-, NO₂^-, NO₃^-, PO₄^3-, NH₄⁺, Ca²⁺, Mg²⁺, Na⁺, and K⁺), and major and minor trace elements (B, Na, Mg, Al, P, S, Cl, K, Ca, Ti, Fe, Zn, Cr, Mn, Cu, As, Pd, F, and Ag). During the campaign, the maximum concentration of several components of TSP (996 μg/m³) was recorded at the Rana Pratap Bagh area, representing a pollution hotspot of Delhi. The maximum concentrations of PAHs were recorded at Udhyog Nagar, a region close to heavily loaded diesel vehicles, small rubber factories, and waste burning areas. Higher content of Cl^- and Cl^-/Na⁺ ratio (>1.7) suggests the presence of nonmarine anthropogenic sources of Cl^- over Delhi. Minimum concentrations of OC, EC, WSOC, PAHs, and WSIS in TSP were observed at Kalkaji, representing the least polluted area in Delhi. Enrichment factor <5.0 at several locations and a significant correlation of Al with Mg, Fe, Ti, and Ca and C/N ratio indicated the abundance of mineral/crustal dust in TSP over Delhi. Principal component analysis (PCA) was also performed for the source apportionment of TSP, and extracted soil dust was found to be the major contributor to TSP, followed by biomass burning, open waste burning, secondary aerosol, and vehicular emissions.

Seasonal variations in carbonaceous species of PM2.5 aerosols at an urban location situated in Indo-Gangetic Plain and its relationship with transport pathways, including the potential sources

The study examines the variation in organic carbon (OC) and elemental carbon (EC) in PM_2.5 concentration at an urban location of Indo-Gangetic Plains (IGP) to understand the impact of seasonality and regional crop residue burning activities. Seasonal cluster analysis of backward air masses and concentration-weighted trajectory (CWT) analysis was performed to identify seasonal transport pathways and potential source regions of carbonaceous aerosols. The mean PM_2.5 level during the study period was 57 ± 41.6 μgm^-3 (5.0-187.3 μgm^-3), whereas OC and EC concentration ranges from 2.8 μgm^-3 to 28.2 μgm^-3 and 1.3 μgm^-3 to 15.5 μgm^-3 with a mean value of 8.4 ± 5.5 μgm^-3 and 5.1 ± 3.3 μgm^-3 respectively. The highest mean PM_2.5 concentration was found during the winter season (111.3 ± 25.5 μgm^-3), which rises 3.6 times compared to the monsoon season. OC and EC also follow a similar trend having the highest levels in winter. Total carbonaceous aerosols contribute ∼38% of PM_2.5 composition. The positive linear trend between OC and EC identified the key sources. HYSPLIT cluster analysis of backward air mass trajectories revealed that during the post-monsoon, winters, pre-monsoon, and monsoon, 71%, 81%, 60%, and 43% of air masses originate within the 500 km radius of IGP. CWT analysis and abundance of OC in post-monsoon and winters season establish a linkage between regional solid-biomass fuel use and crop residue burning activities, including meteorology. Moreover, the low annual average OC/EC ratio (1.75) indicates the overall influence of vehicular emissions. The current dataset of carbonaceous aerosols collated with other Indian studies could be used to validate the global aerosol models on a regional scale and aid in evidence-based air pollution reduction strategies.

Investigating the seasonal variability in source contribution to PM2.5 and PM10 using different receptor models during 2013-2016 in Delhi, India

The present work deals with the seasonal variations in the contribution of sources to PM_2.5 and PM₁₀ in Delhi, India. Samples of PM_2.5 and PM₁₀ were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM_2.5 and PM₁₀ were 131 ± 79 μg m^-3 and 238 ± 106 μg m^-3, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM_2.5 and PM₁₀ were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM_2.5 and PM₁₀ as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM_2.5 and PM₁₀. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.

Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India

The present study attempts to explore and compare the seasonal variability in chemical composition and contributions of different sources of fine and coarse fractions of aerosols (PM_2.5 and PM₁₀) in Delhi, India from January 2013 to December 2016. The annual average concentrations of PM_2.5 and PM₁₀ were 131 ± 79 μg m^-3 (range: 17-417 μg m^-3) and 238 ± 106 μg m^-3 (range: 34-537 μg m^-3), respectively. PM_2.5 and PM₁₀ samples were chemically characterized to assess their chemical components [i.e. organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSICs) and heavy and trace elements] and then used for estimation of enrichment factors (EFs) and applied positive matrix factorization (PMF5) model to evaluate their prominent sources on seasonal basis in Delhi. PMF identified eight major sources i.e. Secondary nitrate (SN), secondary sulphate (SS), vehicular emissions (VE), biomass burning (BB), soil dust (SD), fossil fuel combustion (FFC), sodium and magnesium salts (SMS) and industrial emissions (IE). Total carbon contributes ∼28% to the total PM_2.5 concentration and 24% to the total PM₁₀ concentration and followed the similar seasonality pattern. SN and SS followed opposite seasonal pattern, where SN was higher during colder seasons while SS was greater during warm seasons. The seasonal differences in VE contributions were not very striking as it prevails evidently most of year. Emissions from BB is one of the major sources in Delhi with larger contribution during winter and post monsoon seasons due to stable meteorological conditions and aggrandized biomass burning (agriculture residue burning in and around the regions; mainly Punjab and Haryana) and domestic heating during the season. Conditional Bivariate Probability Function (CBPF) plots revealed that the maximum concentrations of PM_2.5 and PM₁₀ were carried by north westerly winds (north-western Indo Gangetic Plains of India).

Peroxidases from an invasive Mesquite species for management and restoration of fertility of phenolic-contaminated soil

Phenolics drive the global economy, but they also pose threats to soil health and plant growth. Enzymes like peroxidase have the potential to remove the phenolic contaminants from the wastewater; however, their role in restoring soil health and improving plant growth has not yet been ascertained. We fractionated efficient peroxidases (MPx) from leaves of an invasive species of Mesquite, Prosopis juliflora, and demonstrated its superiority over horseradish peroxidase (HRP) in remediating phenol, 3-chlorophenol (3-CP), and a mixture of chlorophenols (CP-M), from contaminated soil. MPx removes phenolics over a broader range of pH (2.0-9.0) as compared with HRP (pH: 7.0-8.0). In soil, replacing H₂O₂ with CaO₂ further increases the phenolic removal efficiency of MPx (≥90% of phenol, ≥ 70% of 3-CP, and ≥90% of CP-M). MPx maintains ~4-fold higher phenolic removal efficiency than purified HRP even in soils with extremely high contaminant concentration (2 g phenolics/kg of soil), which is desirable for environmental applications of enzymes for remediation. MPx treatment restores soil biological processes as evident by key enzymes of soil fertility viz. Acid- and alkaline-phosphatases, urease, and soil dehydrogenase, and improves potential biochemical fertility index of soil contaminated with phenolics. MPx treatment also assists the Vigna mungo test plant to overcome toxicant stress and grow healthy in contaminated soils. Optimization of MPx for application in the field environment would help both in the restoration of phenolic-contaminated soils and the management of invasive Mesquite.

Cookstove Smoke Impact on Ambient Air Quality and Probable Consequences for Human Health in Rural Locations of Southern Nepal

Residential emission from traditional biomass cookstoves is a major source of indoor and outdoor air pollution in developing countries. However, exact quantification of the contribution of biomass cookstove emissions to outdoor air is still lacking. In order to address this gap, we designed a field study to estimate the emission factors of PM2.5 (particulate matter of less than 2.5 µ diameter) and BC (black carbon) indoors, from cookstove smoke using biomass fuel and with smoke escaping outdoors from the roof of the house. The field study was conducted in four randomly selected households in two rural locations of southern Nepal during April 2017. In addition, real-time measurement of ambient PM2.5 was performed for 20 days during the campaign in those two rural sites and one background location to quantify the contribution of cooking-related emissions to the ambient PM2.5. Emission factor estimates indicate that 66% of PM2.5 and 80% of BC emissions from biomass cookstoves directly escape into ambient air. During the cooking period, ambient PM2.5 concentrations in the rural sites were observed to be 37% higher than in the nearby background location. Based on the World Health Organization (WHO)'s AirQ+ model simulation, this 37% rise in ambient PM2.5 during cooking hours can lead to approximately 82 cases of annual premature deaths among the rural population of Chitwan district.

Influence of Vehicular Emissions (NO, NO2, CO and NMHCs) on the Mixing Ratio of Atmospheric Ammonia (NH3) in Delhi, India

Mixing ratios of atmospheric ammonia (NH₃), nitric oxide (NO), carbon monoxide (CO), nonmethane hydrocarbons (NMHCs), and methane (CH₄) were measured to investigate the vehicular emissions, which are a dominant source of atmospheric NH₃ in urban sites of Delhi, India from January 2013 to December 2014. The annual average mixing ratios of NH₃, NO, CO, NMHCs, and CH₄ were 21.2 ± 2.1 ppb, 21.2 ± 6.1 ppb, 1.89 ± 0.18 ppm, 0.67 ± 0.21 ppm and 3.11 ± 0.53 ppm, respectively. Considering NO as a tracer of vehicular plume, ambient NH₃ was correlated with NO during peak traffic hour in the morning (7:00-10:00 h) and evening (17:00-19:00 h) and observed significant positive correlation between them. Result reveals that the mixing ratio of atmospheric NH₃ significantly positive correlated with traffic related pollutants (NO, CO, and NHHCs) during all the seasons (winter, summer, and monsoon). During winter, the average mixing ratio of atmospheric NH₃ was increased by 1.2-3.5 ppb in the morning peak hour, whereas increased by 0.3-1.6 ppb in the evening peak hour. Similarly, an increase in NH₃ mixing ratio was observed during summer (morning: 1.2-2.7 ppb and evening: 1.5-1.6 ppb) and monsoon (morning: 0.4-3.6 ppb and evening: 0.9-1.4 ppb) seasons. The results emphasized that the traffic could be one of the dominant source of ambient NH₃ at the urban site of Delhi, as illustrated by positive relationships of NH₃ with traffic related co-pollutants (NO, CO and NMHCs).

Seasonal variation, source apportionment and source attributed health risk of fine carbonaceous aerosols over National Capital Region, India

Deteriorating air quality with high levels of fine particulate matter (PM_2.5) over National Capital Region (NCR) of India is one of the serious environmental and scientific issues. In this paper, PM_2.5 samples were collected for 24 h twice or thrice a week during December 2016-December 2017 at three sites [Delhi (IG), Modinagar (MN) and Mahendragarh (HR)] over NCR to analyse the carbonaceous aerosols. Source apportionment of PM_2.5 was attempted using Principal Component analysis (PCA) and Positive Matrix Factorization (PMF) based on the analysed carbonaceous fractions [Organic carbon, Elemental carbon, Secondary organic carbon (SOC)]. Organic compounds: alkanes, hopanes, steranes, polycyclic aromatic hydrocarbons (PAHs), phthalates, levoglucosan and n-alkanoic acids were analysed to distinguish the emission sources. Total Carbonaceous Aerosols (TCA) contributed significantly (∼26%) to PM_2.5 which revealed their importance in source apportionment. Estimated SOC contributed 43.2%, 42.2% and 58.2% to OC and 5.4%, 5.3% and 7.8% to PM_2.5 at IG, MN and HR sites respectively. PCA and PMF apportion five emission sources i.e., vehicular emissions (34.6%), biomass burning (26.8%), cooking emissions (15.7%), plastic and waste burning (13.5%) and secondary organic carbon (9.5%) for PM_2.5. Source attributed health risk has also been calculated in terms of Lung cancer risk (LCR) associated with PAHs exposure and concluded that vehicular emissions (40.3%), biomass burning (38.1%), secondary organic carbon (12.8%) contributed higher to LCR (503.2 × 10^-5; ∼503 cases in 1,00,000). Health risk assessment combined with source apportionment inferences signifies the immediate implementation of emissions reduction strategies with special target on transport sector and biomass burning over the NCR of India.

Source apportionment and health risk assessment of organic constituents in fine ambient aerosols (PM2.5): A complete year study over National Capital Region of India

Fine ambient aerosols (PM_2.5) levels in the atmosphere are continuously worsening over Delhi and National Capital Region (NCR) of India. Complete source profiles are required to be assessed for implementation of proper mitigation measures over the NCR. In this study, emission sources of PM_2.5 are reported for the NCR of India for samples collected during December 2016 to December 2017 at three sampling sites in Delhi, Uttar Pradesh and Haryana. Organic constituents (n-alkanes, isoprenoid hydrocarbons, polycyclic aromatic hydrocarbons, phthalates, levoglucosan and n-alkanoic acids) in PM_2.5 were measured to apportion the sources over the study area. Source apportionment of PM_2.5 was performed using organic constituents by Positive Matrix Factorization (PMF) and Principal Component Analysis (PCA). Health risk associated with organic pollutants [PAHs and carcinogen BEHP bis(2-ethylhexyl) phthalate] demonstrated the threat of PM_2.5 exposure via inhalation. Transport pathways of air masses were evaluated using 3-day backward trajectories and observed that some air masses originated from local sources along with long-range transport which influenced the PAHs concentration during most of the study period over the NCR. PMF and PCA resulted in the five major emission sources [vehicular emissions (32.2%), biomass burning (30%), cooking emissions (16.8%), plastic burning (13.4%), mixed sources (7.6%) including biogenic and industrial emissions] for PM_2.5 over the sampling sites. The present study reveals that transport sector is a major source to be targeted to reduce the vehicular emissions and consequent health risks associated with organic pollutants especially PAHs.

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM₁₀ (particulate matter with aerodynamic diameter ≤ 10 μm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM₁₀ sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM₁₀. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM₁₀ concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 μg m^-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM₁₀ mass concentration over the IGP region of India.