Chemical characterization of PM1.0 aerosol in Delhi and source apportionment using positive matrix factorization

Source apportionment of fine aerosol particles of water-soluble and carbonaceous species measured in semi-urban (Kharagpur) and megacity (Kolkata) atmospheres over the eastern Indo-Gangetic plain: Chemical characterisation, relative abundance and anthropogenic contributions

We conducted the source apportionment of fine aerosol particles (aerodynamic diameter ≤1.6μm) collected with the indigenously designed-fabricated submicron aerosol sampler (SAS) in the eastern Indo-Gangetic plain (IGP) semi-urban (Kharagpur, KGP) and megacity (Kolkata, KOL) atmospheres, examining the chemical characteristics at KGP (January 2015-February 2016), and accentuating their abundance and the sources of anthropogenic pollution relative to KOL. The fine water-soluble inorganic ions (WSII) at KGP predominantly constituted Ca2+ (52 %) and equivalent amounts (12 % each) of Cl-, Mg2+ and secondary inorganic aerosols (sum of SO42-, NO3- and NH4+). The annual mean of SO42- at KGP was twice (thrice) larger than NO3- (NH4+); this of organic carbon (OC) was thrice elemental carbon (EC), with secondary OC being 37 % of the total OC. The concordance in peaks of OC with K+ concentrations was identified during the seasonal open biomass burning at KGP (November and May). While the annual mean of OC (EC) concentration at KGP was slightly lower than (nearly equivalent to) KOL; K+, NO3-, NH4+ and F- concentrations at KOL were twice larger than KGP. Source quantification using Positive Matrix Factorization (PMF) revealed the regional dust with crustal elements marked as clean (polluted) at KGP (KOL) constituted the largest fractional contribution among the six identified factors at both KGP and KOL. The combustion-derived anthropogenic pollution comprising about 60 % (50 %) of fine particles at KOL (KGP) was predominantly from the transportation sector (in vehicular emissions and regional dust), coal combustion (industries) and open biomass burning at KOL; it was from brick kilns, residential biofuel combustion, and open biomass burning at KGP. The source-wide distribution of measured aerosol species showed their emergence from largely different sources at KGP and KOL; thereby suggesting a prioritised strategy for sustainable emissions mitigation considering the prominent sources of combustion-derived anthropogenic pollution and aerosol species for megacity and semi-urban atmospheres.

Insight Study of Trace Elements in PM2.5 During Nine Years in Delhi, India: Seasonal Variation, Source Apportionment, and Health Risks Assessment

This study investigated the concentrations, seasonal variations, sources, and human health risks associated with exposure to heavy elements (As, Al, Pb, Cr, Mn, Cu, Zn, and Ni) of PM2.5 at an urban location of Delhi (28° 38' N, 77° 10' E; 218 m amsl), India, from January 2013 to December 2021. The average mass concentration of PM2.5 throughout the study period was estimated as 127 ± 77 µg m-3, which is exceeding the National Ambient Air Quality Standards (NAAQS) limit (annual: 40 µg m-3; 24 h: 60 µg m-3). The seasonal mass concentrations of PM2.5 exhibited at the order of post-monsoon (192 ± 110 µgm-3) > winter (158 ± 70 µgm-3) > summer (92 ± 44 µgm-3) and > monsoon (67 ± 32 µgm-3). The heavy elements, Al (1.19 µg m-3), Zn (0.49 µg m-3), Pb (0.43 µg m-3), Cr (0.21 µg m-3), Cu (0.21 µg m-3), Mn (0.07 µg m-3), and Ni (0.14 µg m-3) exhibited varying concentrations in PM2.5, with the highest levels observed in the post-monsoon season, followed by winter, summer, and monsoon seasons. Six primary sources throughout the study period, contributing to PM2.5 were identified by positive matrix factorization (PMF), such as dust (paved/crustal/soil dust: 29.9%), vehicular emissions (17.2%), biomass burning (15.4%), combustion (14%), industrial emissions (14.2%), and Br-rich sources (9.2%). Health risk assessments, including hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR), were computed based on heavy elements concentrations in PM2.5. Elevated HQ values for Cr and Mn linked with adverse health impacts in both adults and children. High carcinogenic risk values were observed for Cr in both adults and children during the winter and post-monsoon seasons, as well as in adults during the summer and monsoon seasons. The combined HI value exceeding one suggests appreciable non-carcinogenic risks associated with the examined elements. The findings of this study provide valuable insights into the behaviour and risk mitigation of heavy elements in PM2.5, contributing to the understanding of air quality and public health in the urban environment of Delhi.

Association between long-term exposure to ambient particulate matter and pulmonary function among men and women in typical areas of South and North China

Background

Studies comparing the effects of different sizes and concentrations of ambient particulate matter (PM) on pulmonary function in different regions and sexes remain sparse.

Objectives

To investigate the associations of different sizes and levels of long-term ambient PM exposure with pulmonary function among people of different sexes in typical areas of South and North China.

Methods

In 2021, a total of 1,592 participants aged 20-73 years were recruited to participate in the pulmonary function test from the baseline survey of the Diverse Life-Course Cohort (DLCC) in typical areas of Guangdong Province and Hebei Province. The three-year (2018-2020) average ambient PM concentrations were assessed from the ChinaHighPM₁ dataset, ChinaHighPM_2.5 dataset and ChinaHighPM₁₀ dataset. Mean differences in pulmonary function were used in multilevel models for different regions and sexes.

Results

We discovered significant associations of ambient PM exposure with reduced forced vital capacity (FVC) and increased forced expiratory volume in 1 s/forced vital capacity ratio (FEV₁/FVC) among men and lower levels of FEV₁ and FVC among women, such that a 5-μg/m³ concentration increase in PM₁, PM_2.5, and PM₁₀ was associated with decreases in FVC of 122.1 ml (95% confidence interval (CI): 30.8, 213.4), 54.6 ml (95% CI: 15.8, 93.3) and 42.9 ml (95% CI: 12.7, 73.1) and increases in FEV₁/FVC of 2.2% (95% CI: 0.6, 3.9), 1.1% (95% CI: 0.4, 1.9) and 0.9% (95% CI: 0.3, 1.5) among men and decreases in FEV₁ of 51.1 ml (95% CI: 9.7, 92.4), 21.6 ml (95% CI: 4.3, 38.9) and 16.7 ml (95% CI: 3.3, 30.1) and in FVC of 77.8 ml (95% CI: 10.0, 145.6), 38.7 ml (95% CI: 9.0, 68.5) and 31.1 ml (95% CI: 8.1, 54.1) among women in Hebei Province. There was no association between ambient PM and pulmonary function in Guangdong Province.

Conclusion

Long-term exposure to different sizes and concentrations of ambient PM were associated with FEV₁ and FVC among men and women differently. The impact of ambient PM on FVC should be of greater concerned.

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.

Identification of pollutant sources and evaluation of water quality improvement alternatives of a large river

While pollutants are the most important factors for the deterioration of surface water quality, the identification of major pollutant sources for rivers is challenging, especially in areas with diverse land covers and multiple pollutant inputs. This study aims to identify the significant pollutant sources from the tributaries that are affecting the water quality and identify the limiting nutrient for algal growth in the Geum river to provide a management alternative for an improvement of the water quality. The positive matrix factorization (PMF) was applied for pollutant source identification and apportionment of the two major tributaries, Gab-cheon and Miho-cheon. Positive matrix factorization identifies three and two major pollutant sources for Gab-cheon and Miho-cheon, respectively. For Gab-cheon, wastewater treatment plants, urban, and agricultural pollution are identified as major pollutant sources. Furthermore, for Miho-cheon, agricultural and urban pollution were identified as major pollutant sources. Total phosphorus (TP) is also identified as a limiting nutrient for algal growth in the Geum river. Water quality control scenarios were formulated and improvement of water quality in the river locations was simulated and analyzed with the Environmental Fluid Dynamic Code (EFDC). Scenario results were evaluated using a water quality index. The reduction of total phosphorus (TP) from the tributaries has greatly improved the water quality, especially algal bloom in the downstream stations.

Estimating causal links of long-term exposure to particulate matters with all-cause mortality in South China

BACKGROUND

The association between long-term particulate matter (PM) exposure and all-cause mortality has been well-documented. However, evidence is still limited from high-exposed cohorts, especially for PM₁ which is smaller while more toxic than other commonly investigated particles. We aimed to examine the potential casual links of long-term PMs exposure with all-cause mortality in high-exposed areas.

METHODS

A total of 580,757 participants in southern China were enrolled during 2009-2015 and followed up to 2020. The annual average concentration of PM₁, PM_2.5, and PM₁₀ at 1 km² spatial resolution was assessed for each residential address through validated spatiotemporal models. We used marginal structural Cox models to estimate the PM-mortality associations which were further stratified by sociodemographic, lifestyle factors and general exposure levels.

RESULTS

37,578 deaths were totally identified during averagely 8.0 years of follow-up. Increased exposure to all 3 PM size fractions were significantly associated with increased risk of all-cause mortality, with hazard ratios (HRs) of 1.042 (95 % confidence interval (CI): 1.037-1.046), 1.031 (95 % CI: 1.028-1.033), and 1.029 (95 % CI: 1.027-1.031) per 1 μg/m³ increase in PM₁, PM_2.5, and PM₁₀ concentrations, respectively. We observed greater effect estimates among the elderly (age ≥ 65 years), unmarried participants, and those with low education attainment. Additionally, the effect of PM₁, PM_2.5, and PM₁₀ tend to be higher in the low-exposure group than in the general population.

CONCLUSIONS

We provided comprehensive evidence for the potential causal links betweenlong-term PM exposureand all-cause mortality, and suggested stronger links for PM₁compared to large particles and among certain vulnerable subgroups.

Variabilities of δ13C and carbonaceous components in ambient PM2.5 in Northeast India: Insights into sources and atmospheric processes

A year-long sampling campaign of ambient PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 mm) at a regional station in the North-Eastern Region (NER) of India was performed to understand the sources and formation of carbonaceous aerosols. Mass concentration, carbon fractions (organic and elemental carbon), and stable carbon isotope ratio (δ¹³C) of PM_2.5 were measured and studied along with cluster analysis and Potential Source Contribution Function (PSCF) modelling. PM_2.5 mass concentration was observed to be highest during winter and post-monsoon seasons when the meteorological conditions were relatively stable compared to other seasons. Organic carbon (OC) concentration was more than two times higher in the post-monsoon and winter seasons than in the pre-monsoon and monsoon seasons. Air mass back trajectory cluster analysis showed the dominance of local and regional air masses during winter and post-monsoon periods. In contrast, long-range transported air masses influenced the background site in pre-monsoon and monsoon. Air mass data and PSCF analysis indicated that aerosols during winter and post-monsoon are dominated by freshly generated emissions from local sources along with the influence from regional transport of polluted aerosols. On the contrary, the long-range transported air masses containing aged aerosols were dominant during pre-monsoon. No significant variability was observed in the range of δ¹³C values (-28.2‰ to -26.4‰) during the sampled seasons. The δ¹³C of aerosols indicates major sources to be combustion of biomass/biofuels (C3 plant origin), biogenic aerosols, and secondary aerosols. The δ¹³C variability and cluster/PSCF modelling suggest that aged aerosols (along with enhanced photo-oxidation derived secondary aerosols) influenced the final δ¹³C during the pre-monsoon. On the other hand, lower δ¹³C in winter and post-monsoon is attributed to the freshly emitted aerosols from biomass/biofuels.

Effect of Biomass Burning, Diwali Fireworks, and Polluted Fog Events on the Oxidative Potential of Fine Ambient Particulate Matter in Delhi, India

We investigated the influence of biomass burning (BURN), Diwali fireworks, and fog events on the ambient fine particulate matter (PM_2.5) oxidative potential (OP) during the postmonsoon (PMON) and winter season in Delhi, India. The real-time hourly averaged OP (based on a dithiothreitol assay) and PM_2.5 chemical composition were measured intermittently from October 2019 to January 2020. The peak extrinsic OP (OP_v: normalized by the volume of air) was observed during the winter fog (WFOG) (5.23 ± 4.6 nmol·min^-1·m^-3), whereas the intrinsic OP (OP_m; normalized by the PM_2.5 mass) was the highest during the Diwali firework-influenced period (29.4 ± 18.48 pmol·min^-1·μg^-1). Source apportionment analysis using positive matrix factorization revealed that traffic + resuspended dust-related emissions (39%) and secondary sulfate + oxidized organic aerosols (38%) were driving the OP_v during the PMON period, whereas BURN aerosols dominated (37%) the OP_v during the WFOG period. Firework-related emissions became a significant contributor (∼32%) to the OP_v during the Diwali period (4 day period from October 26 to 29), and its contribution peaked (72%) on the night of Diwali. Discerning the influence of seasonal and episodic sources on health-relevant properties of PM_2.5, such as OP, could help better understand the causal relationships between PM_2.5 and health effects in India.

Global Emissions of Hydrogen Chloride and Particulate Chloride from Continental Sources

Gaseous and particulate chlorine species play an important role in modulating tropospheric oxidation capacity, aerosol water uptake, visibility degradation, and human health. The lack of recent global continental chlorine emissions has hindered modeling studies of the role of chlorine in the atmosphere. Here, we develop a comprehensive global emission inventory of gaseous HCl and particulate Cl- (pCl), including 35 sources categorized in six source sectors based on published up-to-date activity data and emission factors. These emissions are gridded at a spatial resolution of 0.1° × 0.1° for the years 1960 to 2014. The estimated emissions of HCl and pCl in 2014 are 2354 (1661-3201) and 2321 (930-3264) Gg Cl a-1, respectively. Emissions of HCl are mostly from open waste burning (38%), open biomass burning (19%), energy (19%), and residential (13%) sectors, and the major sources classified by fuel type are combustion of waste (43%), biomass (32%), and coal (25%). Emissions of pCl are mostly from biofuel (29%) and open biomass burning processes (44%). The sectoral and spatial distributions of HCl and pCl emissions are very heterogeneous along the study period, and the temporal trends are mainly driven by the changes in emission factors, energy intensity, economy, and population.

Meteorology governs the variation of Delhi's high particulate-bound chloride levels

A significant number of past studies have reported Delhi to witness some of the highest levels of particulate-bound chloride compared to anywhere else in the world. The present study employs long-term, highly time-resolved chloride measurements at the IIT Delhi campus from February 2020 to April 2021. The present work sheds light on the dependence of high chloride levels in Delhi on the winds from the northwest direction. The study makes use of linear regression models and stepped linear models to quantify the role of meteorological variables in driving the seasonal variation of chloride in Delhi. The results indicate that ∼85-88% of the variation in chloride concentration observed in Delhi can be attributed to meteorological parameters, mainly temperature (T), relative humidity (RH), and percentage of wind incoming from the northwest (%NW). The results also suggest that the primary chloride emissions remain relatively consistent year-round, and are regionally transported from Delhi's northwest. The results of this study provide valuable insights in understanding the nature of the sources and the variability associated with the chloride levels in Delhi and thus provide a basis for future emission control strategies.

Enhanced secondary aerosol formation driven by excess ammonia during fog episodes in Delhi, India

The Indo-Gangetic Plain (IGP) has high wintertime fine aerosol loadings that significantly modulate the widespread fog formation and sustenance. Here, we investigate the potential formation of secondary inorganic aerosol driven by excess ammonia during winter fog. Physicochemical properties of fine aerosols (PM₁ and PM_2.5) and trace gases (HCl, HONO, HNO₃, SO₂, and NH₃) were simultaneously monitored at hourly resolution using Monitor for AeRosols and Gases in Ambient air (MARGA-2S) for the first time in India. Results showed that four major ions, i.e., Cl^-, NO₃^-, SO₄^2-, and NH₄⁺ contributed approximately 97% of the total measured inorganic ionic mass. The atmosphere was ammonia-rich in winter and ammonium was the dominant neutralizer with aerosol neutralization ratio (ANR) close to unity. The correlation between ammonium and chloride was ≥0.8, implying the significant formation of ammonium chloride during fog in Delhi. Thermodynamical model ISORROPIA-II showed the predicted PM₁ and PM_2.5 pH to be 4.49 ± 0.53, and 4.58 ± 0.48 respectively which were in good agreement with measurements. The ALWC increased from non-foggy to foggy periods and a considerable fraction of fine aerosol mass existed in the supermicron size range of 1-2.5 μm. The sulfur oxidation ratio (SOR) of PM₁, PM_2.5 reached up to 0.60, 0.75 in dense fog and 0.74, 0.87 when ambient RH crossed a threshold of 95%, much higher than non-foggy periods (with confidence level of ≥95%) pointing to enhanced formation of secondary aerosol in fog.

Diurnal variability in the spectral characteristics and sources of water-soluble brown carbon aerosols over Delhi

It is well established that light-absorbing organic aerosols (commonly known as brown carbon, BrC) impact climate. However, uncertainties remain as their contributions to absorption at different wavelengths are often ignored in climate models. Further, BrC exhibits differences in absorption at different wavelengths due to the variable composition including varying sources and meteorological conditions. However, diurnal variability in the spectral characteristics of water-soluble BrC (hereafter BrC) is not yet reported. This study presents unique measurement hitherto lacking in the literature. Online measurements of BrC were performed using an assembled system including a particle-into-liquid sampler, portable UV-Visible spectrophotometer with liquid waveguid capillary cell, and total carbon analyzer (PILS-LWCC-TOC). This system measured the absorption of ambient aerosol extracts at the wavelengths ranging from 300 to 600 nm with 2 min integration time and water-soluble organic carbon (WSOC) with 4 min integration time over a polluted megacity, New Delhi. Black carbon, carbon monoxide (CO), nitrogen oxides (NOx), and the chemical composition of non-refractory submicron aerosols were also measured in parallel. Diurnal variability in absorption coefficient (0.05 to 65 Mm^-1), mass absorption efficiency (0.01 to 3.4 m^-2 gC^-1) at 365 nm, and absorption angstrom exponent (AAE) of BrC for different wavelength range (AAE_300-400: 4.2-5.8; AAE_400-600: 5.5-8.0; and AAE_300-600: 5.3-7.3) is discussed. BrC chromophores absorbing at any wavelength showed minimum absorption during afternoon hours, suggesting the effects of boundary layer expansion and their photo-sensitive/volatile nature. On certain days, a considerable presence of BrC absorbing at 490 nm was observed during nighttime that disappears during the daytime. It appeared to be associated with secondary BrC. Observations also infer that BrC species emitted from the biomass and coal burning are more absorbing among all sources. A fraction of BrC is likely associated with trash burning, as inferred from the spectral characteristics of Factor-3 from the PMF analysis of BrC spectra. Such studies are essential in understanding the BrC characteristics and their further utilization in climate models.

Tracers from Biomass Burning Emissions and Identification of Biomass Burning

The major organic compositions from biomass burning emissions are monosaccharide derivatives from the breakdown of cellulose, generally accompanied by small amounts of straight-chain, aliphatic, oxygenated compounds, and terpenoids from vegetation waxes, resins/gums, and other biopolymers. Levoglucosan from cellulose can be utilized as a specific or general indicator for biomass combustion emissions in aerosol samples. There are other important compounds, such as dehydroabietic acid, syringaldehyde, syringic acid, vanillic acid, vanillin, homovanillic acid, 4-hydroxybenzoic acid, and p-coumaric acid, which are additional key indicators of biomass burning. In this review, we will address these tracers from different types of biomass burning and the methods used to identify the sources in ambient aerosols. First, the methods of inferring biomass burning types by the ratio method are summarized, including levoglucosan/mannose, syringic acid/vanillic acid, levolgucosan/K+, vanillic acid/4-hydroxybenzoic acid, levoglucosan/OC, and levoglucosan/EC to infer the sources of biomass burning, such as crop residual burning, wheat burning, leaf burning, peatland fire, and forest fire in Asia. Second, we present the source tracer ratio methods that determine the biomass combustion types and their contributions. Finally, we introduce the PCA (Principal component analysis) and PMF (Positive matrix factor) methods to identify the type of biomass burning and its contributions according to emission factors of different species in various plants such as softwood, hardwood, and grass.

Assessment of PM10 and PM2.5 over Ghaziabad, an industrial city in the Indo-Gangetic Plain: spatio-temporal variability and associated health effects

This study examined the PM₁₀ and PM_2.5 concentration, associated mortality, and transport pathways in Ghaziabad which is an industrial city in the Indo-Gangetic Plain. To achieve this, PM (both PM₁₀ and PM_2.5) and meteorological parameters were measured from June 2018 to May 2019 at 2 locations and analyzed together with data from a 3rd location in Ghaziabad. The highest daily average PM₁₀ and PM_2.5 concentrations were ~ 1000 µg m^-3 and ~ 450 µg m^-3, respectively. At each of the three locations, the annual mean PM₁₀ concentrations were ~ 260 ± 150 µg m^-3 while the PM_2.5 concentrations were 140 ± 90 µg m^-3. Nonparametric Spearman rank correlation analysis between meteorological parameters and PM concentrations indicated that ventilation coefficient was anti-correlated with PM concentration during the post-monsoon and winter seasons (the most polluted seasons) with rank correlation values of approximately - 0.50. Multiple linear regression (MLR) revealed that the variability in local meteorological parameters account for ~ 50% variability (maximum) in PM₁₀ mass during the monsoon and PM_2.5 during the post-monsoon season. For long-range sources, cluster and concentrated weighted trajectory (CWT) analyses utilizing regional meteorology showed the impact of transported PM from sources in Arabian sea through western India in monsoon and from parts of South Asia through Northwestern IGP and neighboring cities in Uttar Pradesh in other seasons. Finally, mortality estimates show that the number of deaths attributable to ambient PM_2.5 in Ghaziabad were ~ 873 per million individuals which was ~ 70% higher than Delhi.

Chemical Composition and Source Apportionment of Total Suspended Particulate in the Central Himalayan Region

The present study analyzes data from total suspended particulate (TSP) samples collected during 3 years (2005–2008) at Nainital, central Himalayas, India and analyzed for carbonaceous aerosols (organic carbon (OC) and elemental carbon (EC)) and inorganic species, focusing on the assessment of primary and secondary organic carbon contributions (POC, SOC, respectively) and on source apportionment by positive matrix factorization (PMF). An average TSP concentration of 69.6 ± 51.8 µg m−3 was found, exhibiting a pre-monsoon (March–May) maximum (92.9 ± 48.5 µg m−3) due to dust transport and forest fires and a monsoon (June–August) minimum due to atmospheric washout, while carbonaceous aerosols and inorganic species expressed a similar seasonality. The mean OC/EC ratio (8.0 ± 3.3) and the good correlations between OC, EC, and nss-K+ suggested that biomass burning (BB) was one of the major contributing factors to aerosols in Nainital. Using the EC tracer method, along with several approaches for the determination of the (OC/EC)pri ratio, the estimated SOC component accounted for ~25% (19.3–29.7%). Furthermore, TSP source apportionment via PMF allowed for a better understanding of the aerosol sources in the Central Himalayan region. The key aerosol sources over Nainital were BB (27%), secondary sulfate (20%), secondary nitrate (9%), mineral dust (34%), and long-range transported mixed marine aerosol (10%). The potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses were also used to identify the probable regional source areas of resolved aerosol sources. The main source regions for aerosols in Nainital were the plains in northwest India and Pakistan, polluted cities like Delhi, the Thar Desert, and the Arabian Sea area. The outcomes of the present study are expected to elucidate the atmospheric chemistry, emission source origins, and transport pathways of aerosols over the central Himalayan region.

Variation in chemical composition and sources of PM2.5 during the COVID-19 lockdown in Delhi

The Government of India (GOI) announced a nationwide lockdown starting 25th March 2020 to contain the spread of COVID-19, leading to an unprecedented decline in anthropogenic activities and, in turn, improvements in ambient air quality. This is the first study to focus on highly time-resolved chemical speciation and source apportionment of PM_2.5 to assess the impact of the lockdown and subsequent relaxations on the sources of ambient PM_2.5 in Delhi, India. The elemental, organic, and black carbon fractions of PM_2.5 were measured at the IIT Delhi campus from February 2020 to May 2020. We report source apportionment results using positive matrix factorization (PMF) of organic and elemental fractions of PM_2.5 during the different phases of the lockdown. The resolved sources such as vehicular emissions, domestic coal combustion, and semi-volatile oxygenated organic aerosol (SVOOA) were found to decrease by 96%, 95%, and 86%, respectively, during lockdown phase-1 as compared to pre-lockdown. An unforeseen rise in O₃ concentrations with declining NO_x levels was observed, similar to other parts of the globe, leading to the low-volatility oxygenated organic aerosols (LVOOA) increasing to almost double the pre-lockdown concentrations during the last phase of the lockdown. The effect of the lockdown was found to be less pronounced on other resolved sources like secondary chloride, power plants, dust-related, hydrocarbon-like organic aerosols (HOA), and biomass burning related emissions, which were also swayed by the changing meteorological conditions during the four lockdown phases. The results presented in this study provide a basis for future emission control strategies, quantifying the extent to which constraining certain anthropogenic activities can ameliorate the ambient air. These results have direct relevance to not only Delhi but the entire Indo-Gangetic plain (IGP), citing similar geographical and meteorological conditions common to the region along with overlapping regional emission sources. SUMMARY OF MAIN FINDINGS: We identify sources like vehicular emissions, domestic coal combustion, and semi-volatile oxygenated organic aerosol (SVOOA) to be severely impacted by the lockdown, whereas ozone levels and, in turn, low-volatility oxygenated organic aerosols (LVOOA) rise by more than 95% compared to the pre-lockdown concentrations during the last phase of the lockdown. However, other sources resolved in this study, like secondary chloride, power plants, dust-related, hydrocarbon-like organic aerosols (HOA), and biomass burning related emissions, were mainly driven by the changes in the meteorological conditions rather than the lockdown.

Development and evaluation of air pollution-linked quality of life (AP-QOL) questionnaire: insight from two different cohorts

In this study, the air pollution-related quality of life (AP-QOL) questionnaire was carried out in two geographically and economically different groups including New Delhi (Megacity) and Hamirpur, Himachal Pradesh (town), and APE scores were linked with respiratory and cardiovascular illness. The APE-Score was developed by AP-QOL questionnaire responses using Delphi technique and further analyzed using principal component analysis (PCA). For reliability of APE-Score and AP-QOL questionnaire, α-Cronbach's test and basic statistics were performed. The linear mixed-effect model and odds ratios were used to evaluate air pollution exposure and health outcomes. Overall, 720 academicians and 276 security guards were invited to participate in the questionnaire. Cronbach's α coefficients ranged from 0.70 to 0.84 indicated significant reliability in the AP-QOL questionnaire conducted in this study. Substantial variation in respiratory symptoms and their medical history were found - 76.9% ([95% confidential interval (CI)]: (- 83.8, - 66.9) (p < 0.05)) and - 28.6% (95% CI: (- 37.8, - 18.0) (p < 0.05)), respectively, with interquartile range (IQR) increase of APE score. The odds ratios (ORs) of respiratory medical history (MH Res.) showed a significant increase from 1.01 to 1.35 for low to high air pollution exposure in the academic group of IIT Delhi. Interestingly, for an academic group of NITH, the ORs for medical history of cardiovascular (MH Card.) showed an increase from 1.08 to 1.13 for low to high APE which was not the case for IIT Delhi academicians.

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 PM_2.5) 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 PM_2.5 (NR-PM_2.5 + 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. PM_2.5 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.

Silver linings in the dark clouds of COVID-19: Improvement of air quality over India and Delhi metropolitan area from measurements and WRF-CHIMERE model simulations

The current study examines the impact of the COVID-19 lockdown (25th March until May 17, 2020) period in particulate matter (PM) concentrations and air pollutants (NO_x, SO₂, CO, NH₃, and O₃) at 63 stations located at Delhi, Uttar Pradesh and Haryana states within the Delhi-NCR, India. Large average reductions are recorded between the stations in each state such as PM₁₀ (-46 to -58%), PM_2.5 (-49 to -55%), NO₂ (-27 to -58%), NO (-54% to -59%), CO (-4 to -44%), NH₃ (-2 to -38%), while a slight increase is observed for O₃ (+4 to +6%) during the lockdown period compared to same periods in previous years. Furthermore, PM and air pollutants are significantly reduced during lockdown compared to the respective period in previous years, while a significant increase in pollution levels is observed after the re-opening of economy. The meteorological changes were rather marginal between the examined periods in order to justify such large reductions in pollution levels, which are mostly attributed to traffic-related pollutants (NO_x, CO and road-dust PM). The WRF-CHIMERE model simulations reveal a remarkable reduction in PM_2.5, NO₂ and SO₂ levels over whole Indian subcontinent and mostly over urban areas, due to limitation in emissions from the traffic and industrial sectors. A PM_2.5 reduction of -48% was simulated in Delhi in great consistency with measurements, rendering the model as a powerful tool for simulations of lower pollution levels during lockdown period.

Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources

The submicron particulate matter (PM₁) and fine particulate matter (PM_2.5) are very important due to their greater adverse impacts on the natural environment and human health. In this study, the daily PM₁ and PM_2.5 samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin, China. The collected samples were analyzed for the carbonaceous fractions, inorganic ions, elemental species, and specific marker sugar species. The chemical characterization of PM₁ and PM_2.5 was based on their concentrations, compositions, and characteristic ratios (PM₁/PM_2.5, AE/CE, NO₃^-/SO₄^2-, OC/EC, SOC/OC, OM/TCA, K+/EC, levoglucosan/K⁺, V/Cu, and V/Ni). The average concentrations of PM₁ and PM_2.5 were 32.4 µg/m³ and 53.3 µg/m³, and PM₁ constituted 63% of PM_2.5 on average. The source apportionment of PM₁ and PM_2.5 by positive matrix factorization (PMF) model indicated the main sources of secondary aerosols (25% and 34%), biomass burning (17% and 20%), traffic emission (20% and 14%), and coal combustion (17% and 14%). The biomass burning factor involved agricultural fertilization and waste incineration. The biomass burning and primary biogenic contributions were determined by specific marker sugar species. The anthropogenic sources (combustion, secondary particle formation, etc) contributed significantly to PM₁ and PM_2.5, and the natural sources were more evident in PM_2.5. This work significantly contributes to the chemical characterization and source apportionment of PM₁ and PM_2.5 in near-port cities influenced by the diverse sources.

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.

Reactive oxygen species production and inflammatory effects of ambient PM2.5 -associated metals on human lung epithelial A549 cells "one year-long study": The Delhi chapter

The fine particulate matter (PM_2.5) was collected at academic campus of Indian Institute of Technology, Delhi, India from January-December 2017. The PM_2.5 samples were analysed for carcinogenic (Cd, Cr, As, Ni, and Pb) and non-carcinogenic (V, Cu, Zn, Fe) trace metals and their elicited effects on carcinoma epithelial cell line A549. Toxicological testing was done with ELISA kit. Same analyses were repeated for standard reference material (NIST-1648a) represents urban particulate matter. The student-t test and spearman correlation were used for data analysis. The seasonality in PM_2.5 mass concentration and chemical composition showed effect on biological outcomes. The PM_2.5 in post-monsoon and winter had higher amount of trace metals compared to mass collected in pre-monsoon and monsoon. Following the trend in PM mass concentration significantly (p < 0.5) lower cell viability was observed in post-monsoon and winter compared to other two seasons. NIST UPM 1648(a) samples always had higher cytotoxicity compared to ambient PM_2.5 Delhi sample. Strong association of Chromium, Nickel, Cadmium, and Zinc was observed with cell viability and reactive oxygen species (ROS) production. In winter IL-6, IL-8 production were 2.8 and 3 times higher than values observed in post-monsoon and 53 and 9 times higher than control. In winter season trace metals As, Cu, Fe, in pre-monsoon Cr, Ni, As, Pb, V, and Fe, in post-monsoon Cd and V strongly correlated with ROS generation. ROS production in winter and pre-monsoon seasons found to be 2.6 and 1.3 times higher than extremely polluted post-monsoon season which had 2 to 3 times higher PM_2.5 concentration compared to winter and pre-monsoon. The result clearly indicated that the presence of Fe in winter and pre-monsoon seasons catalysed the ROS production, probably OH˙ radical caused high cytokines production which influenced the cell viability reduction, while in post-monsoon PM majorly composed of Pb, As, Fe and Cu and affected by photochemical smog formation showed significant association between ROS production with cell viability. Overall, in Delhi most toxic seasons for respiratory system are winter and post-monsoon and safest season is monsoon.

Characterization of ambient PM1 at a suburban site of Agra: chemical composition, sources, health risk and potential cytotoxicity

The present study was conducted at a University campus of Agra to determine concentrations of crustal and trace elements in submicron mode (PM₁) particles to reveal sources and detrimental effects of PM₁-bound metals (Cr, Cd, Mn, Zn, As, Co, Pb, Cu and Ni) in samples collected in the foggy (1 December 2016-17 January 2017) and non-foggy periods (1 April 2016-30 June 2016). Samples were collected twice a week on preweighed quartz fibre filters (QM-A 47 mm) for 24 h using Envirotech APM 577 (flow rate 10 l min^-1). Mass concentration of PM₁ was 135.0 ± 28.2 and 54.0 ± 18.5 µg/m³ during foggy and non-foggy period, respectively; crustal and trace elements were 13 and 4% during foggy and 11 and 3% in the non-foggy period. Source identification by PCA (principal component analysis) suggested that biomass burning and coal combustion was the prominent sources in foggy period followed by resuspended soil dust, industrial and vehicular emission, whereas in non-foggy period resuspended soil dust was dominant followed by biomass burning and coal combustion, industrial and vehicular emissions. In both episodes, Mn has the highest Hq (hazard quotient) value and Cr has the highest IlcR (Incremental Lifetime Cancer Risk) value for both adults and children. In vitro cytotoxicity impact on macrophage (J774) cells was also tested using MTT assay which revealed decreasing cell viability with increasing particle mass.

Real-time measurement and source apportionment of elements in Delhi's atmosphere

Delhi, the capital of India, suffers from heavy local emissions as well as regional transport of air pollutants, resulting in severe aerosol loadings. To determine the sources of these pollutants, we have quantified the mass concentrations of 26 elements in airborne particles, measured by an online X-ray fluorescence spectrometer with time resolution between 30 min and 1 h. Measurements of PM₁₀ and PM_2.5 (particulate matter <10 μm and < 2.5 μm) were conducted during two consecutive winters (2018 and 2019) in Delhi. On average, 26 elements from Al to Pb made up ~25% and ~19% of the total PM₁₀ mass (271 μg m^-3 and 300 μg m^-3) in 2018 and 2019, respectively. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, non-exhaust, an S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). All factors were resolved in both size ranges (but varying relative concentrations), comprising the following contributions to the elemental PM₁₀ mass (in % average for 2018 and 2019): Cl-Br-Se (41.5%, 36.9%), dust (27.6%, 28.7%), non-exhaust (16.2%, 13.7%), S-rich (6.9%, 9.2%), SFC1 + SFC2 (4%, 7%), Pb-Sn-Se (2.3%, 1.66%), Cu-Cd-Pb (0.67%, 2.2%) and Cr-Ni-Mn (0.57%, 0.47%). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the Pb-Sn-Se, Cl-Br-Se and SFC2 factors prevailed for northwest winds (Pakistan, Punjab, Haryana and Delhi), while the Cu-Cd-Pb and S-rich factors originated from east (Nepal and Uttar Pradesh) and the Cr-Ni-Mn factor from northeast (Uttar Pradesh). In contrast, SFC1, dust and non-exhaust were not associated with any specific wind direction.

Linking aerosol characteristics of size distributions, core potential pathogens and toxic metal(loid)s to wastewater treatment process

Wastewater treatment plants (WWTPs) play important roles in water purification but are also important source of aerosols. However, the relationship between aerosol characteristics and wastewater treatment process remains poorly understood. In this study, aerosols were collected over a 24-month period from a WWTP using a modified anaerobic-anoxic-oxic process. The aerated tank (AerT) was characterized by the highest respiratory fraction (RF) concentrations (861-1525 CFU/m³) and proportions (50.76%-65.96%) of aerosol particles. Fourteen core potential pathogens and 15 toxic metal(loid)s were identified in aerosols. Mycobacterium was the genus that aerosolized most easily in fine grid, pre-anoxic tank, and AerT. High wastewater treatment efficiency may increase the emission of RF and core potential pathogens. The median size of activated sludge, richness of core potential pathogens in wastewater, and total suspended particulates were the most influential factors directly related to the RF proportions, core community of potential pathogens, and composition of toxic metal(loid)s in WWTP aerosols, respectively. Relative humidity, temperature, input and removal of biochemical oxygen demand, dissolved oxygen, and mixed liquor suspended solids could also directly or indirectly affect the aerosol characteristics. This study enhances the mechanistic understanding of linking aerosol characteristics to treatment processes and has important implications for targeted manipulation.

Chemical characterization and source apportionment of ambient nanoparticles: a case study in Hanoi, Vietnam

PM_0.1 has been believed to have adverse short- and long-term effects on human health. However, the information of PM_0.1 that is needed to fully evaluate its influence on human health and environment is still scarce in many developing countries. This is a comprehensive study on the levels, chemical compositions, and source apportionment of PM_0.1 conducted in Hanoi, Vietnam. Twenty-four-hour samples of PM_0.1 were collected during the dry season (November to December 2015) at a mixed site to get the information on mass concentrations and chemical compositions. Multiple linear regression analysis was utilized to investigate the simultaneous influence of meteorological factors on fluctuations in the daily levels of PM_0.1. Multiple linear regression models could explain about 50% of the variations of PM_0.1 concentrations, in which wind speed is the most important variable. The average concentrations of organic carbon (OC), elemental carbon (EC), water-soluble ions (Ca²⁺, K⁺, Mg²⁺, Na⁺, NH₄⁺, Cl^-, NO₃^-, SO₄^2-, C₂O₄^2-), and elements (Be, Al, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, Tl, Pb, Na, Fe, Mg, K, and Ca) were 2.77 ± 0.90 μg m^-3, 0.63 ± 0.28 μg m^-3, 0.88 ± 0.39 μg m^-3, and 0.05 ± 0.02 μg m^-3, accounting for 51.23 ± 9.32%, 11.22 ± 2.10%, 16.28 ± 2.67%, and 1.11 ± 0.94%, respectively. A positive matrix factorization model revealed the contributions of five major sources to the PM_0.1 mass including traffic (gasoline and diesel emissions, 46.28%), secondary emissions (31.18%), resident/commerce (12.23%), industry (6.05%), and road/construction (2.92%).

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).

Source apportionment of fine particulate matter over a National Park in Central India

PM_2.5 mass and chemical constituents were measured over Van Vihar National Park (VVNP), a forested location within Bhopal. Positive Matrix Factorization (USEPA PMF5) was applied to two-year long (2012 and 2013) measurements of PM_2.5 chemical species including water-soluble inorganic ions, organic, pyrolitic, and elemental carbon, and trace elements for the quantitative apportionment of PM_2.5 mass. The model resolved seven factors. A combination of source profiles, temporal evolution, and potential source locations were used to identify these factors as secondary sulfate, combustion aerosol, re-suspended crustal dust, pyrolysis carbon-rich aerosol, biomass burning aerosol, secondary nitrate, and sea salt with mean contributions of 24.8%, 23.6%, 17.3%, 15.7%, 11%, 4.1%, 0.8%, respectively, to the PM_2.5 mass during the study period. Rest of the mass was unapportioned. Inter-annual and seasonal variability of sources contributing to PM_2.5 mass were also assessed. Combustion aerosol and pyrolysis carbon-rich aerosol were responsible for several high PM_2.5 mass concentration episodes at the sampling location. Re-suspended crustal dust was also found to be contributing to episodic highs in PM_2.5 mass. Biomass burning aerosol contribution to PM_2.5 mass increased during stubble burning months in central and northern India. Conditional Bivariate Probability Function (CBPF) and Potential Source Contribution Function (PSCF) analyses were used to identify local and regional source locations (and/or preferred transport pathways) of aerosol sources, respectively. It was found that PM_2.5 at the study was mostly regionally transported and that the predominant regional source locations were Chhattisgarh, northern and south-eastern parts of Madhya Pradesh, western Uttar Pradesh, Delhi, Haryana, Rajasthan, and the Arabian Sea. The outcomes of this study are expected to strengthen the air quality management plans for both VVNP and the city. Further, it is hoped that the results of this study will provide inputs to validate emissions inventories as well as climate model outputs over the region.

Effect of aeration mode on aerosol characteristics from the same wastewater treatment plant

Aeration and mechanical agitation are the main drivers of aerosol generation in wastewater treatment plants (WWTPs). However, the effect of aeration mode on aerosol characteristics remains poorly understood. In this study, horizontal rotor aeration and fine bubble aeration in the same WWTP were selected to identify the effect on the emission, size distribution, microbial and chemical composition. For bacteria, fungi, Enterobacteriaceae, Staphylococcus aureus, and Pseudomonas aeruginosa in aerosols, the horizontal rotor aeration had higher contributions to the emissions than the fine bubble aeration. Horizontal rotor aeration generated a more coarse fraction (size > 7 μm) and a comparable respirable fraction (RF; size < 3.3 μm) compared with those of fine bubble aeration. More types of potential pathogens were generated by horizontal rotor aeration. The most easily aerosolized genera generated by horizontal rotor aeration and fine bubble aeration, were Trichosporon and Mycobacterium, with the aerosolization factors of 633.70 and 192.56, respectively. For Cl^-, SO₄^2-, NO₃^-, Zn, Ba, Cd, Sc, V, Rb, Ca, K, Ca, K, Mg, Na and Si in the aerosols, the contributions of fine bubble aeration were higher than those of horizontal rotor aeration. Due to the aerosol specialty from the different aeration modes, targeted manipulations should be employed to reduce the exposure risks.

Seasonal and spatial variability of secondary inorganic aerosols in PM2.5 at Agra: Source apportionment through receptor models

The present study was conducted at sub-urban and rural site of Agra. The main aim of this study was to characterize WSII in terms of spatial, seasonal and formation characteristics and identify the major sources responsible for the pollution of WSII in PM_2.5 particles using different source apportionment models. Since biomass burning is one of the most important sources of PM_2.5 pollution in Agra, a case study was also conducted at rural site to investigate the contribution of biomass burning from cooking activities using different types of fuels. PM_2.5 mass concentrations were higher at sub-urban site (91.0 ± 50.8 μg/m³) than at rural site (77.1 ± 48.6 μg/m³). WSII contributed 50.0% and 45.8% of annual average PM_2.5 mass at both sites. The aerosols were ammonium rich and were therefore alkaline in nature. Aerosol acidity characteristics studied using AIM-II model showed that the aerosols were slightly less acidic at rural site than at sub-urban site. SO₄^2-, NO₃^- and NH₄⁺ were the major contributors of WSII and their formation was favoured mainly in winter. Although, WSII showed slight variations in seasonal and spatial characteristics, the major sources of pollution were found to be similar. Four sources were identified as biomass burning (29.1% and 27.4%), secondary aerosols (26.2% and 22.5%), coal combustion (22.3% and 26.9%) and soil dust (22.4% and 23.1%) at sub-urban and rural sites. The results of case study showed that among different types of biomass fuels cow dung cakes showed maximum PM_2.5 emissions while LPG showed minimum PM_2.5 emissions.

Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China

Particulate matter with aerodynamic diameters ≤1 μm (PM₁) has a greater impact on the human health but has been less studied due to fewer ground observations. This study attempts to improve the retrieval accuracy and spatial resolution of satellite-based PM₁ estimates using the new ground-based monitoring network in China. Therefore, a space-time extremely randomized trees (STET) model is first developed to estimate PM₁ concentrations at a 1 km spatial resolution from 2014 to 2018 across mainland China. The STET model can derive daily PM₁ concentrations with an average across-validation coefficient of determination of 0.77, a low root-mean-square error of 14.6 μg/m³, and a mean absolute error of 8.9 μg/m³. PM₁ concentrations are generally low in most areas of China, except for the North China Plain and Sichuan Basin where intense human activities and poor natural conditions are prevalent, especially in winter. Moreover, PM₁ pollution has greatly decreased over the past 5 years, benefiting from emission control in China. The STET model, incorporating the spatiotemporal information, shows superior performance in PM₁ estimates relative to previous studies. This high-resolution and high-quality PM₁ data set in China (i.e., ChinaHighPM₁) can be greatly useful for air pollution studies in medium- or small-scale areas.

Characteristics and exposure risks of potential pathogens and toxic metal(loid)s in aerosols from wastewater treatment plants

Aerosols from wastewater treatment plants (WWTPs) are considered to be potentially hazardous to on-site employees and surrounding residents. However, their harmful components and their effects remain poorly understood. In this study, the characteristics, responsible factors, sources and exposure risks of potential pathogens and toxic metal(loid)s in aerosols from four WWTPs were investigated. There were 21 potential pathogens and 15 toxic metal(loid)s detected in the aerosols. Arcobacter and Fe were the dominant taxa responsible for the dissimilarity of the potential pathogen population and toxic metal(loid) composition between the aerosols and the wastewater/sludge, respectively. Both meteorological factors and sources affected pathogen and toxic metal(loid) composition. The potential pathogens and toxic metal(loid)s in indoor aerosols mainly originated from wastewater/sludge, while those in outdoor aerosols originated from wastewater/sludge and ambient air. The highest respirable fraction (<3.30 μm) concentrations and proportions were detected at the aeration units. Non-carcinogenic and carcinogenic risks of toxic metal(loid)s for both adults and children were found within and/or around WWTPs, and non-carcinogenic risks of bacteria for children were found at downwind, suggesting the need for active safeguard procedures, such as that employees wear masks and work clothes, covering the main emission sites, and collecting and destroying of aerosols.

Cellular Responses to Exposure to Outdoor Air from the Chinese Spring Festival at the Air-Liquid Interface

The Spring Festival is the most important holiday in China. During this time, the levels of particulate matter (PM) as well as gaseous copollutants significantly increase because of the widespread enjoyment of fireworks. The expression patterns of microRNAs may serve as valuable signatures of exposure to environmental constituents. We exposed macrophages to the whole stream of outdoor air at the air-liquid interface aiming at closely approximating the physiological conditions and the inhalation situation in the lung. 58 miRNAs were up-regulated, and 68 miRNAs were down-regulated in the night of the New Year's Eve (exposure group E2N1) compared to filtered-air exposed control cells. The target genes of the up-regulated miRNAs were enriched in immunity- and inflammation-linked pathways, such as the TLR-NF-κB pathway. Compared to the E2N1 group, 29 miRNAs were up-regulated, and 23 miRNAs were down-regulated in the cells exposed to air from the daytime of the Chinese New Year with higher concentrations of particles, SO₂, and nitrogen oxide. The target genes of the up-regulated miRNAs were mostly enriched in apoptosis, adhesion, and junction-related pathways. These results preliminarily unravel part of the toxic mechanisms of air constituents and provide clues for discovering the main drivers of air pollution-induced disorders.

Aerosols from a wastewater treatment plant using oxidation ditch process: Characteristics, source apportionment, and exposure risks

The study of aerosol dispersion characteristics in wastewater treatment plants (WWTPs) has attracted extensive attention. Oxidation ditch (OD) is a commonly implemented process during biological wastewater treatment. This study assessed the component characteristics, source apportionment, and exposure risks of aerosols generated from a WWTP using the OD process (AWO). The results indicated that the aeration part of oxidation ditch (ODA) exhibited the highest concentrations and proportions of the respiratory fractions (RF) of bacteria, Enterobacteriaceae, Staphylococcus aureus, and Pseudomonas aeruginosa. Some pathogenic or opportunistic-pathogenic bacteria and carcinogenic metal(loid)s were detected in the AWO. The source apportionment results indicated that the outdoor wastewater treatment processes and ambient air contributed to the constitution of the AWO. The indoor aerosols were mainly constituted by composition of the wastewater treatment process such as the sludge dewatering room (SDR). The pathogenic or opportunistic-pathogenic bacteria with eight genera (Colinsella, Dermatophilus, Enterobactor, Erycherichia-Shigella, Ledionella, Selenomonas, Xanthobacter, and Veillonella) were largely attributed to wastewater or sludge. The risk assessment suggested that inhalation was the main exposure pathway for aerosols (including bacteria and metal(loid)s). Additionally, As indicated the highest non-carcinogenic risks. Furthermore, As, Cd, and Co were associated with high carcinogenic risks. The ODA and sludge dewatering room (SDR) indicated the highest carcinogenic and non-carcinogenic risks of metal(loid)s, respectively. Thus, the AWO should be sufficiently researched and monitored to mitigate their harmful effects on human health, particularly with regard to the health of the site workers.

Ambient PM1 air pollution, blood pressure, and hypertension: Insights from the 33 Communities Chinese Health Study

No evidence exists concerning the association between blood pressure and ambient particles with aerodynamic diameter ≤ 1.0 µm (PM₁), a major component of PM_2.5 (≤ 2.5 µm) particles, and potentially causing more hazardous health effects than PM_2.5. We aimed to examine the associations of blood pressure in adults with both PM₁ and PM_2.5 in China. In 2009, we randomly selected 24,845 participants aged 18-74 years from 33 communities in China. Using a standardized mercuric-column sphygmomanometer, we measured blood pressure. Long-term exposure (2006-08) to PM₁ and PM_2.5 were estimated using a spatial statistical model. Generalized linear mixed models were used to evaluate the associations between air pollutants and blood pressure and hypertension prevalence, controlling for multiple covariates. A 10-μg/m³ increase in PM₁ was significantly associated with an increase of 0.57 (95% CI 0.31-0.83) mmHg in systolic blood pressure (SBP), 0.19 (95% CI 0.03-0.35) mmHg increase in diastolic blood pressure (DBP), and a 5% (OR=1.05; 95% CI 1.01-1.10) increase in odds for hypertension. Similar associations were detected for PM_2.5. Furthermore, PM_1-2.5 showed no association with blood pressure or hypertension. In summary, both PM₁ and PM_2.5 exposures were associated with elevated blood pressure levels and hypertension prevalence in Chinese adults. In addition, most of the pro-hypertensive effects of PM_2.5 may come from PM₁. Further longitudinal designed studies are warranted to validate our findings.

Ambient PM1 air pollution and cardiovascular disease prevalence: Insights from the 33 Communities Chinese Health Study

BACKGROUNDS

Evidence on the association between long-term exposure to particulate matter with aerodynamic diameter ≤2.5 μm (PM_2.5) and cardiovascular disease (CVD) is scarce in developing countries. Moreover, few studies assessed the role of the PM₁ (≤1.0 μm) size fraction and CVD. We investigated the associations between PM₁ and PM_2.5 and CVD prevalence in Chinese adults.

METHODS

In 2009, we randomly recruited 24,845 adults at the age of 18-74 years from 33 communities in Northeastern China. CVD status was determined by self-report of doctor-diagnosed CVD. Three-year (2006-08) average concentrations of PM₁ and PM_2.5 were assigned using a satellite-based exposure. We used spatial Generalized Linear Mixed Models to evaluate the associations between air pollutants and CVD prevalence, adjusting for multiple covariates. Stratified and interaction analyses and sensitivity analyses were also performed.

RESULTS

A 10 μg/m³ increase in long-term exposure to ambient PM₁ levels was associated a 12% higher odds for having CVD (OR = 1.12; 95% CI = 1.05-1.20). Compared to PM₁, association between PM_2.5 and CVD was lower (OR = 1.06; 95% CI = 1.01-1.11). No significant association was observed for PM_1-2.5 (1-2.5 μm) size fraction (OR = 0.98; 95% CI = 0.85-1.13). Stratified analyses showed greater effect estimates in men and the elder.

CONCLUSIONS

Long-term PM₁ exposure was positively related to CVD, especially in men and the elder. In addition, PM₁ may play a greater role than PM_2.5 in associations with CVD. Further longitudinal studies are warranted to confirm our findings.

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.

Chemical characterization and quantitativ e assessment of source-specific health risk of trace metals in PM1.0 at a road site of Delhi, India

This study presents the concentration of submicron aerosol (PM_1.0) collected during November, 2009 to March, 2010 at two road sites near the Indian Institute of Technology Delhi campus. In winter, PM_1.0 composed 83% of PM_2.5 indicating the dominance of combustion activity-generated particles. Principal component analysis (PCA) proved secondary aerosol formation as a dominant process in enhancing aerosol concentration at a receptor site along with biomass burning, vehicle exhaust, road dust, engine and tire tear wear, and secondary ammonia. The non-carcinogenic and excess cancer risk for adults and children were estimated for trace element data set available for road site and at elevated site from another parallel work. The decrease in average hazard quotient (HQ) for children and adults was estimated in following order: Mn > Cr > Ni > Pb > Zn > Cu both at road and elevated site. For children, the mean HQs were observed in safe level for Cu, Ni, Zn, and Pb; however, values exceeded safe limit for Cr and Mn at road site. The average highest hazard index values for children and adults were estimated as 22 and 10, respectively, for road site and 7 and 3 for elevated site. The road site average excess cancer risk (ECR) risk of Cr and Ni was close to tolerable limit (10^-4) for adults and it was 13-16 times higher than the safe limit (10^-6) for children. The ECR of Ni for adults and children was 102 and 14 times higher at road site compared to elevated site. Overall, the observed ECR values far exceed the acceptable level.

Ambient fine particulate matter (PM2.5) in Canadian oil sands communities: Levels, sources and potential human health risk

An investigation of levels and potential sources affecting ambient fine particulate matter (PM_2.5) and associated risk to public health was undertaken at two Canadian oil sands communities (Fort McKay and Fort McMurray) using a 4-year dataset (2010-2013). Geometric mean concentrations of PM_2.5 at Fort McKay and Fort McMurray are not considered high and were 5.47μg/m³ (interquartile range, IQR=3.02-8.55μg/m³) and 4.96μg/m³ (IQR=3.20-7.04μg/m³), respectively. Carcinogenic risks of trace elements were below acceptable (1×10^-6) and/or within tolerable risk (1×10^-4), and non-carcinogenic risks were below a safe level of concern (hazard index=1). Positive matrix factorization (PMF) modeling revealed five sources, where fugitive dust appeared as the major contributor to PM_2.5 mass (Fort McKay: 32%, Fort McMurray: 46%) followed by secondary sulfate (31%, 42%) and secondary nitrate/biomass burning (26%, 8%). Other minor sources included a mining/mobile and a Mn-rich/Mn-Co-Zn-rich source. Source-specific risk values were also estimated and were well below acceptable and safe level of risks. Further work would be needed to better understand the contribution of secondary organic aerosols to PM_2.5 formation in these oil sands communities.