Spatio-temporal variation in chemical characteristics of PM10 over Indo Gangetic Plain of India

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.

Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine

Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. To conduct this present investigation, we obtained high-resolution remotely sensed datasets from 2001 to 2022. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index for Coimbatore city. Numerous non-parametric and descriptive statistical applications were then employed to check the spatial integrity of satellite data products and spatio-temporal trends using Google Earth Engine algorithms. The study reveals most of the southern parts of Coimbatore city witnessed increased LST (0.10 °C/year) together with decreased rainfall (21.5 mm/year). Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods.

Wintertime oxidative potential of PM2.5 over a big urban city in the central Indo-Gangetic Plain

Indo-Gangetic Plain (IGP) experiences a heavy load of particulate pollution impacting the 9 % of the global population living in this region. The present study examines the dithiothreitol (DTT) assay-based oxidative potential (OP) of PM2.5 and the major sources responsible for the observed OP over the central IGP (Kanpur) during winter. The volume normalized OP (OPV) of PM2.5 varied from 2.7 to 10 nmol DTT min-1 m-3 (5.5 ± 1.5) and mass normalized OP (OPM) of PM2.5 varied from 19 to 58 pmol DTT min-1 μg-1 (34 ± 8.0), respectively. Major sources of PM2.5 were identified using the positive matrix factorization (PMF) and the contribution of these sources to observed OP was estimated through multivariate linear regression of OPv with PMF-resolved factors. Although the PM2.5 mass was dominated by secondary aerosols (SA, 28 %), followed by crustal dust (CD, 24 %), resuspended fine dust (RFD, 14 %), traffic emissions (TE, 8 %), industrial emissions (IE, 17 %), and trash burning (TB, 9 %), their proportionate contribution to OP (except SA) was different likely due to differences in redox properties of chemical species coming from these sources. The SA showed the highest contribution (23 %) to observed OP, followed by RFD (19 %), IE (8 %), TE & TB (5 %), CD (4 %), and others (36 %). Our results highlight the significance of determining the chemical composition of particulates along with their mass concentrations for a better understanding of the relationship between PM and health impacts. Such studies are still lacking in the literature, and these results have direct implications for making better mitigation strategies for healthier air quality.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 μg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 μg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 μg m-3), which showed better output due to the frequent observation of PM2.5 >100 μg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 μg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

Influence of Temperature and Relative Humidity on PM2.5 Concentration over Delhi

The present study is an attempt to establish relationship between the concentrations of particulate matter especially (PM2.5) and background meteorological parameters over Delhi, India with the help of statistical and correlative analysis. This work presents the evaluation of air quality in three different locations of Delhi. These locations were selected to fulfil the characteristics as residential, industrial and background locations and performed the analysis for pre and post covid-19, i.e. for 2019 and 2021. The outcome of the study shows that the meteorological parameters have significant influence on the PM2.5 concentration. It was also found that it has a seasonality with low concentration in the monsoon season, moderate in the pre-monsoon season and high during the winters and post-monsoon seasons. However, the statistical and correlative study shows a negative relation with the temperature during the winter, pre-monsoon and post-monsoon and has a positive correlation during the monsoon season. Similarly, it also has been observed that the concentration of PM2.5 shows strong negative correlation with temperature during the high humid conditions, i.e. when the relative humidity is above 50%. However, a weak correlation with ambient temperature has been established during the low humidity condition, i.e. below 50%. The overall study showed that the highest PM2.5 pollution has been observed at residential location followed by industrial and background. The study also concluded that the seasonal meteorology has a complex role in the PM2.5 concentration of the selected areas.

Submicron Aerosol Composition and Source Contribution across the Kathmandu Valley, Nepal, in Winter

The Kathmandu valley experiences an average wintertime PM1 concentration of ∼100 μg m-3 and daily peaks over 200 μg m-3. We present ambient nonrefractory PM1 chemical composition, and concentration measured by a mini aerosol mass spectrometer (mAMS) sequentially at Dhulikhel (on the valley exterior), then urban Ratnapark, and finally suburban Lalitpur in winter 2018. At all sites, organic aerosol (OA) was the largest contributor to combined PM1 (C-PM1) (49%) and black carbon (BC) was the second largest contributor (21%). The average background C-PM1 at Dhulikhel was 48 μg m-3; the urban enhancement was 120% (58 μg m-3). BC had an average of 6.1 μg m-3 at Dhulikhel, an urban enhancement of 17.4 μg m-3. Sulfate (SO4) was 3.6 μg m-3 at Dhulikhel, then 7.5 μg m-3 at Ratnapark, and 12.0 μg m-3 at Lalitpur in the brick kiln region. Chloride (Chl) increased by 330 and 250% from Dhulikhel to Ratnapark and Lalitpur on average. Positive matrix factorization (PMF) identified seven OA sources, four primary OA sources, hydrocarbon-like (HOA), biomass burning (BBOA), trash burning (TBOA), a sulfate-containing local OA source (sLOA), and three secondary oxygenated organic aerosols (OOA). OOA was the largest fraction of OA, over 50% outside the valley and 36% within. HOA (traffic) was the most prominent primary source, contributing 21% of all OA and 44% of BC. Brick kilns were the second largest contributor to C-PM1, 12% of OA, 33% of BC, and a primary emitter of aerosol sulfate. These results, though successive, indicate the importance of multisite measurements to understand ambient particulate matter concentration heterogeneity across urban regions.

Chemical Composition, Sources, and Health Risk Assessment of PM2.5 and PM10 in Urban Sites of Bangkok, Thailand

Of late, air pollution in Asia has increased, particularly in built-up areas due to rapid industrialization and urbanization. The present study sets out to examine the impact that pollution can have on the health of people living in the inner city of Bangkok, Thailand. Consequently, in 2021, fine particulate matter (PM_2.5) and coarse particulate matter (PM₁₀) chemical composition and sources are evaluated at three locations in Bangkok. To identify the possible sources of such particulates, therefore, the principal component analysis (PCA) technique is duly carried out. As determined via PCA, the major sources of air pollution in Bangkok are local emission sources and sea salt. The most significant local sources of PM_2.5 and PM₁₀ in Bangkok include primary combustion, such as vehicle emissions, coal combustion, biomass burning, secondary aerosol formation, industrial emissions, and dust sources. Except for the hazard quotient (HQ) of Ni and Mn of PM_2.5 for adults, the HQ values of As, Cd, Cr, Mn, and Ni of both PM_2.5 and PM₁₀ were below the safe level (HQ = 1) for adults and children. This indicates that exposure to these metals would have non-carcinogenic health effects. Except for the carcinogenic risk (HI) value of Cr of PM_2.5 and PM₁₀, which can cause cancer in adults, at Bangna and Din Daeng, the HI values of Cd, Ni, As, and Pb of PM_2.5 and PM₁₀ are below the limit set by the U.S. Environmental Protection Agency (U.S. EPA). Ni and Mn pose non-carcinogenic risks, whereas Cr poses carcinogenic risks to adults via inhalation, a serious threat to the residents of Bangkok.

Statistical analysis of the temporal change of PM10 levels in the city of Sivas (Turkey)

The objective of this study is to statistically examine the variation of PM10 values measured at three stations in the center of Sivas between the years 2016 and 2020. Hourly PM10 measurement values were taken from three different stations (İstasyon Kavşağı, Meteoroloji, and Başöğretmen AQMSs) in the city center. Then the mean values of the measurements obtained between 2016 and 2020 were compared according to the years and the stations, as well as with the limit values given in the Regulation on Air Quality Assessment and Management(RAQAM). Analyses of variance were conducted to determine any differences between PM10 levels and 24-h limit values of PM10 for Turkey and between PM10 values of stations over the years. Considering the 5-year mean values, the mean value of all PM10 concentrations measured in the city center was calculated as 56.36 µg/m³. No statistical difference was found between the PM10 values measured in 2017 and 2018 at the İstasyon Kavşağı AQMS, and the comparisons of PM10 between stations over the years showed no difference between the Meteoroloji AQMS and the Başöğretmen AQMS in 2019 and 2020. The Spearman's rank-order correlation results of PM10 over the years among the stations in the city showed that the strongest relationship was a moderate one between the years 2019 and 2020 with regard to the İstasyon Kavşağı AQMS. Probable dust transports were examined for the days when PM10 was at its highest, and the conclusion was that desert dust coming from the continent of Africa (south) to the center of Sivas had been effective.

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.

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

Size-segregated airborne fine (PM_2.1) and coarse (PM_>2.1) particulates were measured in an urban environment over central Indo-Gangetic plain in between 2015 and 2018 to get insights into its nature, chemistry and sources. Mean (±1σ) concentration of PM_2.1 was 98 (±76) μgm^-3 with a seasonal high during winter (DJF, 162 ± 71 μgm^-3) compared to pre-monsoon specific high in PM_>2.1 (MAMJ, 177 ± 84 μgm^-3) with an annual mean of 170 (±69) μgm^-3. PM_2.1 was secondary in nature with abundant secondary inorganic aerosols (20% of particulate mass) and water-soluble organic carbon (19%) against metal enriched (25%) PM_>2.1, having robust signature of resuspensions from Earth's crust and road dust. Ammonium-based neutralization of particulate acidity was essentially in PM_2.1 with an indication of predominant H₂SO₄ neutralization in bisulfate form compared to Ca²⁺ and Mg²⁺-based neutralization in PM_>2.1. Molecular distribution of n-alkanes homologues (C₁₇-C₃₅) showed C_max at C₂₃ (PM_2.1) and C₁₈ (PM_>2.1) with weak dominance of odd-numbered n-alkanes. Carbon preference index of n-alkanes was close to unity (PM_2.1: 1.4 ± 0.3; PM_>2.1: 1.3 ± 0.4). Fatty acids (C₁₂-C₂₆) were characterized with predominance of even carbon with C_max at n-hexadecanoic acid (C_16:0). Low to high molecular weight fatty acid ratio ranged from 2.0 (PM_>2.1) to 5.6 (PM_2.1) with vital signature of anthropogenic emissions. Levoglucosan was abundant in PM_2.1 (758 ± 481 ngm^-3) with a high ratio (11.6) against galactosan, emphasizing robust contribution from burning of hardwood and agricultural residues. Receptor model resolves secondary aerosols and biomass burning emissions (45%) as the most influential sources of PM_2.1 whereas, crustal (29%) and secondary aerosols (29%) were found responsible for PM_>2.1; with significant variations among the seasons.

Exceedances and trends of particulate matter (PM2.5) in five Indian megacities

Fine particulate matter (PM_2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM_2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014-2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m³ (2%-8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00-10:00 h) and the lowest level in late afternoon hours (~15:00-16:00 h). We found that the PM_2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20-380 μg/m³) suggest that not only is the annual mean PM_2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.

Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: Rural-urban contrast

Columnar Aerosol Optical Depths (AOD) over an urban area (Chandigarh) and a rural area (Khera, Fatehgarh Sahib district) situated in the Indo-Gangetic Plains (IGP) of India were analysed to study their temporal heterogeneity in terms of interannual, seasonal and monthly variations. Over the last few decades, IGP has become one of the global hotspots of air pollution due to the increased anthropogenic activities such as traffic, industries, agricultural waste burning etc. Level-2 AODs (550 nm) were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard NASA's Terra and Aqua satellites, for a period of 14 years (2005-2018). The climatological mean Terra-MODIS (Aqua-MODIS) AOD over the urban location was ~0.497 ± 0.238 (0.474 ± 0.228), whereas over the rural location it was 0.542 ± 0.269 (0.534 ± 0.282). Linear trend analysis estimated an increase in annual mean Terra-MODIS (Aqua-MODIS) AOD at a rate of ~0.009 (0.013) per year over the urban site; whereas over the rural location the rate of increase was ~0.003 (0.004) per year. Results show that the observed increase is ~1.49% (2.41%) of climatological mean AOD over the urban location for Terra-MODIS (Aqua-MODIS), whereas, over the rural location, it was ~0.50% (0.67%). Using the HYSPLIT trajectory model, it was concluded that, during post-monsoon, the observed high AODs can be related to massive crop residue burning in the IGP region. These AOD trends can also be used to track the regional anthropogenic air-pollution changes. An empirical relation between AOD and PM₁₀ was established, which can be used to estimate PM₁₀ over the urban and rural areas of IGP (using MODIS AODs), complementing the sparse ground-based monitoring. Further, satellite-based air pollution data can be used for baseline assessment and understanding the impact of control policies such as National Clean Air Programme and to support formulate evidence-based pollution control strategies.

Aerosol-associated n-alkanes over Dhauladhar region of North-Western Himalaya: seasonal variations in sources and processes

Particulate n-alkanes are major constituents of organic aerosols (OA). Being primary in origin, chemically stable and thus long-lived, n-alkanes retains source signatures and along with diagnostic parameters have extensively been used to identify source(s) of OA. Systematic, yearlong study was carried out in the Dhauladhar region of North-Western Himalaya (NWH) to investigate dynamics in the composition and concentration of aerosol-associated n-alkanes. PM₁₀ samples were collected for 24 h, once every week, at an urban mid-altitude location (Dharamshala) and a rural low-altitude site (Pohara). Particulate bound n-alkanes were identified and quantified using thermal desorption gas chromatography mass spectrometry (TD-GCMS). Annual mean concentrations of total n-alkanes (TNA) were 211 ± 99 ng m^-3 and 223 ± 83 ng m^-3, while mass fractions of TNA in PM₁₀ were 4410 ± 1759 ppm and 3622 ± 1243 ppm at Dharamshala and Pohara, respectively. At both sites, a slight dominance of odd carbon-numbered n-alkanes was noticed. The TNA concentration and associated diagnostic parameters indicated unique source profiles at rural and urban locations. Significant seasonal variations were attributed to the contrasting land-use settings and meteorological variations. Influence of petrogenic contributions at urban location and predominance of biogenic contributions at rural location were observed in spring and autumn seasons. Preliminary insights on sources of organic aerosols are presented here. The diagnostic parameters allowed apportionment of biogenic and petrogenic sources. Biogenic emissions from agricultural practices viz. harvesting and threshing were predominant in the rural settings, while tourism-led anthropogenic contributions significantly add to petrogenic contributions in urban environment of the NWH region.

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).

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.

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.

The influence of urban stress factors on responses of ground cover vegetation

A comprehensive study was conducted to evaluate the effects of ambient air pollution, land use, and soil properties on ground cover vegetation in the urban area of Varanasi city, situated in the Indo Gangetic Plain of India. Twelve leaf functional traits were assessed on eight most dominant herbaceous species belonging to four angiospermic families in three different land uses with varying air pollution loads and soil properties. Particulate matter (PM₁₀ and TSP), gaseous pollutants (SO₂, NO₂, and O₃), land uses (built-up area, shrub, and grass cover), and soil properties showed significant variability among the land uses. Air pollution was identified as the major stress factor which influenced leaf functional traits of ground cover vegetation followed by soil properties and land uses. Among the plants, Croton sparsiflorus was found to be the most responsive plants to all the factors. Plants responded differently under varying environmental factors as Euphorbia hirta was maximally influenced by air pollution, whereas the effect of land use was maximum in C. sparsiflorus. Influence of soil properties was highest in Digitaria ciliaris and Scoparia dulcis. All the environmental factors in combination maximally influenced non-enzymatic antioxidants (ascorbic acid and polyphenolics) followed by photosynthetic pigments among the different leaf functional traits. Among the environmental factors, NO₂ and PM were identified as the most influencing factors regulating leaf functional traits followed by K level in soil and shrub cover. It can be concluded that responses of different leaf functional traits of ground cover vegetation varied with different environmental factors and responses were mostly species specific.

Stable carbon and nitrogen isotopic composition of PM10 over Indo-Gangetic Plains (IGP), adjoining regions and Indo-Himalayan Range (IHR) during a winter 2014 campaign

For source identification, a field campaign involving simultaneous sampling of particulate matter (PM₁₀) was conducted at eight sampling sites in the Indian mainland during winter 2014. The sampling sites include Delhi (upper IGP), Lucknow (middle IGP), and Kolkata (lower IGP) in the Indo-Gangetic Plains (IGP); Mohal-Kullu and Darjeeling in the Indo-Himalayan Range (IHR). In addition, Ajmer, located upwind of the IGP in NW-India and Giridih and Bhubaneswar, in the downwind to the IGP has also been chosen. To characterize the sources of the ambient PM₁₀, stable isotope ratios of carbon (δ¹³C_TC) and nitrogen (δ¹⁵N_TN) for the total carbon (TC) and total nitrogen (TN) fractions have been considered. Ancillary chemical parameters, such as organic carbon (OC), elemental carbon (EC), and water-soluble ionic components (WSIC) mass concentrations are also presented in this paper. There was very small variation in the daily average δ¹³C_TC ratios (- 24.8 to - 25.9‰) among the sites. Comparison with end-member stable C isotopic signatures of major typical sources suggests that the PM₁₀ at the sites was mainly from fossil fuel and biofuel and biomass combustion. Daily average δ¹⁵N_TN ratios were not observed to vary much between sites either (8.3 to 11.0‰), and the low δ¹⁵N_TN levels also indicate substantial contributions from biofuel and biomass burning of primarily C3 andC4 plant matter. Graphical abstract Scatter plot of the average (± 1 standard deviation (SD)) δ¹³C_TC (‰) compared to δ¹⁵N_TN (‰) at the sampling sites.

Analysis of Compositional Variation and Source Characteristics of Water-Soluble Ions in PM2.5 during Several Winter-Haze Pollution Episodes in Shenyang, China

From 18 February to 13 March 2014 and from 17 December 2016 to 27 January 2017, an online analyzer for monitoring aerosols and gases (MARGA) and an online single particle aerosol mass spectrometer (SPAMS) were used to measure and analyze the concentrations and sources of water-soluble (WS) ions in PM10, PM2.5, and gases (NH3, HNO3, HCl), in Shenyang City, China. During the field campaign, nine haze episodes (or smog episodes, total 582 h) were identified, with 960 identified as non-haze periods. The average mass concentrations of PM2.5 and total water-soluble ions (TWSIs) in PM2.5 during haze episodes were 131 μg·m−3 and 77.2 μg·m−3, 2.3 times and 1.9 times the values in non-haze periods, respectively. The average mass concentration of TWSIs in PM2.5 was 55.9 μg·m−3 (accounting for 55.9% of PM2.5 mass loading), 37.6% of which was sulfate, 31.7% nitrate, 20.0% ammonium, 6.6% chloride, 1.9% potassium, 1.4% calcium, and 0.8% magnesium throughout the campaign. Concentrations of sulfate, nitrate, and ammonium (SNA) secondary pollution ions increased rapidly during haze episodes to as much as 2.2 times, 3.0 times, and 2.4 times higher than during non-haze periods, respectively. Diurnal variations during non-haze periods were significant, while complex pollution was insignificant. Based on changes in the backward trajectories and concentrations of WS ions, the hazy episodes were divided into three types: complex, coal-burning, and automobile exhaust pollution. All complex episodes had high concentrations and greater contributions of ammonium nitrate from complex and automobile exhaust pollution, while the contribution of ammonium sulfate from coal-burning pollution was greater than that of ammonium nitrate. The correlation coefficients among SNA species were very high in complex pollution, with nitrate and sulfate the main forms present. The results of principal component analysis (PCA) were related to emissions from burning coal for heating and from long-range transmission in winter. In the case of exhaust pollution, NO3− accounted for the highest percentage of PM2.5, and NH4+ was more closely related to NO3− than to SO42−. Coal-burning pollution was the most common type of pollution in Shenyang. The contribution of sulfate was higher than that of nitrate. Based on PCA, the contribution of coal-burning emissions varied from 36.7% to 53.6% due to industry, soil sources, and other factors.

Characterization and morphological analysis of individual aerosol of PM10 in urban area of Lucknow, India

Airborne particulate matters were collected during the period of October 2015 to September 2016 in Lucknow at different sampling sites. The annual mean concentration of particulate matter was found to be relatively higher than the limits prescribed by National ambient air quality standards (NAAQS), United State Environmental Protection Agency (USEPA) and World Health Organization (WHO). Particulate matters were studied for morphological analysis, elemental composition and functional group variability with the help of Scanning Electron Microscope-Energy Dispersive Spectroscopy (SEM-EDS) followed by Fourier Transform Infrared spectroscopy (FTIR). Morphological characteristics viz. particle count, aspect ratio, circulatory, roundness, equivalent spherical diameter (ESD) and surface area revealed that the particles were perfectly spherical to irregular in shape. Based on the morphology and elemental composition, four clusters of a particulates namely organic particle with inorganic inclusion, soot, tar balls and aluminosilicates were found. FTIR spectra revealed the presence of sulfate, bisulfate, particulate water, silicate, ammonium, aliphatic carbon, aliphatic alcohol, carbonyl and organic nitrates.

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM_2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM_2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM_2.5 was recorded as 121.9 ± 93.2 μg m^-3 (range 25.1-429.8 μg m^-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM_2.5. Strong seasonal variation was observed in PM_2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM_2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM_2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM_2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM_2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM_2.5 from the north-west India and Pakistan were mostly pertinent.