Characterization of PM2.5 in Delhi: role and impact of secondary aerosol, burning of biomass, and municipal solid waste and crustal matter

Laboratory study on the characteristics of fresh and aged PM1 emitted from typical forest vegetation combustion in Southwest China

The frequency and intensity of forest fires are amplified by climate change. Substantial quantities of PM1 emitted from forest fires can undergo gradual atmospheric dispersion and long-range transport, thus impacting air quality far from the source. However, the chemical composition and physical properties of PM emitted from forest fires and its changes during atmospheric transport remain uncertain. In this study, the evolution of organic carbon (OC), elemental carbon (EC), water-soluble ions, and water-soluble metals in the particulate phase of smoke emitted from the typical forest vegetation combustion in Southwest China before and after photo-oxidation was investigated in the laboratory. Two aging periods of 5 and 9 days were selected. The OC and TC mass concentrations tended to decrease after 9-days aged compared to fresh emissions. OP, OC2, and OC3 in PM1 are expected to be potential indicators of fresh smoke, while OC3 and OC4 may serve as suitable markers for identifying aged carbon sources from the typical forest vegetation combustion in Southwest China. K+ exhibited the highest abundant water-soluble ion in fresh PM1, whereas NO3- became the most abundant water-soluble ion in aged PM1. NH4NO3 emerged as the primary secondary inorganic aerosol emitted from typical forest vegetation combustion in Southwest China. Notably, a 5-day aging period proved insufficient for the complete formation of the secondary inorganic aerosols NH4NO3 and (NH4)2SO4. After aging, the mass concentration of the water-soluble metal Ni in PM1 from typical forest vegetation combustion in Southwest China decreased, while the mean mass concentrations of all other water-soluble metals increased in varying degrees. These findings provide valuable data support and theoretical guidance for studying the atmospheric evolution of forest fire aerosols, as well as contribute to policy formulation and management of atmospheric environment safety and human health.

Coverage of Indoor Smoking and Vaping Restrictions in the U.S., 1990-2021

INTRODUCTION

Secondhand smoke exposure increases the risk of premature death and disease in children and non-smoking adults. As a result, many U.S. states and local jurisdictions have enacted comprehensive indoor smoking restrictions (ISR). Indoor vaping restrictions (IVR) have also been adopted to protect against exposure to secondhand e-cigarette aerosol. This study aimed to quantify state and national U.S. coverage of policies restricting indoor cigarette and e-cigarette use over time.

METHODS

Data from the American Nonsmokers Rights' Foundation on U.S. ISR from 1990 to 2021 and IVR from 2006 to 2021 were analyzed. Combining these data with 2015 U.S. Census population estimates, the percentage of state and national residents covered by partial and comprehensive restrictions in bars, restaurants, and workplaces, were calculated (analysis in 2023-2024) over time.

RESULTS

Between 1990 and 2021, national coverage of comprehensive ISR increased for bars (0% to 67.3%), restaurants (0%-78.2%), and workplaces (0%-77.5%). Partial ISR coverage decreased for bars (14.8%-13.9%), restaurants (40.2%-15.4%) and workplaces (40.2%-22.5%). From 2006 to 2021, comprehensive IVR coverage increased for bars (0%-43.5%), restaurants (0%-51.5%), and workplaces (0%-53.2%). Despite these increases in coverage, by the end of 2021, <50% of the population was protected by comprehensive ISR for bars, restaurants, and workplaces in 19, 12, and 14 states, respectively.

DISCUSSION

The percentage of the U.S. population protected by ISR and IVR has increased over time. However, gaps in coverage remain, which may contribute to disparities in tobacco-related disease and death.

Bottom-up and top-down approaches for estimating road dust emission and correlating it with a receptor model results over a typical urban atmosphere of Indo Gangetic Plain

To investigate the emission and concentration of PM10 and PM2.5-related road dust over Agra, a typical semi-arid urban atmosphere of the Indo Gangetic Plain (IGP), a fine-resolution emission inventory and receptor modeling-based source apportionment was undertaken for the year 2019. On-road, the silt load of Agra (7-55 g/m2 of the road) was found to be 10 to 50 times higher than that reported in advanced countries. The silt load over Agra varied widely depending on road conditions, long-range transport, and land-use pattern. Depending on the silt load, land-use and fleet averaged weight, the annual emission factor for road dust was estimated as 14.3 ± 3.2 (PM10) and 4.4 ± 1.4 (PM2.5) gm/VKT (vehicle kilometer travel). PM10 emission of road dust alone contributed 80 % (29 ± 6 t/d) to the total emission of PM10 and 68 % (9 ± 3 t/d) to PM2.5 of the city with the maximum emission being in industrial areas. Chemical analysis of ambient PM10, PM2.5, and road dust samples showed that the road dust was enriched with geogenic components and was in good agreement with the road dust profile identified from the positive matrix factorization receptor model. The model estimated contribution of road dust (summer and winter combined) to PM10 and PM2.5 ambient air levels was 28 % (67 μg/m3) and 23 % (27 μg/m3) respectively. Summer showed a larger road dust contribution than winter due to strong surface wind and dry road conditions. Results have revealed that the emissions and concentrations of road dust are closely interrelated with road conditions (silt load), land-use patterns, VKT, weight of the vehicles, and micrometeorological conditions. The large road dust emission in IGP cities requires better road conditions and traffic management to curb the emission.

Neurodevelopmental toxicity induced by airborne particulate matter

Airborne particulate matter (PM), the primary component associated with health risks in air pollution, can negatively impact human health. Studies have shown that PM can enter the brain by inhalation, but data on the exact quantity of particles that reach the brain are unknown. Particulate matter exposure can result in neurotoxicity. Exposure to PM poses a greater health risk to infants and children because their nervous systems are not fully developed. This review paper highlights the association between PM and neurodevelopmental toxicity (NDT). Exposure to PM can induce oxidative stress and inflammation, potentially resulting in blood-brain barrier damage and increased susceptibility to development of neurodevelopmental disorders (NDD), such as autism spectrum disorders and attention deficit disorders. In addition, human and animal exposure to PM can induce microglia activation and epigenetic alterations and alter the neurotransmitter levels, which may increase risks for development of NDD. However, the systematic comparisons of the effects of PM on NDD at different ages of exposure are deficient. The elucidation of PM exposure risks and NDT in children during the early developmental stages are of great importance. The synthesis of current research may help to identify markers and mechanisms of PM-induced neurodevelopmental toxicity, allowing for the development of strategies to prevent permanent damage of developing brain.

Four year long simulation of carbonaceous aerosols in India: Seasonality, sources and associated health effects

India's air quality is in a dismal state, with many studies ascribing it to PM_2.5. Most of these corroborate that carbonaceous aerosol (CA) constitute significant fraction of PM_2.5. However, investigations on the effect of long-term meteorological or emission changes on PM_2.5 and its components, and their associated health effects are rare. In this work, WRF-Chem simulations for three seasons over four years (2016-2019) were carried out to cogitate the spatial and temporal changes in PM_2.5 and its components in India. Model predicted PM_2.5 concentrations were in good agreement with the ground-based observations for 25 cities. PM_2.5 was highest in winter and lowest in pre-monsoon. PM_2.5 reduced by ∼8% in Indo-Gangetic Plain (IGP) but increased by ∼38% and ∼130% in south and northeast India, respectively, from 2016 to 2019. IGP witnessed three times higher average PM_2.5 concentrations than south India. No significant interannual change in CA contributions was observed, however, it peaked in the winter season. Other inorganics (OIN) were the major component of PM_2.5, contributing more than 40%. Primary organic aerosol (POA) fractions were higher in north India, while secondary inorganic aerosol (SIA) dominated south India. Transport and residential sectors were the chief contributors to CA across India. Biomass burning contributed up to ∼23% of PM_2.5 in regions of IGP during post-monsoon, with CA fractions up to 50%. Associations between PM_2.5 and its components with daily inpatient admissions from a tertiary care centre in Delhi showed that PM_2.5 and OIN had lower associations with daily hospital admissions than CA. Every 10 μg/m³ increase in POA, black carbon (BC), and secondary organic aerosol (SOA) were associated with ∼1.09%, ∼3.07% and ∼4.93% increase in the risk of daily hospital admissions. This invigorates the need for more policies targeting CA rather than PM_2.5 to mitigate associated health risks, in India.

Interpreting the COVID effect on atmospheric constituents over the Indian region during the lockdown: chemistry, meteorology, and seasonality

Most of the published articles which document changes in atmospheric compositions during the various lockdown and unlock phases of COVID-19 pandemic have made a direct comparison to a reference point (which may be 1 year apart) for attribution of the COVID-mediated lockdown impact on atmospheric composition. In the present study, we offer a better attribution of the lockdown impacts by also considering the effect of meteorology and seasonality. We decrease the temporal distance between the impacted and reference points by considering the difference of adjacent periods first and then comparing the impacted point to the mean of several reference points in the previous years. Additionally, we conduct a multi-station analysis to get a holistic effect of the different climatic and emission regimes. In several places in eastern and coastal India, the seasonally induced changes already pointed to a decrease in PM concentrations based on the previous year data; hence, the actual decrease due to lockdown would be much less than that observed just on the basis of difference of concentrations between subsequent periods. In contrast, northern Indian stations would normally show an increase in PM concentration at the time of the year when lockdown was effected; hence, actual lockdown-induced change would be in surplus of the observed change. The impact of wind-borne transport of pollutants to the study sites dominates over the dilution effects. Box model simulations point to a VOC-sensitive composition.

Particle-bound organic and elemental carbons for source identification of PM < 0.1 µm from biomass combustion

Atmospheric nanoparticles (PM < 0.1 µm) are a major cause of environmental problems and also affect health risk. To control and reduce these problems, sources identification of atmospheric particulates is necessary. Combustion of bituminous coal and biomass including rubber wood, palm kernel, palm fiber, rice stubble, rice straw, maize residue, sugarcane leaves and sugarcane bagasse, which are considered as sources of air quality problems in many countries, was performed. Emissions of particle-bound chemical components including organic carbon (OC), elemental carbon (EC), water-soluble ions (NH₄⁺, Cl^-, NO₃^-, SO₄^2-), elements (Ca, K, Mg, Na) and heavy metals (Cd, Cr, Ni, Pb) were investigated. The results revealed that PM < 0.1 µm from all samples was dominated by the OC component (>50%) with minor contribution from EC (3%-12%). The higher fraction of carbonaceous components was found in the particulates with smaller sizes, and lignin content may relate to concentration of pyrolyzed organic carbon (PyOC) resulting in the differences of OC/EC values. PM emitted from burning palm fiber and rice stubble showed high values of OC/EC and also high PyOC. Non-carbonaceous components such as Cl^-, Cr, Ca, Cd, Ni, Na and Mg may be useful as source indicators, but they did not show any correlation with the size of PM.

A comprehensive review of domestic-open waste burning: recent trends, methodology comparison, and factors assessment

Open burning is a waste management practice performed by many people worldwide, especially in developing countries. Lack of detailed data of open burning practices may lead to a misinterpretation during data analysis, especially when estimating global/local emissions and assessing risks. This study presents a comprehensive review of current research trends, methodological assessments, and factors behind open waste burning practices from published literature. This review used systematic methods such as PRISMA 2020 methodology, a bibliometric approach, and qualitative content analysis to determine and assess 84 articles related to open burning. The results show that environmental risks and emission factors related to open burning incidents at the landfill or residential level are preferable topics that will be rising in the years to come. Coupling methods such as a transect-based approach with a questionnaire survey and mobile-static plume sampling to determine the activities and incidents as baseline data for risk assessment will help researchers gain a robust dataset of open burning emission inventory. In addition, it was found that environmental knowledge and awareness levels influence open burning practices, thereby opening up opportunities for future research.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10163-022-01430-9.

Spatio-temporal variation in fine particulate matter and effect on air quality during the COVID-19 in New Delhi, India

Novel Coronavirus disease has affected almost all the countries; which leads to the pandemic, impacting adversely on environment. The impact on environment during pre-and during lockdowns needs an attention to correlate the pollutants from industrial emissions and other factors. Therefore, the current study demonstrates the changes in fine particulate matter PM_2.5, PM₁₀ and effect on air quality during lockdown. The highest reduction was observed in lockdown I (25 March - 14 April) as compared to others lockdowns (between 15 April and 31st May 2020) due to the complete shutdown of industrial, transport, and construction activities. A significant reduction in PM_2.5 and PM₁₀ from 114.27 μg/m³ and 194.48 μg/m³ for pre-lockdown period to 41.41 μg/m³ and 86.81 μg/m³ for lockdown I was observed. The levels of air quality index fall under satisfactory category for lockdown I whereas satisfactory to moderate category for other lockdowns. The present study revealed a strong correlation between PM_2.5 and PM₁₀ levels during the pre-lockdown period (0.71) and through lockdown IV (0.76), which indicate that change in the PM₁₀ level influences the PM_2.5 level greatly. The findings of the present study could be scaled up nationwide and might be useful in formulating air pollution reduction policies in the future.

Associations between air pollution exposure and birth defects: a time series analysis

Air pollution is a serious environmental problem in China. Birth defects are particularly vulnerable to outdoor air pollution. Our study was to evaluate the association between short-term exposure to air pollutants and the risk of birth defects. Daily data including the air pollutants, meteorological characteristics, and birth records were obtained in Hefei, China, during January 2013 to December 2016. The findings showed that PM_2.5, PM₁₀, SO₂, NO₂, and O₃ exposures were positively correlated with the risk of birth defects. Maternal exposure to PM_2.5 and SO₂ during the 4th to 13th gestational weeks was observed to have a significant association with the risk of birth defects, with the maximum effect in the 7th or 8th week for PM_2.5 and the maximum effect in the 7th week for SO₂. The positively significant exposure windows were the 4th to 14th weeks for PM₁₀, the 4th to 12th weeks for NO₂, and the 26th to 35th weeks for O₃, respectively. The strongest associations were observed in the 8th week for PM₁₀, the 7th week for NO₂, and in the 31st or 32nd week for O_3. The findings of this study demonstrate that air pollutants increase the risk of birth defects among women during pregnancy in Hefei, China, which provide evidence for improving the health of pregnant women and neonates in developing countries, and uncovered potential opportunities to reduce or prevent birth defects by proactive measures during pregnancy.

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.

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

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.

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.

Exposure to Particulate Matter Is Associated With Elevated Blood Pressure and Incident Hypertension in Urban India

Ambient air pollution, specifically particulate matter of diameter <2.5 μm, is reportedly associated with cardiovascular disease risk. However, evidence linking particulate matter of diameter <2.5 μm and blood pressure (BP) is largely from cross-sectional studies and from settings with lower concentrations of particulate matter of diameter <2.5 μm, with exposures not accounting for myriad time-varying and other factors such as built environment. This study aimed to study the association between long- and short-term ambient particulate matter of diameter <2.5 μm exposure from a hybrid spatiotemporal model at 1-km×1-km spatial resolution with longitudinally measured systolic and diastolic BP and incident hypertension in 5342 participants from urban Delhi, India, within an ongoing representative urban adult cohort study. Median annual and monthly exposure at baseline was 92.1 μg/m3 (interquartile range, 87.6-95.7) and 82.4 μg/m3 (interquartile range, 68.4-107.0), respectively. We observed higher average systolic BP (1.77 mm Hg [95% CI, 0.97-2.56] and 3.33 mm Hg [95% CI, 1.12-5.52]) per interquartile range differences in monthly and annual exposures, respectively, after adjusting for covariates. Additionally, interquartile range differences in long-term exposures of 1, 1.5, and 2 years increased the risk of incident hypertension by 1.53× (95% CI, 1.19-1.96), 1.59× (95% CI, 1.31-1.92), and 1.16× (95% CI, 0.95-1.43), respectively. Observed effects were larger in individuals with higher waist-hip ratios. Our data strongly support a temporal association between high levels of ambient air pollution, higher systolic BP, and incident hypertension. Given that high BP is an important risk factor of cardiovascular disease, reducing ambient air pollution is likely to have meaningful clinical and public health benefits.

A framework for PM2.5 constituents-based (including PAHs) emission inventory and source toxicity for priority controls: A case study of Delhi, India

A simple mass-based emission inventory (EI) of PM_2.5 alone does not provide the information on the toxicity of the sources, as not all PM_2.5 particles are equally toxic. The PM_2.5 EI should have three inter-linked versions (i) mass-based, (ii) constituent-based and (iii) source toxicity-based. A framework (applied to the city of Delhi) to prepare constituent and source toxicity-based EI was developed. Mass emission of twelve sources was estimated for 89 constituents. The USEPA's CompTox database was used to estimate threshold concentration for the constituents of PM_2.5 for carcinogenic, chronic and acute health effects. A product of mass emission of the constituent and inverse of its threshold concentration provides an assessment of toxicity of the source. Toxicity was not linearly associated with the mass emission. Road dust, vehicles, coal, dung, wood and coal power plant showed the highest toxicity as presence of metals Cr, Co, Cd, and As make these sources disproportionately more toxic. Among PAHs, Dibenzo (ah)anthracene, showed the highest cancer risk with its 98% emission from vehicles. The soft options replacing wood, crop, coal and dung with LPG, elimination of diesel power generation, burning of waste were simple and effective measures to reduce chronic toxicity by about 40%.

Characterization of atmospheric trace gases and water soluble inorganic chemical ions of PM1 and PM2.5 at Indira Gandhi International Airport, New Delhi during 2017-18 winter

Water soluble inorganic chemical ions of PM₁ and PM_2.5 and atmospheric trace gases were monitored simultaneously on hourly resolution at Indira Gandhi International Airport (IGIA), Delhi during 8 December 2017-10 February 2018. Monitoring was made by MARGA (Monitoring AeRosol and Gases in ambient Air) under winter fog experiment (WIFEX) program of the Ministry of Earth Sciences (MoES), Government of India. The result based on the analysis of the data so generated reveals that Cl^-, NH₄⁺, NO₃^- and SO₄^2- were dominant ions in order which collectively constituted 96.8 and 97.3% of the of the total measured ionic mass in PM₁ and PM_2.5 respectively. Their overall average concentrations in PM₁ were 19.5 ± 19.7, 18.4 ± 10.5, 16.6 ± 8.7 and 10.3 ± 5.7 μg/m³ and in PM_2.5 were 36.0 ± 33.9, 32.7 ± 17.2, 28.5 ± 13.6 and 19.9 ± 13.9 μg/m³. Average concentrations of HCl, HNO₃, HNO₂, SO₂ and NH₃ trace gases were 0.7 ± 0.3, 2.7 ± 1.1, 6.6 ± 4.7, 22.0 ± 12.3 and 25.7 ± 9.1 μg/m³ respectively. Weather parameters along with low mixing height played significant role in the occurrence of high concentration of these chemical species. NH₄⁺ was the prime neutralizer of the acidic components and mostly occurred in (NH₄)₂SO₄/NH₄HSO₄, NH₄NO₃ and NH₄Cl molecular forms. Major sources of these chemical species were fossil fuel combustion in aviation activity and transportation, coal burning in thermal power plants, industrial processes and emissions from biomass burning and agro-based activity. The quality of air with respect to PM_2.5 always remained deteriorated. It became alarming during low visibility period mainly due to high concentration of Cl^-, NO₃^-, SO₄^2- and NH₄⁺. Both meteorological and chemical processes interactively fed each other which occasionally resulted in fog development and visibility degradation. The knowledge gained by this study will help in simulation of atmospheric processes which lead to fog development and dispersal in the Delhi region.

A new perspective of probing the level of pollution in the megacity Delhi affected by crop residue burning using the triple oxygen isotope technique in atmospheric CO2

Air quality in the megacity Delhi is affected not only by local emissions but also by pollutants from crop residue burning in the surrounding areas of the city, particularly the rice straw burning in the post monsoon season. As a major burning product, gaseous CO₂, which is rather inert in the polluted atmosphere, provides an alternative solution to characterize the impact of biomass burning from a new perspective that other common tracers such as particulate matters are limited because of their physical and chemical reactiveness. Here, we report conventional ([CO₂], δ¹³C, and δ¹⁸O) and unconventional (Δ¹⁷O) isotope data for CO₂ collected at Connaught Place (CP), a core area in the megacity Delhi, and two surrounding remote regions during a field campaign in October 18-20, 2017. We also measured the isotopic ratios near a rice straw burning site in Taiwan to constrain their end member isotopic compositions. Rice straw burning produces CO₂ with δ¹³C, δ¹⁸O, and Δ¹⁷O values of -29.02 ± 0.65, 19.63 ± 1.16, and 0.05 ± 0.02‰, respectively. The first two isotopic tracers are less distinguishable from those emitted by fossil fuel combustion but the last one is significantly different. We then utilize these end member isotopic ratios, with emphasis on Δ¹⁷O for the reason given above, for partitioning sources that affect the CO₂ level in Delhi. Anthropogenic fraction of CO₂ at CP ranges from 4 to 40%. Further analysis done by employing a three-component (background, rice straw burning, and fuel combustion) mixing model with constraints from the Δ¹⁷O values yields that rice straw burning contributes as much as ∼70% of the total anthropogenic CO₂, which is more than double of the fossil fuel contribution (∼30%), during the study days.

Cause and Age-specific premature mortality attributable to PM2.5 Exposure: An analysis for Million-Plus Indian cities

In India, a majority population is exposed to high levels of ambient PM_2.5 resulting in adverse health outcomes. Epidemiological studies have associated diseases such as Ischemic Heart Disease (IHD), Cerebrovascular Disease (Stroke), Chronic Obstructive Pulmonary Disease (COPD), Lower Respiratory Infection (LRI), and Lung Cancer (LNC) to long-term PM_2.5 exposure resulting in premature mortality. In the present work, the Integrated Exposure Response (IER) model is used to estimate such premature deaths for the year 2016 in 29 million-plus Indian cities. The city-specific registered deaths data along with information of percent share of cause-specific deaths in the total deaths and measured ambient PM_2.5 concentrations are used to estimate cause-specific baseline mortality in a city. The premature mortality attributable to PM_2.5 exposure is estimated from this baseline mortality. The premature mortality burden attributable to PM_2.5 exposure in these cities is 114,700 (104,100-125,500) deaths from the five causes (IHD, Stroke, COPD, LRI, and LNC). IHD is the leading cause of death accounting for 58% of PM_2.5 related premature deaths, followed by Stroke (22%), COPD (14%), LRI (4%), and LNC (2%) in these 29 cities. The estimated number of PM_2.5 related deaths in productive age group (25 - 50 years) is quite low compared to older people, but the percentage share of these deaths in the cumulative cause-specific baseline deaths is higher for productive age group. Thus, the productive population is considerably at a higher risk of mortality due to PM_2.5 exposure. There is approximately 18% and 70% reduction in premature mortality if these cities can attain National Ambient Air Quality Standards (NAAQS) (40 μg/m³) and the World Health Organization (WHO) guidelines (10 μg/m³) of annual PM_2.5, respectively. The estimates of air pollution related mortality at the city level could assist in city-specific policy formulation for better air pollution control.

The characteristics and mixing states of PM2.5 during a winter dust storm in Ningbo of the Yangtze River Delta, China

Dust particulates play an essential role for the nucleation, hygroscopicity and also contribute to aerosol mass. We investigated the chemical composition, size distribution and mixing states of PM_2.5 using a single-particle aerosol mass spectrometer (SPAMS), Monitor for AeRosols and Gases (MARGA), and off-line membrane sampling from 2018.1.24 to 2018.2.20 at a coastal supersite in Ningbo, a port city in Yangtze River Delta, China. During the study campaign, the eastern part of China had experienced a wide range of cooling, sandstorm, and snowfall processes. The entire sampling campaign was categorized into five sub-periods based on the levels of PM_2.5 and the ratios of PM_2.5/PM₁₀, namely clean (T1), heavy pollution (T2), light pollution (T3), dust (sandstorm) (T4) and cleaning pollution (T5) period. After comparing the average mass spectrum for each period, it shows that the primary ions, such as Ca²⁺and SiO₃^-, rarely coexist with each other within a single particle, but secondary ions generally coexist with these primary ions. Furthermore, the coexistence of each two different ions within a particle does not show distinct variation for the whole study periods. All these suggest that the absorption and partitioning of gaseous contaminants into the surface of primary aerosol through heterogeneous reactions are the major pathways of aging and growth of aerosol; and the merging of particles through collisions usually is insignificant. Although the absolute concentrations of nitrate and sulfate all increased with the PM_2.5 concentrations, the relative equivalent concentrations of NO₃^- and SO₄^2- displayed opposite trends; the relative contribution of sulfate decreased and that of nitrate increased as the increase of pollution. During the dust period, the relative equivalent concentrations of calcium and/or potassium ions in PM_2.5 are significantly higher. This study provided deep insights about the mixing states and characteristics of particulate after long-range transport and a visualization tool for aerosol study.

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.

Pollution Characterization and Source Apportionment of Day and Night PM2.5 Samples in Urban and Suburban Communities of Tianjin (China)

Day and night PM_2.5 samples were collected from two typical urban and suburban communities in Tianjin. The major chemical components in PM_2.5, including the metal elements, polycyclic aromatic hydrocarbons (PAHs), and inorganic water-soluble ions, were monitored. A positive matrix factorization (PMF) model was used to apportion the potential sources of PM_2.5 at the two sites in the daytime and nighttime. The results indicated that the PM_2.5 concentration was higher in the suburban area than in the urban area during the daytime in winter. The daytime and nighttime PAHs concentrations at both sites were both generally higher in winter than in summer. The concentrations of some of the metal elements were higher in summer than in winter. Regional differences and day and night differences in the metals and water-soluble ions commonly existed. The PMF analysis indicated that coal combustion and transportation-related sources were the predominant sources in the urban and suburban areas in the daytime in winter, and secondary aerosols were the most important source for the suburban area in the nighttime in winter. There were more pollution sources of PM_2.5 during the daytime in summer, especially in the suburban area. In the nighttime in summer, the pollution sources of PM_2.5 in the urban and suburbs areas were basically the same, but the source apportionment was quite different.

In utero exposure to fine particulate matter results in an altered neuroimmune phenotype in adult mice

Environmental exposure to air pollution has been linked to a number of health problems including organ rejection, lung damage and inflammation. While the deleterious effects of air pollution in adult animals are well documented, the long-term consequences of particulate matter (PM) exposure during animal development are uncertain. In this study we tested the hypothesis that environmental exposure to PM 2.5 μm in diameter in utero promotes long term inflammation and neurodegeneration. We evaluated the behavior of PM exposed animals using several tests and observed deficits in spatial memory without robust changes in anxiety-like behavior. We then examined how this affects the brains of adult animals by examining proteins implicated in neurodegeneration, synapse formation and inflammation by western blot, ELISA and immunohistochemistry. These tests revealed significantly increased levels of COX2 protein in PM2.5 exposed animal brains in addition to changes in synaptophysin and Arg1 proteins. Exposure to PM2.5 also increased the immunoreactivity for GFAP, a marker of activated astrocytes. Cytokine concentrations in the brain and spleen were also altered by PM2.5 exposure. These findings indicate that in utero exposure to particulate matter has long term consequences which may affect the development of both the brain and the immune system in addition to promoting inflammatory change in adult animals.