Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India

PM2.5-Associated Premature Mortality Attributable to Hot-And-Polluted Episodes and the Inequality Between the Global North and the Global South

Exposure to air pollution and excessive heat during hot-and-polluted episodes (HPEs) may synergistically cause higher health risks globally. Nevertheless, long-term global spatiotemporal characteristics of HPEs and their health impacts remain unclear. Herein, we conducted statistical analyses using reanalysis data of fine particulate matter (PM2.5) and climate together with our derived concentration-response function for HPEs to assess global HPE variations from 1990 to 2019, and to estimate the PM2.5-associated premature mortality during HPEs. Our results reveal that HPE frequency increased significantly globally. HPE PM2.5 intensity in the Global North continuously increased, overpassing the Global South after 2010, indicating a recurred risk of air pollution under climate change in the Global North after several years of emission control endeavors. Globally, we estimated approximately 694,440 (95% CI: 687,996-715,311) total mortalities associated with acute PM2.5 exposure during HPEs from 1990 to 2019, with the Global South accounting for around 80% of these deaths. Among the most vulnerable 15 countries, India had by far the highest mortality burden, and the United States, Russia, Japan, and Germany were particularly highlighted as having higher burdens within the Global North. Our findings highlight the importance of considering environmental inequality between the Global North and the Global South, and co-benefits of air pollution-climate change mitigation during policymaking processes.

Meeting clean air targets could reduce the burden of hypertension among women of reproductive age in India

BACKGROUND

Air pollution is one of the leading risk factors for hypertension globally. However, limited epidemiological evidence exists in developing countries, specifically with indigenous health data and for fine particulate matter (PM2.5) composition. Here, we addressed this knowledge gap in India.

METHODS

Using a logistic regression model, we estimated the association between hypertension (systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg) prevalence among women of reproductive age (WRA, 15-49 years) from the fifth round of the National Family Health Survey and long-term exposure to PM2.5 and its composition, after adjusting for confounders. We also explored the moderating effects of socioeconomic indicators through a multiplicative interaction with PM2.5.

RESULTS

Hypertension prevalence increased by 5.2% (95% uncertainty interval: 4.8%-5.7%) for every 10 μg/m3 increase in ambient PM2.5 exposure. Significant moderating effects were observed among smokers against nonsmokers and for various sociodemographic parameters. Among PM2.5 species, every interquartile range increase in black carbon (BC) and sulphate exposure was significantly associated with higher odds of hypertension than for organic carbon and dust. We estimated that achieving the National Clean Air Program target and World Health Organization air quality guidelines can potentially reduce hypertension prevalence by 2.42% and 4.21%, respectively.

CONCLUSION

Our results demonstrate that increasing ambient PM2.5 exposure is associated with a higher prevalence of hypertension among WRA in India. The risk is not uniform across various PM2.5 species and is higher with BC and sulphate. Achieving clean air targets can substantially reduce the hypertension burden in this population.

Contrasting nature of aerosols over South Asian cities and its surrounding environment

Cross-country assessment of aerosol loading was made over several South Asian megacities using multiple high-resolution remote-sensing database to assess how aerosols vary within the city and its suburbs. Parameters sensitive to aerosol optical and microphysical properties were processed over city-core and its surrounding, separated by a buffer. Cities across the Indo-Gangetic Plain (IGP; AOD:0.52-0.72) along with Mumbai (0.47) and Bangalore (0.46) denote comparatively high aerosol loading against non-IGP cities. City-core specific AOD was invariably high compared to surrounding, however with varying gradient having robust geographical signature. Exceptions to this general trend were in Kathmandu (ΔAOD: 0.07) and Dhaka (ΔAOD: 0.01) while strong positive AOD gradient was noted in Bangalore (+0.11), Colombo (+0.08) and in Mumbai (+0.07). While all mainland cities exhibited robust intraannual variability, distinction between city-core and its surrounding AOD exhibited varying seasonality. City-specific geometric coefficient of variation indicated insignificant association with mean AOD as opposed to European and American cities. Both pixel-based and city-specific analysis revealed a strong increasing trend in AOD with highest magnitude in Varanasi and Bangalore. Aerosol sub-types based on aerosols' sensitivity to UV-absorption and particle size denotes higher relative abundance of carbonaceous smoke aerosols within city-core, without having significant distinction for mineral dusts and urban aerosols.

Vehicular pollution as the primary source of oxidative potential of PM2.5 in Bhubaneswar, a non-attainment city in eastern India

We assessed the oxidative potential (OP) of PM2.5 (n = 230) using dithiothreitol (DTT) assay to identify the major emission sources in Bhubaneswar (20.20°N, 85.80°E), one of the non-attainment cities under the National Clean Air Program, situated on the eastern coast of India. Continuous day and night PM2.5 samples were collected during periods influenced by marine airmass (MAM; April-May 2019) as well as continental airmass (CAM; October 2019-December 2019). Volume normalized DTT (DDTv) activities were approximately two times higher during CAM compared to MAM periods. In contrast, mass normalized DTT activity (DDTm) showed insignificant variations between CAM and MAM periods. This might be due to particulate organic matter, which accounted for more than one-fifth of the PM2.5 mass loading and remained surprisingly invariant during the study periods. Positive matrix factorization (PMF) identified secondary aerosols (MAM: 26% and CAM: 33%) as dominant contributors to PM2.5 mass in both periods. OP, is, however, dominated by vehicular emissions (21%) as identified through multiple linear regression. Conditional Bivariate Probability Function (CBPF) analysis indicated that local sources were the primary drivers for the catalytic activity of PM2.5 in the study region. Additionally, stagnant meteorological conditions, combined with the chemical aging of species during regional transport of pollutants, likely enhanced redox activity of PM2.5 during the CAM period. The study highlights that increasing traffic congestion is primarily responsible for adverse health outcomes in the region. Therefore, it is important to regulate mobility and vehicular movement to mitigate the hazardous impact of PM2.5 in Bhubaneswar.

Spatio-temporal distribution and source contributions of the ambient pollutants in Lucknow city, India

Clean air is imperative to the survival of all life forms on the planet. However, recent times have witnessed enormous escalation in urban pollution levels. It is therefore, incumbent upon us to decipher measures to deal with it. In perspective, the present study was carried out to assess PM10 and PM2.5 loading, metallic constituents, gaseous pollutants, source contributions, health impact and noise level of nine-locations, grouped as residential, commercial, and industrial in Lucknow city for 2019-21. Mean concentrations during pre-monsoon for PM10, PM2.5, SO2 and NO2 were: 138.2 ± 35.2, 69.1 ± 13.6, 8.5 ± 3.3 and 32.3 ± 7.4 µg/m3, respectively, whereas post-monsoon concentrations were 143.0 ± 33.3, 74.6 ± 14.5, 12.5 ± 2.1, and 35.5 ± 6.3 µg/m3, respectively. Exceedance percentage of pre-monsoon PM10 over National Ambient Air Quality Standards (NAAQS) was 38.2% while that for post-monsoon was 43.0%; whereas corresponding values for PM2.5 were 15.2% and 24.3%. Post-monsoon season showed higher particulate loading owing to wintertime inversion and high humidity conditions. Order of elements associated with PM2.5 is Co < Cd < Cr < Ni < V < Be < Mo < Mn < Ti < Cu < Pb < Se < Sr < Li < B < As < Ba < Mg < Al < Zn < Ca < Fe < K < Na and that with PM10 is Co < Cd < Ni < Cr < V < Ti < Be < Mo < Cu < Pb < Se < Sr < Li < B < As < Mn < Ba < Mg < Al < Fe < Zn < K < Na < Ca. WHO AIRQ + ascertained 1654, 144 and 1100 attributable cases per 0.1 million of population to PM10 exposure in 2019-21. Source apportionment was carried out using USEPA-PMF and resolved 6 sources with highest percent contributions including road dust re-entrainment, biomass burning and vehicular emission. It is observed that residents of Lucknow city regularly face exposure to particulate pollutants and associated constituents making it imperative to develop pollution abetment strategies.

Ambient air pollution and daily mortality in ten cities of India: a causal modelling study

BACKGROUND

The evidence for acute effects of air pollution on mortality in India is scarce, despite the extreme concentrations of air pollution observed. This is the first multi-city study in India that examines the association between short-term exposure to PM2·5 and daily mortality using causal methods that highlight the importance of locally generated air pollution.

METHODS

We applied a time-series analysis to ten cities in India between 2008 and 2019. We assessed city-wide daily PM2·5 concentrations using a novel hybrid nationwide spatiotemporal model and estimated city-specific effects of PM2·5 using a generalised additive Poisson regression model. City-specific results were then meta-analysed. We applied an instrumental variable causal approach (including planetary boundary layer height, wind speed, and atmospheric pressure) to evaluate the causal effect of locally generated air pollution on mortality. We obtained an integrated exposure-response curve through a multivariate meta-regression of the city-specific exposure-response curve and calculated the fraction of deaths attributable to air pollution concentrations exceeding the current WHO 24 h ambient PM2·5 guideline of 15 μg/m3. To explore the shape of the exposure-response curve at lower exposures, we further limited the analyses to days with concentrations lower than the current Indian standard (60 μg/m3).

FINDINGS

We observed that a 10 μg/m3 increase in 2-day moving average of PM2·5 was associated with 1·4% (95% CI 0·7-2·2) higher daily mortality. In our causal instrumental variable analyses representing the effect of locally generated air pollution, we observed a stronger association with daily mortality (3·6% [2·1-5·0]) than our overall estimate. Our integrated exposure-response curve suggested steeper slopes at lower levels of exposure and an attenuation of the slope at high exposure levels. We observed two times higher risk of death per 10 μg/m3 increase when restricting our analyses to observations below the Indian air quality standard (2·7% [1·7-3·6]). Using the integrated exposure-response curve, we observed that 7·2% (4·2%-10·1%) of all daily deaths were attributed to PM2·5 concentrations higher than the WHO guidelines.

INTERPRETATION

Short-term PM2·5 exposure was associated with a high risk of death in India, even at concentrations well below the current Indian PM2·5 standard. These associations were stronger for locally generated air pollutants quantified through causal modelling methods than conventional time-series analysis, further supporting a plausible causal link.

FUNDING

Swedish Research Council for Sustainable Development.

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.

Global trends and burden of stroke attributable to particulate matter pollution from 1990 to 2019

OBJECTIVE

To investigate the association between particulate matter and the incidence, disability, and mortality of stroke, we reported the burden of stroke attributable to particulate matter (PM2.5) pollution, including ambient particulate matter pollution (APMP) and household air pollution from solid fuels (HAP), from 1990 to 2019.

METHODS

We retrieved the detailed data on the burden of stroke attributable to PM2.5 from the Global Burden of Disease (GBD) 2019. The number of disability-adjusted life-years (DALYs) and deaths, age-standardized death rates (ASMR), and age-standardized disability-adjusted life-years rates (ASDR) attributable to PM2.5 were estimated by age, sex, geographical location, socio-demographic index (SDI), and stroke subtypes (ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage). The estimated annual percentage change (EAPC) was calculated to assess the trends in ASDR and ASMR during the period 1990-2019.

RESULTS

Regarding stroke subtypes, the proportion of ischemic stroke burden is increasing, while intracerebral hemorrhage carries the heaviest burden. Both APMP and HAP contributed the most to stroke-related deaths and DALYs of stroke among the elderly populations and males. The highest ASDR and ASMR of stroke attributable to APMP were in the middle SDI regions, especially in East Asia. For HAP, the highest ASDR and ASMR were in the low SDI regions, mainly in Oceania. From 1990-2019, in terms of the EAPC results, APMP caused an increased burden of stroke, whereas the impact of HAP significantly fell. The most pronounced increase in ASDR and ASMR for strokes attributed to APMP were in the low-middle SDI and low SDI regions, particularly among the 25-35 age group.

CONCLUSIONS

Stroke attributed to PM2.5 is a global health problem, and the patterns and trends were heterogeneous across APMP and HAP. Targeted interventions should be formulated for APMP and HAP.

Identification of sources of coarse mode aerosol particles (PM10) using ATR-FTIR and SEM-EDX spectroscopy over the Himalayan Region of India

This study attempts to examine the morphological, elemental and physical characteristics of PM10 over the Indian Himalayan Region (IHR) using FTIR and scanning electron microscopy-energy dispersive X-ray (SEM-EDX) analysis. The study aimed at source identification of PM10 by exploring the inorganic ions, organic functional groups, morphology and elemental characteristics. The pollution load of PM10 was estimated as 63 ± 22 μg m-3; 53 ± 16 μg m-3; 67 ± 26 μg m-3 and 55 ± 11 μg m-3 over Mohal-Kullu, Almora, Nainital and Darjeeling, respectively. ATR-FTIR spectrum analysis revealed the existence of inorganic ions (SiO44-, TiO2, SO42-, SO3-, NO3-, NO2-, CO32-, HCO3-, NH4+) and organic functional groups (C-C, C-H, C=C, C≡C, C=O, N-H, C≡N, C=N, O-H, cyclic rings, aromatic compounds and some heterogeneous groups) in PM10 which may arise from geogenic, biogenic and anthropogenic sources. The morphological and elemental characterization was performed by SEM-EDX, inferring for geogenic origin (Al, Na, K, Ca, Mg and Fe) due to the presence of different morphologies (irregular, spherical, cluster, sheet-like solid deposition and columnar). In contrast, particles having biogenic and anthropogenic origins (K, S and Ba) have primarily spherical with few irregular particles at all the study sites. Also, the statistical analysis ANOVA depicts that among all the detected elements, Na, Al, Si, S and K are site-specific in nature as their mean of aw% significantly varied for all the sites. The trajectory analysis revealed that the Uttarakhand, Jammu and Kashmir, the Thar Desert, Himachal Pradesh, Pakistan, Afghanistan, Nepal, Sikkim, the Indo-Gangetic Plain (IGP) and the Bay of Bengal (BoB) contribute to the increased loading of atmospheric pollutants in various locations within the IHR.

High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach

Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85-0.96 and 0.62-0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.

Cumulative effect of PM2.5 components is larger than the effect of PM2.5 mass on child health in India

While studies on ambient fine particulate matter (PM2.5) exposure effect on child health are available, the differential effects, if any, of exposure to PM2.5 species are unexplored in lower and middle-income countries. Using multiple logistic regression, we showed that for every 10 μg m-3 increase in PM2.5 exposure, anaemia, acute respiratory infection, and low birth weight prevalence increase by 10% (95% uncertainty interval, UI: 9-11), 11% (8-13), and 5% (4-6), respectively, among children in India. NO3-, elemental carbon, and NH4+ were more associated with the three health outcomes than other PM2.5 species. We found that the total PM2.5 mass as a surrogate marker for air pollution exposure could substantially underestimate the true composite impact of different components of PM2.5. Our findings provide key indigenous evidence to prioritize control strategies for reducing exposure to more toxic species for greater child health benefits in India.

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

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

Per- and polyfluoroalkyl substances in ambient fine particulate matter in the Pearl River Delta, China: Levels, distribution and health implications

Per- and polyfluoroalkyl substances (PFAS) have attracted worldwide attention as one of persistent organic pollutants; however, there is limited knowledge about the exposure concentrations of PFAS-contained ambient particulate matter and the related health risks. This study investigated the abundance and distribution of 32 PFAS in fine particulate matter (PM2.5) collected from 93 primary or secondary schools across the Pearl River Delta region (PRD), China. These chemicals comprise four PFAS categories which includes perfluoroalkyl carboxylic acids (PFCAs), perfluoroalkyl sulfonic acids (PFSAs), perfluoroalkyl acid (PFAA) precursors and PFAS alternatives. In general, concentrations of target PFAS ranged from 11.52 to 419.72 pg/m3 (median: 57.29 pg/m3) across sites. By categories, concentrations of PFSAs (median: 26.05 pg/m3) were the dominant PFAS categories, followed by PFCAs (14.25 pg/m3), PFAS alternatives (2.75 pg/m3) and PFAA precursors (1.10 pg/m3). By individual PFAS, PFOS and PFOA were the dominant PFAS, which average concentration were 24.18 pg/m3 and 6.05 pg/m3, respectively. Seasonal variation showed that the concentrations of PFCAs and PFSAs were higher in winter than in summer, whereas opposite seasonal trends were observed in PFAA precursors and PFAS alternatives. Estimated daily intake (EDI) and hazard quotient (HQ) were used to assess human inhalation-based exposure risks to PFAS. Although the health risks of PFAS via inhalation were insignificant (HQ far less than one), sufficient attention should be levied to ascertain the human exposure risks through inhalation, given that exposure to PFAS through air inhalation is a long term and cumulative process.

Short-term effects of fine particulate matter constituents on mortality considering the mortality displacement in Zhejiang province, China

BACKGROUND

Evidence linking mortality and short-term exposure to particulate matter (PM_2.5) constituents was sparse. The mortality displacement was often unconsidered and may induce incorrect risk estimation.

OBJECTIVES

To assess the short-term effects of PM_2.5 constituents on all-cause mortality considering the mortality displacement.

METHODS

Daily data on all-cause mortality and PM_2.5 constituents, including sulfate (SO₄^2-), nitrate (NO₃^-), ammonium (NH₄⁺), organic matters (OM), and black carbon (BC), were collected from 2009 to 2020. The mortality effect of PM_2.5 and its constituents was estimated using a distributed lag non-linear model. Stratified analyses were performed by age, sex, and season.

RESULTS

Per interquartile range increases in SO₄^2-, NO₃^-, NH₄⁺, OM, and BC were associated with the 1.42% (95%CI: 0.98, 1.87), 3.76% (3.34, 4.16), 2.26% (1.70, 2.83), 2.36% (2.02, 2.70), and 1.26% (0.91, 1.61) increases in all-cause mortality, respectively. Mortality displacements were observed for PM_2.5, SO₄^2-, NH₄⁺, OM, and BC, with their overall effects lasting for 7-15 days. Stratified analyses revealed a higher risk for old adults (>65 years) and females, with stronger effects in the cold season.

CONCLUSIONS

Short-term exposures to PM_2.5 constituents were positively associated with increased risks of mortality. The mortality displacement should be considered in future epidemiological studies on PM constituents.

DATA AVAILABILITY

Data will be made available on request.

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.

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.

Maternal PM2.5 exposure during gestation and offspring neurodevelopment: Findings from a prospective birth cohort study

Emerging data have suggested the potential role of prenatal PM2.5 exposure as a neurotoxin for offspring. However, the existing results are equivocal, and no study has examined the effects of complex chemical constituents of the particular matter on offspring neurodevelopment. Therefore, in a prospective birth cohort study conducted in Jiangsu, China, we aimed to investigate the association between prenatal exposure to PM2.5 and the neurodevelopment in infants, and further assess the effects of specific chemical constituents of PM2.5. A total of 1531 children who had available data on daily prenatal PM2.5 exposure and completed assessment on neurodevelopment at 1 year old were enrolled. We used the high-performance machine-learning model to estimate daily PM2.5 exposure concentrations at 1 km × 1 km spatial resolution. The combined geospatial-statistical model was applied to evaluate average concentrations of six chemical constituents [organic matter (OM), black carbon (BC), sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and soil dust (Dust)]. The neurodevelopment of children was assessed using Bayley-III Screening Test. After adjusting for confounding factors, the risk of non-optimal gross motor development increased by 31 % for every 10 μg/m3 increase in average PM2.5 exposure during gestation (aRR: 1.31; 95 % CI: 1.04, 1.64). Further analysis of PM2.5 constituents showed that prenatally exposed to high SO42- was associated with the risk of non-optimal gross motor development (aRR: 1.40; 95 % CI: 1.08, 1.81). Null associations were observed for the rest four neurodevelopment domains. Collectively, our study suggested that prenatal exposure to PM2.5, particularly with high SO42- concentration, was associated with children's non-optimal gross motor development at 1 year old. The short- and long-term influences of perinatal PM2.5 exposure on children's neurodevelopment warrant further investigation.