Exposure to particulate matter in India: A synthesis of findings and future directions

An ensemble multi-scale framework for long-term forecasting of air quality

The significance of accurate long-term forecasting of air quality for a long-term policy decision for controlling air pollution and for evaluating its impacts on human health has attracted greater attention recently. This paper proposes an ensemble multi-scale framework to refine the previous version with ensemble empirical mode decomposition (EMD) and nonstationary oscillation resampling (NSOR) for long-term forecasting. Within the proposed ensemble multi-scale framework, we on one hand apply modified EMD to produce more regular and stable EMD components, allowing the long-range oscillation characteristics of the original time series to be better captured. On the other hand, we provide an ensemble mechanism to alleviate the error propagation problem in forecasts caused by iterative implementation of NSOR at all lead times and name it improved NSOR. Application of the proposed multi-scale framework to long-term forecasting of the daily PM2.5 at 14 monitoring stations in Hong Kong demonstrates that it can effectively capture the long-term variation in air pollution processes and significantly increase the forecasting performance. Specifically, the framework can, respectively, reduce the average root-mean-square error and the mean absolute error over all 14 stations by 8.4% and 9.2% for a lead time of 100 days, compared to previous studies. Additionally, better robustness can be obtained by the proposed ensemble framework for 180-day and 365-day long-term forecasting scenarios. It should be emphasized that the proposed ensemble multi-scale framework is a feasible framework, which is applicable for long-term time series forecasting in general.

Rising surface ozone due to anthropogenic activities and its impact on COVID-19 related deaths in Delhi, India

The rapidity and global spread of the COVID-19 pandemic have left several vital questions in the research community requiring coordinated investigation and unique perspectives to explore the relationship between the spread of disease and air quality. Previous studies have focused mainly on the relation of particulate matter concentration with COVID-19-related mortalities. In contrast, surficial ozone has not been given much attention as surface ozone is a primary air pollutant and directly impacts the respiratory system of humans. Hence, we analyzed the relationship between surface ozone pollution and COVID-19-related mortalities. In this study, we have analyzed the variability of various atmospheric pollutants (particulate matter (PM_2.5 and PM₁₀), Nitrogen dioxide (NO₂), Carbon monoxide (CO), and Ozone) in the National Capital Region (NCR) of India during 2020-2021 using station data and investigated the relationship of the air-quality parameters with the COVID-19 related deaths. In northern parts of India, the concentration of particulate matter (PM_2.5 and PM₁₀), Nitrogen dioxide (NO₂), Carbon monoxide (CO), and Ozone remain high during the pre- and post-monsoon seasons due to dust loading and crop residue burning (after winter wheat in April & summer rice in November). The westerly wind brings the polluted airmass from western and northwestern parts to Delhi and National Capital Region during April-June and October-November, and meteorological conditions help raise the concentration of these pollutants. Due to long solar hours and high CO concentrations, the ozone concentration is higher from April to June and September. While comparing major air quality parameters with COVID-19-related deaths, we found a good relationship between surface ozone and COVID-19 mortality in Delhi. We also observed a time lag relationship between ozone concentration and mortality in Delhi, so the exposure to Ozone in a large population of Delhi may have augmented the rise of COVID-19-related deaths. The analysis suggested that ozone has a significant relationship with COVID-19 related mortality in Delhi in comparison to other parameters.

Respiratory Deposition Dose of PM2.5 and PM10 Before, During and After COVID-19 Lockdown Phases in Megacity-Delhi, India

Considerable changes in particulate matter (PM) during COVID-19 lockdown in major cities around the World demand changes in exposure assessment studies of PM. The present study shows variations in respiratory deposition dose (RDD) of both fine (PM_2.5) and coarse (PM₁₀) particles before, during and after Covid-19 lockdown phases at three sites (with different pollution signatures) in Delhi—Alipur, Okhla and Pusa Road. Exposure assessment study showed mean PM_2.5 RDD (± S.D.) (µg/min) for walk and sit mode during before lockdown (BL) as 2.41(± 1.20) and 0.84(± 0.42) for Alipur, 2.71(± 1.60) and 0.94(± 0.56) for Okhla, and 2.54(± 1.28) and 0.88(± 0.44) for Pusa road, which decreased drastically during Lockdown 1(L1) as 0.85(± 0.35) and 0.30(± 0.12) for Alipur, 0.83(± 0.33) and 0.29(± 0.11) for Okhla, and 0.68(± 0.28) and 0.23(± 0.10) for Pusa road, respectively. Mean PM₁₀ RDD (± S.D.) (µg/min) for walk and sit mode during before lockdown (BL) as 3.90 (± 1.73) and 1.36 (± 0.60) for Alipur, 4.74 (± 2.04) and 1.65 (± 0.71) for Okhla, and 4.25 (± 1.69) and 1.48 (± 0.59) for Pusa Road, respectively which decreased drastically during Lockdown 1(L1) as 2.19 (± 0.95) and 0.76 (± 0.33) for Alipur, 1.73 (± 0.67) and 0.60 (± 0.23) for Okhla and, 1.45 (± 0.50) and 0.50 (± 0.17) for Pusa Road, respectively. Significant decrease in RDD concentrations (Both PM_2.5 and PM₁₀) than that of BL phase have been found during Lockdown 1(L1) phase and other successive lockdown and unlock phases—Lockdown 2(L2), Lockdown 3(L3), Lockdown 4(L4) and Unlock1 (UL1) phases. Changes in RDD values during lockdown phases were affected by lesser traffic emission, minimized industrial activities, biomass burning activities, precipitation activities, etc. Seasonal variations of RDD showed Delhites are found exposed to more fine and coarse particles’ RDD (walk and sit modes) before and after lockdown, i.e. during normal days than during lockdown phases showing potential health effects. People in sit condition found less exposed to fine and coarse RDD comparison to those in walk condition both during normal and lockdown days.

Interrelationship of Indoor Particulate Matter and Respiratory Dust Depositions of Women in the Residence of Dhanbad City, India

Women spend relatively more time in indoor environments in developing countries. Exposure to various indoor air pollutants leads them to higher health risks according to household air quality in which they reside. Particulate matter (PM) exposure with their exposure duration inside the household plays a significant role in women's respiratory problems. This study measured size-segregated particulate matter concentrations in 63 residences at different locations. Respiratory dust depositions (RDDs) for 118 women in their different respiratory regions like head airway (HD), tracheobronchial (TB), and alveolar (AL) regions for the three PM size fractions (PM₁₀, PM_2.5, and PM₁) were investigated. For different positions like light exercise and the sitting condition, RDDs values found for AL region were 0.091 μgmin^-1 (SD: 0.067, 0.012-0.408) and 0.028 μgmin^-1 (SD: 0.021, 0.003-0.126) for PM₁₀, 0.325 μgmin^-1 (SD: 0.254, 0.053-1.521) and 0.183 μgmin^-1 (SD: 0.143, 0.031-0.857) for PM_2.5, 0.257 μgmin^-1 (SD: 0.197, 0.043-1.04) and 0.057 μgmin^-1 (SD: 0.044, 0.009-0.233) respectively for PM₁ to females. RDDs values in the AL region significantly increase as PM₁₀ (11%), PM_2.5 (68%), and PM₁ (21%), confirming that for women, the AL region is the most prominent affected zone by fine particles (PM_2.5).

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.

Concentrations, transport characteristics, and health risks of PM2.5-bound trace elements over a national park in central India

Fine particulate matter (PM_2.5) mass and its chemical constituents were measured over Van Vihar National Park (VVNP) in Bhopal, central India. Fine PM collected over two years onto Teflon filters using a Mini-Vol® sampler were analyzed for trace elements using an Energy Dispersive X-ray fluorescence (ED-XRF) spectrometer. The temporal behaviour, dry deposition fluxes and transport pathways of elements, in addition to their health risks were examined in this study. S, K, Si, Al, Ca, and Fe accounted for most of the PM_2.5-bound trace elements (~88% on average). Pronounced seasonality was observed for major elements (S, K, and Cl) and reconstructed soil (estimated as the sum of oxides of crustal elements, i.e., Si, Al, Ca, Fe, and Ti), with winter and post-monsoon season highs, potentially due to source strengths and favourable metrology during these seasons. The synoptic meteorology during these seasons favoured the fetch of particles from highly polluted regions such as the Indo-Gangetic Plain. The estimated average dry depositional flux of each element in this study was comparable to those measured/estimated for each of these species over other urban areas. The sum of the dry deposition flux for crustal elements (1301.9 ± 880.7 μg m^-2 d^-1) was in agreement with global dust cycle models. Air-parcel trajectory cluster analysis revealed that S, K, and Cl were influenced by biomass and coal burning in predominantly in central, and northwestern India, while reconstructed soil was influenced by air masses from the Arabian and Thar deserts. Finally, human exposure risk assessment to carcinogens (As, Cr, Cd, Pb and Ni) and non-carcinogens (Cu, Zn, Mn, V, Hg, Se and Al) revealed that no significant risk was posed by these elements. The assessment in this study was a screening for severe adverse effects, rather than a speciated health assessment. Thus, over the study region, monitoring, health risk assessment and mitigation measures, where needed, must be enhanced to ensure that trace elements induced health effects continue to be within safe levels.

Regional scenario of air pollution in lockdown due to COVID-19 pandemic: Evidence from major urban agglomerations of India

Air pollution in India during COVID-19 lockdown, which imposed on 25th March to 31st May 2020, has brought a significant improvement in air quality. The present paper mainly focuses on the scenario of air pollution level (PM_2.5, PM₁₀, SO₂, NO₂ and O₃) across 57 urban agglomerations (UAs) of India during lockdown. For analysis, India has been divided into six regions - Northern, Western, Central, Southern, Eastern and North-Eastern. Various spatial statistical modelling with composite air quality index (CAQI) have been utilised to examine the spatial pattern of air pollution level. The result shows that concentration of all air pollutants decreased significantly (except O₃) during lockdown. The maximum decrease is the concentration of NO₂ (40%) followed by PM_2.5 (32%), PM₁₀ (24%) and SO₂ (18%). Among 57 UA's, only five - Panipat (1.00), Ghaziabad (0.76), Delhi (0.74), Gurugram (0.72) and Varanasi (0.71) had least improvement in air pollution level considering entire lockdown period. The outcome of this study has an immense scope to understand the regional scenario of air pollution level and to implement effective strategies for environmental sustainability.

Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980-2019 using a machine learning approach

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM_2.5 (particulate matter less than 2.5 μm in diameter) concentrations. This study constructs a gridded near-surface PM_2.5 concentration dataset across China covering 1980-2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM_2.5 concentrations are in excellent agreement with ground measurements, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, emissions and meteorological conditions are also key factors affecting PM_2.5 predictions. From 1980 to 2014, the model-predicted PM_2.5 concentrations increased constantly with the maximum growth rate of 5-10 μg/m³/decade over eastern China. Due to the clean air actions, PM_2.5 concentrations have decreased effectively at a rate over 50 μg/m³/decade in the North China Plain and 20-50 μg/m³/decade over many regions of China during 2014-2019. The newly generated dataset of 1-degree gridded PM_2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.

Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis

As new scientific evidence on health effects of air pollution is generated, air quality guidelines need to be periodically updated. The objective of this review is to support the derivation of updated guidelines by the World Health Organization (WHO) by performing a systematic review of evidence of associations between long-term exposure to particulate matter with diameter under 2.5 µm (PM_2.5) and particulate matter with diameter under 10 µm (PM₁₀), in relation to all-cause and cause-specific mortality. As there is especially uncertainty about the relationship at the low and high end of the exposure range, the review needed to provide an indication of the shape of the concentration-response function (CRF). We systematically searched MEDLINE and EMBASE from database inception to 9 October 2018. Articles were checked for eligibility by two reviewers. We included cohort and case-control studies on outdoor air pollution in human populations using individual level data. In addition to natural-cause mortality, we evaluated mortality from circulatory diseases (ischemic heart disease (IHD) and cerebrovascular disease (stroke) also specifically), respiratory diseases (Chronic Obstructive Pulmonary Disease (COPD) and acute lower respiratory infection (ALRI) also specifically) and lung cancer. A random-effect meta-analysis was performed when at least three studies were available for a specific exposure-outcome pair. Risk of bias was assessed for all included articles using a specifically developed tool coordinated by WHO. Additional analyses were performed to assess consistency across geographic region, explain heterogeneity and explore the shape of the CRF. An adapted GRADE (Grading of Recommendations Assessment, Development and Evaluation) assessment of the body of evidence was made using a specifically developed tool coordinated by WHO. A large number (N = 107) of predominantly cohort studies (N = 104) were included after screening more than 3000 abstracts. Studies were conducted globally with the majority of studies from North America (N = 62) and Europe (N = 25). More studies used PM_2.5 (N = 71) as the exposure metric than PM₁₀ (N = 42). PM_2.5 was significantly associated with all causes of death evaluated. The combined Risk Ratio (RR) for PM_2.5 and natural-cause mortality was 1.08 (95%CI 1.06, 1.09) per 10 µg/m³. Meta analyses of studies conducted at the low mean PM_2.5 levels (<25, 20, 15, 12, 10 µg/m³) yielded RRs that were similar or higher compared to the overall RR, consistent with the finding of generally linear or supra-linear CRFs in individual studies. Pooled RRs were almost identical for studies conducted in North America, Europe and Western Pacific region. PM₁₀ was significantly associated with natural-cause and most but not all causes of death. Application of the risk of bias tool showed that few studies were at a high risk of bias in any domain. Application of the adapted GRADE tool resulted in an assessment of "high certainty of evidence" for PM_2.5 with all assessed endpoints except for respiratory mortality (moderate). The evidence was rated as less certain for PM₁₀ and cause-specific mortality ("moderate" for circulatory, IHD, COPD and "low" for stroke mortality. Compared to the previous global WHO evaluation, the evidence base has increased substantially. However, studies conducted in low- and middle- income countries (LMICs) are still limited. There is clear evidence that both PM_2.5 and PM₁₀ were associated with increased mortality from all causes, cardiovascular disease, respiratory disease and lung cancer. Associations remained below the current WHO guideline exposure level of 10 µg/m³ for PM_2.5. Systematic review registration number (PROSPERO ID): CRD42018082577.

Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010-2016

Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010-2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7-62.7 μg/m3 with prediction bias ranging between 4.6-11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

Impact of Open Burning Refuse on Air Quality: In the Case of "Hidar Sitaten" at Addis Ababa, Ethiopia

Open burning of refuse is one of the key sources that causes high air pollution in Metropolitan cities. This paper identifies pollutant concentration of particulate matter (PM_2.5) emission and air quality index categories with the peak hour interval on Hidar Sitaten day, and present analysis of air quality in Addis Ababa from August 2016 to November 2019. Daily records, with a 1-hour interval, of raw concentration of air pollutant and air quality index data, were obtained from the AirNow website of Addis Ababa central monitoring station. The data collected were analyzed using descriptive statistics of the mean air quality index and concentration of PM_2.5. Accordingly, the study revealed that the peak hour for high pollutant concentration emission ranges between 8 pm to 11 pm hours, and the mean air quality index was more than a moderate level. Particularly, on Hidar Sitaten in 2019 at 9 pm the maximum concentration of PM_2.5 was 8.6 times higher than WHO air quality guideline standard of daily allowance. The highest mean of air quality index and concentration of PM_2.5 recorded was 112 and 44.2 µg/m³ on 21 November 2017, respectively, and it was found to be unhealthy for sensitive groups. This implies that the concentration of PM_2.5 was harmful to people who are unusually sensitive to particulate pollution and have health problems. Therefore, public participation and strong regulations are needed on air quality management to strike a balance between a cultural practice of Hidar Sitaten and healthy air quality.

The Donora Smog Revisited: 70 Years After the Event That Inspired the Clean Air Act

At a storefront museum approximately 25 miles southeast of Pittsburgh, Pennsylvania, a sign reads, "Clean Air Started Here." This is not hyperbole. At the end of October 1948, the communities of Donora and Webster in Pennsylvania were visited by a smog that changed the face of environmental protection in the United States. Conservative estimates showed that 20 individuals died, while an additional 5900-43% of the population of Donora-were affected by the smog. This event led to the first large-scale epidemiological investigation of an environmental health disaster in the United States. Questions remain about the long-term effects of the smog, because higher rates of cardiovascular disease and cancer than were expected were observed in the region in the decade following the smog. Recent work has suggested that environmental contaminants from a bygone era in Donora might have an impact even today. In addition, reports regarding air pollution have indicated that levels of pollutants similar to those estimated to have occurred in Donora are currently present in some rapidly industrializing regions of China and India. Seventy years after the smog, this event still resonates.

Ambient Particulate Air Pollution and Blood Pressure in Peri-urban India

BACKGROUND

Evidence linking long-term exposure to particulate air pollution to blood pressure (BP) in high-income countries may not be transportable to low- and middle-income countries. We examined cross-sectional associations between ambient fine particulate matter (PM2.5) and black carbon (BC) with BP (systolic [SBP] and diastolic [DBP]) and prevalent hypertension in adults from 28 peri-urban villages near Hyderabad, India.

METHODS

We studied 5531 participants from the Andhra Pradesh Children and Parents Study (18-84 years, 54% men). We measured BP (2010-2012) in the right arm and defined hypertension as SBP ≥130 mmHg and/or DBP ≥80 mmHg. We used land-use regression models to estimate annual average PM2.5 and BC at participant's residence. We applied linear and logistic nested mixed-effect models stratified by sex and adjusted by cooking fuel type to estimate associations between within-village PM2.5 or BC and health.

RESULTS

Mean (SD) PM2.5 was 33 µg/m (2.7) and BC was 2.5 µg/m (0.23). In women, a 1 µg/m increase in PM2.5 was associated with 1.4 mmHg higher SBP (95% confidence interval [CI]: 0.12, 2.7), 0.87 mmHg higher DBP (95% CI: -0.18, 1.9), and 4% higher odds of hypertension (95% CI: 0%, 9%). In men, associations with SBP (0.52 mmHg; 95% CI: -0.82, 1.8), DBP (0.41 mmHg; 95% CI: -0.69, 1.5), and hypertension (2% higher odds; 95% CI: -2%, 6%) were weaker. No associations were observed with BC.

CONCLUSION

We observed a positive association between ambient PM2.5 and BP and hypertension in women. Longitudinal studies in this region are needed to corroborate our findings.

Modeling the Impact of an Indoor Air Filter on Air Pollution Exposure Reduction and Associated Mortality in Urban Delhi Household

Indoor exposure to fine particulate matter (PM_2.5) is a prominent health concern. However, few studies have examined the effectiveness of long-term use of indoor air filters for reduction of PM_2.5 exposure and associated decrease in adverse health impacts in urban India. We conducted 20 simulations of yearlong personal exposure to PM_2.5 in urban Delhi using the National Institute of Standards and Technology's CONTAM program (NIST, Gaithersburg, MD, USA). Simulation scenarios were developed to examine different air filter efficiencies, use schedules, and the influence of a smoker at home. We quantified associated mortality reductions with Household Air Pollution Intervention Tool (HAPIT, University of California, Berkeley, CA, USA). Without an air filter, we estimated an annual mean PM_2.5 personal exposure of 103 µg/m³ (95% Confidence Interval (CI): 93, 112) and 137 µg/m³ (95% CI: 125, 149) for households without and with a smoker, respectively. All day use of a high-efficiency particle air (HEPA) filter would reduce personal PM_2.5 exposure to 29 µg/m³ and 30 µg/m³, respectively. The reduced personal PM_2.5 exposure from air filter use is associated with 8-37% reduction in mortality attributable to PM_2.5 pollution in Delhi. The findings of this study indicate that air filter may provide significant improvements in indoor air quality and result in health benefits.

Recent spatial gradients and time trends in Dhaka, Bangladesh, air pollution and their human health implications

Dhaka, the capital of Bangladesh, is among the most polluted cities in the world. This research evaluates seasonal patterns, day-of-week patterns, spatial gradients, and trends in PM2.5 (<2.5 µm in aerodynamic diameter), PM10 (<10 µm in aerodynamic diameter), and gaseous pollutants concentrations (SO2, NO2, CO, and O3) monitored in Dhaka from 2013 to 2017. It expands on past work by considering multiple monitoring sites and air pollutants. Except for ozone, the average concentrations of these pollutants showed strong seasonal variation, with maximum during winter and minimum during monsoon, with the pollution concentration of PM2.5 and PM10 being roughly five- to sixfold higher during winter versus monsoon. Our comparisons of the pollutant concentrations with Bangladesh NAAQS and U.S. NAAQS limits analysis indicate particulate matter (PM2.5 and PM10) as the air pollutants of greatest concern, as they frequently exceeded the Bangladesh NAAQS and U.S. NAAQS, especially during nonmonsoon time. In contrast, gaseous pollutants reported far fewer exceedances throughout the study period. During the study period, the highest number of exceedances of NAAQS limits in Dhaka City (Darus-Salam site) were found for PM2.5 (72% of total study days), followed by PM10 (40% of total study days), O3 (1.7% of total study days), SO2 (0.38% of total study days), and CO (0.25% of total study days). The trend analyses results showed statistically significant positive slopes over time for SO2 (5.6 ppb yr-1, 95% confidence interval [CI]: 0.7, 10.5) and CO (0.32 ppm yr-1, 95% CI: 0.01, 0.56), which suggest increase in brick kilns operation and high-sulfur diesel use. Though statistically nonsignificant annual decreasing slopes for PM2.5 (-4.6 µg/m3 yr-1, 95% CI: -12.7, 3.6) and PM10 (-2.7 µg/m3 yr-1, 95% CI: -7.9, 2.5) were observed during this study period, the PM2.5 concentration is still too high (~ 82.0 µg/m3) and can cause severe impact on human health. Implications: This study revealed key insights into air quality challenges across Dhaka, Bangladesh, indicating particulate matter (PM) as Dhaka's most serious air pollutant threat to human health. The results of these analyses indicate that there is a need for immediate further investigations, and action based on those investigations, including the conduct local epidemiological PM exposure-human health effects studies for this city, in order to determine the most public health effective interventions.

Air pollution associated epigenetic modifications: Transgenerational inheritance and underlying molecular mechanisms

Air pollution is one of the leading causes of deaths in Southeast Asian countries including India. Exposure to air pollutants affects vital cellular mechanisms and is intimately linked with the etiology of a number of chronic diseases. Earlier work from our laboratory has shown that airborne particulate matter disturbs the mitochondrial machinery and causes significant damage to the epigenome. Mitochondrial reactive oxygen species possess the ability to trigger redox-sensitive signaling mechanisms and induce irreversible epigenomic changes. The electrophilic nature of reactive metabolites can directly result in deprotonation of cytosine at C-5 position or interfere with the DNA methyltransferases activity to cause alterations in DNA methylation. In addition, it also perturbs level of cellular metabolites critically involved in different epigenetic processes like acetylation and methylation of histone code and DNA hypo or hypermethylation. Interestingly, these modifications may persist through downstream generations and result in the transgenerational epigenomic inheritance. This phenomenon of subsequent transfer of epigenetic modifications is mainly associated with the germ cells and relies on the germline stability of the epigenetic states. Overall, the recent literature supports, and arguably strengthens, the contention that air pollution might contribute to transmission of epimutations from gametes to zygotes by involving mitochondrial DNA, parental allele imprinting, histone withholding and non-coding RNAs. However, larger prospective studies using innovative, integrated epigenome-wide metabolomic strategy are highly warranted to assess the air pollution induced transgenerational epigenetic inheritance and associated human health effects.

Chemical composition and source apportionment of ambient, household, and personal exposures to PM2.5 in communities using biomass stoves in rural China

Fine particulate matter (PM_2.5) has health effects that may depend on its sources and chemical composition. Few studies have quantified the composition of personal and area PM_2.5 in rural settings over the same time period. Yet, this information would shed important light on the sources influencing personal PM_2.5 exposures. This study investigated the sources and chemical composition of 40 personal exposure, 40 household, and 36 ambient PM_2.5 samples collected in the non-heating and heating seasons in rural southwestern China. Chemical analysis included black carbon (BC), water-soluble components (ions, organic carbon), elements, and organic tracers. Source apportionment was conducted using organic tracer concentrations in a Chemical Mass Balance model. Biomass burning was the largest identified PM_2.5 source contributor to household (average, SD: 48 ± 11%) and exposures (31 ± 6%) in both seasons, and ambient PM_2.5 in winter (20 ± 4%). Food cooking also contributed to household and personal PM, reaching approximately half of the biomass contributions. Secondary inorganic aerosol was the major identified source in summertime ambient PM_2.5 (32 ± 14%), but was present in all samples (summer: 10 ± 3% [household], 13 ± 6% [exposures]; winter: 18 ± 2% [ambient], 7 ± 2% [household], 8 ± 2% [exposures]). Dust concentrations and fractional contribution to total PM_2.5 were higher in summer exposure samples (7 ± 4%) than in ambient or household samples (6 ± 1% and 2 ± 1%, respectively). Indoor sources comprised up to one-fifth of ambient PM_2.5, and outdoor sources (vehicles, secondary aerosols) contributed up to 15% of household PM_2.5. While household sources were the main contributors to PM_2.5 exposures in terms of mass, inorganic components of personal exposures differed from household samples. Based on these findings, health-focused initiatives to reduce harmful PM_2.5 exposures may consider a coordinated approach to address both indoor and outdoor PM_2.5 source contributors.

Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India

Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m³, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7⁻35.2, regional range: 8.5⁻42.0), compared to an average of 53.7 months (95% CI: 46.3⁻61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.

Indoor air pollution in rural north-east India: Elemental compositions, changes in haematological indices, oxidative stress and health risks

Chronic smoke exposure, emitted by biomass fuel burning leads to many diseases, which are originated due to oxidative stress. The present study investigated the levels of PM_2.5, PM₁₀ and PM_2.5 bound trace metals released during cooking with fuelwood and subsequent changes in haematological parameters along with oxidative stress in rural tribal women of northeast India exposed to wood smoke. The levels of PM_2.5, PM₁₀ and trace metals associated with PM_2.5 (nickel, cobalt, manganese, zinc, cadmium, lead and copper) were measured. In addition, blood samples were analyzed for concentrations of different blood related parameters (haemoglobin, platelet count, red blood cells and white blood cells) and levels of antioxidants (reduced glutathione, superoxide dismutase, and catalase). Plasma malondialdehyde (MDA) was measured as a biomarker of lipid peroxidation. Health risk assessment was done to assess the potential risk posed by inhalation of fine particles emitted from cooking with fuel wood. Levels of both PM_2.5 and PM₁₀ were higher in wood users compared to LPG users during cooking period (644.4 ± 368.3 µg/m³ vs 50 ± 23.8 µg/m³; 915 ± 441.3 µg/m³ vs 83.3 ± 33 µg/m³) and it exceeded the permissible limits of WHO. Levels of trace metals during the cooking period in fuel wood users were significantly higher than LPG users (p = 0.01). After controlling possible confounders, both platelet count and white blood cells (WBC) had a significant positive association with PM_2.5and PM₁₀. Similarly, haemoglobin had a negative association with both PM_2.5 and PM₁₀. Depleted levels of antioxidant enzymes and increase in lipid peroxidation (MDA) suggest a close association with pollutants released from wood smoke, indicating oxidative stress in women who used fuelwood for cooking. The total hazard quotient (HQ) of 0.11 was within the acceptable limit (i.e., 1.0). The total excess lifetime cancer risk (ELCR) was 5.4 × 10^-6 which is five times higher than the acceptable limit of 1.0 × 10^-6. Individual carcinogenic risk of Ni (2.3 × 10^-6) and Cd (3.1 × 10^-6) were also higher compared to acceptable limit. These results indicate that tribal women cooking with wood are at greater risk of developing cancer and also give support to the positive association between wood smoke and oxidative stress.

When, Where, and What? Characterizing Personal PM2.5 Exposure in Periurban India by Integrating GPS, Wearable Camera, and Ambient and Personal Monitoring Data

Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM_2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM_2.5 concentrations; questionnaire, GPS, and wearable camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m³ (SD 24.6) for men and 39.7 μg/m³ (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM_2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.

Impact of Maternal Air Pollution Exposure on Children's Lung Health: An Indian Perspective

Air pollution has become an emerging invisible killer in recent years and is a major cause of morbidity and mortality globally. More than 90% of the world’s children breathe toxic air every day. India is among the top ten most highly polluted countries with an average PM₁₀ level of 134 μg/m³ per year. It is reported that 99% of India’s population encounters air pollution levels that exceed the World Health Organization Air Quality Guideline, advising a PM_2.5 permissible level of 10 μg/m³. Maternal exposure to air pollution has serious health outcomes in offspring because it can affect embryonic phases of development during the gestation period. A fetus is more prone to effects from air pollution during embryonic developmental phases due to resulting oxidative stress as antioxidant mechanisms are lacking at that stage. Any injury during this vulnerable period (embryonic phase) will have a long-term impact on offspring health, both early and later in life. Epidemiological studies have revealed that maternal exposure to air pollution increases the risk of development of airway disease in the offspring due to impaired lung development in utero. In this review, we discuss cellular mechanisms involved in maternal exposure to air pollution and how it can impact airway disease development in offspring. A better understanding of these mechanisms in the context of maternal exposure to air pollution can offer a new avenue to prevent the development of airway disease in offspring.

Wearable camera-derived microenvironments in relation to personal exposure to PM2.5

Data regarding which microenvironments drive exposure to air pollution in low and middle income countries are scarce. Our objective was to identify sources of time-resolved personal PM_2.5 exposure in peri-urban India using wearable camera-derived microenvironmental information. We conducted a panel study with up to 6 repeated non-consecutive 24 h measurements on 45 participants (186 participant-days). Camera images were manually annotated to derive visual concepts indicative of microenvironments and activities. Men had slightly higher daily mean PM_2.5 exposure (43 μg/m³) compared to women (39 μg/m³). Cameras helped identify that men also had higher exposures when near a biomass cooking unit (mean (sd) μg/m³: 119 (383) for men vs 83 (196) for women) and presence in the kitchen (133 (311) for men vs 48 (94) for women). Visual concepts associated in regression analysis with higher 5-minute PM_2.5 for both sexes included: smoking (+93% (95% confidence interval: 63%, 129%) in men, +29% (95% CI: 2%, 63%) in women), biomass cooking unit (+57% (95% CI: 28%, 93%) in men, +69% (95% CI: 48%, 93%) in women), visible flame or smoke (+90% (95% CI: 48%, 144%) in men, +39% (95% CI: 6%, 83%) in women), and presence in the kitchen (+49% (95% CI: 27%, 75%) in men, +14% (95% CI: 7%, 20%) in women). Our results indicate wearable cameras can provide objective, high time-resolution microenvironmental data useful for identifying peak exposures and providing insights not evident using standard self-reported time-activity.

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions

This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM_2.5) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM_2.5 concentrations during June 2015-May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM_2.5 concentration was ∼30 μg m^-3 (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m^-3) during 2-5% of observation days. Average concentrations were ∼25 μg m^-3 higher during winter than during monsoon and ∼8 μg m^-3 higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM_2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM_2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations.

Comparison of Ground-Based PM2.5 and PM10 Concentrations in China, India, and the U.S

Urbanization and industrialization have spurred air pollution, making it a global problem. An understanding of the spatiotemporal characteristics of PM_2.5 and PM₁₀ concentrations (particulate matter with an aerodynamic diameter of less than 2.5 μm and 10 μm, respectively) is necessary to mitigate air pollution. We compared the characteristics of PM_2.5 and PM₁₀ concentrations and their trends of China, India, and the U.S. from 2014 to 2017. Particulate matter levels were lowest in the U.S., while China showed higher concentrations, and India showed the highest. Interestingly, significant declines in PM_2.5 and PM₁₀ concentrations were found in some of the most polluted regions in China as well as the U.S. No comparable decline was observed in India. A strong seasonal trend was observed in China and India, with the highest values occurring in winter and the lowest in summer. The opposite trend was noted for the U.S. PM_2.5 was highly correlated with PM₁₀ for both China and India, but the correlation was poor for the U.S. With regard to reducing particulate matter pollutant concentrations, developing countries can learn from the experiences of developed nations and benefit by establishing and implementing joint regional air pollution control programs.

Exposures to fine particulate matter (PM2.5) and birthweight in a rural-urban, mother-child cohort in Tamil Nadu, India

BACKGROUND

Exposure to PM_2.5 (fine particulate matter <less than 2.5µm in aerodynamic diameter) related to ambient and household air pollution has been associated with low birthweight. Few of these studies, however, have been conducted in high exposure settings that are commonly encountered in low and middle income countries (LMICs).

OBJECTIVES

We examined whether PM_2.5 exposures during pregnancy were associated with birthweight in an integrated rural-urban, mother-child cohort in the state of Tamil Nadu, India.

METHODS

We recruited 1285 pregnant women in the first trimester of pregnancy from primary health care centers and urban health posts and followed them until birth to collect antenatal care data and birthweight. We estimated pregnancy period PM _2.5 exposures through direct serial measurements of 24-h household PM_2.5 concentrations, performed across each trimester. Mothers also completed detailed questionnaires to provide data on covariates related to household, socio-economic, demographic and maternal health characteristics. The association between PM_2.5 exposures and birth weight was assessed using linear and logistic regression models that controlled for potential confounders.

RESULTS

A 10-μg/m³ increase in pregnancy period PM_2.5 exposures was associated with a 4g (95% CI: 1.08g, 6.76g) decrease in birthweight and 2% increase in prevalence of low birthweight [odds ratio(OR) = 1.02; 95%CI:1.005,1.041] after adjusting for gestational age, infant sex, maternal BMI, maternal age, history of a previous low birth weight child, birth order and season of conception.

CONCLUSIONS

The study provides some of the first quantitative effects estimates for linking rural-urban PM_2.5 exposures and birthweight in India, adding important evidence for this association from high exposure settings in LMICs, that also experience dual health burdens from ambient and household air pollution. Study results also point to the need for considering maternal PM_2.5 exposures alongside other risk factors for low birthweight in India.

Spatio-Temporal Variation of Particulate Matter(PM2.5) Concentrations and Its Health Impacts in a Mega City, Delhi in India

Rising concentration of air pollution and its associated health effects is rapidly increasing in India, and Delhi, being the capital city, has drawn our attention in recent years. This study was designed to analyze the spatial and temporal variations of particulate matter (PM_2.5) concentrations in a mega city, Delhi. The daily PM_2.5 concentrations monitored by the Central Pollution Control Board (CPCB), New Delhi during November 2016 to October 2017 in different locations distributed in the region of the study were used for the analysis. The descriptive statistics indicate that the spatial mean of monthly average PM_2.5 concentrations ranged from 45.92 μg m^-3 to 278.77 μg m^-3. The maximum and minimum spatial variance observed in the months of March and September, respectively. The study also analyzed the PM_2.5 air quality index (PM_2.5-Air Quality Index (AQI)) for assessing the health impacts in the study area. The AQI value was determined according to the U.S. Environmental Protection Agency (EPA) system. The result suggests that most of the area had the moderate to very unhealthy category of PM_2.5-AQI and that leads to severe breathing discomfort for people residing in the area. It was observed that the air quality level was worst during winter months (October to January).

Status of indoor air pollution (IAP) through particulate matter (PM) emissions and associated health concerns in South Asia

Exposure to particulate emissions poses a variety of public health concerns worldwide, specifically in developing countries. This review summarized the documented studies on indoor particulate matter (PM) emissions and their major health concerns in South Asia. Reviewed literature illustrated the alarming levels of indoor air pollution (IAP) in India, Pakistan, Nepal, and Bangladesh, while Sri Lanka and Bhutan are confronted with relatively lower levels, albeit not safe. To our knowledge, data on this issue are absent from Afghanistan and Maldives. We found that the reported levels of PM₁₀ and PM_2.5 in Nepal, Pakistan, Bangladesh, and India were 2-65, 3-30, 4-22, 2-28 and 1-139, 2-180, 3-77, 1-40 fold higher than WHO standards for indoor PM₁₀ (50 μg/m³) and PM_2.5 (25 μg/m³), respectively. Regarding IAP-mediated health concerns, mortality rates and incidences of respiratory and non-respiratory diseases were increasing with alarming rates, specifically in India, Pakistan, Nepal, and Bangladesh. The major cause might be the reliance of approximately 80% population on conventional biomass burning in the region. Current review also highlighted the prospects of IAP reduction strategies, which in future can help to improve the status of indoor air quality and public health in South Asia.

Health Effects of Ambient Air Pollution in Developing Countries

The deleterious effects of ambient air pollution on human health have been consistently documented by many epidemiologic studies worldwide, and it has been calculated that globally at least seven million deaths are annually attributable to the effects of air pollution. The major air pollutants emitted into the atmosphere by a number of natural processes and human activities include nitrogen oxides, volatile organic compounds, and particulate matter. In addition to the poor ambient air quality, there is increasing evidence that indoor air pollution also poses a serious threat to human health, especially in low-income countries that still use biomass fuels as an energy resource. This review summarizes the current knowledge on ambient air pollution in financially deprived populations.

PM2.5 exposure in highly polluted cities: A case study from New Delhi, India

Personal exposure (PE) to air pollutants is driven by a combination of pollutant concentrations in indoor and outdoor environments, and time-activity pattern of individuals. The objectives of this study were to estimate personal exposure to PM_2.5 and black carbon (BC), and assess the representability of ambient air quality monitoring stations to serve as surrogates for PE in New Delhi. Personal exposure to air pollutants (PM_2.5-PE and BC_PE) was measured using portable, battery-operated instruments (PM_2.5- pDR1500 and BC- microAethalometer AE51) in a small cohort of healthy adults (n=12 in summer, n=6 in winter) with no occupational exposure. Average PM_2.5-PE and BC_PE (µg/m³) were 53.9±136 and 3.71±4.29 respectively, in summer and 489.2±209.2 and 23.3±14.9 respectively, in winter. Activities associated with highest exposure levels were cooking and indoor cleaning for PM_2.5, and commuting for BC. Within transport microenvironments, autorickshaws were found to be the most polluted, and lowest BC exposure was registered in public buses. Comparison of fixed-site ambient monitoring data showed a higher correlation with personal exposure dataset in winter compared to summer (r² of 0.51 (winter) and 0.21 (summer); 51% (winter) and 20% (summer)). This study highlights the need for detailed assessment of PE to air pollutants in Indian cities, and calls for a denser network of monitoring stations for better exposure assessment.

Integrated assessment of exposure to PM2.5 in South India and its relation with cardiovascular risk: Design of the CHAI observational cohort study

While there is convincing evidence that fine particulate matter causes cardiovascular mortality and morbidity, little of the evidence is based on populations outside of high income countries, leaving large uncertainties at high exposures. India is an attractive setting for investigating the cardiovascular risk of particles across a wide concentration range, including concentrations for which there is the largest uncertainty in the exposure-response relationship. CHAI is a European Research Council funded project that investigates the relationship between particulate air pollution from outdoor and household sources with markers of atherosclerosis, an important cardiovascular pathology. The project aims to (1) characterize the exposure of a cohort of adults to particulate air pollution from household and outdoor sources (2) integrate information from GPS, wearable cameras, and continuous measurements of personal exposure to particles to understand where and through which activities people are most exposed and (3) quantify the association between particles and markers of atherosclerosis. CHAI has the potential to make important methodological contributions to modeling air pollution exposure integrating outdoor and household sources as well as in the application of wearable camera data in environmental exposure assessment.

The health burden and economic costs averted by ambient PM2.5 pollution reductions in Nagpur, India

National estimates of the health and economic burdens of exposure to ambient fine particulate matter (PM_2.5) in India reveal substantial impacts. This information, often lacking at the local level, can justify and drive mitigation interventions. Here, we assess the health and economic gains resulting from attainment of WHO guidelines for PM_2.5 concentrations - including interim target 2 (IT-2), interim target 3 (IT-3), and the WHO air quality guideline (AQG) - in Nagpur district to inform policy decision making for mitigation. We conducted a detailed assessment of concentrations of PM_2.5 in 9 areas, covering urban, peri-urban and rural environments, from February 2013 to June 2014. We used a combination of hazard and survival analyses based on the life table method to calculate attributed annual number of premature deaths and disability-adjusted life years (DALYs) for five health outcomes linked to PM_2.5 exposure: acute lower respiratory infection for children <5years, ischemic heart disease, chronic obstructive pulmonary disease, stroke and lung cancer in adults ≥25years. We used GBD 2013 data on deaths and DALYs for these diseases. We calculated averted deaths, DALYs and economic loss resulting from planned reductions in average PM_2.5 concentration from current level to IT-2, IT-3 and AQG by the years 2023, 2033 and 2043, respectively. The economic cost for premature mortality was estimated as the product of attributed deaths and value of statistical life for India, while morbidity was assumed to be 10% of the mortality cost. The annual average PM_2.5 concentration in Nagpur district is 34±17μgm^-3 and results in 3.3 (95% confidence interval [CI]: 2.6, 4.2) thousand premature deaths and 91 (95% CI: 68, 116) thousand DALYs in 2013 with economic loss of USD 2.2 (95% CI: 1.7, 2.8) billion in that year. It is estimated that interventions that achieve IT-2, IT-3 and AQG by 2023, 2033 and 2043, would avert, respectively, 15, 30 and 36%, of the attributed health and economic loss in those years, translating into an impressively large health and economic gain. To achieve this, we recommend an exposure-integrated source reduction approach.