Valuing burden of premature mortality attributable to air pollution in major million-plus non-attainment cities of India

Effectiveness of respiratory face masks in reducing acute PM2.5 pollution exposure during peak pollution period in Delhi

The cities of North India, such as Delhi, face a significant public health threat from severe air pollution. Between October 2021 and January 2022, 79 % of Delhi's daily average PM2.5 (Particulate matter with an aerodynamic diameter ≤ 2.5 μm) values exceeded 100 μg/m3 (the permissible level being 60 μg/m3 as per Indian standards). In response to this acute exposure, using Respiratory Face Masks (RFMs) is a cost-effective solution to reduce immediate health risks while policymakers develop long-term emission control plans. Our research focuses on the health and economic benefits of using RFMs to prevent acute exposure to PM2.5 pollution in Delhi for different age groups. Our findings indicate that, among the fifty chosen RFMs, M50 has greatest potential to prevent short-term excess mortality (908 in age ranges 5-44), followed by M49 (745) and M48 (568). These RFMs resulted in estimated economic benefits of 500.6 (46 %), 411.1 (37 %), and 313.4 (29 %) million Indian Rupee (INR), respectively during October-January 2021-22. By wearing RFMs such as M50, M49, and M48 during episodes of bad air quality, it is estimated that 13 % of short-term excess mortality and associated costs could be saved if at least 30 % of Delhi residents followed an alert issued by an operational Air Quality Early Warning System (AQEWS) developed by the Ministry of Earth Sciences. Our research suggests that RFMs can notably decrease health and economic burdens amid peak PM2.5 pollution in post-monsoon and winter seasons until long-term emission reduction strategies are adopted. It is suggested that an advisory may be crafted in collaboration with statutory bodies and should be disseminated to assist the vulnerable population in using RFMs during winter. The analysis presented in this research is purely science based and outcomes of study are in no way to be construed as endorsement of product.

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Epidemiological trends and age-period-cohort effects on cardiovascular diseases burden attributable to ambient air pollution across BRICS

Long-term exposure to ambient air pollution raises the risk of deaths and morbidity worldwide. From 1990 to 2019, we observed the epidemiological trends and age-period-cohort effects on the cardiovascular diseases (CVD) burden attributable to ambient air pollution across Brazil, Russia, India, China, and South Africa (BRICS). The number of CVD deaths related to ambient particulate matter (PM) pollution increased nearly fivefold in China [5.0% (95% CI 4.7, 5.2)] and India [5.7% (95% CI 5.1, 6.3)] during the study period. The age-standardized CVD deaths and disability-adjusted life years (DALYs) due to ambient PM pollution significantly increased in India and China but decreased in Brazil and Russia. Due to air pollution, the relative risk (RR) of premature CVD mortality (< 70 years) was higher in Russia [RR 12.6 (95% CI 8.7, 17.30)] and India [RR 9.2 (95% CI 7.6, 11.20)]. A higher period risk (2015-2019) for CVD deaths was found in India [RR 1.4 (95% CI 1.4, 1.4)] followed by South Africa [RR 1.3 (95% CI 1.3, 1.3)]. Across the BRICS countries, the RR of CVD mortality markedly decreased from the old birth cohort to young birth cohorts. In conclusion, China and India showed an increasing trend of CVD mortality and morbidity due to ambient PM pollution and higher risk of premature CVD deaths were observed in Russia and India.

A scoping review of human health co-benefits of forest-based climate change mitigation in Europe

Climate change is a pressing global challenge with profound implications for human health. Forest-based climate change mitigation strategies, such as afforestation, reforestation, and sustainable forest management, offer promising solutions to mitigate climate change and simultaneously yield substantial co-benefits for human health. The objective of this scoping review was to examine research trends related to the interdisciplinary nexus between forests as carbon sinks and human health co-benefits. We developed a conceptual framework model, supporting the inclusion of exposure pathways, such as recreational opportunities or aesthetic experiences, in the co-benefit context. We used a scoping review methodology to identify the proportion of European research on forest-based mitigation strategies that acknowledge the interconnection between mitigation strategies and human impacts. We also aimed to assess whether synergies and trade-offs between forest-based carbon sink capacity and human co-benefits has been analysed and quantified. From the initial 4,062 records retrieved, 349 reports analysed European forest management principles and factors related to climate change mitigation capacity. Of those, 97 studies acknowledged human co-benefits and 13 studies quantified the impacts on exposure pathways or health co-benefits and were included for full review. Our analysis demonstrates that there is potential for synergies related to optimising carbon sink capacity together with human co-benefits, but there is currently a lack of holistic research approaches assessing these interrelationships. We suggest enhanced interdisciplinary efforts, using for example multideterminant modelling approaches, to advance evidence and understanding of the forest and health nexus in the context of climate change mitigation.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Environmental management: a country-level evaluation of atmospheric particulate matter removal by the forests of India

Particulate matter (PM) is a critical air pollutant, responsible for an array of ailments leading to premature mortality worldwide. Nature-based solutions for mitigation of PM and especially role of forests in mitigating PM from an ecosystem perspective are less explored. Forests provide a natural pollution abatement strategy by providing a surface area for the deposition of PM. Depending on their structure and composition, forests have varying capacities for PM adsorption, which is again less explored. Hence, in the present study, we evaluate the removal capacity of PM by the forest-type groups of India. Deposition flux and total PM removal across sixteen forest types were estimated based on the 2019 dataset of PM using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. Externality values and PM removal costs by industrial equipment were used for associating an economic value to the air pollution abatement service by forests. The total PM2.5 removal by forests in 2019 was estimated to be 1361.28 tons and PM10 was estimated to be 303,658.27 tons. Deposition of PM was found to be high in littoral and swamp forests, tropical semi-evergreen forests, tropical moist deciduous forests, and sub-tropical pine forests. Tropical dry deciduous forests had the highest net weight % removal of PM with 39% removal for PM2.5 and 39% removal for PM10. The air pollution abatement service by forests for PM removal was 188 M US dollars (USD) with externality-based removal service by forests of 2009 M USD. The net PM removed by all forests of India was estimated to be approximately worth ₹ 470-648 Crore (59-81 million dollars) for PM2.5 and worth ₹56,746-1,22,617 Crore (7093-15,327 million dollars) for PM10 based on valuation using value transfer method. The study concludes that forests can be a significant contributor to PM reduction at a global level. Especially for India's National Clean Air Programme and further research and policy considerations, the findings would be extremely useful.

Evaluating the real-world emissions of diesel passenger Car in Indian heterogeneous traffic

A 30 portable emission measurement system (PEMS) test was conducted in this study to examine the effect of driving modes (aggressive and normal) and road type (urban and rural) on tailpipe emissions. Driving modes were assessed using relative positive acceleration and velocity × positive acceleration factors. The findings revealed that aggressive and normal driving modes differed significantly on urban and rural roads, as evident from paired sample t-test (p < 0.05). Furthermore, aggressive driving exhibited more prominent speed and acceleration on rural roads, while normal driving modes showed consistent acceleration or speed patterns regardless of road conditions as observed from kernel density estimation and box plot analysis. Emission rates (CO, CO2, HC, and NOx) significantly varied between aggressive and normal driving modes on urban and rural roads, as indicated by paired sample t-test analysis (p < 0.05). Aggressive driving increased CO2, CO, and HC emission rates for acceleration and deceleration modes by 18% to 40% compared to normal driving. Aggressive driving modes increased the emission factors (CO, HC, and CO2) by 5% to 25% compared to the normal driving mode on both urban and rural roads. Moreover, the NOx emission factors were also found significant during normal driving conditions on urban roads. This study provides real-world emission factors of diesel cars considering the impact of route, vehicle familiarity, and driving behavior induced by varying traffic conditions, which will contribute to improve the current emissions inventory on both a local and global level.

Verbal autopsy to assess causes of mortality among the economically productive age group in the tribal region of Melghat, central India

Background & Objectives

Verbal autopsy (VA) is the systematic and retrospective inquiry (from relatives) about the symptoms of an illness prior to death. In tribal India, 67-75 per cent of deaths occur at home with an unknown cause of death (CoD). Hence, the aim of this study was to determine the CoD in the 16-60 yr age group utilizing VA.

Methods

A prospective, community based longitudinal study was conducted in 32 tribal villages in the Melghat region of Maharashtra, between 2004 and 2020. Number of deaths and VAs in 16-60 yr age group were collected by village health workers (VHWs) and supervisors, verified by five different persons (internal-external) and cross-checked by three VA interpretation trained physicians. A modified version of WHO VA was used. Cause-specific mortality fractions were calculated.

Results

Of the 1011 deaths recorded, mortality in males was significantly higher than females (P<0.001). A total of 763 VAs were conducted which revealed that tuberculosis was the leading CoD, followed by jaundice, heart diseases, diarrhoea, central nervous system infections and suicide. Suicides were significantly more common among males than in females (P=0.046). Significantly, more deaths occurred during the monsoon (P=0.002), especially diarrhoeal deaths (P=0.024).

Interpretation & conclusions

The findings of this study suggest that, in Indian tribal areas, infectious diseases are the leading causes of morbidity and one of the major causes of deaths in economically productive age group. Intensified VHW-mediated interventions are required to reduce the premature deaths.

The contribution of industrial emissions to ozone pollution: identified using ozone formation path tracing approach

Wintertime meteorological conditions are usually unfavorable for ozone (O₃) formation due to weak solar irradiation and low temperature. Here, we observed a prominent wintertime O₃ pollution event in Shijiazhuang (SJZ) during the Chinese New Year (CNY) in 2021. Meteorological results found that the sudden change in the air pressure field, leading to the wind changing from northwest before CNY to southwest during CNY, promotes the accumulation of air pollutants from southwest neighbor areas of SJZ and greatly inhibits the diffusion and dilution of local pollutants. The photochemical regime of O₃ formation is limited by volatile organic compounds (VOCs), suggesting that VOCs play an important role in O₃ formation. With the developed O₃ formation path tracing (OFPT) approach for O₃ source apportionment, it has been found that highly reactive species, such as ethene, propene, toluene, and xylene, are key contributors to O₃ production, resulting in the mean O₃ production rate (P_O3) during CNY being 3.7 times higher than that before and after CNY. Industrial combustion has been identified as the largest source of the P_O3 (2.6 ± 2.2 ppbv h^-1), with the biggest increment (4.8 times) during CNY compared to the periods before and after CNY. Strict control measures in the industry should be implemented for O₃ pollution control in SJZ. Our results also demonstrate that the OFPT approach, which accounts for the dynamic variations of atmospheric composition and meteorological conditions, is effective for O₃ source apportionment and can also well capture the O₃ production capacity of different sources compared with the maximum incremental reactivity (MIR) method.

Driving factors behind the continuous increase of long-term PM2.5-attributable health burden in India using the high-resolution global datasets from 2001 to 2020

Air pollution is the fourth leading global risk factor, whereas in India air pollution is reported as the highest risk factor with millions of premature deaths every year. Despite implementation of several air pollution control plans, PM_2.5 levels over India have not noticeably reduced. PM_2.5-associated health burdens in India have increased significantly in past decades. A fine resolution (0·01° × 0·01°) analysis of PM_2.5-attribulable premature deaths (rather than the coarse-level analysis) may elucidate the reason for this increase and inform and effective start-of-the-art state-level and national emission control strategies. This study quantified the spatiotemporal dynamics of PM_2.5-attributable premature deaths from 2001 to 2020 and applied a decomposition analysis to dissect the contribution of various associated parameters, such as PM_2.5 concentration, population distribution and disease-specific baseline death rate. Results show significant spatiotemporal variations of PM_2.5 and associated health burden in India. During the study period, population weighted PM_2.5 value increased from 46.0 to 59.5 μg/m³ and associated non-communicable death increased around 87.6 %, from 1050 [95 % (CI): 880-1210] thousand to 1970 (95 % CI: 1658-2259) thousand. The states of Uttar Pradesh, Bihar, West Bengal, Maharashtra, Rajasthan, and Madhya Pradesh had the highest PM_2.5-attributable deaths. In these states, non-accidental deaths increased from 232.1, 112.7, 81.4, 79.1, 66.3 and 58.5 thousand in 2001 to 424.1, 226.7, 156.2, 154.5, 123.3 and 119.7 thousand in 2020. In per capita population (/10⁵ population), the highest PM_2.5-attributable deaths were observed in Delhi, Uttar Pradesh, Bihar, Haryana and Punjab. Throughout the study period, demographic changes outweighed the health burden and were responsible for ~62.8 % increase of PM_2.5-related non-accidental deaths across India, whereas the change in PM_2.5 concentration influenced only 18.7 %. The change in baseline mortality rate impacts differently for the estimation of disease-specific mortality changes. Our findings suggest more dynamic and comprehensive policies at state-specific level, especially for North India is very indispensable for the overall decrease of PM_2.5-related deaths in India.

Is safe distance enough to prevent COVID-19? Dispersion and tracking of aerosols in various artificial ventilation conditions using OpenFOAM

The current COVID-19 pandemic has underlined the importance of learning more about aerosols and particles that migrate through the airways when a person sneezes, coughs and speaks. The coronavirus transmission is influenced by particle movement, which contributes to the emergence of regulations on social distance, use of masks and face shield, crowded assemblies, and daily social activity in domestic, public, and corporate areas. Understanding the transmission of aerosols under different micro-environmental conditions, closed, or ventilated, has become extremely important to regulate safe social distances. The present work attempts to simulate the airborne transmission of coronavirus-laden particles under different respiratory-related activities, i.e., coughing and speaking, using CFD modelling through OpenFOAM v8. The dispersion coupled with the Discrete Phase Method (DPM) has been simulated to develop a better understanding of virus carrier particles transmission processes and their path trailing under different ventilation scenarios. The preliminary results of this study with respect to flow fields were in close agreement with published literature, which was then extended under varied ventilation scenarios and respiratory-related activities. The study observed that improper wearing of mask leads to escape of SARS-CoV-2 containminated aerosols having a smaller aerodynamic diameter from the gap between face mask and face, infecting different surfaces in the vicinity. It was also observed that aerosol propagation infecting the area through coughing is a faster phenomenon compared to the propagation of coronavirus-laden particles during speaking. The study's findings will help decision-makers formulate common but differentiated guidelines for safe distancing under different micro-environmental conditions.