Disability-adjusted life years and economic cost assessment of the health effects related to PM2.5 and PM10 pollution in Mumbai and Delhi, in India from 1991 to 2015

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.

Co-Benefit Assessment of Active Transportation in Delhi, Estimating the Willingness to Use Nonmotorized Mode and Near-Roadway-Avoided PM2.5 Exposure

This study aims to estimate the avoided mortalities and morbidities and related economic impacts due to adopting the nonmotorized transportation (NMT) policy in Delhi, India. To this aim, an integrated quantitative assessment framework is developed to estimate the expected environmental, health, and economic co-benefits from replacing personal motorized transport with NMT in Delhi, taking into account the inhabitants' willingness to use NMT (walking and cycling) mode. The willingness to accept NMT is estimated by conducting a cross-sectional survey in Delhi, which is further used to estimate the expected health benefits from both increased physical activity and near-roadway-avoided PM_2.5 exposure in selected traffic areas in 11 major districts in Delhi. The value of a statistical life (VSL) and cost of illness methods are used to calculate the economic benefits of the avoided mortalities and morbidities from NMT in Delhi. The willingness assessment indicates that the average per capita time spent walking and cycling in Delhi is 11.054 and 2.255 min, respectively. The results from the application of the NMT in Delhi show the annual reduction in CO₂ and PM_2.5 to be 121.5 kilotons and 138.9 tons, respectively. The model estimates the expected co-benefits from increased physical activities and reduced PM_2.5 exposure at 17,529 avoided cases of mortality with an associated savings of about USD 4870 million in Delhi.

Quantifying the multiple environmental, health, and economic benefits from the electrification of the Delhi public transport bus fleet, estimating a district-wise near roadway avoided PM2.5 exposure

This study investigates the co-benefits from the utilization of the battery-electric bus (BEB) fleet in the Delhi public transportation system as a part of the Delhi electric vehicles policy 2020. To this aim, an integrated quantitative assessment framework is developed to estimate the expected environmental, health, and economic co-benefits from replacing the currently existing public bus fleet with the new BEBs in Delhi. First, the model estimates the avoided emissions from deploying the BEB fleet, using a detailed battery energy simulation model, considering the impact of the battery capacity loss on the annual operational time (hours of service) of the BEB. The annual operational time of the BEB is greatly affected by its battery degradation, which results in time lost due to charging the battery. This indicates that the annual passenger-kilometer (PKM) delivered by the BEB is less than the regular bus, under the same traveling condition. Second, considering fine particles (PM_2.5) as the most health-harming pollutant, the model calculates the near roadway avoided PM_2.5 exposure in the selected traffic zones of 11 major districts of Delhi, using a Gaussian dispersion model. Third, the near roadway avoided PM_2.5 exposure is further used in a health impact assessment model, which considers concentration-response functions for several diseases to evaluate the public health benefits from introducing the BEB fleet in Delhi. The research findings indicate that, the utilization of the new BEB fleet leads to a 74.67% reduction in the total pollutant emissions from the existing bus fleet in Delhi. The results of the integrated co-benefits assessment reveal a significant reduction in PM_2.5 emissions (44 t/y), leading to avoidance of mortality (1370 cases) and respiratory diseases related hospital admissions (2808 cases), respectively, and an annual savings of about USD 383 million from the avoided mortality and morbidity cases in Delhi.

Effect of Short- to Long-Term Exposure to Ambient Particulate Matter on Cognitive Function in a Cohort of Middle-Aged and Older Adults: KoGES

Exposure to ambient air pollution and its threat to human health is a global concern, especially in the elderly population. Therefore, more in-depth studies are required to understand the extent of the harmful effects of particulate matter (PM) based on duration and levels of exposure. An investigation was conducted to determine the association between short- (1-14 days), medium- (1, 3, and 6 months), and long-term (1, 2, and 3 years) exposure to air pollutants (PM_2.5 and PM₁₀) and cognitive function among Koreans (4175 participants, mean age 67.8 years, 55.2% women) aged over 50 years. Higher levels of PM_2.5 exposure for short to long term and PM₁₀ exposure for medium to long term were found to be associated with decreased cognitive function, as indicated by lower scores of the Mini-Mental State Examination adopted in Korean (K-MMSE). There were significant effect modifications by sex, age group, alcohol consumption, physical activity, and smoking status in the association between long-term PM_2.5 and PM₁₀ exposure and cognitive function. These findings, which underscore the importance of the efforts to reduce the exposure levels and durations of air pollutants, especially in the vulnerable elderly population, provide evidence for establishing more stringent policies for air pollution regulations.

Integrating quantitative and qualitative approaches to assess wintertime illness-related absenteeism and its direct and indirect costs among the private sector in Ulaanbaatar

Causes for employee absenteeism vary. The commonest cause of work absenteeism is “illness-related.” Mongolia’s capital city, Ulaanbaatar, experiences high employee absenteeism during the winter than during other seasons due to the combination of extreme cold and extreme air pollution. We identified direct and indirect costs of absenteeism attributed to air pollution among private-sector employees in Ulaanbaatar. Using a purposive sampling design, we obtained questionnaire data for 1,330 employees working for private-sector companies spanning six economic sectors. We conducted 26 employee focus groups and 20 individual employer in-depth interviews. We used both quantitative and qualitative instruments to characterize the direct and indirect costs of absence due to illnesses attributed to severe air pollution during wintertime. Female employees and employees with a young child at home were more likely to be absent. Respiratory diseases accounted for the majority of reported air pollution-related illnesses. All participants perceived that air pollution adversely affected their health. Individual employee direct costs related to absence totaled 875,000 MNT ($307.10) for an average of three instances of three-day illness-related absences during the winter. This sum included diagnostic and doctor visit-related, medication costs and hospitalization costs. Non-healthcare-related direct cost (transportation) per absence was 50,000₮ ($17.60). Individual indirect costs included the value of lost wages for the typical 3-day absence, amounting to 120,000₮ ($42.10). These total costs to employees, therefore, may amount to as much as 10% of annual income. The majority of sick absences were unpaid. Overall, the cost of wintertime absences is substantial and fell disproportionately on female employees with young children.

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

Accelerating growth due to industrialization and urbanization has improved the Indian economy but simultaneously has deteriorated human health, environment, and ecosystem. In the present study, the associated health risk mortality (age > 25) and welfare loss for the year 2017 due to excess PM_2.5 concentration in ambient air for 31 major million-plus non-attainment cities (NACs) in India is assessed. The cities for the assessment are prioritised based on population and are classified as ‘X’ (> 5 million population) and ‘Y’ (1–5 million population) class cities. Ground-level PM_2.5 concentration retrieved from air quality monitoring stations for the NACs ranged from 33 to 194 µg/m³. Total PM_2.5 attributable premature mortality cases estimated using global exposure mortality model was 80,447 [95% CI 70,094–89,581]. Ischemic health disease was the leading cause of death accounting for 47% of total mortality, followed by chronic obstructive pulmonary disease (COPD-17%), stroke (14.7%), lower respiratory infection (LRI-9.9%) and lung cancer (LC-1.9%). 9.3% of total mortality is due to other non-communicable diseases (NCD-others). 7.3–18.4% of total premature mortality for the NACs is attributed to excess PM_2.5 exposure. The total economic loss of 90,185.6 [95% CI 88,016.4–92,411] million US$ (as of 2017) was assessed due to PM_2.5 mortality using the value of statistical life approach. The highest mortality (economic burden) share of 61.3% (72.7%) and 30.1% (42.7%) was reported for ‘X’ class cities and North India zone respectively. Compared to the base year 2017, an improvement of 1.01% and 0.7% is observed in premature mortality and economic loss respectively for the year 2024 as a result of policy intervention through National Clean Air Action Programme. The improvement among 31 NACs was found inconsistent, which may be due to a uniform targeted policy, which neglects other socio-economic factors such as population, the standard of living, etc. The study highlights the need for these parameters to be incorporated in the action plans to bring in a tailored solution for each NACs for better applicability and improved results of the programme facilitating solutions for the complex problem of air pollution in India.

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

In this study, monthly particulate matter (PM_2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM_2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.

Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis

BACKGROUND

We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies.

METHODS

The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies.

RESULTS

We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China (n = 14), India (n = 6) and the USA (n = 3). PM_2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM_2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM₁₀.

CONCLUSION

253 premature deaths per million population are associated with exposure to ambient PM_2.5. We observed an unstable estimate for PM₁₀, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM₁₀ and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM_2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

Association Between Air Pollution and COVID-19 Pandemic: An Investigation in Mumbai, India

Spatial hot spots of COVID-19 infections and fatalities are observed at places exposed to high levels of air pollution across many countries. This study empirically investigates the relationship between exposure to air pollutants that is, sulfur dioxide, nitrogen dioxide, and particulate matter (SO2, NO2, and PM10) and COVID-19 infection at the smallest administrative level (ward) of Mumbai City in India. The paper explores two hypotheses: COVID-19 infection is associated with air pollution; the pollutants act as determinants of COVID-19 deaths. Kriging is used to assess the spatial variations of air quality using pollution data, while information on COVID-19 are retrieved from the database of Mumbai municipality. Annual average of PM10 in Mumbai over the past 3 years is much higher than the WHO specified standard across all wards; further, suburbs are more exposed to SO2, and NO2 pollution. Bivariate local indicator of spatial autocorrelation finds significant positive relation between pollution and COVID-19 infected cases in certain suburban wards. Spatial Auto Regressive models suggest that COVID-19 death in Mumbai is distinctly associated with higher exposure to NO2, population density and number of waste water drains. If specific pollutants along with other factors play considerable role in COVID-19 infection, it has strong implications for any mitigation strategy development with an objective to curtail the spreading of the respiratory disease. These findings, first of its kind in India, could prove to be significant pointers toward disease alleviation and better urban living.

Association Between Air Pollution and COVID-19 Pandemic: An Investigation in Mumbai, India

Spatial hot spots of COVID-19 infections and fatalities are observed at places exposed to high levels of air pollution across many countries. This study empirically investigates the relationship between exposure to air pollutants that is, sulfur dioxide, nitrogen dioxide, and particulate matter (SO₂, NO₂, and PM₁₀) and COVID-19 infection at the smallest administrative level (ward) of Mumbai City in India. The paper explores two hypotheses: COVID-19 infection is associated with air pollution; the pollutants act as determinants of COVID-19 deaths. Kriging is used to assess the spatial variations of air quality using pollution data, while information on COVID-19 are retrieved from the database of Mumbai municipality. Annual average of PM₁₀ in Mumbai over the past 3 years is much higher than the WHO specified standard across all wards; further, suburbs are more exposed to SO₂, and NO₂ pollution. Bivariate local indicator of spatial autocorrelation finds significant positive relation between pollution and COVID-19 infected cases in certain suburban wards. Spatial Auto Regressive models suggest that COVID-19 death in Mumbai is distinctly associated with higher exposure to NO₂, population density and number of waste water drains. If specific pollutants along with other factors play considerable role in COVID-19 infection, it has strong implications for any mitigation strategy development with an objective to curtail the spreading of the respiratory disease. These findings, first of its kind in India, could prove to be significant pointers toward disease alleviation and better urban living.

Travelling to polluted cities: a systematic review on the harm of air pollution on international travellers' health

RATIONALE FOR REVIEW

In 2019, approximately, 1.4 billion people travelled internationally. Many individuals travel to megacities where air pollution concentrations can vary significantly. Short-term exposure to air pollutants can cause morbidity and mortality related to cardiovascular and respiratory disease, with the literature clearly reporting a strong association between short-term exposure to particulate matter ≤2.5 μm and ozone with adverse health outcomes in resident populations. However, limited research has been conducted on the health impacts of short-term exposure to air pollution in individuals who travel internationally. The objective of this systematic review was to review the evidence for the respiratory and cardiovascular health impacts from exposure to air pollution during international travel to polluted cities in adults aged ≥18 years old.

KEY FINDINGS

We searched PubMed, Scopus and EMBASE for studies related to air pollution and the health impacts on international travellers. Of the initially identified 115 articles that fit the search criteria, 6 articles were selected for the final review. All six studies found indications of adverse health impacts of air pollution exposure on international travellers, with most of the changes being reversible upon return to their home country/city. However, none of these studies contained large populations nor investigated vulnerable populations, such as children, elderly or those with pre-existing conditions.

CONCLUSIONS

More research is warranted to clearly understand the impacts of air pollution related changes on travellers' health, especially on vulnerable groups who may be at higher risk of adverse impacts during travel to polluted cities.

Air Pollution Health Risk Assessment (AP-HRA), Principles and Applications

Air pollution is a major public health problem. A significant number of epidemiological studies have found a correlation between air quality and a wide variety of adverse health impacts emphasizing a considerable role of air pollution in the disease burden in the general population ranging from subclinical effects to premature death. Health risk assessment of air quality can play a key role at individual and global health promotion and disease prevention levels. The Air Pollution Health Risk Assessment (AP-HRA) forecasts the expected health effect of policies impacting air quality under the various policy, environmental and socio-economic circumstances, making it a key tool for guiding public policy decisions. This paper presents the concept of AP-HRA and offers an outline for the proper conducting of AP-HRA for different scenarios, explaining in broad terms how the health hazards of air emissions and their origins are measured and how air pollution-related impacts are quantified. In this paper, seven widely used AP-HRA tools will be deeply explored, taking into account their spatial resolution, technological factors, pollutants addressed, geographical scale, quantified health effects, method of classification, and operational characteristics. Finally, a comparative analysis of the proposed tools will be conducted, using the SWOT (strengths, weaknesses, opportunities, and threats) method.

Ambient particulate matter triggers dysfunction of subcellular structures and endothelial cell apoptosis through disruption of redox equilibrium and calcium homeostasis

Ambient particulate matter (APM) is becoming a global environmental problem that seriously jeopardizes public health. Previous evidence hinted that APM correlates to cardiovascular diseases. As a potential target, equilibrium of endothelial cell is a prerequisite for vascular health which could be vulnerably attacked by particles, but the specific mechanisms whereby APM damages endothelial cells have not been fully elucidated. In the current study, based on two classical mechanisms of oxidative stress and intracellular calcium overload, we aimed to explore their roles in APM-induced endothelial cell apoptosis from the perspective of subcellular levels, including endoplasmic reticulum (ER) stress and mitochondrial dysfunction. As a result, PM SRM1648a results in oxidative stress and calcium overload in EA.hy926 cells. Additionally, ERs and mitochondria could be severely disturbed by particles in morphology and function, characterized by swelling ERs, mitochondrial fission and disappearance of cristae, coupled with ER damage, mtROS overproduction and significant reduction in mitochondrial membrane potential (MMP). Adverse effects on these organelles are the prime culprits of following apoptosis in endothelial cells. Fortunately, additional antioxidants and calcium inhibitors could mitigate cellular lesion through improvement of subcellular function. Intriguingly, antioxidants relieve cell stress via both mitochondrial and ER stress-mediated pathways, whereas the role of calcium modulators in cell apoptosis is independent of the mitochondrial pathway but could be explained by amelioration of ER stress. In conclusion, our data basically revealed that internalized PM SRM1648a triggers oxidative stress and calcium influx in EA.hy926 endothelial cells, followed by multiple subcellular damage and eventually contributes to cell death, during which antioxidants and calcium inhibitors confer protective effects.

Economic losses and willingness to pay for haze: the data analysis based on 1123 residential families in Jiangsu province, China

Haze pollution is a key obstacle for environmental management faced by China and many other developing countries. The survey on residential families' economic losses and willingness to pay (WTP) are regarded as an essential reference for the implementation of environmental policies for haze treatment. For Jiangsu province of China, the authors of this paper first conducted three qualitative interviews with respectively meteorologists, meteorological administrators, and residents, a questionnaire was then elaborately designed, and subsequent surveys of 1123 families were administered in Jiangsu province. Further, the authors investigated measurements of direct economic losses by using the contingent valuation method (CVM) and explored influential factors of WTP by utilizing the binary logistic regression. From this survey, the estimated total economic loss incurred by haze disasters and total treatment cost for haze-related diseases were respectively 22.38 billion (in RMB) and 8.4 billion for Jiangsu province. 55.9% of residential families were willing to pay 11.6 billion RMB annually (51.97% of total loss) for haze treatment, leaving a shortage of 11.05 billion RMB, which the government is responsible to pay. These findings provide empirical information reflecting the opinions of communities and residential families, useful for the governments and industrial sectors to design environmental policies to meet the requirements of the public and control environmental pollution in an effective way to achieve sustainable development.

Numerical simulations of different sectoral contributions to post monsoon pollution over Delhi

The National Capital Region (NCR) of India, Delhi, has experienced high post-monsoon pollution along with several peak pollution episodes in recent years. Diwali, the festival of lights, which is among the biggest festivals of India celebrated during the post-monsoon season, is also considered a pollution event associated as it is with the lighting of a large number of firecrackers. 2016 Diwali pollution episode continued for a week creating severe discomfort to residents of Delhi, prompting the judiciary to ban the sale and use of firecrackers in Delhi from 2017 onwards. The current study analyzes different sectoral and temporal emissions contribution to the 2016 post monsoonal pollution episode over Delhi using a fully coupled chemical transport model. The findings of the study indicate that aerosols produced from crop residue open burning at the northwestern states contributed more than 60% of the total simulated surface concentration during the period under study. Model experimental simulations show that despite emissions from within the city, what explains the severity of pollution over Delhi during the period under consideration is an additional pollution load emanating from these intense crop open burning sessions from nearby areas. Further, model simulations show that while Diwali emissions can elevate the pollution load over Delhi, the effects do not last beyond 48 h. It is found that the stagnation of the pollutants several days beyond the 2016 Diwali day was due to favorable meteorological conditions like low surface temperature, lower boundary layer height, and weak northwesterly winds. The study shows that in order to improve air quality in Delhi during the post-monsoon period, mitigation efforts should target the adjacent rural areas, especially when there is massive burning of crop residue in those areas.

PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model

At present particulate matter (PM_2.5) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM_2.5), and then quantifies the health effects of PM_2.5 pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure-response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM_2.5 pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM_2.5 pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335-248,914)) and six million, respectively; and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77-4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) has been linked to various adverse health outcomes. PM_2.5 arises from both natural and anthropogenic sources, and PM_2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM_2.5 measurements, potentially limiting the accuracy of PM_2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM_2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM_2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM_2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R². The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

Towards practical indoor air phytoremediation: A review

Indoor air quality has become a growing concern due to the increasing proportion of time people spend indoors, combined with reduced building ventilation rates resulting from an increasing awareness of building energy use. It has been well established that potted-plants can help to phytoremediate a diverse range of indoor air pollutants. In particular, a substantial body of literature has demonstrated the ability of the potted-plant system to remove volatile organic compounds (VOCs) from indoor air. These findings have largely originated from laboratory scale chamber experiments, with several studies drawing different conclusions regarding the primary VOC removal mechanism, and removal efficiencies. Advancements in indoor air phytoremediation technology, notably active botanical biofilters, can more effectively reduce the concentrations of multiple indoor air pollutants through the action of active airflow through a plant growing medium, along with vertically aligned plants which achieve a high leaf area density per unit of floor space. Despite variable system designs, systems available have clear potential to assist or replace existing mechanical ventilation systems for indoor air pollutant removal. Further research is needed to develop, test and confirm their effectiveness and safety before they can be functionally integrated in the broader built environment. The current article reviews the current state of active air phytoremediation technology, discusses the available botanical biofiltration systems, and identifies areas in need of development.

A systematic review of financial implications of air pollution on health in Asia

Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.

Toxicity potential of particles caused by particle-bound polycyclic aromatic hydrocarbons (PPAHs) at two roadside locations and relationship with traffic

This study assessed exposure by the roadside to highly toxic particle-bound polycyclic aromatic hydrocarbons (PPAHs) that are known to adsorb preferentially on fine particles, aerodynamic diameter (d_p ≤ 1 μm). The real-time air quality measurements were conducted in March, April, and May 2015 in Kanpur at two busy roadside locations: one outside IIT Kanpur main gate, IG, and another by a residential area, M3. The locations show varying land use type and traffic density. Higher averaged daily concentrations of PM₁₀, PM_2.5, and PM₁ were observed at IG (PM₁₀ 700-800 μg/m³) owing to nature and high density of traffic, and occurrence of biomass burning nearby. Statistically significant relation (R² > 90%, p < 0.05) between PM₁ and PM_2.5 highlights the influence of mobile sources on particle load at IG. IG, the busier location, had higher daily averaged concentration of aggregate PPAHs (104 ng/m³) than M3 which is located near a residential area (38 ng/m³). In contrast, the higher average daily value of PC/DC ratio (mass per unit surface area of PPAHs on nanoparticles) at M3 (4.87 ng/mm²) than at IG (4.08 ng/mm²) suggests that PAHs of greater mass occur on particles at M3. Finer particles are known to adsorb pollutants of a larger mass that are likely to be more toxic in case of PAHs suggest that ambient air at M3 has more toxicity potential. However, this inference is not based on chemical analyses, and chemical characteristics must also be taken into account for the detailed assessment of health risk. The multiple path dosimetry model (MPPD-v3.04) reveals that the 99.02% of PM₁₀ inhaled, 77.01% of PM_2.5 and 34.54% of PM₁ are deposited in the outermost (head) region of the human respiratory tract.

Urban local air quality management framework for non-attainment areas in Indian cities

Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NO_x and PM_2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country.

Air pollution and public health: the challenges for Delhi, India

Mitigating the impact of pollution on human health worldwide is important to limit the morbidity and mortality arising from exposure to its effect. The level and type of pollutants vary in different urban and rural settings. Here, we explored the extent of air pollution and its impacts on human health in the megacity of Delhi (India) through a review of the published literature. The study aims at describing the extent of air pollution in Delhi, the magnitude of health problems due to air pollution and the risk relationship between air pollution and associated health effects. We found 234 published articles in the PubMed search. The search showed that the extent of air pollution in Delhi has been described by various researchers from about 1986 onwards. We synthesized the findings and discuss them at length with respect to reported values, their possible interpretations and any limitations of the methodology. The chemical composition of ambient air pollution is also discussed. Further, we discuss the magnitude of health problem with respect to chronic obstructive pulmonary diseases (COPD), bronchial asthma and other illnesses. The results of the literature search showed that data has been collected in last 28 years on ambient air quality in Delhi, though it lacks a scientific continuity, consistency of locations and variations in parameters chosen for reporting. As a result, it is difficult to construct a spatiotemporal picture of the air pollution status in Delhi over time. The number of sites from where data have been collected varied widely across studies and methods used for data collection is also non-uniform. Even the parameters studied are varied, as some studies focused on particulate matter ≤10 μm in aerodynamic diameter (PM10) and those ≤2.5 μm in aerodynamic diameter (PM2.5), and others on suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM). Similarly, the locations of data collection have varied widely. Some of the sites were at busy traffic intersections, some on the terraces of offices and residential houses and others in university campuses or airports. As a result, the key question of the extent of pollution and its distribution across various parts of the city could be inferred. None of the studies or a combination of them could present a complete picture of the burden of diseases like COPD, bronchial asthma and other allergic conditions attributable to pollution in Delhi. Neither could it be established what fraction of the burden of the above diseases is attributable to ambient air pollution, given that other factors like tobacco smoke and indoor air pollution are also contributors to the causation of such diseases. In our discussion, we highlight the knowledge gaps and in the conclusion, we suggested what research can be undertaken to fill the these research gaps.

Burden of disease attributed to ambient PM2.5 and PM10 exposure in 190 cities in China

Particulate air pollution is becoming a serious public health concern in urban cities of China. Association of disability-adjusted life years (DALYs) and economic loss with air pollution-related health effects demand quantitative analysis for correctional measures in air quality. This study applies an epidemiology-based exposure-response function to obtain the quantitative estimate of health impact of particulate matter PM_2.5 and PM₁₀ across 190 cities of China during years 2014-2015. The annual average concentration of PM_2.5 and PM₁₀ is 57 ± 18 μg/m³ (ranging from 18 to 119 μg/m³) and 97.7 ± 34.2 μg/m³ (ranging from 33.5 to 252.8 μg/m³), respectively. Based on the present study, the total estimated annual premature mortality due to PM_2.5 is 722,370 [95% confidence interval (CI) = 322,716-987,519], 79% of which accounts for adult cerebrovascular disease (stroke) and ischemic heart disease (IHD). The premature mortality in megacities is very high, such as Chongqing (25,162/year), Beijing (19,702/year), Shanghai (19,617/year), Tianjin (13,726/year), and Chengdu (12,356/year). PM₁₀ pollution has caused 1,491,774 (95% CI = 972,770-1,960,303) premature deaths (age >30) in China. Further, 3,614,064 cases of chronic bronchitis (CB); 13,759,894 cases of asthma attack among all ages; 191,709 COPD-related hospital admission (HA) cases; 499,048 respiratory-related HA; 357,816 cerebrovascular HA; and 308,129 cardiovascular-related HA due to PM₁₀ pollution have been estimated during 2014-2015. Chongqing, Beijing, Baoding, Tianjin, and Shijiazhuang are the top five contributors to pollution-related mortality, accounting for 3.10, 2.71, 2.49, 2.20, and 2.02%, respectively, of the total deaths caused by PM₁₀ pollution. The total DALYs associated with PM_2.5 and PM₁₀ pollution in China is 7.2 and 20.66 million in 2014-2015, and mortality and chronic bronchitis shared about 93.3% of the total DALYs for PM₁₀. During this period, the economic cost of health impact due to PM₁₀ is approximately US$304,122 million, which accounts for about 2.94% of China's gross domestic product (GDP). Megacities are expected to contribute relatively more to the total costs. The present methodology could be used as a tool to help policy makers and pollution control board authorities, to further analyze costs and benefits of air pollution management programs in China.