Continued Rise in Health Burden from Ambient PM2.5 in India under SSP Scenarios Until 2100 despite Decreasing Concentrations

Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM2.5) exposure in India from the 2020s (2016-2020) to the 2100s (2095-2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM2.5 concentrations decreased in all SSP scenarios except for SSP3-7.0, with the lowest concentration observed in SSP1-2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12-5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.

Fugitive road dust particulate matter emission inventory for India: A field campaign in 32 Indian cities

This research delves into the pivotal issue of road dust emissions and their profound ramifications on air quality across diverse regions of India. In pursuit of this objective, the study initiated a comprehensive field campaign to estimate silt loading (sL) values and evaluate the distribution of vehicles at 259 locations spanning 32 Indian cities. Remarkable disparities in sL values were observed across different road types and states. Notably, sites in Rajasthan, characterized by its arid Aravalli range and industrial activities, emerged as stark outliers, exhibiting significantly elevated sL values (up to 137 g/m2) compared to their counterparts. The regional analysis goes further to elucidate the relation between climatic conditions, topography, and silt loading. As a broader trend, roads in North India have higher sL values in contrast to those in South India. Further, a comprehensive particulate matter road dust emission inventory for the entire India in the year 2022 was developed using the vehicle registration data from 1352 road transport offices nationwide, in conjunction with the data from the field campaign concerning sL values and vehicle counts. Specific states such as Rajasthan, Uttar Pradesh, Maharashtra, Karnataka, and Gujarat emerged as the predominant contributors to road dust emissions. These states not only exhibit elevated sL values, but also account for a substantial proportion of the total registered vehicles in India, thereby underscoring the pressing imperative for effective mitigation measures. Weather Research and Forecasting coupled with chemistry (WRF-Chem) simulations, using this emission inventory, reveal that PM2.5 concentrations stemming from road dust exceed the World Health Organization guidelines in 55 % of the states across India. Further analysis delineates that more than 10,000 lives are annually lost due to PM2.5 pollution attributable to road dust in India, with the potential to salvage 10 % of these lives by paving all roads throughout the country.

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

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

Modeling polycyclic aromatic hydrocarbons in India: Seasonal variations, sources and associated health risks

Atmospheric polycyclic aromatic hydrocarbons (PAHs) are in high levels in developing countries like India. However, limited measurements are inadequate for better understanding of their ambient levels and health effects. This study predicted PAHs concentrations in atmosphere and estimated their sources and health risks in India in four representative months of winter, pre-monsoon, monsoon and post-monsoon in 2015 using an updated version of the Community Multiscale Air Quality model (CMAQ). Predicted PAHs were in agreement with observations from literature. Surface 16-PAHs were highest in winter, with a peak value of 2.5 μg/m³ and population-weighted average of 0.5 μg/m³ in northern and eastern India, where biomass burning and coal combustion were chief contributors. Pre-monsoon and monsoon had lower concentrations ∼0.2 μg/m³. The incremental lifetime cancer risk (ILCR) was greater than 4E-4 in many industrial and urban areas. Exposure to PAHs resulted in 7431 excess lifetime cancer cases. Coal combustion and biomass burning were major contributors to ILCR, followed by gas and oil activities. Much higher health risks were observed in urban than in rural areas. India showed much higher levels of total PAHs and cPAHs than the U.S but moderately less than China.

In-kitchen aerosol exposure in twelve cities across the globe

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM_2.5 and PM₁₀) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with ∼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM_2.5 (PM₁₀) concentrations were in Dhaka 185 ± 48 (220 ± 58) μg m^-3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) μg m^-3. Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM_2.5 and PM₁₀ by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM_2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m³; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions.

Air Pollution and Cognitive Impairment across the Life Course in Humans: A Systematic Review with Specific Focus on Income Level of Study Area

Cognitive function is a crucial determinant of human capital. The Lancet Commission (2020) has recognized air pollution as a risk factor for dementia. However, the scientific evidence on the impact of air pollution on cognitive outcomes across the life course and across different income settings, with varying levels of air pollution, needs further exploration. A systematic review was conducted, using Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Guidelines to assess the association between air pollution and cognitive outcomes across the life course with a plan to analyze findings as per the income status of the study population. The PubMed search included keywords related to cognition and to pollution (in their titles) to identify studies on human participants published in English until 10 July 2020. The search yielded 84 relevant studies that described associations between exposure to air pollutants and an increased risk of lower cognitive function among children and adolescents, cognitive impairment and decline among adults, and dementia among older adults with supportive evidence of neuroimaging and inflammatory biomarkers. No study from low- and middle-income countries (LMICs)was identified despite high levels of air pollutants and high rates of dementia. To conclude, air pollution may impair cognitive function across the life-course, but a paucity of studies from reLMICs is a major lacuna in research.

Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters

PM_2.5 concentrations in most of the Indian cities are in alarming levels. However, the current network of 308 monitoring stations are heterogeneously placed and do not cover many parts of the country. This limits the ability of agencies to measure the concentration which people are exposed to. In this study, ground level PM_2.5 concentrations and the associated risk and mortality in India using satellite based AOD data for the year 2015 was estimated to identify the state specific number of more monitoring sites required. Results indicate that average PM_2.5 concentrations were 89 μg/m³, which caused 1.61 million deaths including 0.34 million Chronic Obstructive Pulmonary Disease (COPD) deaths, 0.2 million Lung Cancer (LC) deaths, 0.53 million Ischemic Heart Disease (IHD) deaths and 0.70 million deaths due to Stroke. The years of life lost (YLL) per 1000 population due to exposure to PM_2.5 indicated Delhi (North-India) to be severely affected by PM_2.5 resulting in 227.47 years of life lost and was closely followed by Bihar (Eastern-India) (225.18), Rajasthan (Western-India) (225.05) and Uttar Pradesh (Northern-India) (213.16). Eastern India had the highest population weighted concentration (102.09 μg/m³) and contributed to 23.46% of premature mortality and was followed by Central (75.32 μg/m³) and Northern India (75.12 μg/m³), thus indicating severity of air pollution in India and need for its constant monitoring. As per Indian regulatory agency's guidelines, India still needs 1638 more air quality monitoring stations, of which North-Indian states require maximum number of additional stations i.e. 400, followed by 382 in eastern states.

Source apportionment of size-segregated atmospheric particles and the influence of particles deposition in the human respiratory tract in rural and urban locations of north-east India

Aerosol samples were collected using eight stage non-viable Andersen cascade impactor at three urban and two rural sites in north-east India during 2018 covering three seasons i.e., winter, summer and monsoon. The size-segregated samples collected in the selected locations were carefully analysed in terms of deposition in human respiratory tract using inhalation and deposition curves. Seasonal variation of fractional deposition of particulate matter (PM) in human respiratory tract was observed. For example, during winter, in one of the urban sites i.e., S3 (0.61) the maximum deposition was in Pulmonary (P) region, while in the case of other sites, the maximum deposition was in Nasopharyngeal (NOPL) region. Regional deposition in P was high in S1 and S3 when compared with other sites. Vehicular emissions was dominant in both S1 and S3 in P, while biomass burning being dominant in S3 which could be the reason for maximum deposition in P. Positive matrix factorization (PMF) revealed five to eight factors at each individual site in NOPL, tracheobronchial (TB) and P regions: biomass burning (accounting for 7-32% of PM), coal combustion (14-27%), construction dust (9-25%), dust emissions (17-28%), industrial emissions (12-26%), oil refinery (18%), secondary aerosols (17-33%) and vehicular emissions (12-39%). Dominant sources in urban and rural areas were vehicular emissions and dust emissions, respectively. Therefore, the present study highlights the importance of analyzing source apportionment of PM at ultrafine scale and forms a basis upon which the future air quality studies and mitigation strategies can be formulated in this region.

Effect of restricted emissions during COVID-19 on air quality in India

The effectiveness and cost are always top factors for policy-makers to decide control measures and most measures had no pre-test before implementation. Due to the COVID-19 pandemic, human activities are largely restricted in many regions in India since mid-March of 2020, and it is a progressing experiment to testify effectiveness of restricted emissions. In this study, concentrations of six criteria pollutants, PM₁₀, PM_2.5, CO, NO₂, ozone and SO₂ during March 16th to April 14th from 2017 to 2020 in 22 cities covering different regions of India were analysed. Overall, around 43, 31, 10, and 18% decreases in PM_2.5, PM₁₀, CO, and NO₂ in India were observed during lockdown period compared to previous years. While, there were 17% increase in O₃ and negligible changes in SO₂. The air quality index (AQI) reduced by 44, 33, 29, 15 and 32% in north, south, east, central and western India, respectively. Correlation between cities especially in northern and eastern regions improved in 2020 compared to previous years, indicating more significant regional transport than previous years. The mean excessive risks of PM reduced by ~52% nationwide due to restricted activities in lockdown period. To eliminate the effects of possible favourable meteorology, the WRF-AERMOD model system was also applied in Delhi-NCR with actual meteorology during the lockdown period and an un-favourable event in early November of 2019 and results show that predicted PM_2.5 could increase by only 33% in unfavourable meteorology. This study gives confidence to the regulatory bodies that even during unfavourable meteorology, a significant improvement in air quality could be expected if strict execution of air quality control plans is implemented.

Sericin-coated polyester based air-filter for removal of particulate matter and volatile organic compounds (BTEX) from indoor air

Particulate matter and volatile organic compounds have emerged as a prime environmental concern with increasing air pollution in metropolitan cities leading to lung and heart-related issues. This paper describes a facile and novel method for fabrication of polyester based air filter via surface coating with Sericin for imparting effective removal of particulate matter and volatile organic compounds. A simple dip-coating method followed by thermal fixation has been adopted to coat Sericin on the polyester fiber. The developed changes in surface functionality and morphology of the polyester fiber were confirmed by Attenuated total reflection Fourier-transform infrared spectroscopy and Field emission scanning electron microscopy analysis. The fabricated air filter was tested for removal of particulate matter (generated burning incense stick) and volatile organic compounds (generated vaporizing gasoline), in an indoor chamber. The Sericin coated filter was able to remove the PM_2.5 and PM ₁₀ (from 1000 μg/m³ level to 5 μg/m³ in a 6.28 m³ chamber) within 27 and 23 min of operation, respectively. The fabricated filter very effectively removed particulate matter for 2160 cycles with intermittent washing. The Sericin-coated air filter also proved very effective for removal of volatile organic compounds (Benzene, Toluene, Ethylbenzene and Xylene) from an indoor chamber at a varying initial concentration of 100-1000 μg/m³. The adsorption behavior was described by Langmuir-Freundlich (sips) isotherm and pseudo-first order kinetics with minimal error. The maximum adsorption capacity (mg/g) obtained with Sips Isotherm fitting followed the order Xylene (6.97)>Ethyl Benzene (5.68)> Toluene (5.35) >Benzene (4.78).

Source apportionment of organic pollutants in fine and coarse atmospheric particles in Doha, Qatar

In this study, we investigated the sources of organic pollutants associated with fine (PM_2.5) and coarse (PM_2.5-10) atmospheric particulate matter in Doha, Qatar based on an eight-month sampling campaign conducted from May to December 2015. Multiple organic compound tracers including 36 PAH members, 25 n-alkane homologs, 17 hopanes, and 12 steranes were used for organic aerosols source apportionment. Source apportionment based on specific molecular markers, molecular diagnostic ratios/indices, and positive matrix factorization (PMF) modeling showed that similar sources are responsible for both fine- and coarse-particle organic pollutants. PMF analysis showed that biogenic aerosols, fugitive dust emissions, gasoline engine emissions, diesel engine emissions, and heavy oil combustion were the five main pollution sources of organic aerosols, which agreed well with the results from the diagnostic ratios analysis. The conditional bivariate probability functions (CPF) and potential source contribution function (PSCF) indicated that both regional (i.e., mixed biogenic/secondary particles and oil refinery/shipping emissions) and local sources contributed to airborne organic aerosol concentrations observed at the site, depending on the wind speed and direction. It appears that the relatively high levels of organic pollutants were contributed by local anthropogenic sources, such as fossil fuel combustion, vehicular emissions, and fugitive dust emissions. The high levels of local contributions indicated that there might be great opportunities for Qatar to considerably reduce emissions so that population exposures to carbonaceous aerosols and the public health risks associated with air pollution can be minimized. Implications: Multiple organic tracers and various source apportionment techniques have been used for convincing source apportionment. It was found that both long-range and local sources have a significant impact on atmospheric carbonaceous particles in the area, depending on the wind conditions. Relatively high levels of organic pollutants attributed to local anthropogenic sources indicate that there are great opportunities for Qatar to establish and implement more efficient pollution control measures and policies. Regional sources such as petroleum refineries and shipping-vessels emissions in the Gulf region should also be regulated and managed through regional cooperation to improve the air quality in the region.

Characterization and health risks of criteria air pollutants in Delhi, 2017

Severe air pollution events were observed frequently in north India in recent years especially at its capital, Delhi. Criteria air pollutants data at 10 sites for 2017 in Delhi were analyzed. The results show annual fine particulate matter (PM_2.5) concentrations exceeded the National Ambient Air Quality Standards (NAAQS) of 60 μg/m³ at all sites from 105.51 (site 10) to 143.23 μg/m³ (site 7). Sub-urban sites (site 8, 9 and 10) had lower PM_2.5 concentrations than urban sites. Coarse PM (PM₁₀) and ozone (O₃) were also important with annual averages of 399.56 μg/m³ and 75.69 ppb, respectively. Peak PM_2.5 occurred at the Diwali in early November and Christmas. Only PM₁₀ showed a significant weekly difference with a weekdays/weekends ratio of ∼1.5. PM_2.5/PM₁₀ ratio in episode days with PM_2.5 of >60 μg/m³ was higher than non-episode days. Pearson correlation coefficients show O₃ was negatively related with CO, SO₂, and NO₂, while PM_2.5 was positively related to these pollutants. Analysis of two extreme events from Nov. 6th to Nov. 14th and Dec. 18th to Dec. 26th shows that meteorological conditions with low wind speed and warm temperature kept PM_2.5 concentrations at a high level during these events. Backward trajectory and cluster analysis show the wind coming from northwest of Delhi, where massive anthropogenic emissions were generated, led to high concentrations of air pollutants to Delhi. Health risk analysis reveals that PM_2.5 and PM₁₀ were the two major pollutants threatening public health among the six criteria pollutants.

Responses of PM2.5 and O3 concentrations to changes of meteorology and emissions in China

Tremendous efforts have been made to reduce the severe air pollution in China since 2013. However, the annual and peak fine particulate matter (PM_2.5) concentrations during severe events in winter did not always reduce as expected. This is partially due to the inter-annual variation of meteorology, which affects the emission, transport, transformation, and deposition processes of air pollutants. In this study, the responses of PM_2.5 and ozone (O₃) concentrations to changes in emission and meteorology from 2013 to 2015 were investigated based on ambient measurements and the Community Multi-Scale Air Quality (CMAQ) model simulations with anthropogenic emissions. It is found that emission reductions in 2014 and 2015 effectively reduced PM_2.5 concentrations by 23.9 and 43.5 μg/m³, respectively, but was partially counteracted by unfavorable meteorology. The negative effects from unfavorable meteorology were significant in extreme pollution events. For example, in December 2015, unfavorable meteorology caused a great increase (90 μg/m³) of PM_2.5 in Beijing. Reduction of primary PM and gaseous precursors led to 13.4 and 16.5 ppb increase of O₃-8 h daily concentrations in the summertime in 2014 and 2015 in comparison of 2013, which was likely caused by the increase of solar actinic flux due to PM reduction. In addition, reduction of nitrogen oxides (NOx) emissions in areas with negative NOx-O₃ sensitivity could lead to an increase of O₃ formation when the reduction of volatile organic compounds (VOCs) was not sufficient. This unintended enhanced O₃ formation could also lead to higher O₃ in downwind areas. This study emphasizes the role of meteorology in pollution control, validates the effectiveness of PM_2.5 control measures in China, and highlights the importance of appropriate joint reduction of NOx and VOCs to simultaneously decrease O₃ and PM_2.5 for higher air quality.

Investigating the PM2.5 mass concentration growth processes during 2013-2016 in Beijing and Shanghai

The North China Plain and the Yangtze River Delta are the two of the most heavily polluted regions in China. Observational studies revealed that 'explosive' PM_2.5 mass concentration growths frequently occurred in the two regions. This study analyzed all the PM_2.5 mass concentration growth processes from clean condition (i.e., <35 μg m^-3) to heavy pollution condition (i.e., >150 μg m^-3) in Beijing (BJ) and Shanghai (SH), two representative cities of the two regions, using hourly monitored PM_2.5 concentrations during 2013-2016. 173 and 76 growth processes were identified in BJ and SH, respectively. PM_2.5 rising rates (PMRR) and dynamic growth durations were calculated to illustrate the characteristics of the growth processes. Hourly particulate chemical composition data and meteorological data in BJ and SH were further analyzed. The 4-year averaged PMRR of PM_2.5 total mass were similarly of 7.11 ± 9.82 μg m^-3 h^-1 in BJ and 6.71 ± 6.89 μg m^-3 h^-1 in SH. A decreasing trend was found for the PM_2.5 growth processes in two cities from 2013 to 2016, reflecting the effectiveness of emission controls implemented in the past years. The contributions of particulate components to the PM_2.5 total mass growth were different in BJ and SH. Average PMRR value of PM₁ organic aerosols (OA), SO₄^2-, NO₃^-, and NH₄⁺ in BJ was 1.90, 0.95, 0.82, and 0.53 μg m^-3 h^-1, respectively. Average PMRR of PM_2.5 OA, SO₄^2-, NO₃^-, and NH₄⁺ in SH was 1.70, 1.18, 1.99 and 1.14 μg m^-3 h^-1, respectively. Based on the contributions of different components, the PM_2.5 mass concentration growth processes in BJ and SH were proposed to be classified into 'other components-dominant growth processes', 'all components-contributing growth processes', 'one or more explosive secondary components-dominant growth processes', and 'mixed-factor growth processes'. Potential source contribution function analysis and the meteorological condition analysis showed that source origins and prevailing wind for the two cities during different categories of growth processes had substantial difference. The important source areas included Hebei and Shandong for BJ, and Jiangsu and Anhui for SH. The dominant wind directions during growth processes were northeast, south and southwest in BJ, and were west to north in SH. The results suggested the contributing components, the prevailing wind conditions, and the formation processes were substantially different in the two cities, despite the similar PMRR of PM_2.5 total mass during the growth processes between BJ and SH. Future research is needed to study the detailed formation mechanisms of the different PM_2.5 mass concentration growth processes in the two cities.

Characterization of PM10 and Impact on Human Health During the Annual Festival of Lights (Diwali)

BACKGROUND

Diwali is a Hindu holiday observed each autumn in India, where it is known as the 'celebration of lights'. Burning of fireworks on this day leads to air and noise pollution, causing adverse effects to human health.

OBJECTIVES

To monitor and analyze air quality and noise levels in a residential college campus in northeast India over Diwali 2015.

METHODS

Components of PM₁₀, including metals (cadmium (Cd), cobalt (Co), iron (Fe), zinc (Zn) and nickel (Ni)), ions (calcium (Ca²⁺), ammonium (NH_{4 +}), sodium (Na⁺), potassium (K⁺), chloride (Cl^-), nitrate (NO_3-) and sulfate (SO₄ ^2-)) and bacterial counts were studied for a period of ten days in November 2015. In addition, a health-based survey of patients attending the institute's hospital during those days was conducted to evaluate the risk level due to fireworks burning.

RESULTS

The mean PM₁₀ concentration during Diwali, 311 μg/m³, was 81% higher than other days and 3.1-times higher the Indian National Ambient Air Quality Standards. While noise levels were increased by 65%, the concentration of bacteria in PM₁₀ was reduced by 39% during Diwali compared to other days. The concentrations of metals, cations and anions were increased by 51%, 72% and 77%, respectively. A health study conducted during the analysis period revealed an increase in hospital admissions in the campus due to respiratory symptoms. The higher concentrations of metals during the Diwali period resulted in a 0.5% increase in the hazard index.

CONCLUSIONS

The present study suggests that reducing fireworks during Diwali could reduce pollutant concentrations and result in potential health benefits.

PARTICIPANT CONSENT

Obtained.

ETHICS APPROVAL

The study and survey design were approved by the Institutional Bioethics Committee of the Indian Institute of Technology, Guwahati.

COMPETING INTERESTS

The authors declare no competing financial interests.

Methane emissions from a landfill in north-east India: Performance of various landfill gas emission models

Rapid urbanization and economic growth has led to significant increase in municipal solid waste generation in India during the last few decades and its management has become a major issue because of poor waste management practices. Solid waste generated is deposited into open dumping sites with hardly any segregation and processing. Carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) are the major greenhouse gases that are released from the landfill sites due to the biodegradation of organic matter. In this present study, CH₄ and CO₂ emissions from a landfill in north-east India are estimated using a flux chamber during September, 2015 to August, 2016. The average emission rates of CH₄ and CO₂ are 68 and 92 mg/min/m², respectively. The emissions are highest in the summer whilst being lowest in winter. The diurnal variation of emissions indicated that the emissions follow a trend similar to temperature in all the seasons. Correlation coefficients of CH₄ and temperature in summer, monsoon and winter are 0.99, 0.87 and 0.97, respectively. The measured CH₄ in this study is in the range of other studies around the world. Modified Triangular Method (MTM), IPCC model and the USEPA Landfill gas emissions model (LandGEM) were used to predict the CH₄ emissions during the study year. The consequent simulation results indicate that the MTM, LandGEM-Clean Air Act, LandGEM-Inventory and IPCC models predict 1.9, 3.3, 1.6 and 1.4 times of the measured CH₄ emission flux in this study. Assuming that this higher prediction of CH₄ levels observed in this study holds well for other landfills in this region, a new CH₄ emission inventory (Units: Tonnes/year), with a resolution of 0.1⁰ × 0.1⁰ has been developed. This study stresses the importance of biodegradable composition of waste and meteorology, and also points out the drawbacks of the widely used landfill emission models.

Wet deposition of sulfur and nitrogen in Jiuzhaigou National Nature Reserve, Sichuan, China during 2015-2016: Possible effects from regional emission reduction and local tourist activities

In order to understand the impacts of regional emission changes and local tourism on sulfur and nitrogen wet deposition in Jiuzhaigou National Nature Reserve of southwestern China, wet deposition was monitored at a background site (Rize) and a tourist-affected site (PE: park entrance) in the reserve during 2015-2016. The observation data were compared between Rize and PE and between 2010-2011 and 2015-2016 monitoring campaigns. Also, the observation data were used in the Positive Matrix Factorization (PMF) model to identify the major sources of sulfur and nitrogen wet deposition. The results show that although local tourism emissions had considerable contributions to NH₄⁺, NO₂^-, NO₃^-, and SO₄^2- concentrations in wet deposition (p < 0.05), most of the annual Volume Weighted Mean (VWM) concentrations of these four ions were likely from emissions outside Jiuzhaigou. Annual wet deposition fluxes of the four ions were also affected more by precipitation and regional emissions than by local emissions. Although annual precipitation was higher at Rize (818 mm) during 2015-2016 than at another background site near Long Lake (LL: 752 mm) during 2010-2011, the annual concentrations and fluxes of SO₄^2- and NO₃^- wet deposition decreased by 77% and 74% for SO₄^2- and by 12% and 19% for NO₃^-, respectively, most likely due to regional emission reductions. Similar large reductions in SO₄^2- and NO₃^- concentrations have been also found in some other sites in southwestern China. In contrast, the annual concentration and flux of NH₄⁺ wet deposition at Rize during 2015-2016 were 1.4 and 1.2 times of that measured at LL during 2010-2011, respectively. The results of source apportionment analysis and tour bus emission estimates suggest that elevated NH₄⁺ wet deposition was possibly related to NH₃ emissions from local tour buses, but additional studies on NH₃ emissions from tour buses in the reserve are needed to confirm this.

Source apportionment of PM2.5 in North India using source-oriented air quality models

In recent years, severe pollution events were observed frequently in India especially at its capital, New Delhi. However, limited studies have been conducted to understand the sources to high pollutant concentrations for designing effective control strategies. In this work, source-oriented versions of the Community Multi-scale Air Quality (CMAQ) model with Emissions Database for Global Atmospheric Research (EDGAR) were applied to quantify the contributions of eight source types (energy, industry, residential, on-road, off-road, agriculture, open burning and dust) to fine particulate matter (PM_2.5) and its components including primary PM (PPM) and secondary inorganic aerosol (SIA) i.e. sulfate, nitrate and ammonium ions, in Delhi and three surrounding cities, Chandigarh, Lucknow and Jaipur in 2015. PPM mass is dominated by industry and residential activities (>60%). Energy (∼39%) and industry (∼45%) sectors contribute significantly to PPM at south of Delhi, which reach a maximum of 200 μg/m³ during winter. Unlike PPM, SIA concentrations from different sources are more heterogeneous. High SIA concentrations (∼25 μg/m³) at south Delhi and central Uttar Pradesh were mainly attributed to energy, industry and residential sectors. Agriculture is more important for SIA than PPM and contributions of on-road and open burning to SIA are also higher than to PPM. Residential sector contributes highest to total PM_2.5 (∼80 μg/m³), followed by industry (∼70 μg/m³) in North India. Energy and agriculture contribute ∼25 μg/m³ and ∼16 μg/m³ to total PM_2.5, while SOA contributes <5 μg/m³. In Delhi, industry and residential activities contribute to 80% of total PM_2.5.

Source apportionment of PM2.5 in Baton Rouge, Louisiana during 2009-2014

Particulate matter with aerodynamic diameter <2.5μm (PM_2.5) chemical composition data from the Speciation Trends Network (STN) site located in Baton Rouge, Louisiana were analyzed using the receptor Positive Matrix Factorization (PMF) model version 5.0. The PM_2.5 samples were collected every third day from January 2009 to December 2014. Seven sources were identified, including secondary sulfate, secondary nitrate, industrial emissions, traffic, crustal dust, road dust and sea salt. The contributions of these seven sources to PM_2.5 total mass were 38.4%, 17.6%, 18.7%, 11.5%, 6.1%, 4.2% and 3.6%, respectively. Secondary sulfate, industrial emissions and secondary nitrate were the top three sources. The contributions of industrial emissions and crustal dust have been rising in recent years while that of traffic and sea salt were decreasing. Secondary sources were higher than primary sources during the winter. The crustal and road dust were dominant during the summer, while traffic was more significant during the fall compared to other seasons. During summer, traffic emission and crustal dust were driven by northeast-north winds, traffic is also driven by northeast-north winds in winter, while industry emissions and sea salt were driven by prevailing west and northwest winds during other seasons. PM mass clearly showed the synergetic effects of local sources and distance sources. Thus, measurements and strategies should focus on not only local sources, but also regional transport. Attention should also be paid to industrial and traffic sources since they also account for secondary sources in addition to the primary contributions.

Modeling dry and wet deposition of sulfate, nitrate, and ammonium ions in Jiuzhaigou National Nature Reserve, China using a source-oriented CMAQ model: Part II. Emission sector and source region contributions

A source-oriented Community Multiscale Air Quality (CMAQ) model driven by the meteorological fields generated by the Weather Research and Forecasting (WRF) model was used to study the dry and wet deposition of nitrate (NO3(-)), sulfate (SO4(2-)), and ammonium (NH4(+)) ions in the Jiuzhaigou National Nature Reserve (JNNR), China from June to August 2010 and to identify the contributions of different emission sectors and source regions that were responsible for the deposition fluxes. Contributions from power plants, industry, transportation, domestic, biogenic, windblown dust, open burning, fertilizer, and manure management sources to deposition fluxes in JNNR watershed and four EANET sites are determined. In JNNR, 96%, 82%, and 87% of the SO4(2-), NO3(-) and NH4(+) deposition fluxes are in the form of wet deposition of the corresponding aerosol species. Industry and power plants are the two major sources of SO4(2-) deposition flux, accounting for 86% of the total wet deposition of SO4(2-), and industry has a higher contribution (56%) than that of power plants (30%). Power plants and industry are also the top sources that are responsible for NO3(-) wet deposition, and contributions from power plants (30%) are generally higher than those from industries (21%). The major sources of NH4(+) wet deposition flux in JNNR are fertilizer (48%) and manure management (39%). Source-region apportionment confirms that SO2 and NOx emissions from local and two nearest counties do not have a significant impact on predicted wet deposition fluxes in JNNR, with contributions less than 10%. While local NH3 emissions account for a higher fraction of the NH4(+) deposition, approximately 70% of NH4(+) wet deposition in JNNR originated from other source regions. This study demonstrates that S and N deposition in JNNR is mostly from long-range transport rather than from local emissions, and to protect JNNR, regional emission reduction controls are needed.

Modeling dry and wet deposition of sulfate, nitrate, and ammonium ions in Jiuzhaigou National Nature Reserve, China using a source-oriented CMAQ model: Part I. Base case model results

A source-oriented Community Multiscale Air Quality (CMAQ) model driven by the meteorological fields generated by the Weather Research and Forecasting (WRF) model was used to study the dry and wet deposition of nitrate (NO3(-)), sulfate (SO4(2-)), and ammonium (NH4(+)) ions in the Jiuzhaigou National Nature Reserve (JNNR), China from June to August 2010 and to identify the contributions of different emission sectors and source regions that were responsible for the deposition fluxes. The model performance is evaluated in this paper and the source contribution analyses are presented in a companion paper. The results show that WRF is capable of reproducing the observed precipitation rates with a Mean Normalized Gross Error (MNGE) of 8.1%. Predicted wet deposition fluxes of SO4(2-) and NO3(-) at the Long Lake (LL) site (3100 m a.s.l.) during the three-month episode are 2.75 and 0.34 kg S(N) ha(-1), which agree well with the observed wet deposition fluxes of 2.42 and 0.39 kg S(N) ha(-1), respectively. Temporal variations in the weekly deposition fluxes at LL are also well predicted. Wet deposition flux of NH4(+) at LL is over-predicted by approximately a factor of 3 (1.60 kg N ha(-1)vs. 0.56 kg N ha(-1)), likely due to missing alkaline earth cations such as Ca(2+) in the current CMAQ simulations. Predicted wet deposition fluxes are also in general agreement with observations at four Acid Deposition Monitoring Network in East Asia (EANET) sites in western China. Predicted dry deposition fluxes of SO4(2-) (including gas deposition of SO2) and NO3(-) (including gas deposition of HNO3) are 0.12 and 0.12 kg S(N) h a(-1) at LL and 0.07 and 0.08 kg S(N) ha(-1) at Jiuzhaigou Bureau (JB) in JNNR, respectively, which are much lower than the corresponding wet deposition fluxes. Dry deposition flux of NH4(+) (including gas deposition of NH3) is 0.21 kg N ha(-1) at LL, and is also much lower than the predicted wet deposition flux. For both dry and wet deposition fluxes, predictions from the 12-km resolution nested domain are similar to those from the 36-km resolution parent domain.

Significant Contributions of Isoprene to Summertime Secondary Organic Aerosol in Eastern United States

A modified SAPRC-11 (S11) photochemical mechanism with more detailed treatment of isoprene oxidation chemistry and additional secondary organic aerosol (SOA) formation through surface-controlled reactive uptake of dicarbonyls, isoprene epoxydiol and methacrylic acid epoxide was incorporated in the Community Multiscale Air Quality Model (CMAQ) to quantitatively determine contributions of isoprene to summertime ambient SOA concentrations in the eastern United States. The modified model utilizes a precursor-origin resolved approach to determine secondary glyoxal and methylglyoxal produced by oxidation of isoprene and other major volatile organic compounds (VOCs). Predicted OC concentrations show good agreement with field measurements without significant bias (MFB ∼ 0.07 and MFE ∼ 0.50), and predicted SOA reproduces observed day-to-day and diurnal variation of Oxygenated Organic Aerosol (OOA) determined by an aerosol mass spectrometer (AMS) at two locations in Houston, Texas. On average, isoprene SOA accounts for 55.5% of total predicted near-surface SOA in the eastern U.S., followed by aromatic compounds (13.2%), sesquiterpenes (13.0%) and monoterpenes (10.9%). Aerosol surface uptake of isoprene-generated glyoxal, methylglyoxal and epoxydiol accounts for approximately 83% of total isoprene SOA or more than 45% of total SOA. A domain wide reduction of NOx emissions by 40% leads to a slight decrease of domain average SOA by 3.6% and isoprene SOA by approximately 2.6%. Although most of the isoprene SOA component concentrations are decreased, SOA from isoprene epoxydiol is increased by ∼16%.

Atmospheric wet deposition of sulfur and nitrogen in Jiuzhaigou National Nature Reserve, Sichuan Province, China

In the last two decades, remarkable ecological changes have been observed in Jiuzhaigou National Nature Reserve (JNNR). Some of these changes might be related to excessive deposition of sulfur (S) and nitrogen (N), but the relationship has not been quantified due to lack of monitoring data, particularly S and N deposition data. In this study, we investigated the concentrations, fluxes, and sources of S and N wet deposition in JNNR from April 2010 to May 2011. The results show that SO4(2-), NO3-, and NH4+ concentrations in the wet deposition were 39.4-170.5, 6.2-34.8, and 0.2-61.2 μeq L(-1), with annual Volume-Weighted Mean (VWM) concentrations of 70.5, 12.7, and 13.4 μeq L(-1), respectively. Annual wet deposition fluxes of SO4(2-), NO3-, and NH4+ were 8.06, 1.29, and 1.39 kg S(N)ha(-1), respectively, accounting for about 90% of annual atmospheric inputs of these species at the monitoring site. The results of Positive Matrix Factorization (PMF) analysis show that fossil fuel combustion, agriculture, and aged sea salt contributed to 99% and 83% of annual wet deposition fluxes of SO4(2-) and NO3-, respectively. Agriculture alone contributed to 89% of annual wet deposition flux of NH4+. Although wet deposition in JNNR was polluted by anthropogenic acids, the acidity was largely neutralized by the Ca2+ from crust and 81% of wet deposition samples had a pH higher than 6.00. However, acid rain mainly caused by SO4(2-) continued to occur in the wet season, when ambient alkaline dust concentration was lower. Since anthropogenic emissions have elevated S and N deposition and caused acid rain in JNNR, further studies are needed to better quantify the regional sources and ecological effects of S and N deposition for JNNR.

Simulating near-road reactive dispersion of gaseous air pollutants using a three-dimensional Eulerian model

In this study, the TAMNROM-3D model, a 3D Eulerian near-road air quality model with vehicle induced turbulence parameterization and a MOVES based emission preprocessor, is tested using near-road gaseous pollutants data collected near a rural freeway with 34% heavy duty vehicle traffic. Exhaust emissions of gasses from the vehicles are estimated using a lumped vehicle classification scheme based on the number of vehicle axles and the default county-level MOVES vehicle fleet database. The predicted dilution of CO and NOx in the downwind direction agrees well with observation, although the total NOx emission has to be scaled to 85% of its original emission rate estimated by the MOVES model. Using the atmospheric turbulent diffusion coefficient parameterization of Degrazia et al. (2000) with variable horizontal turbulent diffusion coefficient (Kxx) leads to slightly better predictions than a traditional non-height-dependent Kxx parameterization. The NO2 concentrations can be better predicted when emission of total NOx is split into NO and NO2 using the NO2 to NOx ratio of 29% measured near the road. Simulations using the SAPRC99 photochemical mechanism do not show significant changes in the predicted NO and NO2 concentrations near the road compared to simulations using a simple three-reaction mechanism that involves only NOx and O3. A regional air quality simulation in Houston, Texas during a high O3 episode in August 2000 shows that using the NO2 to NOx ratio of 29% instead of the traditional 5% leads to as much as 6ppb increase in 8-h O3 predictions.