Exposure to respirable and fine dust particle over North-Central India: chemical characterization, source interpretation, and health risk analysis

Unmasking the veil of PM2.5 pollution: A comprehensive analysis of health effects, economic losses, and environmental implications in North Indian States

This research paper analyses particulate matter (PM2.5) pollution data from five Indian states to assess health impacts, including premature deaths from lung cancer (LC), chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), stroke, and lower respiratory infections (LRI). Intake factors (iF), effect factors (EF), and characterization factors (CF) were used to quantify pollutant inhalation, health impact per unit intake, and potential health impact in Disability-Adjusted Life Years (DALYs), respectively. We found localities like Jahangirpuri in Delhi (DL) (3.25E-04-4.01E-04), Faridabad in Haryana (HR)(1.1E-04-9.59E-05), Bhiwadi in Rajasthan (RJ) (6.76E-05-9.73E-05), Ghaziabad in Uttar Pradesh (UP) (7.49E-05-1.32E-04) and Gobindgarh in Punjab (PB) (4.14E-05-5.16E-05) had much higher iFs than the previously reported pan- India value of 7.60E-06. Ghaziabad of UP recorded the highest EF of 4.02E+ 02 DALYs per kilogram PM2.5 inhalation in 2021. High iF and EF values resulted in alarmingly high CF values across all the monitoring stations of the states. The Jahangirpuri of DL recorded the highest CF of 9.88E+ 01 DALYs per kg of PM2.5. The combined five diseases PM2.5-induced mortality cases for five states were assessed to be 171,551 in 2019, which increased to 175,140 in 2022. UP had the largest share of combined mortality cases in 2022, where mortality cases due to stroke grew highest at 29.48 % during 2019-2022, followed by IHD (27.6 %), COPD (20.85 %), and LC (20.35 %). The study estimated economic loss from PM2.5 mortality for five diseases in five states from 2019 to 2022, ranging from 197,964 million US$ to 276,541 million US$. According to the research, the economic burden would still be worrisome if policy interventions can achieve a targeted 30 % PM2.5 reduction across five states by 2024. The study underscores the imperative need for a time-bound holistic approach to decimate air pollution in north Indian states.

Foliar image-based characterization of airborne particulate matter in an urban area and its implications for remediation

This study addresses the pervasive issue of particulate matter (PM) emission in urban areas, proposing a better approach using scanning electron microscope (SEM) techniques to identify plant species effective in airborne PM removal. Conducted in Bilaspur city, the research strategically selected six plant species across four distinct sites and applied the SEM-Image J method for analysis, yielding significant insights, especially in the respirable PM range. Among the tested plant species, Senna Siamea and Dalbergia Sissoo emerged as consistent and standout performers, displaying the highest PM removal efficiency across all sites. Notably, the smaller leaves of Senna siamea and Dalbergia sissoo prevent PM from being resuspended in the air by strong winds, enhancing their overall performance in combating PM pollution. The SEM-EDS analysis was then employed for morphological and chemical characterizations of the PM, revealing anthropogenic sources as the primary contributors to pollution. Hazardous elements, including arsenic (As), antimony (Sb), iron (Fe), indium (In), terbium (Tb), chlorine (Cl), and iodine (I), were identified, underscoring potential health risks associated with the PM composition. The study underscores the significance of SEM-EDS based plant selection for mitigating airborne PM pollution and improving air quality. Senna Siamea and Dalbergia Sissoo are identified as top choices for effective PM removal, marking a significant step towards sustainable urban environments. The findings contribute valuable insights into the chemical makeup of PM, facilitating a deeper understanding of its sources and potential health implications. Overall, this research serves as a crucial step in developing strategies to combat air pollution and fosters the creation of healthier and more sustainable urban environments.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Year-long and seasonal differences of PM2.5 chemical characteristics and their role in the viability of human lung epithelial cells (A549)

Fine particulate matters-PM2.5 in the air can have considerable negative effects on human health and the environment. Various human cell-based studies examined the effect of PM2.5 on human health in different cities of the world using various chemical parameters. Unfortunately, limited information is available regarding the relationship between toxicity and chemical characteristics of PM2.5 collected in Istanbul, Türkiye, located in one of the most populated cities in the world. To investigate the chemical characteristics and cytotoxicity of PM2.5 in Istanbul, samples were collected for 12 months, then potentially toxic metals, oxidative potential, and particle indicators (e.g., functional groups and elements) were determined, and the cytotoxicity of PM2.5 on human A549 lung alveolar epithelial cells was examined. The mean PM2.5 mass concentration was 24.0 ± 17.4 µg m-3 and higher in cold months compared to other seasons. Moreover, the results of the metals, elemental, and functional groups indicated that seasonal and monthly characteristics were influenced by the regional anthropogenic sources and photochemistry input. The cytotoxicity results also showed that the viability of A549 cells was reduced with the exposure of PM2.5 (30-53%) and higher cytotoxicity was obtained in summer compared to the other seasons due to the impact of the metals, elements, and oxidative characteristics of PM2.5.

Prediction of water quality under the impacts of fine dust and sand storm events using an experimental model and multivariate regression analysis

Many regions of the world, especially arid and semiarid areas, occasionally experience fine dust and sandstorms, known environmental problems that make normal life difficult. Since the intrusion of large amounts of dust into treatment plants may significantly change the water quality indices, the main goal of this study was to estimate these indices during the events, which can help decision-makers to improve water quality. To achieve relationships using nonlinear multivariate regression analysis, a long-term (three years: April 2017-February 2020) experimental study of water quality parameters including total dissolved solids (TDS), hydrogen content (pH), electrical conductivity (EC), chlorine (Cl), total hardness, sodium (Na), and magnesium (Mg) for water samples from wastewater treatment plants in Sistan region (Iran) was conducted where is one of the most popular regions in the world with high amount of annual fine dust level. Analysis of ANOVA showed that of all the independent parameters considered in this study, water quality parameters strongly correlated with monthly mean sand and dust storm index (SDSI), wind speed, temperature, and the number of monthly windy days. For the regression analysis, 25 months of data were used for the simulation process and 10 months for validation. The final results showed that the relationships obtained from the nonlinear multivariate regression analysis could predict the water quality indices very well (with R2 more than 0.75) except for Mg with R2 equal to 0.55. In addition, the maximum mean relative error belongs to Mg (10.8%) and then Na (9.9%) whereas the minimum mean relative error belongs to pH (2.6%) and then EC (2.9%).

Physico-chemical characterization of indoor settled dust in Children's microenvironments in Ikeja and Ota, Nigeria

Indoor dust is a collection of particles identified as a major reservoir for several emerging indoor chemical pollutants. This study presents indoor dust particles' morphology and elemental composition in eight children's urban and semi-urban microenvironments (A-H) in Nigeria. Samples were collected using a Tesco vacuum cleaner and analyzed with scanning electron microscopy coupled with an energy-dispersive X-ray (SEM-EDX). The morphology results confirm the presence of alumino silicates, mineral particles and flakes, fly ash and soot, and soot aggregates deposited on alumino silicate particles in the sampled microenvironments. These particles may trigger serious health concerns that directly or indirectly affect the overall well-being of children. From the EDX analysis, the trend of elements (w/w %) in the dust particles across the sampled sites was silicon (386) > oxygen (174)> aluminium (114) > carbon (34.5) > iron (28.0) > calcium (16.7) > magnesium (14.2) > sodium (7.92) > potassium (7.58) > phosphorus (2.22) > lead (2.04) > manganese (1.17) > titanium (0.21). Lead (Pb), a toxic and carcinogenic heavy metal, was observed in locations A and B. This is a concern without a safe lead level because of the neurotoxicity effect on children. As a result, further research on the concentrations, bioavailability, and health risk assessment of heavy metals in these sampled locations is recommended. Furthermore, frequent vacuum cleaning, wet moping and adequate ventilation systems will significantly reduce the accumulation of indoor dust-bound metals.

COVID-19-associated 2020 lockdown: a study on atmospheric black carbon fall impact on human health

The mean mass concentrations of black carbon (BC)_, biomass burning (BC)_bb, and fossil fuel combustion (BC)_ff have been estimated during March-May 2020 (during the COVID-19 outbreak) and March-May 2019 at a semiarid region of Agra over the Indo-Gangetic basin region. The daily mean mass concentration of BC in 2020 and 2019 was 3.9 and 6.9 µg m^-3, respectively. The high monthly mean mass concentration of BC was found to be 4.7, 3.4 and 3.3 µg m^-3 in Mar-2020, Apr-2020, and May-2020, respectively, whereas in Mar-2019, Apr-2019, and May-2019 was 7.7, 7.5 and 5.4 µg m^-3, respectively. The absorption coefficient (b_abs) and absorption angstrom exponent (AAE) of black carbon were calculated. The highest mean AAE was 1.6 in the year 2020 (Mar-May 2020) indicating the dominance of biomass burning. The mean mass concentration of fossil fuel (BC)_ff and biomass burning (BC)_bb is 3.4 and 0.51 µg m^-3, respectively, in 2020 whereas 6.4 and 0.73 µg m^-3, respectively, in 2019. The mean fraction contribution of BC with fossil fuel (BC)_ff was 82.1 ± 13.5% and biomass burning (BC)_bb was 17.9 ± 4.3% in 2020, while in 2019, fossil fuel (BC)_ff was 86.7 ± 13.5% and biomass burning (BC)_bb was 13.3 ± 6.7%. The population-weighted mean concentration of BC, fossil fuel (BC)_ff, and biomass burning (BC)_bb has been calculated. The health risk assessment of BC has been analyzed in the form of attributable relative risk factors and attributed relative risk during the COVID-19 outbreak using AirQ + v.2.0 model. The attributable relative risk factors of BC were 20.6% in 2020 and 29.4% in 2019. The mean attributed relative risk per 10,000,000 populations at 95% confidence interval (CI) due to BC was 184.06 (142.6-225.2) in 2020 and 609.06 (418.3-714.6) in 2019. The low attributed factor and attributed relative risk in 2020 may be attributed to improvements in air quality and a fall in the emission of BC. In 2020, due to the COVID-19 pandemic, the whole country faced the biggest lockdown, ban of the transportation of private vehicles, trains, aircraft, and construction activities, and shut down of the industry leading to a fall in the impact of BC on human health. Overall, this was like a blessing in disguise. This study will help in future planning of mitigation and emission control of air pollutants in large and BC in particular. It only needs a multipronged approach. This study may be like torch bearing to set path for mitigation of impacts of air pollution and improvement of air quality.

Response of PM2.5-bound elemental species to emission variations and associated health risk assessment during the COVID-19 pandemic in a coastal megacity

The coronavirus (COVID-19) pandemic is disrupting the world from many aspects. In this study, the impact of emission variations on PM_2.5-bound elemental species and health risks associated to inhalation exposure has been analyzed based on real-time measurements at a remote coastal site in Shanghai during the pandemic. Most trace elemental species decreased significantly and displayed almost no diel peaks during the lockdown. After the lockdown, they rebounded rapidly, of which V and Ni even exceeded the levels before the lockdown, suggesting the recovery of both inland and shipping activities. Five sources were identified based on receptor modeling. Coal combustion accounted for more than 70% of the measured elemental concentrations before and during the lockdown. Shipping emissions, fugitive/mineral dust, and waste incineration all showed elevated contributions after the lockdown. The total non-carcinogenic risk (HQ) for the target elements exceeded the risk threshold for both children and adults with chloride as the predominant species contributing to HQ. Whereas, the total carcinogenic risk (TR) for adults was above the acceptable level and much higher than that for children. Waste incineration was the largest contributor to HQ, while manufacture processing and coal combustion were the main sources of TR. Lockdown control measures were beneficial for lowering the carcinogenic risk while unexpectedly increased the non-carcinogenic risk. From the perspective of health effects, priorities of control measures should be given to waste incineration, manufacture processing, and coal combustion. A balanced way should be reached between both lowering the levels of air pollutants and their health risks.

Determination and assessment of elemental concentration in the atmospheric particulate matter: a comprehensive review

The elemental concentrations of atmospheric particulate matter (PM) have a detrimental effect on human health in which some elemental species have carcinogenic nature. In India, significant variations have found in the practices adapted from sampling to analysis for the determination and assessment of the elemental concentration in PM. Therefore, Indian studies (2011-2020) on the related domain are summarized to impart consistency in the field and laboratory practices. Further, a comparative analysis with other countries has also been mentioned in the relevant sections to evaluate its likeness with Indian studies. To prepare this study, literature has been procured from reputed journals. Subsequently, each step from sampling to analysis has thoroughly discussed with quality assurance and control (QA/QC) compliance. In addition, a framework has been proposed that showed field and laboratory analysis in an organized manner. Consequently, this study will provide benefit to novice researcher and improve their understanding about the related subject. Also, it will assist other peoples/bodies in framing the necessary decisions to carry out this study.

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.

The short-term effect of PM2.5/O3 on daily mortality from 2013 to 2018 in Hefei, China

This research intends to explore the short-term impacts of PM_2.5/O₃ on daily death in Hefei from 2013 to 2018. Data on daily death of Hefei residents, meteorological factors, and air pollutants were collected from Jan 1, 2013, to Dec 31, 2018. The correlation between PM_2.5/O₃ and daily death in Hefei during the research period was studied by time series analysis. From 2013 to 2018, there were 61,683 non-accidental deaths, including 27,431 cardiovascular deaths, 5587 respiratory deaths, 20,921 malignant tumor deaths, and 1674 diabetes deaths, in Hefei. Annual mean concentrations of PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ in Hefei were 66.18, 92.37, 39.75, 15.39, 930, and 79.08 μg m^-3, respectively. An increase of 10 μg m^-3 in PM_2.5 was related with 0.53% (95% CI 0.31-0.75%), 0.93% (95% CI 0.60-1.26%), 0.90% and (95% CI 0.23-1.57%) increase in non-accidental, cardiovascular and respiratory diseases mortality, respectively. The association between ozone and mortality was not significant. In cold seasons, PM_2.5 had a stronger effect on the deaths resulting from non-accidental, cardiovascular, and respiratory diseases. The effect of O₃ on deaths was not significantly different between the cold season and the warm season. Women and the elders (over 65 years) were at high risk of being affected by PM_2.5/O₃. Short-term exposure to PM_2.5 was positively correlated with increased deaths due to non-accidental, cardiovascular and respiratory diseases in Hefei. Females and elders were more vulnerable to PM_2.5/O₃ exposure. No significant associations were observed between ozone and deaths from non-accidental, cardiovascular, respiratory, malignant tumors, and diabetes diseases.

Biogeochemistry of Household Dust Samples Collected from Private Homes of a Portuguese Industrial City

The main objectives of the present study were to (i) investigate the effects of mineralogy and solid-phase distribution on element bioaccessibility and (ii) perform a risk assessment to calculate the risks to human health via the ingestion pathway. Multiple discriminant analysis showed that the dust chemistry discriminates between indoor and outdoor samples. The solid-phase distribution of the elements in indoor dust indicated that a large proportion of zinc, nickel, lead, copper, and cobalt is associated with an aluminum oxy-hydroxides component, formed by the weathering of aluminum silicates. This component, which seems to influence the mobility of many trace elements, was identified for a group of indoor dust samples that probably had a considerable contribution from outdoor dust. An iron oxide component consisted of the highest percentage of chromium, arsenic, antimony, and tin, indicating low mobility for these elements. The bioaccessible fraction in the stomach phase from the unified BARGE method was generally high in zinc, cadmium, and lead and low in nickel, cobalt, copper, chromium, and antimony. Unlike other potentially toxic elements, copper and nickel associated with aluminum oxy-hydroxides and calcium carbonates were not extracted by the stomach solutions. These trace elements possibly form stable complexes with gastric fluid constituents such as pepsin and amino acid. Lead had a hazard quotient >1, which indicates the risk of non-carcinogenic health effects, especially for children.