Introduction: Atmospheric aerosol particles significantly impact ecosystems,global climate, cultural heritage, and public health. Air pollution is a majorglobal health concern, contributing to roughly one quarter of total globalmortality, with fine Particulate Matter (PM₂.₅) being particularly harmful.Long-term exposure to elevated PM₂.₅ and gaseous pollutants such as sulfurdioxide, nitrogen dioxide, ozone, and carbon monoxide increases the riskof severe health effects, including chronic respiratory and cardiovasculardiseases. Materials and methods: Air quality trends in Uzbekistan, primarily inTashkent, were analyzed using Air Quality Index (AQI) data, focusing onwintertime PM₂.₅ levels. Health outcomes were assessed through statisticalanalysis of Ministry of Health records from 2012 to 2024, with particularattention to respiratory tract infection–related mortality. Comparative analysiswas performed between urban and rural populations, and vulnerable groups,including children and senior citizens, were identified. Results: Analysis revealed that wintertime PM₂.₅ concentrations in Tashkentwere approximately six times higher than World Health Organizationrecommended limits. Statistical evaluation indicated a significant 24.94%increase in respiratory tract infection–related deaths in Tashkent during thestudy period (p<0.05), whereas rural areas showed no significant growth.Urban air pollution, primarily from residential heating and anthropogenicactivities, was identified as a major contributor. Children and elderlypopulations were most affected. Conclusion: These findings demonstrate the substantial health impacts ofurban air pollution in Uzbekistan, particularly in low- and middle-incomeurban settings. The study emphasizes the urgent need for targeted air qualitymanagement strategies to mitigate pollution-related health risks, protectvulnerable populations, and improve public health outcomes
Introduction: The nature of heavy pollution incidence that plagues the South East Asian (SEAR) Region and the African region demands the understanding of air pollution dynamics within these regions to inform policy formulation to improve environmental health. This study therefore aims to grasp the transformation of air pollutants in the last 10 years in the two regions and their potential to influence respiratory health. Materials and methods: This study used the 6th edition of the ambient air quality data from the WHO website, which was revised and published on January 22, 2024. 1609 dataset was used for this research, spanning the 16 countries. Results: The results of the analysis show that in the last 10 years, the mean PM10 (64.15 ± 40.38 g/m³), PM2.5 (22.98 ± 23.65 g/m3), and NO₂ (8.83 ± 7.99 g/m³) were 64.15 ± 40.38 g/m³, 22.98 ± 23.65 g/m³, and 8.83 ± 7.99 g/m³, respectively. Consequently, the air quality index for PM10 and PM2.5 stands at 57.73 and 96.59 for the African Region and 55.53 and 74.61 for SEAR, indicating a satisfactory air quality. The principal component analysis showed that NO₂ exposure and monitoring explained 39.91% of the variance in the data, while component 2 (PM10 and PM2.5) explained 19.43%. The regression model showed that PM10 temporal coverage can be used to predict NO₂ concentration. Indicating that better cover for PM10 can be used to estimate NO2 concentration. Conclusion: This study has highlighted that temporal coverage can be a useful means for air pollutant estimation. Hence, governments should increase monitoring of air pollutants, in this peak era of industrialisation to capture the many unquantified contaminants
Introduction: Haul roads are one of the main sources of dust in mines. Dust pollution not only causes lung diseases, but also reduces the useful vision of machine drivers, slows down trucks, and interferes with transportation operations. On the other hand, machines' operation in dust increases their depreciation and fuel consumption. The popular spraying method is used in mines to overcome this problem. To suppress dust on mining roads, water and oil mulch are commonly used. The first method requires a large amount of water, and the second method has undesirable environmental effects. Therefore, an appropriate alternative method should be found. According to the results, the proposed method may be effective in dust suppression. This process causes both environmental and operational difficulties, including high water consumption, inefficiency in hot and dry areas, and costly water supply in many regions, not to mention its cultural value, soil liquefaction, traffic by the irrigation process, imposing repairing and maintaining costs for sprinklers and ramps, etc. Materials and methods: Soil stabilization is a technique that enhances the engineering and mechanical characteristics of soil, such as its strength, stiffness, formation, and loading capacity using technology and proper materials. The present research aimed at finding out how the physical and strength properties could be improved with the addition of optimum ratios of sugar beet molasses to the ramp soils to make the unpaved roads within the Abyek cement mine region stronger and more durable. Results: As analytical tests for soils containing additives with specified weight percentages, Atterberg limits, compaction, Unconfined Compressive Strength (UCS), and direct shear tests were conducted. The incorporation of sugar beet molasses into both soils resulted in an 11.6% reduction in Optimum Moisture Content (OMC) and increased the Maximum Dry Density (MDD) to 1.955 g/cm³ for AB01 soil and 1.942 g/cm³ for AB02 soil. Conclusion: The optimal result was obtained from direct shear and unconfined compressive tests by adding 2% molasses for AB02 soil and 1% molasses for AB01 soil.
Introduction: The rapid urbanization and heavy traffic in cities raise concerns about health and environmental risks from Potentially Toxic Elements (PTEs). This study analyses the levels of contamination, origins, and exposure hazards of 10 PTEs (Fe, As, Cd, Zn, Cu, Mn, Pb, Cr, Co, Ni) in dust from five public vehicles and five motor parks in Abuja, Nigeria. Materials and methods: Digested samples of park dust were analysed for Fe, Pb, Zn, As, Co, Cr, Cu, Cd, Mn, Ni (ten PTEs) using Atomic Absorption Spectrophotometer (AAS). PTE sources were ascertained using Positive Matrix Factorization (PMF) alongside contamination indicators comprising of Enrichment Factor, Geo-accumulation Index, Contamination Factor and Ecological Risk Factor. A new pollution indicator, the Nemerov Integrated Risk Index (NIRI), was evaluated for consistency with existing methods. Exposure risks (cancer and non-cancer causing) were assessed for commuters. Results: PMF revealed five PTE sources: brake/engine wear (50%), vehicular body wear (1%), tyre wear/lubrication leaks (12%), coal combustion (6%), and vehicular emissions (31%). Cd exhibited the highest contamination levels across all indices. NIRI results aligned with traditional indices, confirming severe Cd pollution. Health risk assessments showed insignificant non- carcinogenic and carcinogenic risks for adults and children, though children were more vulnerable. Conclusion: Traffic-related activities were the dominant sources of PTEs in Abuja’s vehicle and motor park dusts. Cadmium (Cd) exhibited the highest enrichment, exceeding background levels and posing high ecological risk particularly for children, while other PTEs presented low health risks. This study underlines the necessity for targeted mitigation and non-stop monitoring to reduce PTE exposure in urban transit environments.
Introduction: The rapid rate of industrial growth in Delta State, Nigeria, has led to an increase in the emission of airborne pollutants, including Particulate Matter (PM2.5), Sulfur dioxide (SO2), Nitrogen Oxides (NOx), and Volatile Organic Compounds (VOCs), which pose a threat to the environment and the health of the population. This paper utilises Computational Fluid Dynamics (CFD) and Human Health Risk Assessment (HHRA) to simulate the dispersion of pollutants and assess the risks associated with exposure in four industrial areas: Warri/Ekpan, Aladja, Ughelli, and Kwale. Materials and methods: Simulations in three-dimensional CFD of ANSYS Fluent 2024 R1 were conducted using the actual meteorological, topographic and emission parameters provided in NiMet and EIA data. The Navier- Stokes equations were solved with the Realisable k-epsilon turbulence model. The model results were georeferenced and interpreted in ArcGIS Pro 3.2, generating exposure maps by combining the pollutant fields with the population fields. The Hazard Index (HI) and Lifetime Cancer Risk (LCR) were used in quantifying health risks in accordance with USEPA guidelines. Results: The concentrations of VOCs and PM2.5 in the air were 115.6 μg/ m³ and 56.2 μg/m³, respectively, which exceeded the WHO levels. HI values were 14.7-21.4 (adults) and 26.138.0 (children), and LCR values (1.710;- 3.210; -3) represented that there was carcinogenic risk. Conclusion: CFDH-HRA was the most accurate in predicting risks of pollution and exposure, highlighting hotspots in critical zones near Warri and Aladja. The importance of adopting CFD-based control and monitoring to achieve SDGs 3, 9, 11, and 13 lies in creating a cleaner and healthier environment
Globally, air pollution contributes to more than seven million prematuredeaths each year and is responsible for over 3% of all disability-adjusted lifeyears lost. The adverse health impacts of air pollution, especially ParticulateMatter (PM) are extensive, playing a major role in the onset and progressionof coronary artery disease, various respiratory conditions, and multiplepulmonary disorders. Despite extensive evidence documenting the healthimpacts of PM, the underlying biological mechanisms remain only partiallyelucidated. Recent advances in epigenetics, particularly studies focusingon DNA methylation, offer a promising avenue for understanding how PMexposure translates into adverse health effects. An expanding body of researchdemonstrates strong associations between PM exposure and genome-widealterations in DNA methylation, suggesting that these modifications play apivotal role in mediating the biological and health effects of PM exposure.This comprehensive review explores the intricate relationship betweenDNA methylation and PM exposure. Representative epidemiological andexperimental studies emphasize the connections between PM-inducedmethylation alterations and the indirect impact of DNA methylation onhealth. By providing valuable insights into gene-specific alterations, thereview contributes to a deeper understanding of the potential implications ofPM exposure on DNA methylation and its broader health consequences
Introduction: This descriptive-ecological study investigated the seasonal, diurnal, and spatial variations of Carbon monoxide (CO) concentration in Urmia's (Northwest of Iran) ambient air over a six-month period, spanning Winter and Spring. Materials and methods: Sampling was conducted at 20 stations selected from various urban locations. At each station, a portable environmental gas analyzer was used to measure CO concentration during both morning and evening peak traffic hours. Results: The results revealed a significant seasonal and diurnal pattern. The highest CO means were observed in the cold months (January and February), peaking at an average of 6.19 ppm in January evenings. This increase is strongly linked to temperature inversion and heightened heating system usage. Statistical analysis confirmed a highly significant difference (P<0.001) in CO means across months and between morning and evening hours, with concentrations being significantly higher in the evening. Although monthly averages are generally below the 8-h national standard (9 ppm), their proximity to the limit and the registration of high peaks (up to 15.10 ppm) indicate a potential health risk during winter. Spatially, zoning maps showed the central, high-traffic area acts as the main pollution hotspot. Conclusion: The study highlights that even short-term peak CO exposure can be significant, potentially causing headache and behavioral effects. Additionally, the river and surrounding open spaces help reduce pollution, emphasizing the need for integrated air quality management strategies that account for both seasonal and diurnal variations. These findings underscore the critical need for integrated management strategies sensitive to both the time of day and the season.
Introduction: Air fresheners and scented candles release harmful chemicalsindoors, potentially posing health risks with prolonged exposure. Materials and methods: This study investigated the effects of inhalingemissions from these products on growth and locomotor activity in rats. Fortyrats (180–200g) were randomly assigned to four groups: air freshener (A),scented candle (B), combined exposure (D), and control (C). Exposures wereconducted in a controlled inhalation chamber for 10, 20, and 30 days (1 h/day),with 15 min of direct exposure. Environmental parameters (Particulate Matter(PM2.5, PM10), Total Volatile Organic Compounds (TVOCs), Formaldehyde(HCHO), temperature, and humidity) were monitored at three time intervals:0–15 min (emission), 15–30 min (without emission), and 30–60 min (withoutemission), using a portable monitoring device. Results: Significant increases (P≤0.05) in PM2.5, PM10, TVOC, and HCHOwere observed in group D compared to other groups. Rats in group D showedreduced growth rate and locomotor activity. Conclusion: These findings suggest that combined exposure worsens indoorair quality and may impair physiological and behavioral health.
Introduction: This study aimed to evaluate the health impact of a smoke free policy implemented in these facilities in the Seoul metropolitan area, focusing on workers’ subjective symptoms in relation to indoor air–related job environment. Materials and methods: Cross-sectional surveys were conducted among 589 workers employed in billiard halls and screen golf clubs located in Seoul in August 2017 (before policy implementation) and August 2018 (after implementation). Associations between job environment factors and work- time symptoms were examined using chi-squared tests or Fisher’s exact tests and multivariable logistic regression, sequentially adjusting for general and work-related characteristics. Across all symptoms, survey year (2018 vs. 2017) was consistently associated with reduced odds of symptom complaints. Results: Facilities operating mechanical ventilation only or combined natural and mechanical ventilation showed significantly lower odds of all symptoms than those relying solely on natural ventilation. In contrast, workers in facilities with a higher number of windows, facilities where cooking was conducted, or those with more smoking customers generally reported higher odds of symptoms. More frequent ventilation (≥5 times per day) tended to reduce respiratory complaints, whereas insufficient or intermittent ventilation was associated with higher symptom prevalence. Conclusion: Smoke-free regulations, appropriate mechanical ventilation and comprehensive indoor air quality management, including control of cooking and outdoor pollutant infiltration, are needed to further protect the health of workers in these environments. These findings support comprehensive indoor air quality management combining smoke-free policies with adequate mechanical ventilation systems in small indoor sports facilities
Introduction: Airborne microplastics have recently emerged as indoor air pollutants in urban environments due to extensive use of synthetic textiles, furnishings, and plastic-based materials. Continuous inhalation exposure may pose respiratory health risks, particularly in densely occupied spaces with poor ventilation. This study quantifies airborne microplastic concentrations in urban indoor environments and evaluates mitigation strategies based on ventilation improvement and material management. Materials and methods: Air sampling was conducted in residential rooms, classrooms, and office spaces using low-volume active air samplers fitted with quartz microfiber filters (flow rate 16.7 L/min; duration 8 h). Microplastics were identified and counted using optical microscopy, while polymer types were confirmed through Fourier Transform Infra-Red (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) analysis. Environmental parameters including PM2.5 concentration, Air Exchange Rate (AER), relative humidity, temperature, and occupancy density were simultaneously recorded. Respiratory exposure risk was estimated using inhalation dose and Hazard Quotient (HQ) calculations. Results: Microplastics were detected in all indoor environments, with mean concentrations of 600 particles/m3 in residences, 1050 in offices, and 1180 in classrooms. Fibers dominated (68%), mainly polyester and polypropylene. Higher concentrations were associated with low ventilation (AER 0.4 h−1), high occupancy density (0.85 persons/m2), and elevated PM2.5 levels (>45 μg/m3). Estimated inhalation exposure ranged from 2.1 to 3.8 particles/kg/day, and HQ exceeded the safe threshold (1.32) in poorly ventilated classrooms. Increasing AER to 1.2 h−1 reduced concentrations by 39%, replacing synthetic textiles lowered fiber proportion to 41%, and reducing occupancy to 0.55 persons/m2 decreased inhalation dose to 2.1 particles/kg/day and HQ to 0.78. Conclusion: Airborne microplastics are prevalent in indoor environments and may contribute to respiratory health risks, especially under low ventilation and occupancy. Enhancing ventilation, indoor materials, and occupancy reduce concentrations and risks, underscoring importance of indoor air management strategies.
Introduction: Exposure to air pollution heightens respiratory vulnerability, particularly during pandemics. The COVID-19 lockdowns in Iran provided a natural experiment to investigate how reduced human activity influenced air quality across 31 provinces. Understanding these environmental responses is vital for informing sustainable public health and pollution mitigation policies. Materials and methods: Satellite-derived data on air pollutants and air quality and meteorological variables were obtained for all 31 provinces of Iran from Sentinel-5P, the GLDAS-2 dataset developed by National Aeronautics and Space Administration (NASA), and Google Earth Engine (GEE). The study covered two COVID-19 lockdown periods and their corresponding pre-pandemic periods from the previous year. The evaluated air quality indices consisted of Carbon monoxide (CO), Water Vapor (H₂O,) Nitrogen dioxide (NO₂), Ozone (O₃), Sulfur dioxide (SO₂), Absorbing Aerosol Index (AER), and Atmospheric Formaldehyde (HCHO). Meteorological covariates comprised temperature, pressure, precipitation, and wind speed. Sparse temporal data were reconstructed using FDA and FPCA, representing Functional Data Analysis and Functional Principal Component Analysis, respectively. Three Function-on-Function (FOF) regression models— standard, smooth, and principal component-based—were developed, with and without meteorological adjustments. Model performance was assessed using R², AIC, and BIC, representing the coefficient of determination, Akaike Information Criterion, and Bayesian Information Criterion, respectively. Results: Air pollutant levels significantly declined during both lockdowns compared with the corresponding pre-pandemic periods, with spatial variations influenced by meteorological and industrial factors. Incorporating meteorological covariates markedly improved model accuracy, particularly for NO₂ and CO. The principal component-based FOF model provided the best fit, explaining over 80% of variance in major pollutants. Conclusion: COVID-19 lockdowns produced measurable, regionally heterogeneous improvements in air quality across Iran. Integrating meteorological adjustments and advanced functional regression approaches enhances environmental modeling and supports evidence-based air pollution control strategies during health emergencies.
Declining Indoor Air Quality (IAQ) in confined spaces and with insufficientventilation poses serious health risks in industrial and office environments.The presence of volatile organic compounds in indoor air can cause humandiseases, highlighting the need for effective and sustainable air qualityimprovement strategies. Phytoremediation offers an efficient, eco-friendlyapproach for removing contaminants from air, soil, and water, with plantspecies differing in their absorption capacities. This systematic review,conducted following Preferred Reporting Items for Systematic reviews andMeta-Analyses (PRISMA) guidelines, identifies plant species effective inthe phytoremediation of airborne benzene and toluene and examines factorsinfluencing their performance. Comprehensive searches of Scopus, Web ofScience, Google Scholar, ProQuest, MagIran, and Irandoc databases retrievedrelevant studies through a three-stage screening process (title, abstract, andfull-text review). Findings highlight Hedera helix and Epipremnum aureumas the most frequently used and efficient species for benzene and tolueneremoval. Moreover, enhancing factors such as increasing plant exposuretime to pollutants, repeated injection cycles, and modifications to plantcharacteristics or substrate can significantly improve phytoremediationefficiency. Comparative analysis of conventional and enhanced methodsrevealed that enhanced phytoremediation plants improve both the rate andextent of pollutant removal and can serve as cost-effective, sustainable,and eco-friendly strategies for controlling IAQ. Findings also suggest thatselecting appropriate plant species and designing combined systems canmaximize IAQ improvement and promote human health in industrial andoffice settings. Overall, phytoremediation, particularly using multiple plantspecies under optimized environmental conditions, can effectively enhanceindoor air quality and guide the design of practical phytoremediation systems
Introduction: Ambient fine Particulate Matter (PM2.5) pollution is increasingly recognized as a critical environmental issue in rapidly urbanizing and industrializing cities of developing nations. This study aimed to quantify the zone-specific health burden attributable to PM2.5 exposure in Surat, India. Materials and methods: PM2.5 data were obtained from a network of low- cost sensors operating across three major land-use zones: residential (West), commercial (Central), and industrial (South). PM2.5 data collected over one year (October 2022 to September 2023) were combined with population projections and cause-specific mortality rates from national datasets. Two established Health Impact Assessment (HIA) tools, AirQ+ and BenMAP-CE, were utilized to estimate premature mortality associated with PM2.5 levels exceeding WHO air quality standards. Results: The industrial zone exhibited the highest annual mean PM2.5 (86.6 μg/m3) and correspondingly the most significant premature mortality burden, primarily from ischemic heart disease, chronic obstructive pulmonary disease, and stroke. The commercial and residential zones exhibited comparatively lower pollution levels; however, notable mortality impacts were associated with higher population densities. Both AirQ+ and BenMAP-CE models produced consistent mortality estimates, highlighting the relationship between pollution concentration and demographic factors in urban health risks. Elevated incidences of acute lower respiratory infections among children under five were also identified in the industrial zone. Conclusion: In order to lower health hazards, the results highlight the necessity of zone-specific emission reduction measures and additional strengthening of Surat's particle emission trading scheme. The integrated framework offers a practical approach for evaluating urban health and air quality in developing nations.
Clean rooms play an important role in electronics industries, specifically forprecision manufacturing by strictly regulating pollution control, contaminants,and pressure. Airflow design and particle behavior are key determinants ofcontamination control performance in clean rooms. Despite technologicaladvances, suboptimal air distribution remains a major contributor to particledispersion and energy inefficiency. The review systematically examinesexperimental and modeling studies on airflow management and clean roomperformance improvement and their real-world application. A search wasconducted in Scopus, PubMed, and Web of Science using keywords related toclean rooms, air pollution, and airflow. Following Preferred Reporting Itemsfor Systematic reviews and Meta-Analyses (PRISMA) guidelines, studiespublished from 2000 up to November 2025 were screened, and 25 studieswere selected. To improve air flow and pollution control, various factorswere identified along with their strategies and results. The factors identifiedwere classified into four major classifications and their strategies and resultswere analyzed. These classifications included architectural and structuralfactors (e.g., room length, floor height), ventilation and air flow factors (e.g.,velocity, air change rate, fan-filter-unit configuration), environmental andoperational factors (e.g., activity of personnel, SCARA robot, season), andpollutant characteristics (e.g., particle size, and density). Current evidenceindicates that performance improvement of clean rooms requires integratingthese factors into a multi-objective framework balancing cleanliness, energydemand, and process stability. We propose a framework connecting differentparameters to measurable cleanliness and energy efficiency indices
Introduction: This study investigates the temporal trends of Sand and Dust Storms (SDS) and Particulate Matter (PM2.5 and PM10 ) concentrations across nine major Iranian cities from 2011 to 2022, assessing long-term air pollution patterns and associated environmental challenges. Materials and methods: PM concentration data were obtained from air quality monitoring stations. Dust storm events were identified using World Meteorological Organization (WMO) criteria. The primary objective was to analyze spatiotemporal variations and trends, which were statistically assessed using Sen’s slope method. Results: PM10 levels consistently exceeded WHO annual guidelines by 2.13 to 12 times, while PM2.5 levels were 1.37 to 6.89 times higher. Ahvaz recorded the highest cumulative SDS hours (25,318), followed by Yazd (10,062) and Tabriz (9,609), with annual stormy days reaching up to 66. most pronounced increases in PM10 occurred in Ahvaz, Yazd, and Tabriz. Maximum annual PM10 concentrations were observed in Ahvaz (179.8 µg/m3 in 2013) and Yazd (135.85 µg/m3 in 2022), whereas peak PM2.5 levels were reported in Ahvaz (57.2 µg/m3 in 2022) and Shiraz (43.37 µg/m3 in 2019). Conclusion: The escalating intensity and frequency of SDS are linked to regional climate variability, drought, and land degradation. The findings underscore an urgent need for comprehensive mitigation strategies, including desertification control, sustainable land management, and enhanced regional cooperation to address these critical environmental and public health challenges.
Introduction: Asthma is a respiratory disease, the severity of which is affected by air pollutants and environmental factors. Predicting asthma severity can help in disease monitoring and control. The objective of this research is to develop a model for predicting the severity of asthma based on environmental and demographical factors using machine learning. Materials and methods: Data was obtained from different districts in Uttarakhand, India, from government sources. Asthma severity was the output feature or dependent feature, while the input features or independent features were air pollutants such as Particulate Matters (PM2.5, PM10), Nitrogen dioxide (NO₂), Sulfur dioxide (SO₂), Ozone (O₃), Carbon monoxide (CO), environmental factors (temperature, humidity, wind speed) and socio- economic factors (age, gender) in addition to a pollution index. Logistic Regression, Random Forest and XGBoost machine learning models were used for multi-class classification. The metrics for model performance were accuracy, precision, recall and F1-score. Results: Logistic Regression had the highest accuracy (98%) compared to Random Forest and XGBoost (both 89%). It had goo) with an F1-score of 0.00 (support=1). Conclusion: Our findings show the potential of machine learning models, especially class performance with F1-scores of 0.99 (class 0) and 0.96 (class 1). But all models could not predict the minority class (class 2). Logistic Regression, to predict asthma severity from environmental data. But it has limitations due to the exclusion of various factors like smoking, obesity, genetics, previous asthma, and medication
Introduction: Microplastics (MPs) pollution has become a significant global environmental concern, with various sources contributing to its spread. However, the release of MPs from healthcare waste disposal systems, which often involve shredding plastic waste, has not been widely studied. This research investigates the presence and concentration of MPs in the air surrounding autoclave and hydroclave devices at hospital waste disposal sites in Tehran. Materials and methods: A cross-sectional study was conducted from May to August 2024 in eight hospitals in Tehran, encompassing both autoclave and hydroclave systems. Air samples were collected from distances of 0,5, and 10 m from the waste management units during their operation and when they were off. A total of 48 samples were analyzed for microplastic particles using light microscopy and Raman spectroscopy to determine particle characteristics such as size, shape, and color. Results: The average concentration of MPs in the air surrounding autoclave and hydrocalve devices was 45±43 (N/m3) and higher concentrations were observed when the devices were active. No significant differences were observed between the autoclave and hydroclave systems. Microplastic particles in the air of the disinfected areas were mainly fibrous (95%) and black (70%), and the average particle length was 34.93 μm. Smaller particles, which pose more health risks, were the most common particles. Conclusion: Hospital waste disposal units, especially their shredder systems, are a significant source of airborne MPs. These emissions, especially through inhalation are a potential health risk. This study highlights the need for further research and mitigation strategies to reduce microplastic emissions in healthcare settings
Introduction: Air pollution poses significant public health risks in industrial regions, with stroke mortality emerging as a critical outcome. This study examines the association between air pollutant exposure and stroke mortality in Arak, Iran - an industrial city with consistently poor air quality exceeding WHO thresholds. Materials and methods: We conducted a time-series analysis of 1,010 stroke deaths (2019-2022) using zero-inflated negative binomial regression to model over-dispersed mortality data. Pollutant concentrations (PM2.5 , PM10,NO₂, O₃, SO₂) were collected from four monitoring stations representingindustrial, traffic, and residential z ones. Effects were assessed for short-term (1-3 months) and long-term (6-24 months) exposures, with adjustment for meteorological and demographic confounders. Results: NO₂ demonstrated the strongest short-term association (2-month RR: 1.50, 95% CI: 1.30-1.75, p<0.001). PM10 showed a slight increase in risk at the 2-month lag (RR: 1.06, 95% CI: 0.98–1.14), although it was not statistically significant. Long-term PM2.5 exposure significantly increased mortality risk (24-month RR: 1.20, 95% CI: 1.05-1.58). A possible inverse association was observed for SO₂ (2-month RR: 0.59, 95% CI: 0.36–0.97), while O₃ effects varied over time. Conclusion: Industrial emissions (particularly NO₂ and particulate matter) significantly contribute to stroke mortality in Arak. The identified exposure response relationships highlight the importance of stricter emission controls on vehicular and industrial sources and targeted health interventions for high-risk populations. Further investigation of pollutant interactions is also essential to better understand their combined effects on stroke mortality.
A growing body of evidence implicates ambient air pollution in the exacerbation of clinical outcomes after SARS-CoV-2 infection. To synthesize this evidence, we performed a global systematic review and meta-analysis to precisely quantify the associations between exposure to specific atmospheric ontaminants and the subsequent risks of COVID-19-related mortality and hospital admission. Our methodology adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, involving a comprehensive search of scientific databases for literature published until the end of August 2025. From this search, 44 publications were deemed eligible for inclusion. We employed random-effects models to compute summary Risk Ratios (RRs) representing the change in health risk per 1 µg/m³ increment in atmospheric pollutant concentration. Our findings indicate that long term exposure to fine Particulate Matter (PM2.5), coarse Particles (PM₁₀), Nitrogen dioxide (NO₂), and Sulfur dioxide (SO₂) significantly increased the likelihood of fatal outcomes from COVID-19. The respective pooled RRs were 1.046 (95% CI: 1.031–1.062), 1.079 (95% CI: 1.005–1.154), 1.017 (95% CI: 1.004–1.029), and 1.077 (95% CI: 1.021–1.133). Acute exposures to ambient PM2.5 and NO₂ concentrations were similarly associated with increased mortality, demonstrating risk ratios of 1.043 (95% CI: 1.033–1.053) and 1.033 (95% CI: 1.019–1.048) respectively per 1 µg/m³ increment. Additionally, both acute and chronic exposures to PM2.5, PM₁₀, and NO₂ showed significant associations with higher COVID-19 hospitalization rates. This meta-analysis provides robust quantitative suggestion that ambient PM2.5, PM10, NO₂, and SO₂ are significant and modifiable risk factors for severe COVID-19 outcomes. These results emphasize the critical need for enhanced air quality standards as a fundamental element of public health policy to alleviate the impact of COVID-19 and bolster defenses against forthcoming respiratory epidemics.
Introduction: Research on indoor air pollution using settled dust as a medium is limited in India; therefore, this study presents the first comprehensive assessment of Total mercury (THg) in settled indoor dust across various indoor microenvironments in the Ernakulam district of Kerala state, located in southwestern India. Materials and methods: Sampling was conducted in the third week of February and the first week of March 2022 (n=32) in seven types of indoor microenvironments. Passive sampling was employed for the collection of settled dust samples, and THg in the dust samples was analysed using a Direct Mercury Analyser (Milestone DMA-80, USA). Results: The average THg concentration across all sampled environments was 0.90±0.66 mg/kg. Correlation analysis revealed a moderate (r=0.48) but statistically significant relationship (p<0.05) between THg levels and population density, likely due to contaminants brought to the indoor spaces by the people. Health risk evaluation based on hazard quotient (HQ) for ingestion and dermal exposures suggested that ingestion is the primary route of mercury exposure, with museums posing a high HQing value (0.0295) and furniture making shops posing a low HQing value (0.0001). Conclusion: This study highlights the need for mercury monitoring in urban built environments and the possible sources of mercury contamination in various indoor microenvironments. The study suggests protective measures for personal protection from dust exposure. Finally, the study concludes by suggesting the requirement for broader surveillance of mercury in various built environments in India.
