Introduction: Perchloroethylene (PCE) is widely used in dry cleaning and has been linked to hepatorenal toxicity. We aimed to assess the relationship between occupational PCE exposure, oxidative stress, and biomarkers of liver and kidney function. Materials and methods: We conducted a cross-sectional study of 30 male Iranian dry-cleaning workers and 30 frequency-matched controls. Personal full-shift air samples were collected for PCE. Serum biomarkers of oxidative stress ; Malondialdehyde (MDA), Superoxide Dismutase (SOD) and Catalase (CAT); and organ function; Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), Alkaline Phosphatase (ALP), bilirubin, creatinine, urea; were measured. Results: Exposed workers had a mean Time-Weighted Average (TWA) of 29 ppm, exceeding the 25-ppm Occupational Exposure Limit (OEL). Compared with controls, Malondialdehyde (MDA), Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), and creatinine were higher (p<0.05), while SOD and CAT were lower (p<0.05); ALP and bilirubin did not differ. Within the exposed group, longer employment was associated with worsening oxidative and hepatorenal markers. Multivariate regression analysis confirmed that PCE exposure remained a significant independent predictor of oxidative stress (MDA, SOD, CAT) even after adjusting for age and smoking. Conclusion: Findings indicate oxidative stress accompanies subclinical liver injury and early renal impact at prevailing occupational exposures. Reducing PCE through engineering controls and safer technologies should be prioritized.

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: 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.

Introduction: Emissions from cooking activities are among the major sources of indoor and ambient air pollution. Materials and methods: This experimental research aims to explore the levels of 16 gaseous Polycyclic Aromatic Hydrocarbons (PAHs) during calf meat frying in laboratory, utilizing different frying temperatures (i.e 150, 190, and 240 °C) and oils (non-frying oil and, frying oil). Furthermore, non-cancer and cancer risks were also assessed. For the purpose of the study, 36 air samples were taken during meat frying and analyzed by a Gas chromatography mass spectrometry (Agilent GC8890, USA) equipped with a Flame Ionization Detector (FID) for 16 PAH compounds. Results: The concentration of ∑16PAHs during meat frying using sunflower oil and frying oil use varied from 5.037-10.025 µg/m3 and 3.978-8.075 µg/ m3, respectively. Hazard Quotients (HQs) associated with PAHs exposure during meat frying using frying oil for cooks, adults and children were in the range of 0.440-1.338 (0.769), 0.503-1.527 (0.879) and 0.504-1.531 (0.881), respectively. For frying oil, HQ values were in the ranges of 0.32-1.19 (0.69), 0.37-1.36 (0.79), and 0.37-1.36 (0.79) for cooks, adults, and children, respectively. The inhalation cancer risk values through exposure to meat using sunflower oil for cooks, adults and children were 1.4E-04-4.2E-04 (2.4E-04), 2.8E-05-8.6E-05 (4.9E-05), and 7.7E-06-2.3E-05 (1.3E-05), respectively. For frying oil, the cancer risk values were as: 1.0E-04-3.7E-04 (2.2E-04) for cooks, 2.1E-05-7.6E-05 (4.4E-05) for adults and 5.63E-06-2.08E-05 (1.2E- 05) for children. Conclusion: This study showed high levels of PAHs during meat frying indicating health risks for children and adults. The research’s results have practical use for public health professionals and policy makers, for regular monitoring of indoor PAHs during cooking and the development of policies to reduce exposure to these air pollutants in enclosed spaces.

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 contaminants 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: Anthropogenic CO2 emission is a pressing global challenge wreaking serious health, environmental and socioeconomic havocs which require urgent attention. Given these undesirable outcomes and the enormous contribution of some set of countries to global CO2 emissions; this study investigated the long-run effects of globalization, technology innovation and renewable energy consumption on CO2 emissions in top 5 CO2-emitting countries across the globe. Materials and methods: To achieve the study objective, annual CO2 emissions, globalization, technology innovation, renewable energy and economic growth data of the top 5 CO2-emitting countries spanning from 1990 to 2022 was analysed using panel autoregressive distributed lag modelling technique and Dumitrescu-Hurlin panel causality test. Results: CO2 emissions, globalization, technology innovation, renewable energy consumption and economic growth were found to have long-run relationship in top 5 CO2-emitting countries. Particularly, renewable energy consumption was found to have negative effect on CO2 emissions while globalization and technology innovation were found to have positive direct effects on CO2 emissions. However, globalization and technology innovation had inhibitive interaction effect on CO2 emissions. Findings also revealed mutually reinforcing causal relationship between economic growth and CO2 emissions; and between technology innovation and CO2 emissions. Conclusion: The findings underscore the fact that urgent prioritisation of renewable energy consumption and international relationships which encourage the transfer, development and adoption of environment-friendly technological innovations will reduce CO2 emissions and its undesirable environmental, health and socioeconomic effects in top 5 CO2-emitting countries.

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 representing industrial, traffic, and residential zones. 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 invers 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.

Introduction: Traffic noise modeling is a rapidly growing field. Researchers are continually improving existing models and creating new ones that take into consideration complex aspects such as traffic flow patterns and the influence of geography. This study aims to test few models that may be suitable for the Indian scenario along with development of new model. Materials and methods: In the present study, evaluation and modeling of traffic noise have been carried out. The study was carried out in 20 locations in Raipur city. Half of the locations were selected for validation of results, and half were selected for studying the best-suited model for our selected area. Six models best suited to our location were selected after performing the literature review in brief. Traffic data was collected, and models were tested. Results: On comparing the data, it was found that out of six models, the Burgess model was found to be the most accurate, as its predicted noise levels are consistently closest to the measured noise levels across all ten locations. But the coefficient of correlation (R) for this model was found to be in the range of 0.31 to 0.64. Burgess model uses the framework of concentric zones to analyse how noise varies based on location within a city, taking into account factors such as land use, population density, and the types of activities prevalent in each zone. Further, we developed our own model by using the multiple regression method and validated our results. On performing the statistical analysis, highest value of R2 (0.83 and 0.82) were found for locations PL1 and PL8 respectively. Mean Absolute Deviation (MAD) values ranged from 0.859 to 2.175, and Root Mean Squared Error (RMSE) values ranged from 0.884 to 2.203 for all locations. Conclusion: The high R² values, close to 1, and the low RMSE values indicate that our model fits the data well. Therefore, we can conclude that the developed model is highly suitable for predicting noise levels at our location.

Introduction: Indoor air quality plays a significant role in students' health and productivity. The present study attempts to examine the impact of air pollution on subjective thermal comfort and explores how the interaction between thermal conditions and Particulate Matter (PM) affects students' thermal comfort and health. Materials and methods: The data were collected through objective and subjective methods. The objective method consists the measurement of air pollution and meteorological parameters using the particle counter PCE-MPC At the same time, subjective questionnaires were developed to obtain data relative to the students' sensations, preferences, and indoor environment during two periods of student occupancy and under two conditions: one with closed windows and one with natural ventilation. Results: Findings show that the average indoor and outdoor PM concentrations exceed the World Health Organization (WHO) standard. These suggest that universities would benefit from upgrading their heating systems and providing humidifiers. Results also highlight the difference between Predicted Mean Vote (PMV) and Thermal comfort; Thermal Sensation Vote (TSV), Thermal Preference Vote (TPV) and the need for adopted strategies in the perceived thermal comfort assessments. Additionally, the static results indicated the significant impact of PM on both TSV and TPV (P values<0.05) regardless of whether the windows are open or closed. Conclusion: To our knowledge, this is the first study conducted in Algeria to evaluate the effects of air pollution on students' perceived thermal comfort. The results underline the importance of addressing indoor air quality and prioritising natural ventilation strategies to enhance both student well-being and academic performance.

Climate change is not only contributing to the proliferation of infectious and vector-borne communicable diseases is a major concern, but also escalating the risk of extreme weather among community, in which research on climate change adaptation using advanced technology is necessary. This study aimed to investigate research trend on climate change adaptation in Indonesia concerning on the utilization of novel technology and artificial intelligence. This study employed bibliographic analysis using Scopus article database during 2000-2023. The total sampling technique was used, in which every relevant document within inclusion criteria were included in the study. The analysis was conducted in R Studio, in which network analysis was measured by VOSviewer. A total of 1,858 articles is identified. The annual of publication growth rate is 17.77%, with the average citation per document is 29. The university situated in Java Island-Indonesia was leading institution for publication. Sustainability and Biodiversitas are the most prominent journals. The scholars with high publication and citation are Yulianto (13 articles) and Murdiyarso (1,819 citation). Eight clusters have been recorded, with the most prominent term is “climate change”, "adaptation", "flood", "remote sensing", "agriculture", and "vulnerability". This study found the research interest on climate change adaptation is elevating each year in Indonesia. The application of advanced technology, such as artificial intelligence, machine learning, and Internet of Things (IoT) remains relatively unexplored. Therefore, future research on climate change adaptation using advanced technology in Indonesia is needed to provide comprehensive knowledge, enhance predictive capabilities, and provide innovative solution to manage the effect of climate change.