Introduction: Air pollution, particularly Particulate Matter (PM), poses a significant global health threat due to its deep penetration into the respiratory system, leading to or exacerbating cardiovascular and respiratory morbidities and mortalities, increased hospital admissions, and premature death. This study aimed to analyze the temporal trends of PM10 and PM2.5 concentrations in Khorramabad city and to quantify their associated health impacts over a five-year period (2013-2017). Materials and methods: In this descriptive-analytical study, hourly concentration data for PM10 and PM2.5 from 2013-2017 were obtained from the Khorramabad Environmental Protection Agency. Data underwent rigorous validation based on World Health Organization (WHO) criteria and Z-score method in SPSS to ensure reliability and remove outliers. Time-series analysis and visualization of pollutant variations performed using R software and the Openair package. Health effect quantification, including estimations of attributable mortality and morbidity, conducted using the AirQ2.2.3 model, integrating air quality data with epidemiological parameters such as Relative Risk (RR) and Baseline Incidence (BI). Results: The study revealed an overall decreasing trend in PM10 and PM2.5 concentrations from 2013 to 2016, with a notable increase in PM10 concentration observed in 2017. The annual average concentrations of PM10 and PM2.5 for the entire study period were estimated at approximately 65 μg/m3 and 35 μg/m3, respectively, significantly exceeding WHO air quality guidelines. Quantification of health effects indicated a total of 1634 attributable deaths due to PM10 exposure over the five years, comprising 530 cardiovascular deaths and 103 respiratory deaths. For PM2.5, the total attributable deaths were estimated at 933 individuals. The highest health burden for PM10 related to total respiratory visits (1341 cases) and cardiovascular deaths (530 cases). Both pollutants exhibited similar diurnal and weekly patterns, with peaks during morning and evening rush hours and mid-week days, and higher concentrations during warm seasons, influenced by dust storms and agricultural burning. Conclusion: The study reveals consistently high levels of PM10 and PM2.5 in Khorramabad, especially during warmer seasons, leading to a substantial public health burden. These findings emphasize the critical need for effective interventions and long-term strategies to control air pollution and safeguard community health.

Introduction: Today, the natural ventilation is emphasized to minimize the energy consumption. It also helps to decrease interior temperatures and maintain internal humidity. In this work, the effect of the rear window opening (factor-1), its position (factor-2) and also the effect of the position of a heat source (factor-3) on Air Exchange Efficiency (AEE) and Heat Source Surface Temperature (HSST) is evaluated. Materials and methods: The Taguchi Design of Experiment (DOE) is applied to shortlist nine simulations with different combinations of the levels for three factors. Then Computational Fluid Dynamics (CFD) simulations were performed and the responses (AEE & HSST) were recorded. The Signal-toNoise (S/N) ratio values are evaluated separately for the responses and the rank table is prepared to see the impact of various factors for the best response value. Analysis of Variance (ANOVA) analysis is performed to evaluate the impact percentage of the factors to obtain the best responses. Results: From the mean S/N plots, the best and the worst combinations of levels of the factors for both responses are identified and then simulated. From the study, it is observed that the rear window opening and the window position has the highest and the lowest impact respectively to obtain the highest AEE. Similarly, the window position and the window opening have almost equal impact on lowering the HSST. Conclusion: The study concludes that proper positioning of window and its opening can be evaluated to get the best AEE and to transfer the maximum heat from the heat source in the room.

Introduction: Carbon dioxide (CO₂), the most abundant greenhouse gas, has reached an atmospheric concentration of 411 ppm, its highest level in the past 650,000 years. To effectively reduce the carbon footprint, accurately measuring these emissions is a crucial first step. This study focuses on quantifying the Carbon dioxide Equivalent (CO2e) emissions from six selected hospitals in Tehran, providing essential data to inform targeted sustainability efforts in the healthcare sector. Materials and methods: This cross-sectional study quantified greenhouse gas emissions from six major hospitals in Tehran using the Aga Khan Development Network (AKDN) Carbon Management Tool, supplemented by emission factors from the UK Department for Environment, Food and Rural Affairs (DEFRA). Data were collected from hospital records and relevant departments using standardized checklists designed according to AKDN guidelines. The sources of emissions assessed included energy consumption, anesthetic gases, inhalation devices, waste management, transportation and supply chain activities. All collected data were converted to Carbon dioxide Equivalent (CO₂e) using established emission factors. Results: The total CO2e emissions from the six hospitals amounted to 28,260.74 tons. Energy consumption was the largest contributor, accounting for 57% (16,182.5 tons) of emissions, followed by anesthetic gases at 40% (11,313.67 tons). Waste management (626.36 tons), transportation (89 tons), inhalation devices (26.75 tons), and supply chain activities (22.58 tons) contributed smaller shares. Conclusion: The study highlights the urgent need for targeted strategies to reduce greenhouse gas emissions in healthcare settings. Recommendations include shifting torenewable energy sources, substituting high global warming potential anesthetic gases with lower-impact alternatives, optimizing supply chain logistics, and improving waste management practices. Implementing these measures can significantly reduce the carbon footprint of hospitals while maintaining quality care. This study provides a foundation for future emission reduction efforts in Iran’s healthcare sector, aligning with global climate goals.

Introduction: Indoor environment contributes to human health and productivity. The absence of climatic control systems may lead to microbial contamination. The air quality of public institutions should be monitored. Therefore, this study was conducted to determine the microbial load of the air in the main library at the University of Guyana and to evaluate the effectiveness of a disinfection intervention on the microbial load of the air. Materials and methods: This was an experimental- observational study involving three phases: analyzing the microbial quality of the air, a disinfection experiment, and a disinfection intervention. Phase 1 was done before the rehabilitation of the library, Phase 2 was done during the rehabilitation, and Phase 3 was carried out after the rehabilitation and the disinfection intervention. Samples were collected on settle plates and incubated. ColonyForming Units (CFUs) were enumerated and the microbial load was determined using a standardised equation. Several disinfectants were tested against two bacteria and a specific disinfection protocol was developed for the disinfection intervention. Results: The bacterial load for Phase 1 (13,114 CFU/m3) and Phase 2 (7,636 CFU/m3) was higher than that of Phase 3 (4,648 CFU/m3). There was an extremely high fungal load (4,067 CFU/m3) before the disinfection intervention but no growth after. Conclusion: We concluded that a high microbial load was found in our study before the disinfection intervention which was considerably diminished after the intervention. We recommend implementing the cleaning regimen we developed as part of the library’s cleaning protocol.

Air pollution remains a critical global health issue that affects children. This bibliometric study analyses trends, research gaps, and key contributors to the literature on air pollution’s impact on children’s health, utilizing data from 1,590 publications indexed in the Scopus database between 1956 and 2024. Hazing’s Publish or Perish and VOS viewer were used to analyse the data. Most studies on air quality focus on medicine (67.42%) and environmental science (41.32%). Key findings indicate that the United States leads in both publication volume and impact, contributing 467 papers and 27,252 total citations, with an h-index of 89. Researchers from institutions such as Harvard T.H. Chan School of Public Health and the University of Southern California are pivotal in advancing the discourse on how air pollution exacerbates conditions like asthma, bronchitis, and long-term cognitive impairments in children. Older foundational studies, particularly those published in the late 2000s, continue to be highly influential for their focus on neuroinflammation and cognitive deficits linked to air pollution. International collaboration is robust, with coauthorship networks between the United States, China, and several European countries. However, more interdisciplinary and longitudinal studies are needed to deepen our understanding of the mechanisms through which air pollution affects children’s health. This study provides insights for future research efforts, strengthens scientific understanding, and supports the development of more effective public health interventions to reduce the burden of air pollution on children worldwide.

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: The effect of air ambient pollution on human health is a widely discussed subject among environmental experts and socio-economists worldwide. Despite a large number of research being conducted on the topic, the relationship between public health and air quality in Indian cities remains questionable. The study was carried out to assess the impact of air pollution on the health of people of different socio-economic section of the society. Materials and methods: A cross-sectional survey was conducted among the residents of Rourkela city who were exposed to air pollution, and the logistic regression model was applied. Results: The findings revealed that human health is significantly impacted by air pollution in terms of flu/fever (46%), runny nose/cold (36 %) and others respiratory and cardiovascular disease. The individuals who were breathing in polluted air were 10.65 times more likely to have gotten sick from air pollution (β=2.37; SE=0.33; p<0.01) when compared to the people who were not breathing in polluted air. In contrast, a rise of one unit in the AQI in Rourkela corresponds to an 8.4% higher chance of being ill due to air pollution-related diseases (β=0.08; SE=0.02; p˂0.01). A high, rising linear trend (R2=0.67) of mortality by major air pollution-related diseases was also recorded in Rourkela during the period of 2016–2022. Hence, it is evidenced that rising AQI values correspond to rising health hazards associated with air pollution in Rourkela. Conclusion: The study's conclusions offer a thorough understanding of the negative impacts of air pollution, locals' perceptions of it, and the practical ramifications for local government when assessing more efficient approaches for reducing pollution.

Introduction: Exposure to particulate matter in the workplace has been identified as a major contributing factor to respiratory diseases including decreased lung function. In developing countries, health and safety of landfill workers receives little attention. Thus, it is necessary to conduct an in-depth investigation into particulate matter exposure and its link to disease. This study to investigate the relationship between exposure of Particulate Matter (PM2.5) on forced vital capacity and respiratory symptoms in landfill workers. Materials and methods: Measurements of PM2.5 parameter air samples were taken using an SNDWAY brand detector from five locations. Interviews using questionnaires with 49 landfill worker respondents were conducted to determine respiratory symptoms. Forced vital capacity was examined with a Voldyne 4000 incentive spirometer and the results were analyzed with chisquare statistical analysis. Results: The highest PM2.5 concentrations occurred during the day in the west, east, and center, which fall into the unhealthy category. Most of the respondents had abnormal forced vital capacity (32 people, 65.3%) and respiratory symptoms (43 respondents, 87.8%). Chi-square analysis showed a statistically significant association between PM2.5 exposure and abnormal FVC with a p-value of 0.002 and an Odds Ratio (OR) of 10.0 (95% CI: 2.41–41.58). Conclusion: This study found a significant association between PM2.5 exposure and abnormal Forced Vital Capacity (FVC) in landfill workers (p = 0.002; OR= 10.0; 95% CI: 2.41–41.58). These findings indicate that workers exposed toPM2.5 are at increased risk of lung function impairment, underscoring the need for routine respiratory health monitoring and targeted exposure mitigation in landfill environments.

Introduction: Air quality forecasting, particularly predicting Particulate Matter (PM2.5 ) concentrations, has gained significant attention due to its critical implications for public health and environmental management. Accurately predicting PM2.5 , a harmful air pollutant associated with respiratory and cardiovascular diseases, is vital for effective air quality management in densely populated urban areas. Materials and methods: This study uses various meteorological and environmental data combinations in Tehran, Iran, this study investigates the efficacy of three predictive modeling techniques Auto Regressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) in forecasting daily and monthly PM2.5 levels. The models were evaluated based on performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R² scores. Results: Results indicate that XGBoost excelled in daily predictions when using solely meteorological data, achieving an R² score of 0.998674, while ARIMA demonstrated strong predictive capacity but struggled with added complexity. LSTM maintained reasonable performance amidst increased data input but faced challenges in both daily and monthly forecasts. Monthly predictions from all models proved less reliable, particularly with ARIMA yielding negative R² values, indicating suboptimal performance compared to simplistic models. Conclusion: The findings highlight the importance of model selection and feature engineering in accurately predicting PM2.5 levels. The study suggests a shift towards hybrid modeling approaches and incorporating diverse environmental data to enhance forecasting accuracy in air quality management, particularly for long-term predictions.

Introduction: In the current study of specific air pollutants, including levels of NO2 , SO2, and Particulate Matter (PM10), as well as the Air Quality Index(AQI), has been done on the current state of air quality in Lucknow. Materials and methods: To assess the ambient air quality in Lucknow, this secondary data was recorded from three key sources: Uttar Pradesh Pollution Control Board (UPPCB), Central Pollution Control Board (CPCB), and Centre for Science and Environment (CSE), from five monitoring stations across various areas of the city, including residential areas like Aliganj and Mahanagar, commercial sectors like Hazratganj and Ansal TC, and the industrial sector of Talkatora. Results: The results showed that, within a range of 111.24 to 240.89 μg/m3, the average 24-h PM10 concentration was evaluated as 178.09 μg/m3. The average concentrations of SO2 and NO2 over 24 h ranged between 6.96 and 11.50 and 25.28 and 44.41 μg/m3 respectively. Seasonal fluctuations in PM10, SO2 , and NO2 were observed, with maximum values recorded in winter at 218.20, 10.32, and 41.43 μg/m3 , and minimum values recorded in monsoon season at 123.47, 7.19, and 28.31 μg/m3 , respectively. Maximum values were recorded in winter at 177 μg/m3 , while lowest values were recorded in monsoon at 111 μg/m3. Conclusion: The study focused on monthly and seasonal variations in PM10, SO2, and NO2 levels at five representative locations in Lucknow. Key findings revealed that while the annual PM10 concentration exceeded National Ambient Air Quality (NAAQ) standards. The SO2 and NO2 concentrations remained below recommended levels throughout the year, with lower concentrations observed during the monsoon season compared to summer and winter.