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 20. 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

Introduction: Poor Indoor Air Quality (IAQ) in the growing number of low- income urban houses is closely linked to their unstructured neighbourhood development, poor building quality and unique community behaviour. It has been associated with numerous health issues which determine the occupant’s quality of life. This study proposed an explanatory model to reveal the interactive effect of building, human, and environment, on IAQ in tropical urban houses.
Materials and methods: Particulate Matter (PM), Carbon dioxide (CO₂), airflow, temperature, and relative humidity were continuously measured using calibrated sensors in two seasons. Data on the active ventilation openings, indoor characteristics (material, volume, layout, and indoor porosity), real- time activity, and occupant’s perception were recorded through questionnaire.
Results: The average indoor PM₁₀ and PM_2.5 were 1.8 and 4.8 times higher than World Health Organization (WHO) standard, mostly affected by habitual indoor smoking which increase PM₁₀ and PM_2.5 by 259% and 281%. High cooking intensity increased kitchen CO₂ concentration by 47%. However, 82.75% of the occupants accepted this poor IAQ as neutral, which was correlated to their low education and economic backgrounds. Moreover,
regression analysis showed significant effect of house volume, kitchen layout, and roof structure’s airtightness, on pollutant concentrations.
Conclusion: Low-income occupants have habits and activities that generate high indoor contaminants, worsen by the confined living space with insufficient ventilation, resulting in poor IAQ. Hence, stakeholders should
prioritise educating low-socioeconomic communities about the health risk of high indoor pollution. Beside human activity control, this study offers a new IAQ mitigation perspective on the impact of interior characteristics on pollutant accumulation and dilution inside buildings.

Introduction: Indoor air pollution poses significant health risks, given the substantial time individuals spend indoors. Cool-mist humidifiers have been proposed as a potential intervention for enhancing indoor air quality
by influencing pollutant concentrations. This study investigates the effects of gas dissolution in vapor particles generated by a cool-mist humidifier on indoor air pollutants.
Materials and methods: A controlled laboratory experiment was conducted within a 1 m³ insulated plastic chamber to monitor key parameters, including Carbon monoxide (CO), Carbon dioxide (CO₂), Oxygen (O₂), Total Volatile Organic Compounds (TVOCs), temperature, and Relative Humidity (RH). Pollutants were introduced using a lit candle and formaldehyde, and air quality was measured using a digital gas analyzer (CEM GD-3803) and a TVOC analyzer (QB2000N/T). Baseline pollutant levels without humidification were compared to levels observed with a cool-mist humidifier operating at various humidification rates (110–370 mL/h) over an 8-h period.
Results: The results indicated consistent reductions in CO₂ and TVOC concentrations across all tested humidification rates, accompanied by increases in temperature and relative humidity. CO concentrations exhibited more variable behavior, with alternating increases and decreases over the testing periods.
Conclusion: These findings underscore the potential of cool-mist humidifiers as an effective strategy for reducing indoor air pollutants, particularly CO₂ and TVOCs. This has meaningful implications for enhancing indoor air quality and protecting public health.

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 PM_2.5 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 PM_2.5 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 PM_2.5 concentrations from 2013 to 2016, with a notable increase in PM₁₀ concentration observed in 2017. The annual average concentrations of PM₁₀ and PM_2.5 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 PM_2.5, 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 PM_2.5 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: Climate change-driven droughts have intensified dust storms, expanding their impact to regions that previously experienced little to no dust. One such area is the southern shores of the Caspian Sea.
Materials and methods: This study investigated three severe dust cases along the southern Caspian coast, originating from various sources both inside and outside of Iran. A combination of satellite data, reanalysis data, and numerical model outputs was analyzed. The dust surface concentration output from the
WRF-Chem model’s 36- and 48-h forecasts was qualitatively compared with the dust patterns from MERRA2 reanalysis data.
Results: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data confirmed the presence of dust from near the surface to over 5 km in altitude, allowing dust to cross the Alborz range. Satellite imagery and Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model outputs revealed that dust over the southern Caspian coast originated from three sources: northern Iraq, central Iran, and western Turkmenistan. Comparing Weather Research and Forecasting (WRF)-Chem model outputs with reanalysis data demonstrated that the model accurately predicted dust events along the southern Caspian shores in all three cases, though its precision is not yet suitable for quantitative comparison.
Conclusion: According to the results of this study, dust in the northern provinces of Iran is emitted from three dust sources in northern Iraq, central Iran, and Turkmenistan. Also, the WRF-Chem model has been able to predict the dust transport from these different dust sources to northern Iran. However, it can be stated that the accuracy of the outputs is still not suitable for quantitative comparison.

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: Traffic-Related Air Pollution (TRAP) is currently among the priority environmental issues because of the strong correlation it shares with the occurrence of unwanted respiratory effects, particularly in children.
Air pollution exposure to pollutants such as Nitrogen dioxide (NO₂) and Particulate Matters (PM₂.₅) has been linked to heightened asthmatic attacks. The purpose of this research was to explore the short-term relationship
between the exposure to TRAP and the development of asthma attacks in children, and the necessity for specifically targeted interventions.
Materials and methods: Panel study was done among 150 asthmatic children aged 6-12 years residing in high-traffic urban environments. Levels of TRAP exposure were estimated on a day-to-day basis by implementing a land-use regression model that included traffic density, proximity to major roads, and meteorological conditions. Asthma attacks were documented based on symptoms (wheezing, cough, breathlessness) and relief medication, as per the parents' reporting. Fixed effects Poisson regression was used to estimate pollutant exposure and asthma attack relationships.
Results: Higher exposure to TRAP was strongly linked to asthma attacks. Higher exposure to NO₂ and PM₂.₅ by 10 μg/m³ was linked with 5% and 3% higher asthma attacks, respectively. The results demonstrate the increased
respiratory hazards due to short-term pollution exposure among children.
Conclusion: This research highlights the adverse effect of TRAP on childhood asthma and demands active interventions such as tighter emission controls, urban planning reform, and public education campaigns. Additional studies in mechanisms at the biological level and rigorous policy implementation are needed in an attempt to protect children's respiratory health.

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.