Introduction: In Ghana, the road subsector serves as the primary mode of transport, accounting for 96% of passenger and cargo traffic. Air quality issues have been exacerbated by the prevalence of aged and poorly performing vehicular engines, posing significant health risks. This study, therefore, investigated ambient air quality during the dry season along key roadways in Winneba, located in the Central Region of Ghana. Materials and methods: Stationary monitoring devices, including EPAM-7500 particulate monitors and Aeroqual Series 500 gas monitors were used to measure concentrations of Particulate Matters (PM₂.₅, PM₁₀), Carbon monoxide (CO), Nitrogen Oxides (NOₓ), Sulphur dioxide (SO2), and Volatile Organic Compound (VOCs) including temperature and relative humidity. Data collection was conducted using a purposive rotation among the selected roads, with each monitoring session replicated three times. Results: Winneba junction-WindyBay Avenue (WJ’WBA) exhibited the highest concentrations of CO (2125±182.40 µg/m³) whilst the highest level of NOₓ (198±27.01 µg/m³) was at Winneba central-Donkorkyiem (WC’D). PM2.5 concentrations at WJ’WBA was the lowest (871 ± 79.54 µg/m³), while the Control Road (CR) had highest mean concentration of 902 ± 107.16 µg/m³. The PM10 highest mean level was at WJ’WBA (931±51.29 µg/m³) and lowest at the CR (874±90.42 µg/m³). Levels of SO₂ and VOCs were below the detection limits of the gas monitors. In all, levels of the measured pollutants did not differ significantly (p<0.05) between the sampling locations, but exceeded the pollution thresholds established by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA). All monitored roads were classified as "extremely polluted" based on the Air Quality Index (AQI). The Exceedance Factors (EF) confirmed the severity of pollution levels. Statistical analyses, correlation and regression methods, indicated no significant relationship between weather conditions and air pollution levels. Conclusion: These findings underscore the severity of air quality issues in Winneba and the urgent need for enhanced monitoring systems including the implementation of regular vehicular emission testing and the use of bioindicators for monitoring vehicular pollutants to mitigate both human and environmental health risks.

Introduction:  Air pollution is one of the world’s major global issues. In this research we aimed to calculate the impact of weather factors on air pollution and show the results by using the econometric method of data analysis. After that, we also studied the effect of car exhaust on air pollution in relation to urban congestion and car age. Materials and methods: Data cleaning methods used in this research include as correcting structural errors, dealing with missing data and sorting data. For calculation, correlation analysis was used to find the relationship of the time series dataset, and then used panel model for the test results, which are estimated by least squares method. In correlation analysis, used air quality and weather’s data of Ulaanbaatar city’s last 3 years. Results: As a result of the research, we found that the amount of air pollutant depends on weather factors, that is, location and wind speed have the greatest influence on air pollution. Also the decrease in the amount of sulfur dioxide is due to the ban on burning raw coal in the capital. Our findings indicate that the nitrogen dioxide level in the residential area is high even in the warm season, which is due to congestion and age of vehicles. Conclusion: The most important weather factors affecting air pollution are location and wind direction. In the future, with comprehensive data collection, future research could better identify sources of air pollution and develop effective mitigation strategies.

Introduction: Air pollution can trigger the attack in asthmatic patients if uncontrolled. Previous works focused on controlling pollution by proposing algorithms to predict air pollution. While these prediction algorithms save patients from attack triggers, they have limitations such as prediction accuracy, mathematical complexity, and lack of adequate patient notification systems. Materials and methods: This study proposed a novel Intelligent Air Pollution Prediction (IAPP) algorithm based on optimizing Random Forest Regression (RFR) to predict air pollution and send an alert message to the patient and hospital in real time. Meanwhile, IAPP utilized reliable data from Internet of Things (IoT)-based air pollution detection nodes. The performance of IAPP was evaluated in a real-world environment during the peak pollutant season to test the prediction accuracy of air pollution. Results: Results showed that the proposed IAPP achieved a high prediction accuracy of 99.98% with an R squared value of 0.99. This demonstrated that the IAPP algorithm based on the RFR model can effectively protect asthmatic patients from attack triggers. Conclusion: As a result, the IAPP algorithm reduces hospital visits during high pollution and enables patients to complete their daily activities without obstacles or absence.

Introduction: Air pollution is a significant global health challenge, contributing to the deaths of millions of people annually. Among these pollutants, Particulate Matter (PM2.5) is the most harmful to the respiratory system causing serious health problems. This study focused on predicting PM2.5 in the air of Islamabad, capital of Pakistan by using machine learning and deep learning models. Materials and methods: Two machine learning models (Decision Tree and Random Forest) and four deep learning models including Multi-Layer Neural Network (MLNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) are used in the study. Each model's performance was assessed by using statistical indicators including coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE). These models are also ranked based on their performance by compromise programming technique. Results: Machine learning models performed better in the training phase by achieving higher R2 values of 0.98 and 0.97 but couldn’t maintain the same performance in the testing phase. Whereas the deep learning models performed best in both the training and testing phases. MLNN model attained higher R2 value of 0.98 in training and 0.88 in testing and is evaluated as top-ranked prediction model in predicting particulate matter PM2.5. Whereas, LSTM, GRU, RNN, Decision Tree, and Random Forest are placed at the 2nd, 3rd, 4th, 5th, and 6th positions having R2 values of 0.86, 0.87, 0.82, 0.99, and 0.97 during training and 0.71, 0.69, 0.69, 0.75, and 0.85 respectively during testing. Conclusion: Deep learning models, especially MLNN, showed strong performance in predicting PM2.5 as compared to the machine learning models.

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.

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: 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: 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: 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 PM2.5 were 1.8 and 4.8 times higher than World Health Organization (WHO) standard, mostly affected by habitual indoor smoking which increase PM10 and PM2.5 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.

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.