2026· International Journal of Revolutionary Civil Engineering· Vol 2, pp. 9-15· 0 citations
Abstract
Air pollution is an important international challenge due to its increasing impacts both on public health and the environment. The Air Quality Index simplifies the complex data by transforming the parameters into a single numerical value. However, accurately estimating the AQI stays a difficult because of the complicated connections among the pollutants and weather factors. It is aimed in this study to develop predictive models for AQI using statistical modelling, with a concentration on evaluating the influence of weather variables. The data were gained from publicly available government monitoring stations, specifically the Andhra Pradesh Pollution Control Board, as well as, the Central Pollution Control Board in Visakhapatnam city. Data period was used for the years 2018–2024. The parameters used are: AQI, SO₂, NOx, PM₁₀, PM₂.₅, NH₃, temperature, precipitation, and wind speed. At a particular air quality monitoring station, additional parameters such as NO, NO₂, CO, O₃, benzene, and toluene were included. Linear, polynomial, and exponential models are the models used for the statistical analysis. The results highlighted that the best model with the best effectiveness and fit is the polynomial model, which gives the best performance, the highest R² values (0.889, 0.937, 0.944, 0.900, 0.931, 0.703, and 0.662) across the studied stations were achieved. These findings can support improved air quality management and public health planning in Visakhapatnam city.
Air pollution has become a major environmental and public-health concern worldwide, and understanding its behaviour is essential for effective monitoring and management. This study investigates air-quality patterns across four regions in the Sultanate of Oman—Al Khuwair, Salalah, Al Khoud, and Bediya—using a combination of statistical modelling and machine-learning techniques. Hourly data for 2023, including pollutant concentrations and key meteorological variables, were obtained from the Environment Authority of Oman, cleaned, and pre-processed to construct region-specific datasets. Air Quality Index (AQI) values were calculated for each pollutant and classified into three categories (Good, Moderate, and Unhealthy). Kernel Support Vector Machine (KSVM) and Gaussian Process Regression and models were trained using a 70/30 temporal split to classify AQI levels. Results showed that KSVM achieved the highest accuracy in Salalah (96.97%), Al Khoud (94.33%), and Bediya (93.37%), while Gaussian Process Regression performed best in Al Khuwair (70.32%). In conclusion, this research demonstrates that advanced kernel-based classifiers can effectively model non-linear environmental data, providing a scalable solution for regional environmental management.
Shamssa Abdullah Al-Rahbi, M. Alodat· SISTEMASI· 0 citations
Air pollution has become a major environmental and public health concern due to rapid urbanization, industrial growth, and increasing vehicular emissions. High concentrations of pollutants such as PM2.5, PM10, NO₂, SO₂, CO, and O₃ can significantly impact human health and environmental sustainability. Accurate monitoring and prediction of air quality are therefore essential for effective environmental management and public safety. This paper presents AirAware, a machine learning–based system designed to predict and monitor Air Quality Index (AQI) levels using historical air pollution data and real-time environmental information. The system utilizes the XGBoost algorithm to analyze pollutant parameters and generate accurate AQI predictions and classifications. Data preprocessing techniques such as cleaning, normalization, and SMOTE-based class balancing are applied to improve model performance and ensure reliable predictions across different AQI categories. In addition, the system integrates real-time air pollution data through the OpenWeather API, enabling continuous monitoring of current environmental conditions. The predicted AQI values and pollution trends are displayed through a web-based dashboard, allowing users to visualize air quality patterns and compare real-time data with machine learning predictions. By combining machine learning techniques with real-time data integration, the proposed system provides an effective solution for air quality prediction, monitoring, and environmental awareness.
Neethu Roy, Jeeson Justin· International Journal of Lat...· 0 citations
Concentration of different pollutants such as O₃ , NO₂ , SO₂ , CO, PM₁₀ , and PM2.5 in the atmosphere is called air pollution, which created several challenges in the environment and is considered one of the dangerous phenomenal in the current world, in Afghanistan urbanization in last two decades had significant impact on air quality. There is different source of air pollution in urban areas such as industries, more population, transportation system, less vegetation covers and etc. The main goal of this of this research is to analyze the pollutant levels from 2021 to 2024 in Kabul city by analyzing of six major pollutants including O₃ , NO₂ , SO₂ , CO, PM₁₀ , and PM2.5 and comparing them with national standard and by using the AQI to analyses the air quality. This is a quantitative research; data has been collected from National Environmental protection Agency air quality station based in Darulaman area of Kabul city. Significant annual and seasonal variations in pollutant concentrations were found by the results. O₃ concentrations exceeded the national norm during the winter months, ranging from 14 µg/m³ (June 2023) to 181 µg/m³ (December 2022). Particularly in late 2024, NO₂ and SO₂ exhibited significant peaks, with NO₂ reaching 258 µg/m³ and SO₂ reaching 150 µg/m³, both of which exceeded allowable limits. With maximum readings of 221 µg/m³ and 157 µg/m³, respectively, PM₁ 0 and PM₂ .5 were continuously over national norms for a number of months. According to the analysis of the Air Quality Index (AQI), PM₂ . ₅ was the main pollutant affecting the general quality of the air. The AQI rating was at its lowest (79) in September 2022 (Moderate) and at its worst (207) in January 2021 (Very Unhealthy). Seasonal trends revealed that while air quality improved somewhat during some summer months, especially in 2022, it deteriorated during the winter months of November through January. Overall, the results show a trend toward worsening air quality in 2023 and 2024, particularly as a result of particle matter pollution.
Mujeebullah Ahmadzai, Masihullah Safi, Shafiqullah Rahmani· International Journal of Nat...· 0 citations
Air pollution is a major environmental and public health concern in India, driven by rapid urbanization, industrialization, vehicular emissions, and construction activities. This study presents a comparative analysis of the Air Quality Index (AQI) of five major cities in Maharashtra—Mumbai, Pune, Nagpur, Nashik, and Chhatrapati Sambhajinagar (Aurangabad)—using secondary data from the Central Pollution Control Board (CPCB) and Maharashtra Pollution Control Board (MPCB). The analysis shows that Mumbai and Pune generally record higher AQI levels due to heavy traffic and industrial activities, while Nashik experiences relatively better air quality. AQI tends to worsen during winter and improve during the monsoon because of seasonal meteorological conditions. The study highlights the need for effective pollution control measures, sustainable urban planning, improved public transport, and continuous air quality monitoring to safeguard environmental quality and public health.
Dr.Gangotri S.Nirbhavane· EPRA international journal o...· 0 citations
Aims: This study aims to examine the spatial and seasonal variability of urban air pollution across major traffic corridors in Bengaluru by integrating traffic, meteorological, and air quality data. It also evaluates the performance of machine learning models for predicting PM2.5 concentrations and identifies the key factors influencing air pollution.
Study Design: Quantitative observational study based on environmental data analysis and machine learning.
Place and Duration of Study: The study was conducted across seven major traffic corridors in Bengaluru, India, using traffic, meteorological, and air quality data collected over different seasons.
Methodology: Traffic, air quality, and meteorological datasets from seven traffic corridors were integrated into a unified dataset. The data were preprocessed and analyzed to evaluate spatial and seasonal pollution patterns. Linear Regression, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) models were developed to predict PM2.5 concentrations. Model performance was evaluated using the coefficient of determination (R²).
Results: The analysis revealed considerable spatial variation in PM2.5 concentrations across the selected corridors. Mysore Road and Tumakuru Road recorded the highest pollution levels (>90–100 µg/m³), whereas Jayadeva Junction showed comparatively lower concentrations. Seasonal analysis indicated lower pollution during the monsoon due to rainfall and higher pollution during the post-monsoon and winter seasons because of reduced atmospheric dispersion. Among the evaluated models, Linear Regression achieved the highest predictive performance (R² = 0.45), followed closely by ANN (R² = 0.44). Feature importance analysis indicated that traffic congestion contributed more to PM₂.₅ concentration prediction than the evaluated meteorological variables.
Conclusion: The proposed framework demonstrates the feasibility of predicting PM₂.₅ concentrations using integrated traffic and meteorological data and supports evidence-based traffic management and air quality control strategies for sustainable urban development.
S. Niranjankumar, N. Nandini· Asian Journal of Environment...· 0 citations
Bangladesh being a developing country in South Asia, is facing the challenge of air pollution for a long time now. The capital city Dhaka stands out within top 3 polluted cities in the entire earth. Inside Dhaka metropolitan area, the slums are often regarded as the most polluted locations. Still these areas are not explored properly in terms of slum-specific air pollution state. Thus, this study measures air pollution at 5 major slum areas of the Dhaka metropolitan area i.e. Shagufta, Bauniabadh, Bhasantek, Korail and Ceramic Bazar through an air quality monitoring system (AQMS) installed in each site for 2025. The collected data include values of particulate matter ((PM1, PM2.5, PM4, PM10), gaseous substances (CO2, NO2, SO2) and atmospheric variables (temperature and relative humidity). The collected data was taken into Microsoft excel and cleaning process was performed. Then the data for these variables were grouped by four seasons i.e., summer, monsoon, post-monsoon and winter and after analysis using RStudio, these were compared among the slum locations. This revealed that Korail, Bhasantek and Ceramic bazar are comparatively more polluted in terms of particulate matter, whereas Shagufta is more polluted by other gaseous traces. The analysis indicates winter as the most polluted season and monsoon as the least. Pearson's correlation analysis revealed the interconnectedness among these variables, where particulate matter exhibited high positive correlation with one another. Principal component analysis revealed particulate matter as the dominant pollutant. Under these circumstances, the study provides slum-specific recommendations e.g., awareness campaign, waste management etc.
Md. Maksudur Rahman, Asmaul Husna Siddique· International journal of res...· 0 citations
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