Jul 2026· International Research Journal on Advanced Engineering Hub (IRJAEH)· Vol 4, pp. 4971-4977· 0 citations· 16 references
TL;DR
Experimental results confirm competitive prediction accuracy, real-time responsiveness, and seamless scalability, affirming the IQMMS system's suitability for next-generation smart city deployments.
Abstract
Urban air pollution has emerged as one of the foremost environmental and public health crises confronting rapidly growing cities worldwide. The convergence of industrial expansion, vehicular traffic, and urban sprawl continues to degrade ambient air quality at an alarming pace. Conventional monitoring setups, which rely on fixed sensor nodes and dedicated stations, incur substantial deployment costs and inherently limit spatial coverage. This paper introduces an Intelligent Air Quality Monitoring and Management System (IAQMMS) tailored for smart city ecosystems—a software-centric framework that fuses real-time pollutant data streams, machine learning inference, and predictive analytics to monitor, forecast, and govern urban air pollution. The system exploits publicly accessible air quality application programming interfaces (APIs) and historical pollution repositories to generate forward-looking Air Quality Index (AQI) estimates and pinpoint probable emission hotspots. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture is adopted to simultaneously capture spatial pollutant distributions and temporal concentration dynamics. Supplementary capabilities encompass automated health risk stratification and context-aware precautionary guidance delivered through an interactive dashboard. Experimental results confirm competitive prediction accuracy, real-time responsiveness, and seamless scalability, affirming the system's suitability for next-generation smart city deployments.
Rapid urbanization, industrialization and motorization have intensified air pollution challenges in Indian cities, posing significant risks to public health and sustainable development. Conventional air quality management approaches, largely reactive and static, are insufficient to address the complex and dynamic nature of urban air pollution. This manuscript reviews the role of artificial intelligence (AI) and environmental technologies in advancing air pollution management within the framework of India’s Smart Cities Mission and the National Clean Air Programme (NCAP). AI-driven techniques, including machine learning and deep learning, enable improved air quality forecasting, source apportionment, hotspot identification and decision-support systems through integration of multi-source data from sensors, satellite observations and meteorology. The study further examines environmental technologies targeting pollution reduction across transport, industrial, construction, indoor and outdoor environments, including electric mobility, advanced emission controls, carbon capture, dust mitigation, outdoor air purification and nature-based solutions. An integrated smart city framework is proposed linking data, AI analytics, interventions, monitoring and health-focused outcomes. The findings suggest that coordinated AI-enabled governance and targeted technologies can substantially reduce air pollutant levels, enhance regulatory efficiency and support healthier, climate-resilient urban environments in India.
Dharani Bhaskaran· Romanian Journal of Ecology...· 0 citations
Rapid urbanization, industrialization, increasing vehicle emissions, fossil fuel consumption, and construction activities have significantly deteriorated urban air quality, posing serious risks to public health, the environment, and the economy. Traditional air quality monitoring systems, which rely on fixed monitoring stations and statistical forecasting methods, often lack adequate spatial coverage and fail to capture the complex relationships among environmental factors. Artificial Intelligence (AI) offers an effective alternative by integrating data from IoT sensors, satellite observations, meteorological stations, traffic systems, and historical pollution records to generate accurate real-time air quality predictions. This paper presents an AI-based predictive framework for urban air quality management that combines data preprocessing, feature engineering, machine learning, deep learning, and ensemble models. The framework analyzes key environmental parameters, including particulate matter (PM₂.₅ and PM₁₀), gaseous pollutants, weather conditions, traffic density, and industrial emissions. Advanced preprocessing techniques improve data quality, while algorithms such as Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gradient Boosting enhance forecasting accuracy. The proposed model supports intelligent decision-making by enabling early pollution warnings, optimized traffic management, industrial emission control, and sustainable urban planning. Experimental results demonstrate that AI-based models outperform conventional statistical approaches in prediction accuracy and computational efficiency. Overall, the framework provides a scalable and reliable solution for smart city applications, contributing to healthier, more sustainable, and resilient urban environments.
Seshagiri N, Narendra Karmarkar· International Journal of Mod...· 0 citations
Addressing the growing problem of urban air pollution requires that sophisticated air pollution monitoring and forecasting systems be incorporated into civil engineering practice. New technologies that support smarter and more sustainable urban environments include the Internet of Things (IoT), big data analytics, and machine learning (ML). These technologies allow for real-time data acquisition, high-resolution monitoring, and precise predictive optimization. Continuous, spatially dense air quality monitoring is made possible by IoT-based sensor networks, while big data frameworks can effectively integrate and analyse diverse, multi-source datasets. Meanwhile, ML and deep learning can further improve forecasting accuracy, enabling urban planners and civil engineers to foresee pollution trends and implement proactive mitigation measures. Notwithstanding these developments, challenges persist for data integration, sensor calibration, model transparency, and the reliability of inexpensive monitoring systems. To overcome these constraints, improvements are needed in terms of data accuracy, robust calibration techniques, and the implementation of interpretable ML models. This review emphasizes the vital role of civil engineers in promoting resilient and sustainable urban development, advancing effective air quality management, and safeguarding public health through interdisciplinary collaboration between data scientists and policymakers.
Prakash Pathak, Pradip Jadhao· Ecology, Economy and Society...· 0 citations
Air pollution poses a serious hazard to public health in Cities of India; Among all sources, those resulting from Energy Consumption dominate the deaths caused by air pollution. Economic growth has intensified air pollution and climate change issues at the same time. Existing prediction methods focus on isolated single-site time-series forecasting, ignoring spatial dependencies and cross-city pollution transport, limiting regional management effectiveness. Based on the hourly air quality data of key pollutants in seven cities across India during 2015-2020, this paper first explores their concentrations and correlations; Then it builds a Deep Learning forecasting system consisting of Long Short-Term Memory (LSTM), a convolutional neural network, and long short-term memory networks (CNN-LSTM) and a transformer to investigate its AQI-prediction performance. LSTM can capture long-term temporal dependencies; CNN-LSTM combines spatial-temporal information model integration; Transformers explore long-range relationships through attention mechanisms. Based on the experimental results of this paper, it can be concluded that CNN-LSTM is more effective than other systems. It also has some advantages over single-LSTM and the transformer.
An optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality and demonstrates a robust, scalable, and practical solution for short-term air quality prediction.
Shivatejaswini B, D. B M, Mr. Gattu Prasad· International Scientific Jou...· 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
