Smart Analysis of Air Pollutants In Urban Areas

The Air pollution poses significant challenges to urban areas, impacting public health and environmental quality. Predicting air pollutant levels accurately is crucial for effective environmental management and public health initiatives. This study investigates the efficacy of deep learning and statistical models for predicting air pollutant levels in urban areas, encompassing NO2, O3, SO2, PM 2.5, PM 10, CO, and NH3. Deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are compared against traditional statistical models such as linear regression and autoregressive integrated moving average (ARIMA). The performance evaluation considers factors like accuracy, robustness, and computational efficiency. Moreover, the interpretability of model predictions and their potential for real-time applications in air quality management are examined. Through a comprehensive comparative analysis, this study examines the effectiveness of deep learning and statistical models in predicting air pollution levels. facilitating informed decision-making in urban environmental management and public health initiatives. This research aims to enhance the predictive capabilities of the models. Special attention is given to the temporal and spatial aspects of air pollution, considering the dynamic nature of urban environments.