A Review on Deep Learning Applications in Air Pollution Control Methods

This study delves into the transformative role of deep learning and neural networks in the domain of air pollution control. By focusing on enhanced detection and monitoring, particularly through convolutional and recurrent neural architectures, the research highlights the potential of these technologies to unravel complex patterns within air quality dynamics. Beyond mere detection, these models demonstrate proactive capabilities, enabling the prediction and forecasting of pollution events. This foresight empowers the implementation of adaptive control strategies, effectively minimizing health risks and optimizing resource allocation. However, the study acknowledges challenges related to data quality and interpretability, emphasizing the necessity for interdisciplinary collaboration among machine learning experts, environmental scientists, and policymakers. In synthesizing these findings, the research contributes to the advancement of sustainable strategies for mitigating the impact of air pollution on human health and the environment and also reviews methods of controlling it by deep learning approaches.