A Comprehensive Review Paper on Air Quality Prediction and Remediation in Using Soft Computing

Air quality prediction and remediation are critical components in addressing environmental pollution and its associated health risks. Predictive modelling and remediation techniques are now crucial for tracking air quality levels and carrying out corrective measures due to the growing global worry about air pollution and how it harms both the environment and people’s health. Artificial neural networks (ANN), fuzzy logic, support vector machines (SVM) and genetic algorithms (GA) are applications of soft computing techniques that have demonstrated significant promise in predicting air quality and developing efficient remediation strategies. This paper explores the approach of soft computing techniques in air quality prediction and remediation and panoramic overview of various method used to predict air pollutants like particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulphur dioxide (SO2), and ozone (O3). In order to forecast future impurity level and enable alerts and better air quality management, these models make use of past air quality data, meteorological conditions, and other pertinent variables. In order to improve prediction accuracy, the article also addresses the integration of soft computing techniques with real-time data collecting systems and environmental sensors. Additionally, the remediation component focuses on creating efficient air pollution control strategic by using optimization methods like genetic algorithms and particle swarm optimization. These tactics include deploying pollution-reducing technologies, designing green areas, and placing air purifiers in the best possible locations. Prediction models and remediation techniques work together to provide a comprehensive approach to air quality management, which helps create sustainable urban environments and smart cities. The present research emphasizes the possibilities of soft computing methods in creating adaptive and intelligent systems for air quality forecasting and remediation.