Abstract
Particulate Matter 2.5 (PM2.5) is a major contributor to air pollution and its exposure has substantial health consequences. As a result, precise prediction of PM2.5 concentrations is required in order to establish emission reduction strategies for air quality management. The article presents an Artificial Neural Network (ANN) model to forecast PM2.5 levels in a particular region. The model uses data such as air temperature, carbon monoxide, nitric oxide, nitrogen dioxide, ozone, suspended particles, rainfall, relative humidity, sulfur dioxide, wind direction and wind speed to predict PM2.5 concentrations in the air accurately. The model’s efficacy is evaluated using statistical measures such as the Coefficient of Determination, the Root Mean Squared Error and the Mean Absolute Error. The study results indicate that the ANN model outperforms more traditional statistical models, with R2 values of 0.987, which is higher than the values achieved by the Linear Regression and Decision Tree Regressor models, which are 0.88 and 0.89 respectively. The study’s findings have significant implications for public health and environmental policy, as they can provide more accurate and rapid statistics on air quality. The ability to forecast PM2.5 concentrations can help policymakers and health professionals take proactive measures to mitigate the impact of air pollution on public health.