Application of regression models in pollution monitoring

Linear and nonlinear regression are powerful and even more robust tools for analyzing environmental data to interpret pollution phenomena and predict future trends. This chapter will analyze their application for pollution monitoring. Because of the vast and complex data that must be collected and processed to monitor pollution effectively, sophisticated tools are required to interpret the information significantly. Regression models are strong statistical methods widely used to relate pollution levels to environmental factors, such as temperature, humidity, industrial activities, and vehicular emissions. Linear regressions (which take a simple approach of inputting it into a model to confirm the level of correlation between independent variables (predictors) and the dependent variable (pollution values)) are quite easy to instantiate and interpret. These models discussed trends and forecasting and assumed linearity. Linear regression models cannot control for biological concepts, such as the biological meaning of the data. It discusses the merits and demerits of linear and nonlinear regression techniques for pollution monitoring. It highlights critical features like selecting a model, estimating the parameters, and testing the model to check if the selected models are confirmed and valid. This is a how-to on data cleaning approaches such as normalization, outlier detection, and removal techniques, which are important to model optimal models. The urgency of addressing pollution through robust statistical monitoring is underscored by the increasing industrialization and urbanization worldwide, which have led to significant environmental and public health challenges. Monitoring pollution is fundamental in order to recognize pollution sources, to evaluate environmental impacts, and to propose effective mitigation measures. With the development of hot technological points, such as Internet of Things (IoT) and remote sensing, the accuracy and real-time of the pollution monitoring systems are improved.