Change point detection to analyze air pollution and its economic effects: an exponentially weighted moving average perspective

Air pollution has a direct impact on every society, leading to consequential effects on the economy of a nation. Poor air quality adversely affects human health, resulting in various economic outcomes such as rising healthcare costs, diminished labor productivity, negative impacts on tourism and living standards, increased regulatory expenses for businesses, and heightened economic disparities. Effective control methods are essential to monitor factors influencing the economy, including air quality. The presence of toxic substances in the air reduces air quality, necessitating its monitoring through indices like PM10. Among statistical process control tools, control charts are the most prominent for efficient change point detection. This study introduces a new process monitoring tool that incorporates additional auxiliary information, if available, alongside the main variable of interest. The proposed methodology ensures detection ability remains robust, even under disturbances in the auxiliary variable. Furthermore, mathematical analyses reveal that many existing statistical quality control tools become special cases of the proposed structure for specific sensitivity parameter values. Evaluated through properties of run length distribution, the proposed chart allows control of the robustness-efficiency balance by adjusting its sensitivity parameter. A practical implementation demonstrates the effectiveness of the chart in monitoring air quality data.