Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation

Abstract

Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes damage to the environment. The most important cause of air pollution is the amount and density of substances called “particulate matter” above guidelines. Particulate matter (PM) are mixed liquid droplets and solid particles with advective diameters less than 2.5 μm (PM2.5—fine particles) and between 2.5 and 10 μm (PM2.5–10—coarse particles). PM10 is defined as one that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. It is important to identify the underlying causes of air pollution caused by PM10. In this context, these criteria need to be evaluated to minimize the negative effects of PM10. In the study, monthly average PM10 data obtained from the Air Quality Monitoring Station in Kocaeli, Türkiye, between 2017 and 2023 are used. After determining the criteria for PM10, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking decision-maker opinions. The proposed decision-making model aims to guide obtaining and focusing on the important causes of out-of-limit and dangerous PM10 concentrations in the air. Additionally, PM10 data is analyzed in the context of COVID-19 and a statistical analysis is conducted. One-way Analysis of Variance (ANOVA) is used to evaluate whether there is a significant difference in average monthly data over the years. The Games–Howell test, one of the post-hoc tests, is used for determining differences between groups (years). In addition, monthly PM10 values for the future are estimated using the Expert Modeler tool in the software IBM® SPSS® Statistics 22. The study is important in that it provides a focus on the criteria affecting PM10 with an intuitionistic fuzzy perspective, along with statistical analysis.