A Multi-Country Statistical Analysis Covering Turkey, Slovakia, and Romania in an Educational Framework

Abstract

This paper uses hierarchical regression analysis, a statistically robust method, to explore the correlations between two meteorological parameters and three particulate matter concentrations. The dataset is provided by six sensors located in three cities from three countries, and the measurements were taken simultaneously for three months at each minute. Analyses and calculations were performed with the Statistical Package for the Social Sciences (SPSS). The results underscore that the complexity of air pollution dynamics is affected by the location even when the same type of sensors is used, and emphasize that a one-size-fits-all approach cannot effectively address air pollution. The findings are helpful from three perspectives: for education, to show how to handle and communicate a solution for local communities’ issues about air pollution; for research, to understand how easy a university can generate and analyze open-source data; and for policymakers, to design targeted interventions addressing each country’s challenges.

From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey

“…The distribution of sensors was strategically planned. With its higher population density and relatively few environmental monitoring stations, Adana required a denser sensor network to adequately capture spatial variability in air quality [ 45 , 46 ]. This strategic positioning allows for a more comprehensive assessment of air quality in a rapidly industrialized region experiencing significant urban growth.…”

Unveiling the Air Quality Impacts of Municipal Solid Waste Disposal: An Integrative Study of On-Site Measurements and Community Perceptions

“…Akiner (2020) [18] addressed environmental pollution in the Mediterranean Sea along the Turkish coast, including Adana. Pekdogan et al (2023) [19] investigated particulate matter measurements in Adana, concluding that PM levels were high during the months of measurement. Consequently, air pollutants such as PM 10 , O 3 , CO, NO x , NO, NO, NO 2 , H 2 S, and SO 2 originating from high population density and industrial activities were identified in this region.…”