Using Statistical Methods to Identify the Impact of Solid Fuel Boilers on Seasonal Changes in Air Pollution

Ewa Bakinowska, Alicja Dota (), Rafał Urbaniak, Bartosz Ciupek, Marcin Żurawski and Marek Dębczyński
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Ewa Bakinowska: Faculty of Control, Robotics and Electrical Engineering, Institute of Mathematics, Poznan University of Technology, 3a Piotrowo St., 61-138 Poznan, Poland
Alicja Dota: Faculty of Control, Robotics and Electrical Engineering, Institute of Mathematics, Poznan University of Technology, 3a Piotrowo St., 61-138 Poznan, Poland
Rafał Urbaniak: Faculty of Technology, University of Kalisz, 201-205 Poznańska St., 62-800 Kalisz, Poland
Bartosz Ciupek: Faculty of Environmental Engineering and Energy, Institute of Thermal Energy, Poznan University of Technology, 3 Piotrowo St., 61-138 Poznan, Poland
Marcin Żurawski: Faculty of Technology, University of Kalisz, 201-205 Poznańska St., 62-800 Kalisz, Poland
Marek Dębczyński: Faculty of Technology, University of Kalisz, 201-205 Poznańska St., 62-800 Kalisz, Poland

Energies, 2025, vol. 18, issue 20, 1-27

Abstract: Air pollution with particulate matter (PM), recognized by the EU and WHO as a significant factor affecting human health, is subject to standards. Exceeding these standards on a daily or annual basis poses an increased health risk. This article presents an analysis of data from 2022 to 2024 from the administrative area of Pleszew (Poland), which, in 2023, ranked second in the country in terms of annual PM 10 concentration [µg/m 3 ]. The main cause of the poor air quality is identified as so-called “low emissions” resulting from residential heating using high-emission coal-fired boilers. The methods used in this analysis not only identified the main causes of pollutant emissions but also demonstrated the seasonal impact of these sources on air quality, both on an annual and daily basis. The analysis utilized statistical tools such as a mixed linear regression model and Tukey’s post hoc tests performed after analysis of variance (ANOVA). The obtained regression model of PM 10 concentration on the outside air temperature (defining the intensity of operation of heating devices) clearly indicates the predicted air pollution. Dividing the day into three time intervals proved to be an effective analytical tool enabling the identification of periods with the highest risk of high PM 10 concentrations. The highest average PM 10 concentration values were recorded in the autumn and winter months between 3:00 PM and 9:00 PM. The developed methods can serve as fundamental tools for local government authorities, guiding further energy policy directions for the study area to improve the identified situation. At the same time, daily and hourly air pollution analysis clearly confirmed the characteristics of inefficient heat sources, which will allow residents to protect their health by avoiding spending time outdoors during peak particulate matter concentration hours. Until the energy situation in the region changes, this will continue.

Keywords: air pollution forecasting; linear regression; urban air quality; sensor data modeling; energy-related environmental planning; heating boiler; emission (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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