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Air pollution macro-regions identification using machine learning and spatio-temporal analysis.

Tymoteusz Morawiec1, Mateusz Zareba1, Tomasz Danek1

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Air pollution in Poland, particularly particulate matter (PM), poses significant public health challenges. This study used advanced clustering to map PM10 levels, revealing distinct high-pollution zones in southern urban areas and low-emission northern regions.

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Area of Science:

  • Environmental Science
  • Public Health
  • Spatial Planning

Background:

  • Particulate matter (PM) pollution is a critical environmental and public health issue in Poland.
  • Effective spatial planning requires detailed understanding of pollution distribution.

Purpose of the Study:

  • To conduct a spatio-temporal analysis of particulate matter (PM) pollution in Poland.
  • To identify spatial patterns and local anomalies of PM concentrations using advanced clustering techniques.
  • To inform public health and spatial planning strategies for air quality management.

Main Methods:

  • Utilized data from 173 air quality monitoring stations (2015-2023).
  • Applied unsupervised clustering based on Dynamic Time Warping (DTW) metric for daily and annual timescales.
  • Analyzed over 13 million observations to delineate macroregions and local clusters.

Main Results:

  • Identified four macroregions and sixteen clusters, highlighting significant spatial variations in PM10 concentrations (19.7 to 27.18 µg/m³).
  • Confirmed the influence of topographic, urban, and microclimatic factors on PM distribution.
  • Observed distinct high-PM clusters in urbanized southern Poland (Silesia, Lesser Poland) versus low-emission northern lowlands.

Conclusions:

  • Regional air quality policies require supra-regional coordination for effectiveness.
  • High-resolution data analysis is crucial for effective environmental and public health planning to mitigate air pollution impacts.