Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data

Liu, Xiansheng; Huang, Haiying; Jiang, Yiming; Wang, Tao; Xu, Yanling; Abbaszade, Gülcin; Schnelle-Kreis, Jürgen; Zimmermann, Ralf

Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data

2020

Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM₁₀ air pollution and to investigate the population exposure to the distribution of PM₁₀, daily and monthly PM₁₀ concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM₁₀ concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM₁₀ levels. Subsequently, population exposure levels were calculated using population-weighted PM₁₀ concentrations. Finally, the power-law distribution was used to test the distribution of PM₁₀ polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM₁₀ concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM₁₀ polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control.

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