Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data
2020
Liu, Xiansheng | Huang, Haiying | Jiang, Yiming | Wang, Tao | Xu, Yanling | Abbaszade, Gülcin | Schnelle-Kreis, Jürgen | Zimmermann, Ralf
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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