Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis

IF 6 3区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Environmental Sciences Europe Pub Date : 2024-08-30 DOI:10.1186/s12302-024-00972-z
Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi
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Abstract

Background

PM10, comprising particles with diameters of 10 µm or less, has been identified as a significant environmental pollutant associated with adverse health outcomes in European cities. Understanding the temporal variation of the relationship between PM10 and geographical parameters is crucial for sustainable land use planning and air quality management in European landscapes. This study utilizes Conditional Inference Forest modeling and partial correlation to examine the impact of geographical factors on monthly average concentrations of PM10 in European suburban and urban landscapes during heating and cooling periods. The investigation focuses on two buffer zones (1000 m and 3000 m circle radiuses) surrounding 1216 European air quality monitoring stations.

Results

Results reveal importance and significant correlations between various geographical variables (soil texture, land use, transportation network, and meteorological) and PM10 quality on a continental scale. In suburban landscapes, soil texture, temperature, roads, and rail density play pivotal roles, while meteorological variables, particularly monthly average temperature and wind speed, dominate in urban landscapes. Urban sites exhibit higher R-squared values during both cooling (0.41) and heating periods (0.61) compared to suburban sites (cooling period R-squared: 0.39; heating period: R-squared: 0.51), indicating better predictive performance likely attributed to the less heterogeneous land use patterns surrounding urban PM10 monitoring sites.

Conclusion

The study underscores the importance of investigating spatial and temporal dynamics of geographical factors for accurate PM10 air quality prediction models in European urban and suburban landscapes. These findings provide valuable insights for policymakers, urban planners, and environmental scientists, guiding efforts toward sustainable and healthier urban environments.

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地理参数对欧洲景观中 PM10 污染的影响:基于机器学习算法的分析
背景可吸入颗粒物 PM10 由直径为 10 微米或更小的颗粒组成,已被确定为与欧洲城市的不良健康后果相关的重要环境污染物。了解 PM10 与地理参数之间关系的时间变化对于欧洲景观的可持续土地利用规划和空气质量管理至关重要。本研究利用条件推理森林建模和部分相关性研究了地理因素对欧洲郊区和城市景观在供暖和降温期间 PM10 月平均浓度的影响。结果显示,在欧洲大陆范围内,各种地理变量(土壤质地、土地利用、交通网络和气象)与 PM10 质量之间存在重要且显著的相关性。在郊区地貌中,土壤质地、温度、道路和铁路密度起着关键作用,而气象变量,尤其是月平均温度和风速,在城市地貌中占主导地位。与郊区相比,城市站点在降温期(0.41)和供暖期(0.61)均表现出更高的 R 平方值(降温期 R 平方值:0.39;供暖期:R 平方值:0.51),这表明城市 PM10 监测点周围的土地利用模式异质性较低,因此具有更好的预测性能。这些研究结果为政策制定者、城市规划者和环境科学家提供了有价值的见解,为实现可持续和更健康的城市环境提供了指导。
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来源期刊
Environmental Sciences Europe
Environmental Sciences Europe Environmental Science-Pollution
CiteScore
11.20
自引率
1.70%
发文量
110
审稿时长
13 weeks
期刊介绍: ESEU is an international journal, focusing primarily on Europe, with a broad scope covering all aspects of environmental sciences, including the main topic regulation. ESEU will discuss the entanglement between environmental sciences and regulation because, in recent years, there have been misunderstandings and even disagreement between stakeholders in these two areas. ESEU will help to improve the comprehension of issues between environmental sciences and regulation. ESEU will be an outlet from the German-speaking (DACH) countries to Europe and an inlet from Europe to the DACH countries regarding environmental sciences and regulation. Moreover, ESEU will facilitate the exchange of ideas and interaction between Europe and the DACH countries regarding environmental regulatory issues. Although Europe is at the center of ESEU, the journal will not exclude the rest of the world, because regulatory issues pertaining to environmental sciences can be fully seen only from a global perspective.
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