ULR 4515 - LGCgE, Laboratoire de Génie Civil et géo-Environnement, Univ. Lille, Univ. Artois, IMT Lille Douai, JUNIA, 3 Rue du Pr Laguesse BP83, F-59000, Lille, France.
UFR3S - Pharmacie, 59000, Lille, France.
Environ Sci Pollut Res Int. 2024 Aug;31(38):50642-50653. doi: 10.1007/s11356-024-34519-9. Epub 2024 Aug 5.
Assessing environmental exposure to pollution is a challenging task, and scientists often use distance-based or proximity indicators when field or modeled data are unavailable. Although buffers are commonly used to represent the impact of a pollution source on neighboring populations, they can result in high-exposure misclassification. Euclidean distance-based indicators offer a promising alternative, but practices vary significantly in the literature. In this study, we aimed to compare several distance-based indicators for multiple environmental contaminants in an industrial and urban area. At the population's grid cell resolution of 200 × 200 m, we compared the distance to the closest source, the average or median distance to all sources, or a restricted number of nearby sources for six types of sources (industries, railways, rail areas, roadways, road crossings, and agricultural patches) against environmental contamination data (PM, NO, and multimetallic contamination in lichens). Our findings revealed that the representativeness of contamination by indicators is significantly affected by the type and number of nearby sources considered. Specifically, we found that considering the distance to the nearest source or the average distance to all sources can lead to exposure misclassifications. The optimal correlation between distance indicators and pollutant levels was observed when considering 10-14 of the closest industrial sources, located within a 4.9- to 5.5-km radius. For rail areas, the optimal number was two to three sources within a 5.4- to 7.4-km radius. For main roads, intersections, and railways, the optimal number of sources varied depending on the pollutant, generally falling within a 3- to 9.4-km radius. Environmental contamination is influenced by the diversity of nearby sources, and considering only one source increases the risk of misclassification. Our results suggest that proximity models are still appropriate for study areas where the etiology of existing health effects is unclear, providing an exploratory analysis before more sophisticated research.
评估环境污染暴露是一项具有挑战性的任务，当缺乏现场或模拟数据时，科学家通常使用基于距离或邻近度的指标。虽然缓冲区通常用于表示污染源对邻近人群的影响，但它们可能导致高暴露错误分类。基于欧几里得距离的指标提供了一种有前途的替代方法，但在文献中实践差异很大。在这项研究中，我们旨在比较工业和城市地区多种环境污染物的几种基于距离的指标。在人口的网格单元格分辨率为 200×200 米的情况下，我们比较了到最近污染源的距离、到所有污染源的平均或中位数距离，或者对于六种类型的污染源（工业、铁路、铁路区域、道路、道路交叉口和农业斑块），考虑到环境污染物数据（PM、NO 和地衣中的多金属污染），最近的附近污染源数量。我们的研究结果表明，指标对污染的代表性受到考虑的附近源的类型和数量的显著影响。具体而言，我们发现考虑最近源的距离或所有源的平均距离可能会导致暴露错误分类。当考虑距离最近的工业源 10-14 个，位于 4.9-5.5 公里半径内时，距离指标与污染物水平之间的最佳相关性观察到。对于铁路区域，最佳数量是 5.4-7.4 公里半径内的两个到三个源。对于主要道路、交叉口和铁路，最佳源数取决于污染物，通常在 3-9.4 公里半径内。环境污染物受到附近源多样性的影响，仅考虑一个源会增加错误分类的风险。我们的结果表明，接近度模型仍然适用于病因尚不清楚的现有健康影响的研究区域，为更复杂的研究之前提供了探索性分析。
