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Using Bayesian networks for environmental health risk assessment
Environmental Research ( IF 7.7 ) Pub Date : 2021-09-15 , DOI: 10.1016/j.envres.2021.112059
Sandra Pérez 1 , Catherine German-Labaume 2 , Sébastien Mathiot 3 , Sylvaine Goix 4 , Philippe Chamaret 4
Affiliation  

The study investigated the potential relationships between air pollution, socio-economy, and proven pathologies (e.g., respiratory, cardiovascular) within an industrial area in Southern France (Etang de Berre), gathering steel industries, oil refineries, shipping, road traffic and experiencing a Mediterranean climate. A total of 178 variables were simultaneously integrated within a Bayesian model at intra-urban scale. Various unsupervised and supervised algorithms (maximum spanning tree, tree-augmented naive classifier) as well as sensitivity analyses were used to better understand the links between all variables, and highlighted correlations between population exposure to air pollutants and some pathologies. Adverse health effects (bronchus and lung cancers for 15–65 years old people) were observed for hydrofluoric acid at low background concentration (<0.003 μg m−3) while exposure to particulate cadmium (0.210–0.250 μg m−3) disrupts insulin metabolism for people over 65 years-old leading to diabetes. Bronchus and lung cancers for people over 65 years-old occurred at low background SO2 concentration (6 μg m−3) below European limit values. When benzo[k]fluoranthene exceeded 0.672 μg m−3, we observed a high number of hospital admissions for respiratory diseases for 15-65 years-old people. The study also revealed the important influence of socio-economy (e.g., single-parent family, people with no qualification at 15 years-old) on pathologies (e.g., cardiovascular diseases). Finally, a diffuse polychlorinated biphenyl (PCB) pollution was observed in the study area and can potentially cause lung cancers.



中文翻译:

使用贝叶斯网络进行环境健康风险评估

该研究调查了法国南部工业区 ( Etang de Berre ) 内空气污染、社会经济和已证实的疾病(例如呼吸系统、心血管疾病)之间的潜在关系),聚集钢铁工业、炼油厂、航运、道路交通,体验地中海气候。在城内规模的贝叶斯模型中同时集成了 178 个变量。各种无监督和监督算法(最大生成树、树增强朴素分类器)以及敏感性分析被用来更好地理解所有变量之间的联系,并突出了人群暴露于空气污染物与某些病理之间的相关性。在低背景浓度 (<0.003 μg m -3 ) 的氢氟酸暴露于颗粒镉 (0.210-0.250 μg m -3) 会扰乱 65 岁以上人群的胰岛素代谢,从而导致糖尿病。65 岁以上人群的支气管癌和肺癌发生在低于欧洲限值的低背景 SO 2浓度(6 μg m -3 )下。当苯并[k]荧蒽超过 0.672 μg m -3时,我们观察到 15-65 岁人群因呼吸系统疾病入院的人数较多。该研究还揭示了社会经济(例如,单亲家庭、15 岁没有资格的人)对病理(例如,心血管疾病)的重要影响。最后,在研究区域观察到弥漫性多氯联苯 (PCB) 污染,可能导致肺癌。

更新日期:2021-09-21
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