European Commission, Joint Research Centre (JRC), Ispra, VA, Italy; Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ-UPSACLAY, 91190 Gif sur Yvette, France; Université Paris-Saclay, INRAE, AgroParisTech, UMR SAD-APT, 91120 Palaiseau, France.
European Commission, Joint Research Centre (JRC), Ispra, VA, Italy.
Environ Int. 2024 Mar;185:108544. doi: 10.1016/j.envint.2024.108544. Epub 2024 Mar 1.
Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Organization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection.
砷（As）是一种用途广泛的重金属微量元素，广泛应用于工业应用。As 是一种致癌物质，通过吸入和摄入会对健康造成危害，并与肝脏、肾脏、肺部和膀胱肿瘤的风险增加有关。在农业环境中，砷制剂的重复使用会导致土壤浓度升高，而土壤浓度还受到环境和管理变量的影响。由于接触 As 会带来风险，因此需要有效的评估工具来支持环境和健康政策。然而，现有的最全面的土壤 As 数据，即土地利用/覆盖面积框架统计调查（LUCAS）数据库，由于检测限较高，存在严重的局限性。尽管检测限符合国际标准化组织的标准，但排除了采用标准数据分析方法的可能性。本工作重点开发了一种新的方法，使用 LUCAS 土壤样本来模拟欧洲土壤中的 As 污染。我们引入了 GAMLSS-RF 模型，这是一种将随机森林与广义加性模型用于位置、比例和形状的新方法。半参数模型可以捕捉输入变量之间的非线性相互作用，同时适应有 censored 和无 censored 观测值，并可以校准以包含来自其他运动数据库的信息。在拟合和验证空间模型后，我们制作了欧洲尺度的 As 浓度图，空间分辨率为 250 m，并根据参考值（即两个行动水平和一个背景浓度）评估了这些模式。我们发现整个欧洲大陆的 As 浓度存在显著差异，北部国家的浓度较低，葡萄牙、西班牙、奥地利、法国和比利时的浓度较高。通过克服现有数据库和方法的局限性，本方法为处理高度 censored 数据提供了一种替代方法。该模型还为评估土壤中 As 污染风险提供了有价值的概率工具，为环境和健康保护的决策提供了信息。
