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Simulation of changes in heavy metal contamination in farmland soils of a typical manufacturing center through logistic-based cellular automata modeling

机译:通过基于逻辑的元胞自动机模型模拟典型制造中心农田土壤中重金属污染的变化

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A customized logistic-based cellular automata (CA) model was developed to simulate changes in heavy metal contamination (HMC) in farmland soils of Dongguan, a manufacturing center in Southern China, and to discover the relationship between HMC and related explanatory variables (continuous and categorical). The model was calibrated through the simulation and validation of HMC in 2012. Thereafter, the model was implemented for the scenario simulation of development alternatives for HMC in 2022. The HMC in 2002 and 2012 was determined through soil tests and cokriging. Continuous variables were divided into two groups by odds ratios. Positive variables (odds ratios >1) included the Nemerow synthetic pollution index in 2002, linear drainage density, distance from the city center, distance from the railway, slope, and secondary industrial output per unit of land. Negative variables (odds ratios <1) included elevation, distance from the road, distance from the key polluting enterprises, distance from the town center, soil pH, and distance from bodies of water. Categorical variables, including soil type, parent material type, organic content grade, and land use type, also significantly influenced HMC according to Wald statistics. The relative operating characteristic and kappa coefficients were 0.91 and 0.64, respectively, which proved the validity and accuracy of the model. The scenario simulation shows that the government should not only implement stricter environmental regulation but also strengthen the remediation of the current polluted area to effectively mitigate HMC.
机译:建立了基于逻辑的定制细胞自动机(CA)模型,以模拟东莞(中国南部的制造中心)农田土壤中重金属污染(HMC)的变化,并发现HMC与相关解释变量(连续和连续)之间的关系。绝对)。该模型在2012年通过HMC的仿真和验证进行了校准。此后,该模型用于2022年HMC的开发替代方案的情景模拟。2002年和2012年的HMC是通过土壤测试和协同克里金法确定的。连续变量按优势比分为两组。正变量(比值大于1)包括2002年的Nemerow综合污染指数,线性排水密度,距市中心的距离,距铁路的距离,坡度和每单位土地的第二产业产出。负变量(优势比<1)包括海拔,距道路的距离,距主要污染企业的距离,距市中心的距离,土壤pH值和距水体的距离。根据Wald统计,分类变量(包括土壤类型,母体材料类型,有机物含量等级和土地使用类型)也显着影响HMC。相对工作特性和κ系数分别为0.91和0.64,证明了该模型的有效性和准确性。情景模拟表明,政府不仅应执行更严格的环境法规,而且应加强对当前污染区的补救,以有效减轻HMC。

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