School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou City, Jiangsu, 221116, China.
School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou City, Jiangsu, 221116, China.
Environ Pollut. 2024 Jul 1;352:124147. doi: 10.1016/j.envpol.2024.124147. Epub 2024 May 10.
Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and rapid monitoring, analysis, and assessment to control environmental risks associated with large CSW dumpsites. We constructed a new composite model (PLS-FL) that uses partial least squares regression (PLSR) and fuzzy logic inference (FLI) to accurately predict heavy metal concentrations in soils and assess pollution risk levels. The potential application of the PLS-FL was tested through a gully type CSW case study. We compared 20 modeling strategies using the PLS-FL: five types heavy metals (Cd, Zn, Pb, Cr and As) * four spectral transformation methods (first derivative (FD), second derivative (SD), reverse logarithm (RL), and continuum removal (CR)) * one variable selection method (competitive adaptive reweighted sampling (CARS)). The results showed that the combination of derivative transformation and CARS was recommended for estimation, with R > 0.80 and R > 0.50. When comparing the PLSR model with four traditional machine learning methods (Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), and KNN), the PLSR model demonstrated the highest average prediction accuracy. Additionally, the FLI process no longer relies on human perception and expert opinion, enhancing the model's objectivity and reliability. The evaluation results revealed that the heavy metal contamination areas of the CSW dumpsite are concentrated at the bottom of the gully, with more severe contamination in the north. Furthermore, a high-risk zone exists in the interim storage area for CSW to the east of the dump. These findings align with the initial detections at the sampling sites and highlight the need for targeted monitoring and control in these areas. The application of the model will empower regulators to quickly assess the overall situation of large-scale heavy metal pollution and provide scientific program and data support for continuous large-scale pollution risk monitoring and sustainable risk management.
煤基固体废物（CSW）堆放场中重金属的持续释放和迁移常常导致生态用地严重侵占和自然环境严重污染。因此，迫切需要进行长期和快速的监测、分析和评估，以控制与大型 CSW 堆放场相关的环境风险。我们构建了一个新的组合模型（PLS-FL），该模型使用偏最小二乘回归（PLSR）和模糊逻辑推理（FLI）来准确预测土壤中重金属浓度并评估污染风险水平。通过对沟型 CSW 案例研究，对 PLS-FL 的潜在应用进行了测试。我们使用 PLS-FL 比较了 20 种建模策略：五种重金属（Cd、Zn、Pb、Cr 和 As）* 四种光谱变换方法（一阶导数（FD）、二阶导数（SD）、反对数（RL）和连续体去除（CR））* 一种变量选择方法（竞争自适应重加权采样（CARS））。结果表明，建议采用导数变换和 CARS 的组合进行估计，R>0.80 和 R>0.50。当将 PLSR 模型与四种传统机器学习方法（支持向量机（SVM）、随机森林（RF）、极限学习机（ELM）和 KNN）进行比较时，PLSR 模型表现出最高的平均预测精度。此外，FLI 过程不再依赖于人类感知和专家意见，增强了模型的客观性和可靠性。评估结果表明，CSW 堆放场的重金属污染区域集中在沟壑底部，北部污染更为严重。此外，在堆放场以东的 CSW 临时储存区存在一个高风险区。这些发现与采样点的初步检测结果一致，表明需要在这些区域进行有针对性的监测和控制。该模型的应用将使监管机构能够快速评估大规模重金属污染的总体情况，并为持续的大规模污染风险监测和可持续风险管理提供科学的方案和数据支持。
