在使用PMF模型的北方工业县的土壤PTE的来源分配:分割策略和不确定性分析
Biling Shi1, Xiao Yang2, Tao Liang2
1Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049, China.
Environmental research
|April 8, 2024
概括
农业土壤的空间异质性使资源分配复杂化. 这项研究通过整合分区处理 (PP) 和不确定性分析来改进正矩阵因子化 (PMF) 建模,提高了潜在有毒元素 (PTE) 的准确性.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 土壤科学 土壤科学
背景情况:
- 农业土壤中潜在有毒元素 (PTE) 的空间异质性挑战了传统的正矩阵因数分解 (PMF) 源分配的准确性.
- 现有的PMF模型可能无法充分解决空间变化,导致PTE污染评估的结果不可靠.
研究的目的:
- 为了提高PMF模拟准确度,在空间异质的农业土壤中分配PTE源.
- 评估与PMF集成的不同分区处理 (PP) 策略的有效性.
- 确定导致PTE污染的主要来源,并评估人类活动的影响.
主要方法:
- 选择了中国北部的一个工业农业中心进行土壤采样.
- 根据工业密度,人口密度和生态风险指数实施了三个分区处理 (PP) 策略 (PP-1,PP-2,PP-3).
- 集成PP策略与PMF建模,并进行不确定性分析来比较模型性能.
主要成果:
- 在农业土壤中观察到 (Cd), (Pb) 和 (Zn) 的显著丰富;Hg和Cd对潜在的生态风险贡献了83.49%.
- 基于生态风险指数的分区处理策略PP-3,加上PMF,产生了具有较低不确定性的最佳建模结果.
- 自然来源,肥料/杀虫剂,大气沉积,采矿和炼被确定为PTE的主要贡献者.
- 与标准PMF相比,人为来源,特别是肥料/农药和大气沉积的贡献增加了 (分别为1.64%和5.91%).
结论:
- 将适当的分区处理 (PP) 与PMF集成,可显著提高空间异质土壤PTE污染源分配的准确性.
- 基于生态风险指数的分区 (PP-3) 提供了在这种环境中对PMF建模的最可靠方法.
- 这些发现支持通过准确识别人类贡献来实施针对土壤PTE污染的有针对性的控制策略.
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