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在现场规模上对土壤重金属污染源的遥感分析方法,考虑源-沉关系

Yulong Wang¹, Bin Zou¹, Xuegang Zuo¹

¹School of Geosciences and Info-physics, Central South University, Changsha 410083, China.

The Science of the total environment

|June 19, 2024

概括

这项研究引入了一种新的遥感方法,以精确地识别土壤重金属 (HM) 污染源,包括直接的垃圾填埋场和潜在的热点,在现场规模. 该方法加强了采矿区的污染控制策略.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
地质化学地质化学

背景情况:

传统的重金属 (HM) 污染源的识别仅限于区域规模,由于空间异质性,无法精确确定特定的地点.
现有的方法忽略了来自历史人类活动的潜在污染源,只关注直接的固体废物填埋场.

研究的目的:

开发一种新的遥感分析方法,用于在现场范围内识别直接和潜在的HM污染源.
纳入源下沉关系,并解决传统HM污染识别方法的局限性.

主要方法:

高分辨率多光谱图像的监督分类,用于直接提取污染源 (固体废物填埋场).
机器学习建模与高光谱图像用于绘制基于HM分布的潜在污染源.
为网格化污染源分析开发源识别算法.

主要成果:

在 (Cd) 污染的矿区成功地提取了21个固体废物垃圾场,准确度为~90%,卡帕值为0.80.
确定了4167个HM污染热点,它们具有最佳的Cd反转精度 (Rv2=0.91,RMSEv=4.27,RPDv=3.02).
在已识别的直接和潜在污染源之间展示了高度的空间相似性.

结论:

拟议的遥感方法准确地识别了现场规模的直接和潜在的土壤HM污染源.
这种创新方法为准确预防和控制土壤HM污染提供了有价值的方法参考.
该方法在Cd污染的矿区的验证强调了其实际适用性.

关键词:

污染污染污染超光谱图像是一种超光谱图像.机器学习是机器学习.采矿区是一个采矿区.源标识来源的标识.尾尾池是一个尾池.

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