State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; State Key Laboratory of Geological Processes and Mineral Resources(GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan, 430074, China.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Sci Total Environ. 2019 Apr 1;659:363-371. doi: 10.1016/j.scitotenv.2018.12.330. Epub 2018 Dec 22.
The spatial distribution of potentially toxic metals (PTMs) has been shown to be related to anthropogenic activities. Several auxiliary variables, such as those related to remote sensing data (e.g. digital elevation models, land use, and enhanced vegetation index) and soil properties (e.g. pH, soil type and cation exchange capacity), have been used to predict the spatial distribution of soil PTMs. However, these variables are mostly focused on natural processes or a single aspect of anthropogenic activities and cannot reflect the effects of integrated anthropogenic activities. Nighttime lights (NTL) images, a representative variable of integrated anthropogenic activities, may have the potential to reflect PTMs distribution. To uncover this relationship and determine the effects on evaluation precision, the NTL was employed as an auxiliary variable to map the distribution of PTMs in the United Kingdom. In this study, areas with a digital number (DN) ≥ 50 and an area > 30 km were extracted from NTL images to represent regions of high-frequency anthropogenic activities. Subsequently, the distance between the sampling points and the nearest extracted area was calculated. Barium, lead, zinc, copper, and nickel concentrations exhibited the highest correlation with this distance. Their concentrations were mapped using distance as an auxiliary variable through three different kriging methods, i.e., ordinary kriging (OK), cokriging (CK), and regression kriging (RK). The accuracy of the predictions was evaluated using the leave-one-out cross validation method. Regardless of the elements, CK and RK always exhibited lower mean absolute error and root mean square error, in contrast to OK. This indicates that using the NTL as the auxiliary variable indeed enhanced the prediction accuracy for the relevant PTMs. Additionally, RK showed superior results in most cases. Hence, we recommend RK for prediction of PTMs when using the NTL as the auxiliary variable.
潜在有毒金属（PTMs）的空间分布与人为活动有关。已经使用了一些辅助变量，例如与遥感数据（例如数字高程模型、土地利用和增强植被指数）和土壤特性（例如 pH 值、土壤类型和阳离子交换能力）相关的那些变量，来预测土壤 PTMs 的空间分布。然而，这些变量主要集中在自然过程或人为活动的单一方面，无法反映综合人为活动的影响。夜间灯光（NTL）图像作为综合人为活动的代表性变量，可能具有反映 PTMs 分布的潜力。为了揭示这种关系并确定对评估精度的影响，将 NTL 用作辅助变量来绘制英国 PTMs 的分布。在这项研究中，从 NTL 图像中提取了数字数（DN）≥50 和面积>30km 的区域，以代表高频人为活动的区域。随后，计算了采样点与最近提取区域之间的距离。钡、铅、锌、铜和镍浓度与该距离表现出最高的相关性。使用距离作为辅助变量，通过三种不同的克里金方法（普通克里金法（OK）、协克里金法（CK）和回归克里金法（RK））绘制了它们的浓度图。使用留一法交叉验证法评估了预测的准确性。无论元素如何，CK 和 RK 始终表现出比 OK 更低的平均绝对误差和均方根误差。这表明，使用 NTL 作为辅助变量确实提高了相关 PTMs 的预测精度。此外，RK 在大多数情况下表现出更好的结果。因此，我们建议在使用 NTL 作为辅助变量时，使用 RK 进行 PTMs 的预测。
