Geostatistical prediction of water lead levels in Flint, Michigan: A multivariate approach.

Geostatistical prediction of water lead levels in Flint, Michigan: A multivariate approach.
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密歇根州弗林特水铅含量的地统计预测:多变量方法。

DOI:
10.1016/j.scitotenv.2018.07.459
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发表时间:
2019
期刊:
The Science of the total environment
影响因子:
--
通讯作者:
Goovaerts,Pierre
Goovaerts,Pierre
中科院分区:
--
文献类型:
--
作者:
Goovaerts,Pierre

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尽管有几个环境危机,很少有研究已经进行了城市范围内的地理空间模型的水铅水平(WLL)在公共分配系统。本文介绍了第一次应用多元地统计学的饮用水中的铅在分配系统,特别是在弗林特,密歇根州。弗林特数据的主要特点之一是通过两种不同的抽样举措收集数据:㈠自愿或房主驱动的抽样,即有关公民决定获得一套检测工具,自己进行抽样(10 717个地点); ㈡国家管理的抽样,即在技术小组对居民进行培训后,每两周在809个选定地点(哨点)收集数据。这两个数据集首先在41周的采样期和每个税收包裹上取平均值,以减弱采样波动,并创建一组由两个协议采样的420个税收包裹。这两个变量显示的相关性为0.62,而他们的直接和交叉半变异函数显示大量的块金效应和7.5公里的长距离。然后,使用协同克里格法对哨点记录的并被市政府官员认为更可靠的WLL进行插值,以解释51,045个住宅税包裹中每个包裹的更密集抽样的自愿数据和有关服务线组成(铅、其他或未知)的信息。交叉验证表明,更高的预测精度的多变量地质统计方法相对于克里金法和平方反比距离加权插值只使用哨兵数据。这一通用程序适用于其他基础设施老化的城市,这些城市的饮用水中的铅含量令人担忧。
Despite several environmental crises, little research has been conducted on citywide geospatial modeling of water lead levels (WLL) in public distribution systems. This paper presents the first application of multivariate geostatistics to lead in drinking water within a distribution system, specifically in Flint, Michigan. One of the key features of the Flint data is their collection through two different sampling initiatives: (i) voluntary or homeowner-driven sampling whereby concerned citizens decided to acquire a testing kit and conduct sampling on their own (10,717 sites), and (ii) State-administered sampling where data were collected bi-weekly at 809 selected sites after training of residents by technical teams (sentinel sites). These two datasets were first averaged over the 41-week sampling period and each tax parcel to attenuate sampling fluctuations and create a set of 420 tax parcels sampled by both protocols. Both variables displayed a correlation of 0.62 while their direct and cross-semivariograms showed substantial nugget effect and a long range of 7.5 km. WLLs recorded at sentinel sites and deemed more reliable by city officials were then interpolated using cokriging to account for the more densely sampled voluntary data and information on service line composition (lead, other, or unknown) available for each of 51,045 residential tax parcels. Cross-validation demonstrated the greater prediction accuracy of the multivariate geostatistical approach relative to kriging and inverse square distance weighting interpolation using only sentinel data. This general procedure is applicable to other cities with aging infrastructure where lead in drinking water is a concern.
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