Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.
Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.
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DOI：
10.1021/es203152a
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发表时间：
2012-03-06
影响因子：
11.4
通讯作者：
Serre, Marc L.
中科院分区：
文献类型：
--
作者：
Messier, Kyle P.;Akita, Yasuyuki;Serre, Marc L.
基于地理信息系统的技术是国家机构和流行病学研究人员用于估计浓度和接触的具有成本效益和效率的方法。然而，预算限制使得全州范围的污染评估变得困难，特别是在地下水介质中。许多研究已经独立地实现了地理编码，土地利用回归和地质统计学，但这是第一次研究整合这些GIS技术的好处，以满足全州范围内的暴露评估的需要。浓度暴露的一种新的框架，集成了地址地理编码，土地利用回归（LUR），低于检测数据建模，贝叶斯最大熵（BME）。一个LUR模型开发的四氯乙烯，占点源和流向。然后，我们将LUR模型集成到BME方法中作为平均趋势，同时还将下面的检测数据建模为截断高斯概率分布函数。通过多级地理编码，我们从以前可用的数据库中增加了可用的PCE数据4.7倍。LUR模型显示干洗店在短距离的显着影响。与具有恒定平均趋势的BME相比，LUR模型作为BME中的平均趋势的集成导致交叉验证均方误差降低7.5%。
Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for Tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.
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个性化暴露评估：人类环境健康研究的有前途的方法。
影响因子：
10.4
作者：
Weis, Brenda K;Balshaw, David;Barr, John R;Brown, David;Ellisman, Mark;Lioy, Paul;Omenn, Gilbert;Potter, John D;Smith, Martyn T;Sohn, Lydia;Suk, William A;Sumner, Susan;Swenberg, James;Walt, David R;Watkins, Simon;Thompson, Claudia;Wilson, Samuel H
通讯作者：
Wilson, Samuel H
DOI：
10.1007/bf00890661
发表时间：
1990-10-01
期刊：
MATHEMATICAL GEOLOGY
影响因子：
--
作者：
CHRISTAKOS, G
通讯作者：
CHRISTAKOS, G
影响因子：
4.9
作者：
Schwanghart, Wolfgang;Kuhn, Nikolaus J.
通讯作者：
Kuhn, Nikolaus J.
影响因子：
10.4
作者：
Bellander, T;Berglind, N;Järup, L
通讯作者：
Järup, L
影响因子：
5.4
作者：
Qian, SS;Reckhow, KH;McMahon, G
通讯作者：
McMahon, G