Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation.
Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation.
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DOI：
10.1007/s10661-023-11283-w
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发表时间：
2023-06-12
影响因子：
3
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中科院分区：
文献类型：
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气象(MET)数据是环境暴露模型的重要输入。虽然利用地理空间技术对暴露潜力进行建模是一种常见做法，但现有研究很少评估输入气象数据对产出结果不确定性程度的影响。这项研究的目的是确定不同的MET数据源对潜在暴露易感性预测的影响。比较了三种风资料来源：北美地区再分析(NIR)数据库、地区机场气象机场报告(METAR)和当地气象台站资料。这些数据源被用作机器学习(ML)驱动的地理信息系统多标准决策分析(GIS-MCDA)地理空间模型的输入，以预测纳瓦霍民族废弃铀矿场的潜在风险。结果表明，从不同的风数据来源得出的结果有很大差异。在使用国家铀资源评估(NURE)数据库在地理加权回归(GWR)中验证了每个来源的结果后，METARS数据与当地气象站数据相结合显示出最高的准确性，平均R2为0.74。我们的结论是，基于当地直接测量的数据(METAR和MET数据)比研究中评估的其他来源产生了更准确的预测。这项研究有可能为未来的数据收集方法提供信息，导致围绕环境暴露敏感性和风险评估做出更准确的预测和更知情的政策决定。
Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.
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DOI：
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发表时间：
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影响因子：
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发表时间：
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期刊：
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影响因子：
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