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首页> 外文期刊>Ground Water Monitoring & Remediation >Factors Influencing Variability in Groundwater Monitoring Data Sets
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Factors Influencing Variability in Groundwater Monitoring Data Sets

机译：影响地下水监测数据集变异性的因素

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"Random" variability in groundwater monitoring data sets reduces the ability to identify long-term concentration trends. This, in turn, increases the time and cost required to evaluate the effectiveness of natural attenuation and other groundwater remedies. To better understand the factors influencing variability in groundwater monitoring results, we have analyzed three large groundwater monitoring data sets. For the three data sets, the long-term trend in contaminant concentration in each well accounted for an average of 30% to 40% of the overall variation in contaminant concentration. Understanding the causes of the remaining variability would support the development of improved groundwater monitoring methods. All three data sets show large differences in the temporal monitoring records between individual wells (e.g., coefficient of variation for monitoring results from individual wells ranges from 0.08 to 4.6) indicating that well and aquifer factors are more important contributors to variability than sample collection and analysis factors. However, the depth to groundwater (R2 - 0.020) and distance between water level and screened interval (R_2 = 0.049) accounted for only a portion of the differences in variability between wells and other aquifer characteristics evaluated and were not correlated with the observed variability in monitoring results. Unidentified factors were apparently much more important contributors to variability than these factors. The monitoring data sets exhibited two distinct timescales for variability: Time-independent variability that was apparent even when wells were re-sampled within a few days and a long-term variability likely associated with the long-term concentration trend. The observation of time-independent variability suggests that frequent monitoring of contaminated monitoring wells serves primarily to characterize sources of variability unrelated to the long-term trend of primary interest.
机译：地下水监测数据集中的“随机”变化降低了识别长期浓度趋势的能力。反过来，这增加了评估自然衰减和其他地下水补救措施有效性所需的时间和成本。为了更好地理解影响地下水监测结果变化的因素，我们分析了三个大型地下水监测数据集。对于这三个数据集，每口井中污染物浓度的长期趋势平均占污染物浓度总体变化的30％至40％。了解剩余变异性的原因将有助于开发改进的地下水监测方法。所有这三个数据集都显示出各个井之间的时间监测记录存在较大差异（例如，各个井的监测结果的变异系数范围为0.08至4.6），这表明井和含水层因素比样品收集和分析对变化的影响更大。因素。然而，地下水的深度（R2-0.020）和水位与筛分间隔之间的距离（R_2 = 0.049）仅占部分井之间的变异性差异和评估的其他含水层特征的一部分，并且与观测到的变异性不相关。监测结果。与这些因素相比，未确定的因素显然是导致变异性的重要因素。监测数据集显示了两个不同的时间尺度变化：即使在几天之内重新采样孔，时间独立变化也很明显；长期变化可能与长期浓度趋势相关。与时间无关的变异性的观察表明，对受污染的监测井的频繁监测主要用于表征与主要关注的长期趋势无关的变异性来源。

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