Admin at 22:02, 26 April 2022

2022-04-26T22:02:16Z

Jhurley at 17:52, 20 June 2018

2018-06-20T17:52:49Z

Jhurley at 17:39, 20 June 2018

2018-06-20T17:39:27Z

Admin: 1 revision imported

2018-05-04T20:57:33Z

1 revision imported

Admin: 1 revision imported

2018-05-04T20:53:42Z

1 revision imported

Admin: 1 revision imported

2018-05-04T20:50:22Z

1 revision imported

Admin: 1 revision imported

2018-05-04T16:58:29Z

1 revision imported

Admin: 1 revision imported

2018-05-04T15:58:29Z

1 revision imported

Admin: 1 revision imported

2018-05-04T14:58:29Z

1 revision imported

Admin: 1 revision imported

2018-05-04T13:56:42Z

1 revision imported

@@ Line 4: / Line 4: @@
 '''Related Article(s):'''
 *[[Long-Term Monitoring (LTM)]]
 *[[Long-Term Monitoring (LTM) - Data Analysis]]
-'''CONTRIBUTOR(S):''' [[Dr. Thomas McHugh]] and [[Dr. David Adamson, P.E.]]
+'''Contributor(s):''' [[Dr. Thomas McHugh]] and [[Dr. David Adamson, P.E.]]
 '''Key Resource(s):'''
 *[http://dx.doi.org/10.1111/j.1745-6592.2011.01337.x Factors influencing variability in groundwater monitoring data sets]<ref name="McHugh2011">McHugh, T.E., Beckley, L.M., Liu, C.Y. and Newell, C.J., 2011. Factors influencing variability in groundwater monitoring data sets. Groundwater Monitoring & Remediation, 31(2), 92-101. [http://dx.doi.org/10.1111/j.1745-6592.2011.01337.x doi: 10.1111/j.1745-6592.2011.01337.x]</ref>
@@ Line 30: / Line 32: @@
 Sampling event-to-sampling event data variability in groundwater monitoring results may be associated with: 1) analytical variability at the laboratory, 2) sample collection, or 3) signal variability (i.e., inherent variability within the aquifer). We discuss each of these issues directly below.
-# '''Analytical Variability''': When all samples are analyzed at the same laboratory, laboratory variability is not an important source of monitoring variability. Contaminant concentrations in field duplicate samples typically show a relative percent difference (RPD) of <10%. This level of variability is usually much smaller than the variation in contaminant concentration between samples collected from the same monitoring well over days or weeks<ref name="McHugh2011"/>. It is also smaller than the variability observed between paired samples collected from a monitoring well on the same day, but at two different purge volumes. Although commercial laboratories have very high precision (i.e., they report very similar results when analyzing the same sample multiple times), results reported by different laboratories for the same sample commonly differ by 30% or more due to differences in instrument calibration<ref>Wehrmann, H.A., Barcelona, M.J., Varljen, M.D., and Blinkiewicz, G., 1996. Groundwater contamination by volatile organic compounds: site characterization, spatial and temporal variability. Illinois State Water Survey Contract Report 591, Table 9. [[Media:Wehrmann-1996-GW_Contamination_by_VOC_Site_Charac._Spatial_and_Temporal_Var_.pdf|Report pdf]]</ref><ref>Keith, S.J., Wilson, L.G., Fitch, H.R. and Esposito, D.M., 1983. Sources of spatial-temporal variability on groundwater quality data and methods of control. Groundwater Monitoring & Remediation, 3(2), 21-32. [http://dx.doi.org/10.1111/j.1745-6592.1983.tb01196.x doi: 10.1111/j.1745-6592.1983.tb01196.x]</ref>. Thus, switching sample analysis from one laboratory to another may result in a one-time change in monitoring results<ref name="McHugh2011"/>. <br />
+#'''Analytical Variability''': When all samples are analyzed at the same laboratory, laboratory variability is not an important source of monitoring variability. Contaminant concentrations in field duplicate samples typically show a relative percent difference (RPD) of <10%. This level of variability is usually much smaller than the variation in contaminant concentration between samples collected from the same monitoring well over days or weeks<ref name="McHugh2011" />. It is also smaller than the variability observed between paired samples collected from a monitoring well on the same day, but at two different purge volumes. Although commercial laboratories have very high precision (i.e., they report very similar results when analyzing the same sample multiple times), results reported by different laboratories for the same sample commonly differ by 30% or more due to differences in instrument calibration<ref>Wehrmann, H.A., Barcelona, M.J., Varljen, M.D., and Blinkiewicz, G., 1996. Groundwater contamination by volatile organic compounds: site characterization, spatial and temporal variability. Illinois State Water Survey Contract Report 591, Table 9. [//www.enviro.wiki/images/8/85/Wehrmann-1996-GW_Contamination_by_VOC_Site_Charac._Spatial_and_Temporal_Var_.pdf Report pdf]</ref><ref>Keith, S.J., Wilson, L.G., Fitch, H.R. and Esposito, D.M., 1983. Sources of spatial-temporal variability on groundwater quality data and methods of control. Groundwater Monitoring & Remediation, 3(2), 21-32. [http://dx.doi.org/10.1111/j.1745-6592.1983.tb01196.x doi: 10.1111/j.1745-6592.1983.tb01196.x]</ref>. Thus, switching sample analysis from one laboratory to another may result in a one-time change in monitoring results<ref name="McHugh2011" />. <br />
-# '''Sample Collection Method Variability''': In general, the sample collection method does not affect event-to-event variability in groundwater monitoring results. A study comparing two [[Groundwater Sampling - No-Purge/Passive]] methods (SNAP and HydraSleeve) and three variations of low flow purge sampling at two sites found that the collection methods had little impact on event-to-event variability<ref name= "Kulkarni2016">Kulkarni, P.R., Krebs, C.J., Britt, S., Newell, C.J., McHugh, T.E., in review. Effect of groundwater sampling methods on short-term variability and concentration bias: two field sites.</ref>. However, an analysis of monitoring datasets from three sites found that results obtained using a HydraSleeve sampler may be low biased and are more variable in some settings<ref>McHugh, T.E., Kulkarni, P.R., Beckley, L.M., Newell, C.J. and Zumbro, M., 2015. Negative bias and increased variability in VOC concentrations using the HydraSleeve in monitoring wells. Groundwater Monitoring & Remediation, 36, 79–87. [http://onlinelibrary.wiley.com/doi/10.1111/gwmr.12141/abstract doi: 10.1111/gwmr.12141]</ref>.<br />
+#'''Sample Collection Method Variability''': In general, the sample collection method does not affect event-to-event variability in groundwater monitoring results. A study comparing two [[Groundwater Sampling - No-Purge/Passive]] methods (SNAP and HydraSleeve) and three variations of low flow purge sampling at two sites found that the collection methods had little impact on event-to-event variability<ref name="Kulkarni2016">Kulkarni, P.R., Krebs, C.J., Britt, S., Newell, C.J., McHugh, T.E., in review. Effect of groundwater sampling methods on short-term variability and concentration bias: two field sites.</ref>. However, an analysis of monitoring datasets from three sites found that results obtained using a HydraSleeve sampler may be low biased and are more variable in some settings<ref>McHugh, T.E., Kulkarni, P.R., Beckley, L.M., Newell, C.J. and Zumbro, M., 2015. Negative bias and increased variability in VOC concentrations using the HydraSleeve in monitoring wells. Groundwater Monitoring & Remediation, 36, 79–87. [http://onlinelibrary.wiley.com/doi/10.1111/gwmr.12141/abstract doi: 10.1111/gwmr.12141]</ref>.<br />
-# '''Signal Variability''': Signal variability (i.e., the actual concentration variability in the water within the aquifer) is the most important source of variability in groundwater monitoring results<ref name="McHugh2011"/><ref name="McHugh2015b">McHugh, T.E., Kulkarni, P.R., Krebs, C., Newell, C.J., Sanford, B., 2015. Methods for minimization and management of variability in long-term groundwater monitoring results. Final Report ER-201209. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-201209/ER-201209 ER201209]</ref>. In addition to long-term source attenuation, contaminant concentrations in water moving through an aquifer (and through a monitoring well) vary up and down in response to a wide variety of <u>solute transport</u> factors including groundwater flow direction, groundwater elevation, and variations in degradation rates. At most sites, these factors result in a contaminant plume that is spatiotemporally variable. This variability in contaminant distribution creates an inherent level of inconsistency in groundwater monitoring results. As a result, groundwater samples collected days or weeks apart from the same monitoring well using the same sampling method commonly show high variations in contaminant concentrations relative to the expected change based on the long-term attenuation rate.  Although perhaps unintuitive, monitoring well construction, well placement within the aquifer, and sampling method have little or no impact on this inherent variability<ref name="McHugh2011"/>.
+#'''Signal Variability''': Signal variability (i.e., the actual concentration variability in the water within the aquifer) is the most important source of variability in groundwater monitoring results<ref name="McHugh2011" /><ref name="McHugh2015b">McHugh, T.E., Kulkarni, P.R., Krebs, C., Newell, C.J., Sanford, B., 2015. Methods for minimization and management of variability in long-term groundwater monitoring results. Final Report ER-201209. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-201209/ER-201209 ER201209]</ref>. In addition to long-term source attenuation, contaminant concentrations in water moving through an aquifer (and through a monitoring well) vary up and down in response to a wide variety of <u>solute transport</u> factors including groundwater flow direction, groundwater elevation, and variations in degradation rates. At most sites, these factors result in a contaminant plume that is spatiotemporally variable. This variability in contaminant distribution creates an inherent level of inconsistency in groundwater monitoring results. As a result, groundwater samples collected days or weeks apart from the same monitoring well using the same sampling method commonly show high variations in contaminant concentrations relative to the expected change based on the long-term attenuation rate.  Although perhaps unintuitive, monitoring well construction, well placement within the aquifer, and sampling method have little or no impact on this inherent variability<ref name="McHugh2011" />.
 These issues provide the foundation for a conceptual model of factors contributing to short-term data variability (Fig. 2).
@@ Line 38: / Line 40: @@
 ==Approaches to Manage Variability in LTM Results==
-In general, groundwater sampling methods should be selected based on cost, ease of use, and sample volume requirements. This is because, for most sites, sample collection methods seem to have little or no impact on data variability<ref name= "Kulkarni2016"/><ref name="McHugh2015b"/>
+In general, groundwater sampling methods should be selected based on cost, ease of use, and sample volume requirements. This is because, for most sites, sample collection methods seem to have little or no impact on data variability<ref name="Kulkarni2016" /><ref name="McHugh2015b" />
-[[Long-Term Monitoring Data Analysis]] should utilize methods and procedures that account for the inherent event-to-event variability in monitoring results. The ESTCP program supported the development of a new method to evaluate attenuation rates and optimize monitoring frequency based on the short-term variability and long-term attenuation rate at a particular site<ref name="McHugh2015b"/>. This evaluation method has been incorporated into an Excel-based spreadsheet tool called The Monitoring Optimization and Trend Analysis Toolkit<ref name= "McHugh2015c">McHugh, T.E., Kulkarni, P.R., Beckley, L.M., Newell, C.J., Strasters, B., 2015. Methods for minimization and management of variability in long-term groundwater monitoring results. A new method to optimize monitoring frequency and evaluate long-term concentration trends, Technical Report, Task 2 & 3. ER-201209. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-201209 ER-201209]</ref>. This tool helps a user evaluate remedy effectiveness by evaluation of attenuation rates and spatial differences in attenuation rates. It quantitatively accounts for short-term variability in helping the user answer the following questions:
+[[Long-Term Monitoring Data Analysis]] should utilize methods and procedures that account for the inherent event-to-event variability in monitoring results. The ESTCP program supported the development of a new method to evaluate attenuation rates and optimize monitoring frequency based on the short-term variability and long-term attenuation rate at a particular site<ref name="McHugh2015b" />. This evaluation method has been incorporated into an Excel-based spreadsheet tool called The Monitoring Optimization and Trend Analysis Toolkit<ref name="McHugh2015c">McHugh, T.E., Kulkarni, P.R., Beckley, L.M., Newell, C.J., Strasters, B., 2015. Methods for minimization and management of variability in long-term groundwater monitoring results. A new method to optimize monitoring frequency and evaluate long-term concentration trends, Technical Report, Task 2 & 3. ER-201209. [https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-201209 ER-201209]</ref>. This tool helps a user evaluate remedy effectiveness by evaluation of attenuation rates and spatial differences in attenuation rates. It quantitatively accounts for short-term variability in helping the user answer the following questions:
-# How much monitoring data do I need to determine a site’s long-term source attenuation rate with a defined level of accuracy or confidence?
+#How much monitoring data do I need to determine a site’s long-term source attenuation rate with a defined level of accuracy or confidence?
-# Do any individual wells appear to be attenuating more slowly than the source as a whole?
+#Do any individual wells appear to be attenuating more slowly than the source as a whole?
 ==Summary==
-Short-term variability in groundwater concentration vs. time data makes it more difficult to discern trends and make remediation decisions. A study of the sources of variability concluded that analytical lab variability is relatively small, as is the way monitoring wells are constructed and sampled. A new tool is available to help answer questions about how much data are needed to understand underlying trends in groundwater monitoring temporal data<ref name="McHugh2015c"/>.
+Short-term variability in groundwater concentration vs. time data makes it more difficult to discern trends and make remediation decisions. A study of the sources of variability concluded that analytical lab variability is relatively small, as is the way monitoring wells are constructed and sampled. A new tool is available to help answer questions about how much data are needed to understand underlying trends in groundwater monitoring temporal data<ref name="McHugh2015c" />.
 ==References==
-<references/>
+<references />
 ==See Also==
-*[https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Monitoring/ER-1705  Improved Understanding of Sources of Variability in Groundwater Sampling for Long-Term Monitoring Programs]

← Older revision		Revision as of 17:52, 20 June 2018
Line 8:		Line 8:


−	'''CONTRIBUTOR(S):''' [[Dr. ~~Thomas McHugh \|~~ Thomas McHugh]] and [[Dr. David Adamson, P.E. ~~\| David Adamson~~]]	+	'''CONTRIBUTOR(S):''' [[Dr. Thomas McHugh]] and [[Dr. David Adamson, P.E.]]

← Older revision		Revision as of 17:39, 20 June 2018
Line 8:		Line 8:


−	'''CONTRIBUTOR(S):''' [[Dr. Thomas McHugh]] and [[Dr. David Adamson, P.E.]]	+	'''CONTRIBUTOR(S):''' [[Dr. Thomas McHugh \| Thomas McHugh]] and [[Dr. David Adamson, P.E. \| David Adamson]]

← Older revision	Revision as of 20:57, 4 May 2018
(No difference)

← Older revision	Revision as of 20:53, 4 May 2018
(No difference)

← Older revision	Revision as of 20:50, 4 May 2018
(No difference)

← Older revision	Revision as of 16:58, 4 May 2018
(No difference)

← Older revision	Revision as of 15:58, 4 May 2018
(No difference)

← Older revision	Revision as of 14:58, 4 May 2018
(No difference)

← Older revision	Revision as of 13:56, 4 May 2018
(No difference)

Long-Term Monitoring (LTM) - Data Variability - Revision history

Admin at 22:02, 26 April 2022

Jhurley at 17:52, 20 June 2018

Jhurley at 17:39, 20 June 2018

Admin: 1 revision imported

Admin: 1 revision imported

Admin: 1 revision imported

Admin: 1 revision imported

Admin: 1 revision imported

Admin: 1 revision imported

Admin: 1 revision imported