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A Case Study Perspective on Working with ProUCL and a State Environmental Agency in Determining Background Threshold Values.

Daniel DL - Int J Environ Res Public Health (2015)

Bottom Line: Due to limited resources, some state agencies have set ProUCL as a standard by which all state-mandated environmental analyses are compared, despite the EPA's more open acceptance of other software products and methodologies.In the current case study, several such instances arose and substantial resources were invested to demonstrate the appropriateness of alternative methodologies, sometimes without acquiring acceptance by the state despite sound statistical demonstration.In particular, efforts were made to address: inappropriate outlier detection, upper tolerance limit (UTL) calculations based on gamma distributions when non-detects were present, and inappropriate use of nonparametric UTL formulas.

View Article: PubMed Central - PubMed

Affiliation: Applied Statistics, New Mexico State University, Department 3CQ, Post Office Box 30001, Las Cruces, NM 88003, USA. ddaniel@nmsu.edu.

ABSTRACT
ProUCL is a software package made available by the Environmental Protection Agency (EPA) to provide environmental scientists with better tools with which to conduct statistical analyses. ProUCL has been in production for over ten years and is in its fifth major version. In time, it has included more sophisticated and appropriate analysis tools. However, there is still substantial criticism of it among statisticians for its various omissions and even its philosophical approach. Due to limited resources, some state agencies have set ProUCL as a standard by which all state-mandated environmental analyses are compared, despite the EPA's more open acceptance of other software products and methodologies. As such, it can be difficult for state-supervised sites to convince the state to allow the use of more appropriate methodologies or different software. In the current case study, several such instances arose and substantial resources were invested to demonstrate the appropriateness of alternative methodologies, sometimes without acquiring acceptance by the state despite sound statistical demonstration. In particular, efforts were made to address: inappropriate outlier detection, upper tolerance limit (UTL) calculations based on gamma distributions when non-detects were present, and inappropriate use of nonparametric UTL formulas.

No MeSH data available.


(a) Original Gamma(3, 3) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (b) Original Gamma(7, 1) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (c) Original Gamma(1, 0.2) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green).
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ijerph-12-12905-f002: (a) Original Gamma(3, 3) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (b) Original Gamma(7, 1) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (c) Original Gamma(1, 0.2) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green).

Mentions: Figure A1 shows the probability density functions (pdfs) for these distributions. From this plot one can see that the second distribution is approaching symmetry and somewhat approximates a normal distribution. Figure A2a–c show some results of the simulations implementing both the Gamma ROS method and the EM method. In the simulations, samples of size n = 12 were generated (since the simulations were targeted at the situation in the study we were working on) and NDs were inserted based on a DL that was selected in a manner that would produce an average of three NDs in a sample (since this was fairly common for the samples in the study). In Figure A2a–c, the pdf of the original gamma distribution from which simulation data were generated is shown with a heavy black curve. For each figure, 100 data sets were randomly generated and the EM method was used to estimate the gamma parameters-the corresponding pdfs are displayed in blue; and the gamma ROS method was used to estimate parameters for the same 100 data sets—these pdfs are displayed in green. In each of Figure A2a–c, the pdf’s obtained by the EM method generally approximates the original pdf more closely than the pdf’s obtained from the gamma ROS method.


A Case Study Perspective on Working with ProUCL and a State Environmental Agency in Determining Background Threshold Values.

Daniel DL - Int J Environ Res Public Health (2015)

(a) Original Gamma(3, 3) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (b) Original Gamma(7, 1) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (c) Original Gamma(1, 0.2) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4627007&req=5

ijerph-12-12905-f002: (a) Original Gamma(3, 3) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (b) Original Gamma(7, 1) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green); (c) Original Gamma(1, 0.2) pdf (black) for simulated data with n = 12 with an average of three NDs, and pdf’s estimated by the EM method (blue) and the GROS method (green).
Mentions: Figure A1 shows the probability density functions (pdfs) for these distributions. From this plot one can see that the second distribution is approaching symmetry and somewhat approximates a normal distribution. Figure A2a–c show some results of the simulations implementing both the Gamma ROS method and the EM method. In the simulations, samples of size n = 12 were generated (since the simulations were targeted at the situation in the study we were working on) and NDs were inserted based on a DL that was selected in a manner that would produce an average of three NDs in a sample (since this was fairly common for the samples in the study). In Figure A2a–c, the pdf of the original gamma distribution from which simulation data were generated is shown with a heavy black curve. For each figure, 100 data sets were randomly generated and the EM method was used to estimate the gamma parameters-the corresponding pdfs are displayed in blue; and the gamma ROS method was used to estimate parameters for the same 100 data sets—these pdfs are displayed in green. In each of Figure A2a–c, the pdf’s obtained by the EM method generally approximates the original pdf more closely than the pdf’s obtained from the gamma ROS method.

Bottom Line: Due to limited resources, some state agencies have set ProUCL as a standard by which all state-mandated environmental analyses are compared, despite the EPA's more open acceptance of other software products and methodologies.In the current case study, several such instances arose and substantial resources were invested to demonstrate the appropriateness of alternative methodologies, sometimes without acquiring acceptance by the state despite sound statistical demonstration.In particular, efforts were made to address: inappropriate outlier detection, upper tolerance limit (UTL) calculations based on gamma distributions when non-detects were present, and inappropriate use of nonparametric UTL formulas.

View Article: PubMed Central - PubMed

Affiliation: Applied Statistics, New Mexico State University, Department 3CQ, Post Office Box 30001, Las Cruces, NM 88003, USA. ddaniel@nmsu.edu.

ABSTRACT
ProUCL is a software package made available by the Environmental Protection Agency (EPA) to provide environmental scientists with better tools with which to conduct statistical analyses. ProUCL has been in production for over ten years and is in its fifth major version. In time, it has included more sophisticated and appropriate analysis tools. However, there is still substantial criticism of it among statisticians for its various omissions and even its philosophical approach. Due to limited resources, some state agencies have set ProUCL as a standard by which all state-mandated environmental analyses are compared, despite the EPA's more open acceptance of other software products and methodologies. As such, it can be difficult for state-supervised sites to convince the state to allow the use of more appropriate methodologies or different software. In the current case study, several such instances arose and substantial resources were invested to demonstrate the appropriateness of alternative methodologies, sometimes without acquiring acceptance by the state despite sound statistical demonstration. In particular, efforts were made to address: inappropriate outlier detection, upper tolerance limit (UTL) calculations based on gamma distributions when non-detects were present, and inappropriate use of nonparametric UTL formulas.

No MeSH data available.