1
|
Sivaganesan M, Willis JR, Karim M, Babatola A, Catoe D, Boehm AB, Wilder M, Green H, Lobos A, Harwood VJ, Hertel S, Klepikow R, Howard MF, Laksanalamai P, Roundtree A, Mattioli M, Eytcheson S, Molina M, Lane M, Rediske R, Ronan A, D'Souza N, Rose JB, Shrestha A, Hoar C, Silverman AI, Faulkner W, Wickman K, Kralj JG, Servetas SL, Hunter ME, Jackson SA, Shanks OC. Interlaboratory performance and quantitative PCR data acceptance metrics for NIST SRM® 2917. Water Res 2022; 225:119162. [PMID: 36191524 PMCID: PMC9932931 DOI: 10.1016/j.watres.2022.119162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Surface water quality quantitative polymerase chain reaction (qPCR) technologies are expanding from a subject of research to routine environmental and public health laboratory testing. Readily available, reliable reference material is needed to interpret qPCR measurements, particularly across laboratories. Standard Reference Material® 2917 (NIST SRM® 2917) is a DNA plasmid construct that functions with multiple water quality qPCR assays allowing for estimation of total fecal pollution and identification of key fecal sources. This study investigates SRM 2917 interlaboratory performance based on repeated measures of 12 qPCR assays by 14 laboratories (n = 1008 instrument runs). Using a Bayesian approach, single-instrument run data are combined to generate assay-specific global calibration models allowing for characterization of within- and between-lab variability. Comparable data sets generated by two additional laboratories are used to assess new SRM 2917 data acceptance metrics. SRM 2917 allows for reproducible single-instrument run calibration models across laboratories, regardless of qPCR assay. In addition, global models offer multiple data acceptance metric options that future users can employ to minimize variability, improve comparability of data across laboratories, and increase confidence in qPCR measurements.
Collapse
Affiliation(s)
- Mano Sivaganesan
- U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, USA
| | - Jessica R Willis
- U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, USA
| | - Mohammad Karim
- Environmental Services Laboratory, City of Santa Cruz, Santa Cruz, CA, USA
| | - Akin Babatola
- Environmental Services Laboratory, City of Santa Cruz, Santa Cruz, CA, USA
| | - David Catoe
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
| | - Alexandria B Boehm
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
| | - Maxwell Wilder
- Department of Environmental Biology, SUNY-ESF, Syracuse, NY, USA
| | - Hyatt Green
- Department of Environmental Biology, SUNY-ESF, Syracuse, NY, USA
| | - Aldo Lobos
- Department of Integrative Biology, University of South Florida, Tampa, FL, USA
| | - Valerie J Harwood
- Department of Integrative Biology, University of South Florida, Tampa, FL, USA
| | - Stephanie Hertel
- U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, USA
| | - Regina Klepikow
- U.S. Environmental Protection Agency, Region 7 Laboratory, Kansas City, KS, USA
| | | | | | - Alexis Roundtree
- Waterborne Disease Prevention Branch, Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Mia Mattioli
- Waterborne Disease Prevention Branch, Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Stephanie Eytcheson
- U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, USA
| | - Marirosa Molina
- U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, USA
| | - Molly Lane
- Annis Water Resources Institute, Grand Valley State University, Muskegon, MI, USA
| | - Richard Rediske
- Annis Water Resources Institute, Grand Valley State University, Muskegon, MI, USA
| | - Amanda Ronan
- U.S. Environmental Protection Agency, Region 2 Laboratory, Edison, NJ, USA
| | - Nishita D'Souza
- Department of Fisheries and Wildlife, Michigan State University, E. Lansing, MI, USA
| | - Joan B Rose
- Department of Fisheries and Wildlife, Michigan State University, E. Lansing, MI, USA
| | - Abhilasha Shrestha
- Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, IL, USA
| | - Catherine Hoar
- Department of Civil and Urban Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Andrea I Silverman
- Department of Civil and Urban Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | | | | | - Jason G Kralj
- National Institute of Standards and Technology, Biosystems and Biomaterials Division, Complex Microbial Systems Group, Gaithersburg, MD, USA
| | - Stephanie L Servetas
- National Institute of Standards and Technology, Biosystems and Biomaterials Division, Complex Microbial Systems Group, Gaithersburg, MD, USA
| | - Monique E Hunter
- National Institute of Standards and Technology, Biosystems and Biomaterials Division, Complex Microbial Systems Group, Gaithersburg, MD, USA
| | - Scott A Jackson
- National Institute of Standards and Technology, Biosystems and Biomaterials Division, Complex Microbial Systems Group, Gaithersburg, MD, USA
| | - Orin C Shanks
- U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, USA.
| |
Collapse
|
2
|
Bohon J, Gonzalez E, Grace C, Harris CT, Jacobsen B, Kachiguine S, Kim D, MacArthur J, Martinez-McKinney F, Mazza S, Nizam M, Norvell N, Padilla R, Potter E, Prakash T, Prebys E, Ryan E, Schumm BA, Smedley J, Stuart D, Tarka M, Torrecilla IS, Wilder M, Zhu D. Use of diamond sensors for a high-flux, high-rate X-ray pass-through diagnostic. J Synchrotron Radiat 2022; 29:595-601. [PMID: 35510992 PMCID: PMC9070720 DOI: 10.1107/s1600577522003022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/19/2022] [Indexed: 06/14/2023]
Abstract
X-ray free-electron lasers (XFELs) deliver pulses of coherent X-rays on the femtosecond time scale, with potentially high repetition rates. While XFELs provide high peak intensities, both the intensity and the centroid of the beam fluctuate strongly on a pulse-to-pulse basis, motivating high-rate beam diagnostics that operate over a large dynamic range. The fast drift velocity, low X-ray absorption and high radiation tolerance properties of chemical vapour deposition diamonds make these crystals a promising candidate material for developing a fast (multi-GHz) pass-through diagnostic for the next generation of XFELs. A new approach to the design of a diamond sensor signal path is presented, along with associated characterization studies performed in the XPP endstation of the LINAC Coherent Light Source (LCLS) at SLAC. Qualitative charge collection profiles (collected charge versus time) are presented and compared with those from a commercially available detector. Quantitative results on the charge collection efficiency and signal collection times are presented over a range of approximately four orders of magnitude in the generated electron-hole plasma density.
Collapse
Affiliation(s)
- J. Bohon
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - E. Gonzalez
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - C. Grace
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - C. T. Harris
- Sandia National Laboratories, Albuquerque, NM 87123, USA
| | - B. Jacobsen
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - S. Kachiguine
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - D. Kim
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - J. MacArthur
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - F. Martinez-McKinney
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - S. Mazza
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - M. Nizam
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - N. Norvell
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - R. Padilla
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - E. Potter
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - T. Prakash
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - E. Prebys
- University of California, Davis, CA 95616, USA
| | - E. Ryan
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - B. A. Schumm
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - J. Smedley
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - D. Stuart
- University of California, Santa Barbara, CA 93106, USA
| | - M. Tarka
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | | | - M. Wilder
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 95064, USA
| | - D. Zhu
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| |
Collapse
|
3
|
Green H, Wilder M, Wiedmann M, Weller D. Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination. Front Microbiol 2021; 12:684533. [PMID: 34475855 PMCID: PMC8406625 DOI: 10.3389/fmicb.2021.684533] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/05/2021] [Indexed: 12/03/2022] Open
Abstract
Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., Escherichia coli, MST markers, and physical–chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.
Collapse
Affiliation(s)
- Hyatt Green
- Department of Environmental Biology, College of Environmental Science and Forestry, State University of New York, Syracuse, NY, United States
| | - Maxwell Wilder
- Department of Environmental Biology, College of Environmental Science and Forestry, State University of New York, Syracuse, NY, United States
| | - Martin Wiedmann
- Department of Food Science, Cornell University, Ithaca, NY, United States
| | - Daniel Weller
- Department of Environmental Biology, College of Environmental Science and Forestry, State University of New York, Syracuse, NY, United States
| |
Collapse
|