A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds
Technological advances and resource constraints present scientists and engineers with renewed challenges in the design of methods to conduct water quality monitoring, and these decisions ultimately determine the degree of project success. Many professionals are exploring alternative lower-cost optio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/17/3110 |
| _version_ | 1797581779140345856 |
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| author | Robert Daren Harmel Heather Elise Preisendanz Kevin Wayne King Dennis Busch Francois Birgand Debabrata Sahoo |
| author_facet | Robert Daren Harmel Heather Elise Preisendanz Kevin Wayne King Dennis Busch Francois Birgand Debabrata Sahoo |
| author_sort | Robert Daren Harmel |
| collection | DOAJ |
| description | Technological advances and resource constraints present scientists and engineers with renewed challenges in the design of methods to conduct water quality monitoring, and these decisions ultimately determine the degree of project success. Many professionals are exploring alternative lower-cost options because of cost constraints, and research and development is largely focused on <i>in situ</i> sensors that produce high temporal resolution data. While some guidance is available, contemporary information is needed to balance water quality monitoring decisions with financial and personnel constraints, while meeting data quality needs. This manuscript focuses on monitoring constituents, such as sediment, nutrients, and pathogens, at the field scale and in small watersheds. Specifically, the impacts on the costs and data quality of alternatives related to site selection, discharge measurement, and constituent concentration measurement, are explored. The present analysis showed that avoiding sites requiring extensive berm construction and the installation of electric power to reach distant sites greatly reduces the initial costs with little impact on data quality; however, other decisions directly impact data quality. For example, proper discharge measurement, high-frequency sampling, frequent site and equipment maintenance, and the purchase of backup power and monitoring equipment can be costly, but are important for high quality data collection. In contrast, other decisions such as the equipment type (mechanical samplers, electronic samplers, or <i>in situ</i> sensors) and whether to analyze discrete or composite samples greatly affect the costs, but have minimal impact on data quality. These decisions, therefore, can be based on other considerations (e.g., project goals, intended data uses, funding agency specifications, and agency protocols). We hope this guidance helps practitioners better design and implement water quality monitoring to satisfy resource constraints and data quality needs. |
| first_indexed | 2024-03-10T23:10:27Z |
| format | Article |
| id | doaj.art-65811a6c2c534316ac31a173dbf4449a |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T23:10:27Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-65811a6c2c534316ac31a173dbf4449a2023-11-19T09:02:22ZengMDPI AGWater2073-44412023-08-011517311010.3390/w15173110A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small WatershedsRobert Daren Harmel0Heather Elise Preisendanz1Kevin Wayne King2Dennis Busch3Francois Birgand4Debabrata Sahoo5Center for Agricultural Resources Research, United State Department of Agriculture–Agricultural Research Service, Fort Collins, CO 80526, USADepartment of Agricultural and Biological Engineering, Penn State University, State College, PA 16801, USASoil Drainage Research Unit, United State Department of Agriculture–Agricultural Research Service, Columbus, OH 43210, USAPioneer Farm, University of Wisconsin—Platteville, Platteville, WI 53818, USADepartment of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USADepartment of Agricultural Sciences, Clemson University, Clemson, SC 29634, USATechnological advances and resource constraints present scientists and engineers with renewed challenges in the design of methods to conduct water quality monitoring, and these decisions ultimately determine the degree of project success. Many professionals are exploring alternative lower-cost options because of cost constraints, and research and development is largely focused on <i>in situ</i> sensors that produce high temporal resolution data. While some guidance is available, contemporary information is needed to balance water quality monitoring decisions with financial and personnel constraints, while meeting data quality needs. This manuscript focuses on monitoring constituents, such as sediment, nutrients, and pathogens, at the field scale and in small watersheds. Specifically, the impacts on the costs and data quality of alternatives related to site selection, discharge measurement, and constituent concentration measurement, are explored. The present analysis showed that avoiding sites requiring extensive berm construction and the installation of electric power to reach distant sites greatly reduces the initial costs with little impact on data quality; however, other decisions directly impact data quality. For example, proper discharge measurement, high-frequency sampling, frequent site and equipment maintenance, and the purchase of backup power and monitoring equipment can be costly, but are important for high quality data collection. In contrast, other decisions such as the equipment type (mechanical samplers, electronic samplers, or <i>in situ</i> sensors) and whether to analyze discrete or composite samples greatly affect the costs, but have minimal impact on data quality. These decisions, therefore, can be based on other considerations (e.g., project goals, intended data uses, funding agency specifications, and agency protocols). We hope this guidance helps practitioners better design and implement water quality monitoring to satisfy resource constraints and data quality needs.https://www.mdpi.com/2073-4441/15/17/3110uncertaintynon-point source pollutionstormwateredge of fieldhydrologic measurementinstrumentation |
| spellingShingle | Robert Daren Harmel Heather Elise Preisendanz Kevin Wayne King Dennis Busch Francois Birgand Debabrata Sahoo A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds Water uncertainty non-point source pollution stormwater edge of field hydrologic measurement instrumentation |
| title | A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds |
| title_full | A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds |
| title_fullStr | A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds |
| title_full_unstemmed | A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds |
| title_short | A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds |
| title_sort | review of data quality and cost considerations for water quality monitoring at the field scale and in small watersheds |
| topic | uncertainty non-point source pollution stormwater edge of field hydrologic measurement instrumentation |
| url | https://www.mdpi.com/2073-4441/15/17/3110 |
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