A review of data quality and cost considerations for water quality monitoring at the field scale and in small watersheds

Authors: Harmel, Daren; PREISENDANZ, H - PENNSYLVANIA STATE UNIVERSITY; King, Kevin; BUSCH, D - UNIVERSITY OF WISCONSIN; BIRGAND, F - NORTH CAROLINA STATE UNIVERSITY; SAHOO, D - CLEMSON UNIVERSITY

Submitted to: Water
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/14/2023
Publication Date: 9/15/2023
Citation: Harmel, R.D., Preisendanz, H.E., King, K.W., Busch, D., Birgand, F., Sahoo, D. 2023. A review of data quality and cost considerations for water quality monitoring at the field scale and in small watersheds. Water. 15(17). Article 3110. https://doi.org/10.3390/w15173110.
DOI: https://doi.org/10.3390/w15173110

Interpretive Summary: Technological advances and resources 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 in situ 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. Specifically, the impacts of decisions related to site selection, discharge measurement, and constituent concentration measurement are explored. The present analysis showed that avoiding sites requiring extensive berm construction and installation of AC power to reach distant sites greatly reduces 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 purchase of backup power and monitoring equipment can be costly but are important for high quality data collection. In contrast, other decisions such as equipment type (mechanical samplers, electronic samplers, or in situ sensors) and whether to analyze discrete or composite samples greatly affect 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 assists practitioners design and implement water quality monitoring that better satisfies resource constraints and data quality needs.

Technical Abstract: Technological advances and resources 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 in situ 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. Specifically, the impacts of decisions related to site selection, discharge measurement, and constituent concentration measurement are explored. The present analysis showed that avoiding sites requiring extensive berm construction and installation of AC power to reach distant sites greatly reduces 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 purchase of backup power and monitoring equipment can be costly but are important for high quality data collection. In contrast, other decisions such as equipment type (mechanical samplers, electronic samplers, or in situ sensors) and whether to analyze discrete or composite samples greatly affect 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 assists practitioners design and implement water quality monitoring that better satisfies resource constraints and data quality needs.