Spineless ‘Monsters’ Low on Cost, High on Water Quality Health

Monitoring nature’s best water quality indicators, Benthic Macro Invertebrates (BMI), costs less than chemical analysis but demands the best in IT to track changing ever-biodiversity.

The bottom-dwellers of water bodies, BMI are aquatic insects at spineless immature stages of life that are small yet large enough to be seen without a microscope. With wide-set eyes and gaping mouths, common names like mites, worms, naiads and snails, to name a few, belie their heroic contributions to the advocacy of good water quality.

Generally, the more different types of macro-invertebrates present, the better the water quality. Some species are more sensitive to pollutants and other environmental disturbances caused by human activity related to agriculture, urbanization or poor management practices. When those species are expected but missing from an aquatic ecosystem, poor aquatic health is a concern. 1

Environmental agencies have been performing benthic monitoring for decades, but the fragmented nature of water quality monitoring has made it difficult to comprehensively manage biological and water chemistry analysis.

screen shot of Pennsylvania Department of Environmental Protection's macroinvertebrate story map, Looking Below the Surface

Identifying where BMI exist and thrive reveal key insights to water chemistry and the impact of water pollution. Benthic surveys provide a more precise, informative process for researchers, water quality regulators, and the public. How many types can you spot in this image?

Biological assessment is a valuable approach to measuring water quality. Lab results from water quality samples only represent water quality at the time the sample was taken. In contrast, BMI are exposed to the water for days or weeks. Unlike fish which swim away from pollutants and disruption, immobile invertebrates speak to long-term quality of environmental health and may confirm lab results received over time.

Traditional lab analysis will continue to play a critical role in water quality monitoring. At the same time, new technologies present opportunities to integrate lab processes and the nuanced study of macro-invertebrates. Both approaches pose challenges to storing, managing and reporting associated complex meta data in order to meet stringent Quality Assurance Project Plan (QAPP) requirements and substantiate claims of clean water.

Different BMI sampling methodologies, diversity of taxonomy, and changing taxa or the classification of organisms have made it more onerous to store ecological data with more conventional water quality monitoring meta data and data.

Now KISTERS’ Water Quality Information System empowers water quality managers and broader water resource departments to track both approaches to discrete monitoring.

Flexible architecture securely houses in a wide variety of data formats which are not limited to field notes, photos and videos recorded by smartphones or tablets; files from probes and sondes; data on weather or algal blooms from satellites; electronic data deliverable (EDD) files from labs; and taxonomic trees which change over time.

Automated processes verify and perform quality control on data collected, especially matching clinical labs orders with test results. The statistics wizard easily calculates benchmarks and derived parameters. Graphs, additional tools and an Esri ArcGIS extension for mapping facilitate deeper investigations of unexpected observations and editing of data points as needed. Quality codes and audit trails fully support the integrity of information reported to regulators, watershed partners or the public.

While discrete monitoring is beneficial, the hurdle to integrate it with continuous monitoring activity may remain. In-situ sensors can collect data on rainfall, water temperature, pH, dissolved oxygen, and more parameters in real time. Comparing coarse resolution data from sampling events or benthic surveys to high volume, high resolution data from sensors is often troublesome. Designed with a focus on advanced time series analysis, KISTERS’ Water Quality Solution conveniently aligns these variations in frequency.

Human machine interface (HMI) empowers water quality managers with new abilities to chart lab results against continuous flow data and rainfall event totals.KISTERS’ platform enables further analysis of hydrological data factors on water quality. Lab results from discrete water quality monitoring samples (symbolized as black triangles) can be charted against continuous data like flow rate (shown in red) or storm events (shown in green and blue) which carry runoff into BMI habitats.

As communities develop, how they sustain clean water supplies that attract new residences and businesses and maintain a high quality of life is important. Water quality monitoring programs that utilize bioindicators and lab testing of physicochemical characteristics realize the most advantages. However, an inaccurate picture results if data are isolated from meta data that prove credibility and continuous monitoring datasets.

Quality control, reliable reporting, and holistic analysis of water quality are harder to achieve in decentralized systems.

Ecosystem shifts rarely point to one specific cause, but the environment provides a number of warning signs if decision-makers are willing to look at the whole body scientific data which include BMI and fish that feed on them. Over the past decade, benthic sampling protocols have been established. And now, user-friendly software that streamlines the complexity of managing the spectrum of water quality data is available.

In partnership with counties in the Pacific Northwest to conservation agencies in the Great Lakes region, from New South Wales, Australia Office of Water to Bundesamt für Umwelt (BAFU) the Swiss Federal Office for the Environment, KISTERS is pleased to power the effective use of nature’s best water quality indicators with its advance information technology solutions.

1Jeff Mitchell. “Learning from ‘Little Monsters,’” The Current (Santa Barbara), July 19, 2018. www.news.ucsb.edu/2018/019125/learning-little-monsters