A new study led by Earth System Science Interdisciplinary Center (ESSIC) scientist Sujay Kaushal introduces a new way to think about water quality monitoring along urban streams that could help researchers more accurately track pollutants across waterways.
Longitudinal stream synoptic (LSS) monitoring is the process of monitoring many points along a stream flowpath across space and time. It is underutilized compared to other methods of water quality monitoring such as routine monitoring at one location, sensor deployments, random sampling surveys, and tracer studies. Those methods monitor streams on relatively small scales – usually a few hundred meters – which can complicate the process of interpreting that data to broadly represent urban watershed patterns and processes. Longitudinal monitoring allows researchers to follow the fate and transport of chemical cocktails downstream rather than only study a single point on a river. When combined with traditional long-term monitoring approaches at one or a few locations, this illustrates a more accurate and holistic understanding of a stream’s chemical composition across its flowpath.
LSS monitoring is uniquely valuable in tracking the transport of chemical cocktails – distinct mixtures of excess organic matter, nutrients, salts, and metals – which can then help researchers trace sources of pollutants and how far they travel along streams and rivers. For example, the team used LSS monitoring to track how a stream’s chemical cocktails changed upstream of a wastewater treatment plant, immediately downstream of the plant, and within streams flowing through forest and wetland conservation areas in regional and national parks.
“We can use clues from multiple chemical cocktails to track their fate and transport,” explains Kaushal, first author on the paper published in Frontiers in Environmental Science and professor at UMD’s Department of Geology, “This type of monitoring helps us connect what happens to water quality from small headwater streams to sensitive receiving waters like drinking water reservoirs or the Chesapeake Bay. ”
Kaushal and his team analyzed longitudinal patterns of chemical mixtures of carbon, nutrients, greenhouse gasses, salts, and metals along 10 flowpaths draining into the Chesapeake Bay region. Each flowpath studied is drained by a watershed experiencing urban degradation, forest and wetland conservation, or stream and floodplain restoration. The team monitored over 300 total sampling sites along a combined steam length of 337 km.
Using this data, the researchers developed a classification system for downstream water quality patterns to analyze longitudinal trends in water quality along streams and rivers and how it changes downstream of a pollution source. To detect longitudinal trends, Kaushal’s team looked at how streams’ chemical compositions changed using high-resolution spatial sampling across groups of stations.
Before this study, researchers had no way of interpreting, conceptualizing, comparing, and standardizing longitudinal water quality trends across space in streams and rivers.
“There are plenty of longitudinal studies in the medical literature where they follow a patient throughout their lifetime. We followed water quality patterns throughout the length of the flowpath — that is a unit that really matters because it links and connects what happens in streams to sensitive receiving waters such as the Chesapeake Bay or a drinking water reservoir,” explains Kaushal, “Not only can we look and see if the chemical concentrations go up or they go down, we can also see if there is transformation of one form of chemical to another.”
For example, Kaushal’s team can see if a pollutant like nitrate – which is responsible for hypoxia and dead zones in the Chesapeake Bay – can be converted by microbes to nitrogen gas, which is harmless. Conversely, they can also see if certain chemicals like heavy metals are converted to more toxic dissolved forms along the flowpath.
By tracing multiple chemicals together in a chemical cocktail approach, Kaushal’s team is able to track how sources of multiple pollutants change over time and identify potential hot spots of natural retention and transformation of contaminants in streams and surrounding wetlands. They can then develop strategies for co-managing groups of pollutants with similar transport and transformation characteristics using conservation- and restoration-based approaches
“You can’t address problems in water quality unless you explicitly know where they are coming from, and this source tracking approach at finer spatial scales is important,” says Kaushal, “Understanding where these patterns and processes occur along a stream or river can be important for targeting stream management efforts.”
The ability to address multiple contaminants at once is fairly novel in stream quality management, which typically focuses on only one contaminant at a time. This type of monitoring and chemical cocktails approach moves towards a more holistic way to monitor and manage water quality. This is particularly important in urban watersheds, where landscapes are heterogeneous and big changes in water quality can occur over relatively smaller spatial scales.
Next, the researchers hope to incorporate longitudinal monitoring into standard watershed assessments and regional monitoring programs. They are currently working with collaborators at Virginia Tech and USGS to track sources of salt pollution to drinking water supplies as part of a partnership with the Washington Metropolitan Council of Governments.
“Evaluating stream restoration successes and failures can be complex, and we are using this new approach to identify where it can work and where there are problem areas along streams,” Kaushal says, “Understanding how water quality changes along a flowpath seems simple and logical, but it is really frontier because most of our monitoring infrastructure and methods are focused on only analyzing changes over time at one or a few locations within a watershed.”