Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
Creation of a low cost, low light bioluminescence sensor for real time biological nitrate sensing in marine environments
The concentration of nitrate (NO3−) in Narragansett Bay has been shown to undergo considerable temporal and spatial variation. However, the dynamics of this flux has never been monitored on a fine-scale (<100 m, < 1 d) or in real-time. Whole-cell bio-reporters are promising candidates for low cost environmental sensing of bioavailable nutrients. Yet difficulties remain in creating sensors for long term deployment in the marine environment. This paper describes the creation and validation of a low-cost sensor using a self-bioluminescent strain of the cyanobacteria Synechococcus elongatus pcc 7942 for the direct measurement of bioavailable nitrate. Nitrate bioavailability was measured by monitoring light emission from a luxAB based promotor fusion to glnA using a light to frequency sensor and single board microcontroller. Sensor designs are presented in this manuscript with specific focus on storage, cell viability, and compatibility with the marine environment. Sensors were able to consistently assess nitrate standards as low as 1 ppm (16.3 μM). Using a wavelet denoising approach to reduce white noise and hardware noise, nitrate detection of standards as low as 0.037 ppm (0.65 μM) was achieved. Good sensitivity and low cost make these sensors ideal candidates for continuous monitoring of biological nitrates in estuarine systems.
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- Award ID(s):
- 1655221
- NSF-PAR ID:
- 10340494
- Date Published:
- Journal Name:
- Environmental Technology
- ISSN:
- 0959-3330
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/09593330.2021.1939792
