An official website of the United States government
Low-cost sensors enable finer-scale spatiotemporal measurements within the existing methane (CH 4 ) monitoring infrastructure and could help cities mitigate CH 4 emissions to meet their climate goals. While initial studies of low-cost CH 4 sensors have shown potential for effective CH 4 measurement at ambient concentrations, sensor deployment remains limited due to questions about interferences and calibration across environments and seasons. This study evaluates sensor performance across seasons with specific attention paid to the sensor's understudied carbon monoxide (CO) interferences and environmental dependencies through long-term ambient co-location in an urban environment. The sensor was first evaluated in a laboratory using chamber calibration and co-location experiments, and then in the field through two 8 week co-locations with a reference CH 4 instrument. In the laboratory, the sensor was sensitive to CH 4 concentrations below ambient background concentrations. Different sensor units responded similarly to changing CH 4 , CO, temperature, and humidity conditions but required individual calibrations to account for differences in sensor response factors. When deployed in-field, co-located with a reference instrument near Baltimore, MD, the sensor captured diurnal trends in hourly CH 4 concentration after corrections for temperature, absolute humidity, CO concentration, and hour of day. Variable performance was observed across seasons with the sensor performing well ( R 2 = 0.65; percent bias 3.12%; RMSE 0.10 ppm) in the winter validation period and less accurately ( R 2 = 0.12; percent bias 3.01%; RMSE 0.08 ppm) in the summer validation period where there was less dynamic range in CH 4 concentrations. The results highlight the utility of sensor deployment in more variable ambient CH 4 conditions and demonstrate the importance of accounting for temperature and humidity dependencies as well as co-located CO concentrations with low-cost CH 4 measurements. We show this can be addressed via Multiple Linear Regression (MLR) models accounting for key covariates to enable urban measurements in areas with CH 4 enhancement. Together with individualized calibration prior to deployment, the sensor shows promise for use in low-cost sensor networks and represents a valuable supplement to existing monitoring strategies to identify CH 4 hotspots.
more »
« less
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.
more »
« less
- Award ID(s):
- 1655221
- 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
More Like this
-
-
Abstract 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.more » « less
-
null (Ed.)To increase the production of crops, chemical fertilizers are used in crop fields. However, underuse or overuse cannot increase crop yields but even decrease them and cause severe environmental problems. Thus, the detection and monitoring of chemical concentration are increasingly important. To build up and monitor a data-based system for a large area, such a method is costly and time-consuming. In this research, we developed a conductive polymer-based sensor to detect nitrate concentrations in soil water. Conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) was used as our sensing material. To increase its conductivity, we used the vacuum phase polymerization method to achieve a high conductive and stable polymer film. The conductivity of the polymer film is 500 S/cm. Our results have demonstrated that the conductive polymer-based sensors have high sensitivity to nitrate solution. The response to 1000 ppm nitrate solution is 47.2% (Response = (Initrate - IDIwate) / IDIwater). The sensors can detect nitrate range from 1ppm to 1000 ppm. The response time is less than 1 minute. This impedance-based sensor will eventually be integrated with the surface acoustic wave sensors, combined with an antenna and a GPR unit for low maintenance, autonomous, and in-situ soil nutrient sensingmore » « less
-
null (Ed.)To increase the production of crops, chemical fertilizers are used in crop fields. However, underuse or overuse cannot increase crop yields but even decrease them and cause severe environmental problems. Thus, the detection and monitoring of chemical concentration are increasingly important. To build up and monitor a data-based system for a large area, such a method is costly and time-consuming. In this research, we developed a conductive polymer-based sensor to detect nitrate concentrations in soil water. Conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) was used as our sensing material. To increase its conductivity, we used the vacuum phase polymerization method to achieve a high conductive and stable polymer film. The conductivity of the polymer film is 500 S/cm. Our results have demonstrated that the conductive polymer-based sensors have high sensitivity to nitrate solution. The response to 1000 ppm nitrate solution is 47.2% (Response = (Initrate - IDIwate) / IDIwater). The sensors can detect nitrate range from 1ppm to 1000 ppm. The response time is less than 1 minute. This impedance-based sensor will eventually be integrated with the surface acoustic wave sensors, combined with an antenna and a GPR unit for low maintenance, autonomous, and in-situ soil nutrient sensing.more » « less
-
Nitrate (NO3) pollution in groundwater, caused by various factors both natural and synthetic, contributes to the decline of human health and well-being. Current techniques used for nitrate detection include spectroscopic, electrochemical, chromatography, and capillary electrophoresis. It is highly desired to develop a simple cost-effective alternative to these complex methods for nitrate detection. Therefore, a real-time poly (3,4-ethylenedioxythiophene) (PEDOT)-based sensor for nitrate ion detection via electrical property change is introduced in this study. Vapor phase polymerization (VPP) is used to create a polymer thin film. Variations in specific parameters during the process are tested and compared to develop new insights into PEDOT sensitivity towards nitrate ions. Through this study, the optimal fabrication parameters that produce a sensor with the highest sensitivity toward nitrate ions are determined. With the optimized parameters, the electrical resistance response of the sensor to 1000 ppm nitrate solution is 41.79%. Furthermore, the sensors can detect nitrate ranging from 1 ppm to 1000 ppm. The proposed sensor demonstrates excellent potential to detect the overabundance of nitrate ions in aqueous solutions in real time.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/09593330.2021.1939792
