Water quality assessment using a portable UV optical absorbance nitrate sensor with a scintillator and smartphone camera

Nitrate contamination of water sources is a global environmental concern. A major source of pollution is agricultural runoff, which can contain decomposed organic matter, fertilizer, and animal or human waste. Nitrate adversely affects the stability of water systems such as dams and rivers and thus also public health. Regulation is essential but difficult to implement, given that measuring nitrates is laborious, and normally done using chemical assays in laboratories. We present a novel portable nitrate sensor that uses a smartphone camera fitted with low-cost optics. The sensor uses ultraviolet absorbance analysis to detect nitrates in water samples and quantify the concentration. The sensor’s absorptivity when a bandpass filter was used was 0.0681 L∙mg−1∙cm−1 compared to 0.0934 L∙mg−1∙cm−1 measured with a spectrophotometer in a laboratory. Measurements by the sensor of the concentration of nitrates in two environmental samples differed from those taken by the spectrophotometer by 19% and 7%. The sensor achieved a nitrate concentration measurement resolution of 0.2 mg∙L−1, and a detection range of 0–5 mg∙L−1, with higher concentrations requiring dilution to quantify. Our tests showed that the smartphone-based nitrate sensor is sufficiently accurate to be used as an inexpensive instrument for nitrate analysis in the field.

Thin-film Nitrate Sensor Performance Prediction Based on Preprocessed Sensor Images

Automating the assessment of sensor quality in the production of thin-film nitrate sensors can yield significant advantages. Currently, the inspection process is extremely time and labor intensive, requiring technicians to manually examine sensors from each batch to determine their performance. Not only is manually examining sensors costly, it also takes days to conclude the results. It is possible to utilize image based learning approach to entirely automate the quality assessment process by accurately predicting the performance of every sensor; this allows for instant performance analysis and rapid changes to the fabrication parameters.The fabrication parameters will directly control the thickness of the ion-selective membrane (ISM) of the nitrate sensor. The thickness of the ISM directly affects the texture on the sensor’s surface. Because of the reliable correlation between sensor performance and sensor surface texture, it allows us to use learning methods to predict sensor performance through images instead of direct measurements.We propose a method to predict sensor quality using noncontact sensor images through a series of image processing techniques followed by machine and deep learning.

Adaptive Learning-based Method for Nitrate Sensor Quality Assessment in On-line Scenarios

Nitrate sensors are commonly used to reflect the nitrate levels of soil conditions in agriculture. In a roll-to-roll system, for manufacturing Thin-Film nitrate sensors, varying characteristics of the ion-selective membrane on screen-printed electrodes are inevitable and affect sensor performance. It is essential to monitor the sensor performance in real-time to guarantee the quality of the products. We applied image processing techniques and offline learning to realize the performance assessment. However, a large variation of the sensor’s data with dynamic manufacturing factors will defeat the accuracy of the prediction system. In this work, our key contribution is to propose a system for predicting the sensor performance in on-line scenarios and making the neural networks efficiently adapt to the new data. We leverage residual learning and Hedge Back-Propagation to the on-line settings and make the predicting network more adaptive for input data coming sequentially. Our results show that our method achieves a highly accurate prediction performance with compact time consumption.

Water quality assessment using a portable UV optical absorbance nitrate sensor with a scintillator and smartphone camera

Nitrate contamination of water sources is a global environmental concern. A major source of pollution is agricultural runoff, which can contain decomposed organic matter, fertilizer, and animal or human waste. Nitrate adversely affects the stability of water systems such as dams and rivers and thus also public health. Regulation is essential but difficult to implement, given that measuring nitrates is laborious, and normally done using chemical assays in laboratories. We present a novel portable nitrate sensor that uses a smartphone camera fitted with low-cost optics. The sensor uses ultraviolet absorbance analysis to detect nitrates in water samples and quantify the concentration. The sensor’s absorptivity when a bandpass filter was used was 0.0681 L∙mg−1∙cm−1 compared to 0.0934 L∙mg−1∙cm−1 measured with a spectrophotometer in a laboratory. Measurements by the sensor of the concentration of nitrates in two environmental samples differed from those taken by the spectrophotometer by 19% and 7%. The sensor achieved a nitrate concentration measurement resolution of 0.2 mg∙L−1, and a detection range of 0–5 mg∙L−1, with higher concentrations requiring dilution to quantify. Our tests showed that the smartphone-based nitrate sensor is sufficiently accurate to be used as an inexpensive instrument for nitrate analysis in the field.

Nutrient Dynamics Buoy (Ndb) Sensor Data and Calibration Report

The Nutrient Dynamics Buoy (NDB) is currently based on a multi-parameter sonde with 7 sensors and includes an integrated Nitrate Sensor and a Phosphate sensor. It also includes a reference PAR1 sensor mounted at the water surface as well as a submersible PAR1 sensor that is immersed at 2m below the water surface together with the mult-parameter sonde, Nitrate sensor and Phosphate sensor.