A Low Cost and Eco-Sustainable Device to Determine the End of the Disinfection Process in SODIS.

The lack of safe drinking water is one of the main health problems in many regions of the world. In order to face it, Solar water disinfection (SODIS) proposes the use of transparent plastic containers, which are filled with contaminated water, and exposed to direct sunlight until enough UV radiation is received to inactivate the pathogens. However, a reliable method for determining the end of the disinfection process is needed. Although several approaches have been proposed in the literature for this purpose, they do not strictly accomplish two critical constraints that are essential in this type of project, namely, low cost and sustainability. In this paper, we propose an electronic device to determine when the lethal UV dose has been reached in SODIS containers, which accomplishes both constraints mentioned above: on the one hand, its manufacturing cost is around EUR 12, which is much lower than the price of other electronic solutions; on the other hand, the device is sufficiently autonomous to work for months with small low-cost disposable batteries, thereby avoiding the use of rechargeable batteries, which are considered hazardous waste at the end of their useful life. In our approach, we first analyze different low cost UV sensors in order to select the most accurate one by comparing their response with a reference pattern provided by a radiometer. Then, an electronic device is designed using this sensor, which measures the accumulated UV radiation and compares this value with the lethal UV dose to determine the end of the disinfection process. Finally, the device has been manufactured and tested in real conditions to analyze its accuracy, obtaining satisfactory results.

An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis.

Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.