ABSTRACT
In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.
ABSTRACT
A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.
ABSTRACT
The determination of the levels of solar radiation incident on the terrestrial surface (W·m-2) is essential for several areas such as architecture, agriculture, health, power generation, telecommunications, and climate forecasting models. The high cost of acquiring and maintaining radiometric equipment makes it difficult to create and expand monitoring networks. It contributes to the limited Brazilian radiometric network and affects the understanding and availability of this variable. This paper presents the development of a new surface solar radiation measurement system based on silicon photodiodes (Si) with a spectral range between 300 nm and 1400 nm incorporating Internet of Things (IoT) technology with an estimated cost of USD 200. The proposed system can provide instantaneous surface solar radiation levels, connectivity to wireless networks and an exclusive web system for monitoring data. For the sake of comparison, the results were compared with those provided by a government meteorology station (INMet). The prototype validation resulted in determination coefficients (R2) greater than 0.95 while the statistical analysis referred to the results and uncertainties for the range of ±500 kJ·m-2, less than 4.0% for the developed prototypes. The proposed system operates similarly to pyranometers based on thermopiles providing reliable readings, a low acquisition and maintenance cost, autonomous operation, and applicability in the most varied climatological and energy research types. The developed system is pending a patent at the National Institute of Industrial Property under registration BR1020200199846.
Subject(s)
Internet of Things , Agriculture , Brazil , Wireless TechnologyABSTRACT
Low-cost air quality sensors are widely used to improve temporal and spatial resolution of air quality data. In Lima, Peru, only a limited number of reference air quality monitors have been installed, which has led to a lack of data for establishing environmental and health policies. Low-cost technology is promising for developing countries because it is small and inexpensive to operate and maintain. However, considerable work remains to be done to improve data quality. In this study, a low-cost sensor was installed with a reference monitor station as the first stage for the calibration process, and a multiple regression model was developed based on reference measurements as an outcome variable using sensor data, temperature, and relative humidity as the predictive parameters. The results show that this particular technology exhibits a promising performance in measuring PM2.5 and PM10 (particulate matter with diameter aerodynamic less than 2.5 µm and 10 µm, respectively); however, the correlation for PM2.5 appears to be better. Temperature and relative humidity data from the sensor were only partially analyzed due to the evident low correlation with the reference meteorological data. The objective of this study is to begin analyzing the performance of low-cost sensors that have already been introduced to the Peruvian market and selecting those that perform better to provide for informed decision-making.