Title : Smart network for water contaminant detection and pollution monitoring based on the Internet of Things (IoT)
Abstract:
Rapid urbanization, population growth, and the overexploitation of resources in cities around the world have created environmental challenges for the monitoring, conservation, and management of resources. The implementation of sensor technologies to collect and analyze water quality data has become increasingly important for achieving sustainable urban development. A Smart Water Pollutant Detection Network (SWPDN) and a device(H2OG) a low-cost, with low energy consumption and high precision for real-time monitoring to quantify pH, total dissolved solids (TDS), electrical conductivity (EC), resistivity (R), and ionic strength (IS) in water using technologies such as the Internet of Things (IoT) was developed to provide a support tool for water management. The system was developed as a web application using technologies such as HTML, CSS, and JavaScript. It continuously receives data via an ESP32S module mounted on an expansion board, which connects various types of sensors. The data is sent to the web application upon user request, as well as to a Google Sheets spreadsheet for backup. The sensors were calibrated using buffer solutions with pH values of 4, 7, and 10, and electrical conductivity (EC) of 1413 µS/cm (Hach). Analysis of variance using a Tukey test was performed by comparing the data obtained by the H2OG and a Hach HQ4200 device using HACH pHC101 and CDC401 sensors as a reference in the wastewater discharge area of a textile workshop. The results indicated that there is no statistically significant difference between the pH value obtained by the H2OG and the HQ4200 (p=0.4305), suggesting that the data sets are homogeneous. The SDT, EC, R, and FI showed significant differences when the data exceeded the calibration curve (p=0.0004). In a range of 200–800 units, these same parameters showed no significant differences (p = 0.6280), a performance that can be attributed to the calibration range. The SWPDN system and the H2OG device represent an efficient solution for environmental monitoring. Further studies are needed to improve the precision and accuracy of certain parameters, explore different water types, and integrate an intelligent agent for trend analysis. The widespread adoption of advanced sensors is a fundamental step toward achieving sustainable urban development in smart cities.
