Вимоги до технічних засобів автоматизованої системи вимірювання гідрохімічних параметрів поверхневих вод із застосуванням IoT-технологій

The technical requirements for an automated system measuring hydrochemical parameters of surface waters have been substantiated, utilizing modern sensor technologies and IoT solutions to ensure continuous data collection, transmission, and centralized monitoring storage. A systematic analysis of water monitoring data from the Zhytomyr territorial community was conducted, which helped identify priority indicators for automated control. These include general physicochemical parameters (pH, turbidity, electrical conductivity), nutrients (ammonium nitrogen, nitrates, phosphates), specific pollutants (iron, manganese), and organic pollution indicators (BOD, COD). The operational principles of main sensor types were analyzed in detail: electrochemical, optical, conductometric, and ion-selective. Key technical requirements and specifications were justified, including measurement ranges, accuracy, temporal stability, resistance to temperature fluctuations, and calibration requirements. Special attention was paid to data transmission parameters, energy-efficient solutions, and anti-fouling protection of sensors. Recommendations were developed for the optimal placement and configuration of 10 automated monitoring stations, taking into account the hydrological characteristics of control points and the level of technogenic load. A differentiated approach to station sensor configuration was proposed, depending on the specifics of each installation site. A maintenance protocol was established, including regular calibration, electrode and membrane replacement, and periodic sensor cleaning to ensure measurement accuracy. An important implementation aspect is the system's integration with cloud services for centralized storage and processing of collected data. This provides operational access to monitoring results and analytical forecasting capabilities. The proposed automated monitoring system will significantly improve surface water quality control efficiency, minimize emergency response time, and ensure compliance with European water quality standards. The obtained results form the basis for further system improvement through the integration of machine learning algorithms for data processing and development of pollution dynamics prediction models. The implementation of such a system will create conditions for sustainable water resource management and reduction of anthropogenic environmental impact.