Water quality monitoring is an important means to control pollutants that may harm the environment and human health. However, water quality monitoring is usually carried out in a laboratory setting, usually using expensive sensor equipment and qualified professional testers. With advances in nanotechnology and engineered materials, several studies have shown that the development of technologies designed to monitor water quality in recent years can reduce the cost of analysis and accelerate the realization of management and decision-making outcomes.
Water is an important natural resource for life on earth and for human activities and, therefore, it is necessary to have abundant clean water to quench thirst, irrigate fields, and sustain all forms of life in the environment. Several sources contribute negatively to changing water quality, mainly caused by human action, such as population growth, industrialization, urbanization, agriculture, domestic sewage, and poor management. To improve water quality, the 2030 Agenda for Sustainable Development established, as a goal for 2030, the availability and sustainable management of water and basic sanitation for all human beings, with the improvement of water quality, the reduction in pollution, and the elimination of dangerous pollutants.
Water quality monitoring allows the identification and quantification of polluting substances that can be compared to acceptable standards for each location, being a strategic management tool for decision-making and the improvement of water quality . Unfortunately, there is still a large gap in water quality data, especially in remote locations and in developing countries, raising concerns about the sustainability of water resources and risks to human health. Furthermore, the main conventional techniques for monitoring water are often expensive, requiring qualified professionals and complex equipment, and which, in many circumstances, do not allow direct analysis in the field with immediate results.
However, several studies have tried to fill the gap and overcome the lack of data provision, such as the incorporation of citizen science, for example, or the development of low-cost and in situ technologies, as will be described throughout this work. The development of low-cost, accessible, and easy-to-handle devices and sensors for water quality analysis can be a viable alternative for obtaining data, improving water quality, and, consequently, the security of the water. Furthermore, in situ measurement contributes to cost minimization, as it eliminates the need for sampling, sample preservation, transport, and laboratory water analysis.
The combination of technologies and water quality sensors with components of microsystems, associated with a software architecture and cloud computing (online), allows the development of a system in the conception of Smart Water Quality Monitoring Systems (SWQMS), from the point of view of the Internet of Things (IoT). In addition, integration with the Big Data system can improve the modeling of the water system, reducing model uncertainties and ensuring more information in the management of risk analysis.
2、Water quality monitoring Parameters
Although there are several parameters for Water quality monitoring, only a few are used as key parameters in the monitoring, which can vary according to the location or the purpose of water use. Regarding water use, according to Boyd and Alley, when intended for human consumption, such as drinking, for example, the water must not have high concentrations of minerals, taste, or odor, and must be free of toxins or pathogenic organisms; for recreation, despite being unsuitable for consumption, the water must not present risks of contagion or diseases through direct contact; for the environment, the water must not contain pollutants that cause adverse effects on flora and fauna.
For Water quality monitoring, Boyd, Alley, the World Health Organization, Spellman, Cotruvo, and Omer present a variety of physical, chemical, and biological parameters for drinking water, superficial water (fresh and saltwater) and groundwater, the sources of pollutants, types of speciation, and the main analysis techniques. In this work, the following were considered as physical parameters: (i) color, (ii) temperature, and (iii) turbidity; and as chemicals: (iv) chlorine, (v) fluorine, (vi) phosphorus, (vii) metals, (viii) nitrogen, (ix) dissolved oxygen, (x) pH, and (xi) redox potential or ORP (Oxidation–Reduction Potential).
In biological monitoring, although it is possible to identify numerous pathogenic species in water, the methods of isolation and the enumeration of such microorganisms make this a complex and time-consuming task, making it impractical to monitor all microorganisms that may be present in water. To solve this problem, the monitoring of biological contamination is conventionally carried out by the analysis of key microorganisms present in human and warm-blooded animal feces, (xii) total coliforms and Escherichia coli being the most-used parameters to assess the microbiological safety of drinking and surface water supplies.
The monitoring of (xiii) algae and cyanobacteria is also important, since in many aquatic ecosystems, including drinking water supplies, there is a proliferation of these microorganisms called Harmful Algal Blooms (HAB). As emerging contaminants, the occurrence of HAB depends on several environmental conditions, such as the presence of nutrients and water temperature, and it is responsible for producing a variety of toxins released into water, which are dangerous for public health.
Many of the parameters mentioned above make up the Water Quality Index (WQI), such as dissolved oxygen, total coliforms, pH, temperature, nitrogen, phosphorus, and turbidity. The WQI appeared in 1960 (Horton Index), being a simple and concise tool that allows the expression of the quality of water bodies and their derivations, such as for recreation, irrigation, and public supply, for example. Nowadays, there are different numbers of models developed by different international organizations and used for WQI calculation, such as the National Sanitation Foundation Water Quality Index (NSFWQI) and the Weighted Arithmetic Water Quality Index (WAWQI), for example.