New Horizons in Environmental and Pollutant Monitoring: Low-Cost Technologies for Detection, Characterization, and Assessment

Dear Colleagues,

The identification and characterization of environmental pollutants represents a significant challenge for researchers, especially due to the dependence on expensive and complex equipment. In order to minimize these drawbacks, the new trends in technology aim to design and test prototype innovative systems that employ optical sensors and machine learning algorithms. These devices should be portable, inexpensive, easy to use and capable of delivering accurate and reliable results in real time.

The need for accessible tools for contaminant detection arises from the problems faced by researchers in using sophisticated equipment that requires considerable time and effort. This motivates the development of intelligent detection systems, supported by learning models implemented through programming languages and machine learning techniques. Such systems have the potential to acquire new skills, facilitating novel and cost-effective processing of optical signals, as well as advanced pattern recognition.

Over the last decade, low-cost sensor technology for monitoring pollution in environmental layers such as air, water, and soil has made great progress. The devices developed enable us to understand the behaviour of pollutants in the different environments where they are deposited and accumulate, which contributes to reducing health impacts. It is emphasized that these devices are easy to use, portable, require little maintenance, and enable continuous and near-real-time monitoring. Additionally, they can be used in citizen science projects and community initiatives, promoting social participation in environmental issues.

Despite their advantages, low-cost sensors face challenges in terms of data reliability due to certain design compromises. The rapid increase in studies and projects based on these sensors has dispersed existing information, making it difficult to consolidate knowledge. It is therefore essential to identify, review, and scientifically validate advances in this area in order to develop increasingly accurate and functional devices. Since 2012, 891 relevant titles have been published that constitute the technical and scientific basis of this field. Current research focuses on highlighting the importance of sensor calibration and performance, the type of processor used, data storage and communication, as well as the possibility of remote and real-time access to the information generated.

Urban and industrial growth has increased pollution and environmental degradation to alarming levels, affecting water availability, soil fertility, and air quality. Against this background, it is essential to implement medium- and long-term monitoring strategies to reduce negative impacts and restore the quality of environmental layers. Traditionally, environmental assessment is carried out by manual sampling and subsequent laboratory analysis. However, new technologies allow for more sustainable and cost-effective methods, such as the integration of Internet of Things (IoT) protocols for real-time monitoring of multiple environmental parameters.

The developed systems use low-cost sensors to measure environmental quality parameters and transmit the data via Wi-Fi networks to cloud platforms based on Internet of Things (IoT) protocols. The results can be displayed on Android smartphones or computers, facilitating remote access and analysis of the information. Scientific research has shown that these systems allow continuous and simultaneous monitoring of environmental quality in different sources and layers, ensuring reliable data for industrial, agricultural and commercial purposes. The use of microcontrollers such as Arduino Nano and inexpensive sensors significantly reduces the cost and complexity of implementation, favouring the validation and effectiveness of the developed systems.

Prof. Dr. Jorge Mendoza-Pérez
Dr. Israel Reyes-Ramírez
Guest Editors

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• sensor
• pollution
• low-cost
• air quality
• water availability
• soil fertility
• monitoring
• calibration
• impacts
• machine learning