LoRa-based Network for Water Quality Monitoring in Coastal Areas

[EN] Agriculture Farming activity near to rivers and coastal areas sometimes imply spills of chemical and fertilizers products in aquifers and rivers. These spill highly affect the water quality in rivers' mouths and beaches close to those rivers. The presence of these elements can worse the quality for its normal use, even for its enjoying. When this polluted water reaches the sea can also have problematic consequences for fauna and flora. For this reason, it is important to rapidly detect where these spills are taking place and where the water does not have the minimum of quality to be used. In this article we propose the design and implementation of a LoRa (Long Range) based wireless sensor network for monitoring the quality of water in coastal areas, rivers and ditches with the aim to generate an observatory of water quality of the monitored areas. This network is composed by several wireless sensor nodes endowed with several sensors to physically measure parameters of water quality, such as turbidity, temperature, etc., and weather conditions such as temperature and relative humidity. The data collected by the sensors is sent to a gateway that forwards them to our storage database. The database is used to create an observatory that will permit the monitoring of the environment where the network is deployed. We test different devices to select the one that presents the best performance. Finally, the final solution is tested in a real environment for checking its correct operation. Two different tests will be carried out. The first test checks the correct operation of sensors and the network architecture while the second test show us the devices performance in terms of coverage.Sendra, S.; Parra-Boronat, L.; Jimenez, JM.; García-García, L.; Lloret, J. (2023). LoRa-based Network for Water Quality Monitoring in Coastal Areas. Mobile Networks and Applications (Online). 28(1):65-81. https://doi.org/10.1007/s11036-022-01994-8658128

Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity

The goal of smart city development is to improve the quality of life by incorporating technology into daily activities. Artificial intelligence (AI) is critical to the ongoing development of future smart cities. The Internet of Things (IoT) idea connects every internet-enabled device for improved access and control. AI in various domains has changed ordinary towns into highly equipped smart cities. Machine learning and deep learning algorithms have proven indispensable in a variety of industries, and they are now being implemented into smart city concepts to automate and improve urban activities and operations on a large scale. IoT and machine learning technology are frequently used in smart cities to collect data from various sources. This article delves deeply into the significance, scope, and developments of AI-based smart cities. It also addresses some of the difficulties and restrictions associated with smart cities powered by AI. The goal of the study is to inspire and encourage academics to create original smart city solutions based on AI technologies

Root Zone Sensors for Irrigation Management in Intensive Agriculture

Crop irrigation uses more than 70% of the world’s water, and thus, improving irrigation efficiency is decisive to sustain the food demand from a fast-growing world population. This objective may be accomplished by cultivating more water-efficient crop species and/or through the application of efficient irrigation systems, which includes the implementation of a suitable method for precise scheduling. At the farm level, irrigation is generally scheduled based on the grower’s experience or on the determination of soil water balance (weather-based method). An alternative approach entails the measurement of soil water status. Expensive and sophisticated root zone sensors (RZS), such as neutron probes, are available for the use of soil and plant scientists, while cheap and practical devices are needed for irrigation management in commercial crops. The paper illustrates the main features of RZS’ (for both soil moisture and salinity) marketed for the irrigation industry and discusses how such sensors may be integrated in a wireless network for computer-controlled irrigation and used for innovative irrigation strategies, such as deficit or dual-water irrigation. The paper also consider the main results of recent or current research works conducted by the authors in Tuscany (Italy) on the irrigation management of container-grown ornamental plants, which is an important agricultural sector in Italy

Monitoring conductivity levels at the Mouth of the swash in Briarcliffe Acres

Conductivity is the ability of water to conduct electricity. It is a measurement of the total amount of dissolved solids. Monitoring water conductivity can lead to a better understanding of pollutants in the environment. During the monitoring samples were taken between Feb 20, 2019 to Feb 17, 2021. These samples were taken from the Head and Mouth of Swash and Cabana road at Briarcliffe Acres in South Carolina. The measurements conducted are conductivity measurements in laboratory with Hach HQ40d reference method: SM 2510 A-2011 and 2510 B-2011. Over 45 samples have a median around 50,075 micro siemens and outliers all the way down to 5,510 at one point with the highest outlier being 54,500

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

Visualization System of Energy-saving for Smartification in Urban and building planning

13301甲第5699号博士（工学）金沢大

Laboratory Directed Research and Development FY-10 Annual Report

The FY 2010 Laboratory Directed Research and Development (LDRD) Annual Report is a compendium of the diverse research performed to develop and ensure the INL's technical capabilities can support the future DOE missions and national research priorities. LDRD is essential to the INL -- it provides a means for the laboratory to pursue novel scientific and engineering research in areas that are deemed too basic or risky for programmatic investments. This research enhances technical capabilities at the laboratory, providing scientific and engineering staff with opportunities for skill building and partnership development

On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants

The deteriorating water environment demands new approaches and technologies to achieve sustainable and smart management of urban water systems. Wireless sensor networks represent a promising technology for water quality monitoring and management. The use of wireless sensor networks facilitates the improvement of current centralized systems and traditional manual methods, leading to decentralized smart water quality monitoring systems adaptable to the dynamic and heterogeneous water distribution infrastructure of cities. However, there is a need for a low-cost wireless sensor node solution on the market that enables a cost-effective deployment of this new generation of systems. This paper presents the integration to a wireless sensor network and a preliminary validation in a wastewater treatment plant scenario of a low-cost water quality monitoring device in the close-to-market stage. This device consists of a nitrate and nitrite analyzer based on a novel ion chromatography detection method. The analytical device is integrated using an Internet of Things software platform and tested under real conditions. By doing so, a decentralized smart water quality monitoring system that is conceived and developed for water quality monitoring and management is accomplished. In the presented scenario, such a system allows online near-real-time communication with several devices deployed in multiple water treatment plants and provides preventive and data analytics mechanisms to support decision making. The results obtained comparing laboratory and device measured data demonstrate the reliability of the system and the analytical method implemented in the device.Ingeniería, Industria y Construcció

New York City’s Green Infrastructure: Impacts on Nutrient Cycling and Improvements in Performance

Urban stormwater runoff from impervious surfaces reduces water quality and ecological diversity in surrounding streams. The problem is exacerbated in older cities with combined sewer systems like New York City, where roughly 30 billion gallons of untreated sewage and stormwater runoff are combined and dumped into the New York harbor annually. Rain gardens and green roofs are designed to naturally manage stormwater, but both performance data and design guidance are limited. In particular, rain gardens are not optimized for nutrient removal, and US green roofs are commonly planted with non-native vegetation, which may not be optimized for water retention. The first of three studies in this dissertation investigates the overall effect of rain gardens on nutrient removal. Engineers have found there to be tradeoffs between rain garden designs that overall favor greater water retention and those that favor removal of pollutant nutrients, as efficient nutrient removal requires designs that drain slowly, and thus absorb less stormwater. Despite these opposing concerns, this dissertation has found that rain gardens constructed in areas with combined sewer systems should focus on water retention, as the benefits of treating increased amounts of water outweigh admitted downsides, such as the leaching of pollutant nutrients contained in rain garden soil. The second study investigates how nutrient pollution can be reduced in rain gardens. To do this, it quantifies the rate that the rain garden’s soil creates nitrogen pollution, by converting nitrogen from organic to inorganic forms, as inorganic nitrogen is more readily washed out of the soil and into water bodies. Conversely, it also quantifies the amount of nitrogen consumed by plants and also nitrogen emitted in gas form. It then uses the results to construct an overall nitrogen mass balance. The results indicate that the soil used to build rain gardens is in fact too nitrogen rich; inorganic nitrogen supplied by the decomposition of organic nitrogen and by stormwater runoff is far greater than required to maintain vegetative health for rain garden plants. The study concludes that altering rain garden soil specifications could reduce nitrogen pollution. The third study finds that “industry-standard” green roofs planted with drought-tolerant Sedum vegetation might not capture as much stormwater as “next-generation” native systems with irrigation and smart detention. Specifically, the study provides crop coefficients demonstrating reduced evapotranspiration in drought tolerant green roof plants compared to native plants. It also found a native roof’s stormwater capture increased with irrigation and the use of a smart runoff detention system, which automatically reduced the volume of water in the cistern that captures roof runoff in advance of a predicted storm. US government agencies are launching multi-billion dollar greening initiatives that include rain gardens and green roofs designed to manage volumes of stormwater runoff. The research here can assist in quantifying performance and improving green infrastructure designs