Developing an Open-Source, Low-Cost, Radon Monitoring System

[Abstract] The United States Environmental Protection Agency (USEPA) and the International Agency for Research on Cancer (IARC) have declared Radon gas a human carcinogen. Spain has several regions with high radon concentrations, Galicia (northwestern Spain) being one with the highest Radon concentration. In this work, we present the development of an open-source and low-cost radon monitoring and alert system. The system has two parts: devices and the backend. The devices integrate a Radon sensor, capable of measuring Radon levels every 10 min, and several environmental sensors capable of measuring temperature, humidity, atmospheric pressure, and air pollution. The devices send all the information to the backend, which stores it, exposes it in a web interface, and uses the historical data to predict the radon levels for the following hours. If the radon levels are predicted to overpass the threshold in the next hour, the system issues an alert via several channels (email and MQTT) to the configured recipients for the corresponding device, allowing them to take measures to lower the Radon concentration. The results of this work indicate that the system allows the radon levels to be greatly reduced and makes the development of a low cost and open-source radon monitoring system feasible. The system scalability allows a network of sensors to be created that can help mitigate the health hazard that high radon concentrations create

Exploring the Feasibility of Low Cost Technology in Rainfall Monitoring: The TREBOADA Observing System

[Abstract] In order to characterize the spatial and temporal variability of the rainfall, we have developed an observation system to monitor the precipitation over the metropolitan area of A Coruña. The observation system (called TREBOADA) consists of a network of rain gauges, comprising gauges operated by the regional weather agency and rain gauges deployed specifically for TREBOADA. The latter ones are built using low cost technology, which significantly reduces the cost of each gauge. Data from the rain gauges are combined with rain observations from the meteorological radar to produce high resolution rain products.Xunta de Galicia; ED431G/0

Developing a Secure Low-Cost Radon Monitoring System

[Abstract] Radon gas has been declared a human carcinogen by the United States Environmental Protection Agency (USEPA) and the International Agency for Research on Cancer (IARC). Several studies carried out in Spain highlighted the high radon concentrations in several regions, with Galicia (northwestern Spain) being one of the regions with the highest radon concentrations. The objective of this work was to create a safe and low-cost radon monitoring and alert system, based on open source technologies. To achieve this objective, the system uses devices, a collection of sensors with a processing unit and a communication module, and a backend, responsible for managing all the information, predicting radon levels and issuing alerts using open source technologies. Security is one of the largest challenges for the internet of things, and it is utterly important in the current scenario, given that high radon concentrations pose a health risk. For this reason, this work focuses on securing the entire end-to-end communication path to avoid data forging. The results of this work indicate that the development of a low-cost, yet secured, radon monitoring system is feasible, allowing one to create a network of sensors that can help mitigate the health hazards that high radon concentrations pose.This project was funded by the Consolidation and Structuring of Competitive Research Units-Competitive Reference Groups (ED431C 2018/49) and Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers (ED431G/01), funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds. This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness, R & D National Plan, via the project BIA2017-86738-R. This research and the APC were also supported by Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration (CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European UnionXunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/2

Indicators Framework for Sustainable Urban Design

[Abstract] Climate change and sustainability have recently been object of study due to the impact on the planet and on human activity of the first and the benefits that could derive from the efficiency of the second. Particularly, urban environments are locations that represent a high percentage of emissions of gases, waste, resources use and so forth. However, they are places where great changes can be made, in an attempt to accomplish the urgent challenge to adapt to current and projected rates of climate change. Research has shown that a fruitful approach to urban sustainability is to describe indicators that measure the effectiveness of current processes of urban infrastructures, analyze areas in need of improvement and measure the effect of any actions taken. The significant feature of this research relies on its global approach, considering both major worldwide used and less widely-spread frameworks and the analysis of the 32 selected tools and guidelines, including over 2000 indicators. The result is a proposed structure of 14 categories and 48 indicators, easily applicable in urban areas, that tries to fulfill basic aspects to obtain a general diagnosis of the sustainable nature of the urban environment, which can serve as support to detect the strongest and weakest areas in terms of their sustainability

Machine Learning-Based Radon Monitoring System

[Abstract] Radon (Rn) is a biological threat to cells due to its radioactivity. It is capable of penetrating the human body and damaging cellular DNA, causing mutations and interfering with cellular dynamics. Human exposure to high concentrations of Rn should, therefore, be minimized. The concentration of radon in a room depends on numerous factors, such as room temperature, humidity level, existence of air currents, natural grounds of the buildings, building structure, etc. It is not always possible to change these factors. In this paper we propose a corrective measure for reducing indoor radon concentrations by introducing clean air into the room through forced ventilation. This cannot be maintained continuously because it generates excessive noise (and costs). Therefore, a system for predicting radon concentrations based on Machine Learning has been developed. Its output activates the fan control system when certain thresholds are reached.This work is supported by Instituto de Salud Carlos III, grant number PI17/01826 Collaborative Project in Genomic Data Integration (CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National Plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)— “A way to build Europe.” This project was also supported by the General Directorate of Culture, Education and University Management of the Xunta de Galicia ED431D 2017/16, the “Drug Discovery Galician Network” Ref. ED431G/01, and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23). This work was also funded by the grant for the consolidation and structuring of competitive research units (ED431C 2018/49) from the General Directorate of Culture, Education and University Management of the Xunta de Galicia, and the CYTED network (PCI2018 093284) funded by the Spanish Ministry of Innovation and Science. This project was also supported by the General Directorate of Culture, Education and University Management of the Xunta de Galicia “PRACTICUM DIRECT” Ref. IN845D-2020/03Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Xunta de Galicia; IN845D-2020/0

Operational thresholds of moored ships at the oil terminal of inner port of A Coruña (Spain)

[Abstract] Minimizing the stay of a vessel in port can lead to improvements in harbor efficiency. Currently, downtimes of cargo operations or their performance reduction because of excessive vessel motion are especially relevant. This work aims to evaluate the operational conditions of moored vessels in the inner port of A Coruña (Spain), comparing them with motion thresholds established by international standards for cargo operations. To this end, motions of 19 ships were monitored. Data analysis revealed large angular motions, particularly roll and yaw, including amplitudes that exceeded the limiting criteria of the standards in most of the analyzed vessels, with no registered downtime. Regarding linear movements, heave and surge recorded lower amplitudes compared to the values of standard thresholds. The specific behavior of each vessel was analyzed in terms of its size, maritime conditions, and mooring location. Field campaigns such as those performed in this work are an effective way of analyzing the operational conditions of ports, which could help in identifying problems in the mooring zone.Ministerio de Economía, Industria y Competitividad; BIA2017-86738-

Development of an Automatic Low-Cost Air Quality Control System: A Radon Application

[Abstract] Air pollution is the fourth-largest overall risk factor for human health worldwide. Ambient air pollution (outdoors) and household air pollution (indoors) cause about 6.5 million premature deaths. The World Health Organization has established that between 3% and 14% of lung cancer cases are due to radon gas, making it the most important cause of lung cancer after smoking. This work presents a fully automated, low-cost indoor air quality control system that can monitor temperature, pressure, humidity, total volatile organic compounds (TOVC), and radon concentration. Using the radon concentration as an air quality measure, we created a prediction algorithm. The system uses those predictions to control a ventilation system automatically. We tested the algorithm for different prediction windows and compared the results with those without the ventilation system in a radon research room. In this room, the radon concentration is high 100% of the time, reaching a level eleven times higher than the recommended limit. The results show that the system can achieve an 86% reduction of the radon concentration, maintaining it low 90% of the time while having the ventilation system on during only 34% of the time. This work demonstrates that we can control air quality using low-cost resources, keeping a household or workplace safe but comfortable.This work was supported by Spanish Ministry of Economy and Competitiveness through the project BIA2017-86738-R and through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. This work is supported in part by grants from the European Social Fund 2014–2020. CITIC (Research Centre of the Galician University System) and the Galician University System (SUG) obtained funds through Regional Development Fund (ERDF), with 80% from the Operational Program ERDF Galicia 2014–2020 and the remaining 20% from the Secretaría Xeral de Universidades of the Galician University System (SUG) (Ref ED431G 2019/01). Additional support was provided by the Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49)Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2018/4

Application of an Analytic Methodology to Estimate the Movements of Moored Vessels Based on Forecast Data

[Abstract]: A port’s operating capacity and the economic performance of its concessions are intimately related to the quality of its operational conditions. This paper presents an analytical methodology for estimating the movements of a moored vessel based on field measurements and forecast data, specifically including ship dimensions and meteorological and maritime conditions. The methodology was tested and validated in the Outer Port of Punta Langosteira, A Coruña, Spain. It was determined that the significant wave height outside the port, and the ratio of the vessel’s length divided by its beam (L/B), are the variables that most influence movements. Furthermore, heave and surge are the movements with a better value of the coefficient of determination (R2 values of 0.71 and 0.67, respectively), the sway (R2 = 0.30) and roll (R2 = 0.27) being the worst when using the available forecast variables of the Outer Port of Punta Langosteira. Despite their low R2 values, sway and roll models are able to estimate the main trends of these movements. The obtained estimators provide good predictions with assumable error values (root mean square error—RMSE and mean absolute error—MAE), showing their potential application as a predictive tool. Finally, as a consequence, the A Coruña Port Authority has included the results of the methodology in its port management system allowing them to predict moored vessel behavior in the port.The authors gratefully acknowledge the financial support of the Spanish Ministry of Economy, Industry and Competitiveness, R & D National Plan, within the project BIA2017-86738-R. In addition, special thanks to the Port Authority of A Coruña, Spain

Modeling of energy efficiency for residential buildings using artificial neuronal networks

The energy efficiency dataset used to support the findings of this study has been deposited in the GitHub repository https://github.com/mereshow/ann-energy-efficiency.git.[Abstract] Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the residential sector. The process of evaluation and qualification of the energy efficiency in existing buildings should contain an analysis of the thermal behavior of the building envelope. To determine this thermal behavior and its representative parameters, we usually have to use destructive auscultation techniques in order to determine the composition of the different layers of the envelope. In this work, we present a nondestructive, fast, and cheap technique based on artificial neural network (ANN) models that predict the energy performance of a house, given some of its characteristics. The models were created using a dataset of buildings of different typologies and uses, located in the northern area of Spain. In this dataset, the models are able to predict the U-opaque value of a building with a correlation coefficient of 0.967 with the real U-opaque measured value for the same building