Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks
1.2. Radon Related Concepts
1.2.1. Mechanisms and Factors That Boost Radon Leakage into Enclosed Spaces
1.2.2. Criteria for the Application of Corrective Measures
1.2.3. Radon Gas Detection Systems
1.3. Use of Expert Systems in Decision-Support Tools
1.3.1. Expert Systems Applied to the Interpretation of External Factors and Environmental Conditions
1.4. Decision Trees
- Step 1: Start from the root node that contains all the independent variables.
- Step 2: For each independent variable find all the potential splitting points by defining a series of regions or nodes that do not overlap each other, and that are associated to the observable values linked to the predictor variable in each one of those regions.
- Step 3: For each one of the previously identified nodes, the subset S that allows one to minimize the impurity of the node into two descending branches giving place to two child nodes, must be found. As it has already been mentioned, the impurity parameter represents the sum of the squared deviations between the dependent variables’ values and the mean value for the region or node . A group might be considered as pure when all its elements belong to the same region, and as impure when all of its elements belong to different regions . When a node is pure, it might be said that a terminal node—or leaf—has been reached. However, when a node is impure it is necessary to determine if it is wished to stop and accept the obtained group, or else continue performing splits considering other independent variables . As it has been already mentioned, there are several indices to measure impurity—such as Gini for classification problems —but in this case, because a regression case will be applied, the RSS metric becomes important, aiming to its minimization as a metric for more homogeneous nodes.
- Step 4: The former process is repeated recursively through the recursive binary splitting method, selecting the predictor variable and the splitting point that guarantees a lower total RSS value by using the subsequent nodes until the tree reaches the maximum size that was assigned to it.
- Step 5: If a stop criterion is reached then the iteration is halted; else the command will return again to Step 2. In relation to the stop criteria, it is necessary to be careful because an early stop might result in a tree that is very small as to represent the structure of the starting data. On the contrary, a late stop might produce a tree that is too large, or even unstable, and with no useful meaning whatsoever .
- Step 6: Finally, after obtaining the value associated to a prediction line, it is proceeded to calculate the root-mean-square error (RMSE) in order to determine the accuracy and precision of the proposed regression model, by calculating the square root of the mean value of the sum of the squared differences between the predicted and the actual values. That is, starting from the initial data, a measure is established of how far away the predicted and the actual values are .
2. Materials and Methods
2.1. Definition of the Methodology
2.1.1. Previous Considerations
2.1.2. Conceptual Design and Description of the Proposed Methodology
- Environmental and atmospheric sensors.
- Radon sensor.
Data Reading, Processing and Interpretation
Monitoring and Alert Generation
- Preventive actions
- Activate forced evacuation and mechanical ventilation.
- De-activate forced evacuation.
- Natural ventilation.
- Checking actions
- Check sensors.
- Check inference.
- Check exposure time.
2.2. Implementation of the Methodology
2.2.1. First Inference System—Correction Factor
Determination of the Correlation Coefficient
2.2.2. Second Inference System—Radon Risk
2.2.3. Regression Trees and Generation of the Spider-Web Diagram—Establishment of Recommendations
3. Case Study and Results
3.1. Data Collection
3.2. Data Reading, Processing and Interpretation
- Radon concentration value: 84 Bq/m3.
- Indoor temperature: 29.6 °C.
- Outdoor temperature: 6.5 °C.
- Atmospheric pressure: 1014.8 mbar.
- Wind speed: 9.5 km/h.
- Collected rainfall: 1.2 mm.
- Relative humidity: 62.3%.
- Correlation coefficient of radon concentration vs. indoor temperature: 0.2047.
- Correlation coefficient of radon concentration vs. outdoor temperature: −0.08535.
- Correlation coefficient of radon concentration vs. temperature difference: 0.155.
- Correlation coefficient of radon concentration vs. atmospheric pressure: 0.05139.
- Correlation coefficient of radon concentration vs. wind speed: 0.04129.
- Correlation coefficient of radon concentration vs. rainfall: −0.3779.
- Correlation coefficient of radon concentration vs. relative humidity: −0.09206.
3.3. Monitoring and Alert Generation
- Activate forced evacuation: 3.667/5.
- De-activate forced evacuation: 1/5.
- Natural ventilation: 2.5/5.
- Check sensors: 0/5.
- Check inference: 0/5.
- Check exposure time: 0/5.
- Unlike early warning systems, the use of expert systems combined with decision trees is not aimed only at minimizing the impact of radon gas concentrations, but it also intends to mitigate the existing effects and to enrich the knowledge base of the system, in a way that allows to keep inserting and identifying factors influencing those gas concentrations.
- When defined as expert systems, the decisions will always be subject to a final review because, even if they may be understood as multi-criteria decision methods, in no case they encompass all the criteria, viewpoints and scenarios that could show up. So, its preventive effects would be a consequence of its evolution as knowledge-based systems, while their derived decisions will always be interpreted by the users, who at any moment may modify them, either inside or outside the methodology.
- The incorporation of modification mechanisms that are not related to the re-programming of the software artifact is one of the key points of the methodology. It clearly increases its versatility of use as well as its adaptation to different scenarios and circumstances. Said scenarios, in principle related to buildings with a residential use and their associated environmental metrics, may be extended with a mere re-definition of the membership functions and the dependent variables, defining through these all the specific circumstances for each scenario where the methodology is wished to be applied. For example, if its use were focused on underground mines then it would be necessary to re-define the intervals and qualifiers of the input variables to the first inference system, as well as removing and considering recommended actions according to their origins. In any case, those changes would be easily implemented.
- The necessary interpretation of the results derived from the algorithm establishes the nature of expert systems, enriches their knowledge base, and incorporates a qualitative control on the uncertainty that is associated to the definition and application of the methodology. With all that, the uncertainty that is inherent both to data collection and to their qualitative interpretation is reduced. Not only the uncertainty from the randomness of measurements is reduced, but also the epistemic is—this one related to the lack of information—and the one related with the own interaction of the systems, algorithms and data collection processes as well. The fact of supervising the risk determination by means of a correction factor, and the constant monitoring that is provided by the definition of the membership functions, allows an expert user to continuously improve the prediction capability of the methodology.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Technology||Air Sampling||Measurement Technique||Duration of Studies||Description of the Technology|
|Activated carbon detector||Passive||Integrating ||Short-term studies (periods generally shorter than one week)||These are based on the capability of active carbon to retain radon gas . After their use, the amount of gamma radiation emitted may be determined in a laboratory.|
|Electret ion chamber detector||Active and passive variants||Integrating||Depending on the ion chamber design, it may allow to carry out short-term or long-term studies ||This type of devices measure the ionization produced when radon atoms disintegrate inside the chamber .|
|Track-etch detector||Passive||Integrating ||Long-term studies||After exposed, the detector is chemically and/or electrochemically etched and analysed in a laboratory to obtain the average radon concentration from the density of tracks produced by radon and its progeny .|
|Scintillation cells||Active||Grab or Continuous ||Commonly used for carrying out continuous measurements  for both short and long cycles||It consists of a cylindrical device coated with a luminescent material , with one of the walls being transparent, and activated by alpha emissions . The emitted photons are captured and amplified by a photomultiplier tube [46,47], thus allowing to determine the radon gas concentration level in air .|
|Gas-filled detector||Active and passive variants||Grab or continuous ||May be used both for short and for long measurement periods||It is a device in which an interaction is produced between the radioactive particles derived from the radon gas present in a chamber, generating ion couples that are attracted to charged electrodes . It may use different gas mixes such as air, argon with a small amount of methane, and argon or helium with small amounts of any halogen element .|
|Solid-state detector||Active and passive variants||Grab or continuous||Can be used both for short and long measurement cycles.||It is based on the interaction of the emitted radiation with a semi-conductor material that produces electron-hole couples that are then collected by charged electrodes . Depending on the type of design it may focus on one or another radiation type, with germanium and silicon being the most commonly used materials .|
|Guideline 1: Design an Artifact (the Proposed Methodology)|
|The artifact, meaning the methodology as detailed in Section 2.2, consists in a helping tool aimed to the process for the prevention of high indoor concentrations of radon gas. In the first place, the calculation is made for the correction factor, from the information collected by a set of sensors, by means of a fuzzy logic-based inference system. Later, said correction factor is combined with the current radon gas concentration within a second inference system, also based on fuzzy logic, from which the radon risk level is obtained. Finally, from the calculated values and the data history it is possible to train regression models based on a decision tree, that will be used to perform recommendations on the levels at which the corrective measures should be applied to reduce the concentration of radon gas. With the objective of automating the calculations and facilitating the understanding of the proposed methodology, the implementation of the system has been carried out into a software artifact defined using the MATLAB® environment developed by The MathWorks, Inc, Natick, MA, US.|
|Guideline 2: Relevance of the problem|
|The problem derived from the inhalation of radon gas results nowadays to be unquestionable, because it is recognized as the second-leading cause of lung cancer after smoking [1,5,6,7,8,9,11]. That is why it becomes very important the development of a methodology that allows to detect and anticipate potential situations in which the current radon gas concentration might get increased inside a building.|
|Guideline 3: Assessment of the design|
|The application of the new methodology is shown in the case study described in Section 3.|
|Guideline 4: Contributions to the field of research|
|The contributions to the field of expert systems are presented in Section 4 and Section 5 of this article.|
|Guideline 5: Rigour in the research|
|The conceptual development of the presented methodology, together with its classification within the field of investigation, has been defined in Section 1. In the same way, the mathematical foundations of this work are supported on the use of fuzzy inference systems, given their proved effectiveness and their capability for handling uncertainty in decision-making processes.|
|Guideline 6: Design as a search|
|In Section 1, the methodology has been framed within the state of the art that is inherent to the field of study.|
|Guideline 7: Communication of the research|
|In Section 5, the main contributions of the new method are presented, as will be the future lines of work.|
|Prevention of Exposure to Harmful Radon Gas Concentrations|
|Design Needs for the Artifact||Technical Requirements||Restrictions Associated to the Environment|
|The methodological process, from data collection to its interpretation, will need as little user interaction as possible||Programming the sensor data reading, and the automatic filtering and labelling of information |
Programming the autonomous-operation inference models
|Errors or corrections that need the intervention of an expert|
|It must have an interface where the recommendations are shown in a graphical way||Graphical interface||The user must have available a device to run the software|
|It must collect environmental information and store it in a convenient way||Definition of a knowledge base supported by common database systems||The applicable restrictions from the devices and the environments where the software is implemented|
|It must process recursively the information||Continuous reading/writing on the database systems||None|
|It must calculate a risk value associated to the exposure to radon gas||Conjoint implementation of the inference systems together with the regression tree||Limitations associated to the algorithms themselves|
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Cerqueiro-Pequeño, J.; Comesaña-Campos, A.; Casal-Guisande, M.; Bouza-Rodríguez, J.-B. Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks. Int. J. Environ. Res. Public Health 2021, 18, 269. https://doi.org/10.3390/ijerph18010269
Cerqueiro-Pequeño J, Comesaña-Campos A, Casal-Guisande M, Bouza-Rodríguez J-B. Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks. International Journal of Environmental Research and Public Health. 2021; 18(1):269. https://doi.org/10.3390/ijerph18010269Chicago/Turabian Style
Cerqueiro-Pequeño, Jorge, Alberto Comesaña-Campos, Manuel Casal-Guisande, and José-Benito Bouza-Rodríguez. 2021. "Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks" International Journal of Environmental Research and Public Health 18, no. 1: 269. https://doi.org/10.3390/ijerph18010269