Multivariate Modeling for Spatio-Temporal Radon Flux Predictions
Nowadays, various fields in environmental sciences require the availability of appropriate techniques to exploit the information given by multivariate spatial or spatio-temporal observations. In particular, radon flux data which are of high interest to monitor greenhouse gas emissions and to assess...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/7/1104 |
| _version_ | 1797589397631139840 |
|---|---|
| author | Sandra De Iaco Claudia Cappello Antonella Congedi Monica Palma |
| author_facet | Sandra De Iaco Claudia Cappello Antonella Congedi Monica Palma |
| author_sort | Sandra De Iaco |
| collection | DOAJ |
| description | Nowadays, various fields in environmental sciences require the availability of appropriate techniques to exploit the information given by multivariate spatial or spatio-temporal observations. In particular, radon flux data which are of high interest to monitor greenhouse gas emissions and to assess human exposure to indoor radon are determined by the deposit of uranium and radio (precursor elements). Furthermore, they are also affected by various atmospheric variables, such as humidity, temperature, precipitation and evapotranspiration. To this aim, a significant role can be recognized to the tools of multivariate geostatistics which supports the modeling and prediction of variables under study. In this paper, the spatio-temporal distribution of radon flux densities over the Veneto Region (Italy) and its estimation at unsampled points in space and time are discussed. In particular, the spatio-temporal linear coregionalization model is identified on the basis of the joint diagonalization of the empirical covariance matrices evaluated at different spatio-temporal lags and is used to produce predicted radon flux maps for different months. Probability maps, that the radon flux density in the upcoming months is greater than three historical statistics, are then built. This might be of interest especially in summer months when the risk of radon exhalation is higher. Moreover, a comparison with respect to alternative models in the univariate and multivariate context is provided. |
| first_indexed | 2024-03-11T01:06:05Z |
| format | Article |
| id | doaj.art-d00ffc0845c64a4d91c80e0c0ca4078a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T01:06:05Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-d00ffc0845c64a4d91c80e0c0ca4078a2023-11-18T19:14:53ZengMDPI AGEntropy1099-43002023-07-01257110410.3390/e25071104Multivariate Modeling for Spatio-Temporal Radon Flux PredictionsSandra De Iaco0Claudia Cappello1Antonella Congedi2Monica Palma3National Future Center of Biodiversity, 90133 Palermo, ItalyDES-Sect. of Mathematics and Statistics, University of Salento, 73100 Lecce, ItalyDES-Sect. of Mathematics and Statistics, University of Salento, 73100 Lecce, ItalyDES-Sect. of Mathematics and Statistics, University of Salento, 73100 Lecce, ItalyNowadays, various fields in environmental sciences require the availability of appropriate techniques to exploit the information given by multivariate spatial or spatio-temporal observations. In particular, radon flux data which are of high interest to monitor greenhouse gas emissions and to assess human exposure to indoor radon are determined by the deposit of uranium and radio (precursor elements). Furthermore, they are also affected by various atmospheric variables, such as humidity, temperature, precipitation and evapotranspiration. To this aim, a significant role can be recognized to the tools of multivariate geostatistics which supports the modeling and prediction of variables under study. In this paper, the spatio-temporal distribution of radon flux densities over the Veneto Region (Italy) and its estimation at unsampled points in space and time are discussed. In particular, the spatio-temporal linear coregionalization model is identified on the basis of the joint diagonalization of the empirical covariance matrices evaluated at different spatio-temporal lags and is used to produce predicted radon flux maps for different months. Probability maps, that the radon flux density in the upcoming months is greater than three historical statistics, are then built. This might be of interest especially in summer months when the risk of radon exhalation is higher. Moreover, a comparison with respect to alternative models in the univariate and multivariate context is provided.https://www.mdpi.com/1099-4300/25/7/1104multiple correlationspace–time coregionalization modelspace–time predictioncokriging |
| spellingShingle | Sandra De Iaco Claudia Cappello Antonella Congedi Monica Palma Multivariate Modeling for Spatio-Temporal Radon Flux Predictions Entropy multiple correlation space–time coregionalization model space–time prediction cokriging |
| title | Multivariate Modeling for Spatio-Temporal Radon Flux Predictions |
| title_full | Multivariate Modeling for Spatio-Temporal Radon Flux Predictions |
| title_fullStr | Multivariate Modeling for Spatio-Temporal Radon Flux Predictions |
| title_full_unstemmed | Multivariate Modeling for Spatio-Temporal Radon Flux Predictions |
| title_short | Multivariate Modeling for Spatio-Temporal Radon Flux Predictions |
| title_sort | multivariate modeling for spatio temporal radon flux predictions |
| topic | multiple correlation space–time coregionalization model space–time prediction cokriging |
| url | https://www.mdpi.com/1099-4300/25/7/1104 |
| work_keys_str_mv | AT sandradeiaco multivariatemodelingforspatiotemporalradonfluxpredictions AT claudiacappello multivariatemodelingforspatiotemporalradonfluxpredictions AT antonellacongedi multivariatemodelingforspatiotemporalradonfluxpredictions AT monicapalma multivariatemodelingforspatiotemporalradonfluxpredictions |