Apportioning radon contamination sources in underground spaces using a grey-box model

被引:0
|
作者
Choi, Yijune [1 ]
Lee, Soonjae [1 ]
机构
[1] Korea Univ, Dept Earth & Environm Sci, 145 Anam Ro, Seoul 02841, South Korea
基金
新加坡国家研究基金会;
关键词
Indoor Air Quality; Geogenic Radon; Apportionment Sources; Grey-Box Model; INDOOR AIR-QUALITY; ENVIRONMENTAL-FACTORS; ENTRY; SOIL; BASEMENT;
D O I
10.1016/j.jhazmat.2024.136707
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The management of radon in indoor environments is essential owing to its carcinogenic risk, with the main sources being gaseous radon from advection and aqueous radon from degassing. However, methods for apportioning indoor radon sources are limited, due to gaps between preliminary physical information and site-specific measurement. This study proposes a method for apportioning sources utilizing a grey-box model that integrates physical theories with data information. Monitoring of indoor air conditioning was conducted and a grey-box model for indoor radon contamination was established using a signal processing system and a physical-based model. Based on the optimized grey-box model, sources of indoor radon were apportioned. The average radon concentration during the monitoring period was 1104 Bq/m3, which was 7.5 times higher than 148 Bq/m3 , the action level recommended by EPA. The grey-box model showed good performance for predicting indoor radon concentrations. Degassing of aqueous radon was identified as the main source of indoor radon. The estimations provided by the grey-box model showed its potential to assess the contribution of each transport mechanism. Apportioning sources through grey-box modeling is a promising method for developing optimized mitigation strategies and bridging data gaps between the preliminary conceptual site model and site investigation.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Sensor fault diagnosis in inland navigation networks based on a grey-box model
    Segovia, P.
    Blesa, J.
    Duviella, E.
    Rajaoarisoa, L.
    Nejjari, F.
    Puig, V.
    IFAC PAPERSONLINE, 2018, 51 (24): : 742 - 747
  • [42] Grey-box modeling and model predictive control for cascade-type PEMFC
    Barzegari, Mohammad M.
    Alizadeh, Ebrahim
    Pahnabi, Amir H.
    ENERGY, 2017, 127 : 611 - 622
  • [43] Using a utility system grey-box model as a support tool for progressive energy management and automation of buildings
    Vítězslav Máša
    Michal Touš
    Martin Pavlas
    Clean Technologies and Environmental Policy, 2016, 18 : 195 - 208
  • [44] Using a utility system grey-box model as a support tool for progressive energy management and automation of buildings
    Masa, Vitezslav
    Tous, Michal
    Pavlas, Martin
    CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2016, 18 (01) : 195 - 208
  • [45] Machine learning assisted identification of grey-box hot metal desulfurization model
    Vuolio, Tero
    Visuri, Ville-Valtteri
    Sorsa, Aki
    Paananen, Timo
    Tuomikoski, Sakari
    Fabritius, Timo
    MATERIALS AND MANUFACTURING PROCESSES, 2023, 38 (15) : 1983 - 1996
  • [46] Adaptive Linear Grey-Box Models for Model Predictive Controller of Residential Buildings
    Yu, Xingji
    Georges, Laurent
    Imsland, Lars
    BUILDSIM NORDIC 2022, 2022, 362
  • [47] Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation
    Gonzalez, Rodrigo A.
    Quintanilla, Paulina
    2024 10TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES, CODIT 2024, 2024, : 2967 - 2972
  • [48] Adaptive control of CSTR using feedback linearization based on grey-box modeling
    Hourfar, Farzad
    Salahshoor, Karim
    PROCEEDINGS OF 2008 IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL, VOLS 1 AND 2, 2008, : 7 - +
  • [49] Modelling the heat consumption in district heating systems using a grey-box approach
    Nielsen, HA
    Madsen, H
    ENERGY AND BUILDINGS, 2006, 38 (01) : 63 - 71
  • [50] SQIRL: Grey-Box Detection of SQL Injection Vulnerabilities Using Reinforcement Learning
    Al Wahaibi, Salim
    Foley, Myles
    Maffeis, Sergio
    PROCEEDINGS OF THE 32ND USENIX SECURITY SYMPOSIUM, 2023, : 6097 - 6114