Using Bayesian networks for environmental health risk assessment

Pérez, Sandra; German-Labaume, Catherine; Mathiot, Sébastien; Goix, Sylvaine; Chamaret, Philippe

doi:10.1016/j.envres.2021.112059

Cited by 14 publications

(5 citation statements)

References 49 publications

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“…Since the nodes are modeled through probability distributions, the uncertainty can be estimated more accurately. It has been applied in many fields such as accident analysis, environmental risk simulation, and decision analysis (Barton et al., 2008; He et al., 2022; Pérez et al., 2022; Shao et al., 2017; Vagnoli & Remenyte‐Prescott, 2022). Laurila‐Pant et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Capturing and quantifying the aggregate effects of multi‐source factors affecting miners' health and well‐being: Construction of Bayesian belief networks

Zhou,

Sun,

Wang

et al. 2023

Stress and Health

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Factors originating at the organizational, work, and individual levels are closely interrelated and intricately intertwined, affecting health rates. There was limited research on the interdependence and aggregate effects between multi‐source factors and occupational health and well‐being (OHW). It is challenging to achieve management goals. Therefore, considering cross‐level factors and across the “work environment‐stress‐exposure‐OHW” chain, individual vulnerability was considered. A Fuzzy Bayesian Belief Network (FBBN) driven by both domain knowledge and data was constructed to carve out the logic between multi‐source factors and OHW. Workers from four coal mines were surveyed twice in 6 months. 714 valid samples were included in the analysis. The interdependencies among multi‐source factors were identified by the Interpretive Structure Modeling method and the visual probability estimation was achieved based on FBBN. It revealed that the work and the organizational level were the root factors. Eight factors involved in work stress were mainly mediating, and actual exposure and individual vulnerability were direct factors. Pathway interventions and joint interventions were proposed. The prediction ability and scheme feasibility of FBBN were verified. The approach developed allows robust assessments of aggregate effects and obtains multi‐source factor importance. This study provides vital insights and evaluation tools for understanding workplace stress and OHW management.

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Section: Literature Reviewmentioning

confidence: 99%

Capturing and quantifying the aggregate effects of multi‐source factors affecting miners' health and well‐being: Construction of Bayesian belief networks

Zhou,

Sun,

Wang

et al. 2023

Stress and Health

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show abstract

“…The urban lifestyle is influenced by the quality of outdoor environments, as these spaces accommodate several social, cultural, and commercial activities, which raises the importance of providing attractive and comfortable outdoor places [12]. However, the urbanization process intrinsically brings negative consequences to environmental quality, as the UHI [3] and air pollution [5]. These issues are worsened when combined with the phenomena caused by global warming, such as heat waves (HWs).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Urban overheating has serious impacts on urban environmental quality and has been already associated with an increase in the local vulnerability and the heat-related mortality and morbidity [4]. Air quality is another concern in urban areas due to the pollution related to anthropogenic activities (e.g., industrial activities and the transport sector) [5]. Indeed, air pollution is considered the principal environmental risk to human health nowadays [6], and some studies already stated that air pollution exposure is associated with higher levels of mortality and higher incidences of cardiovascular and respiratory diseases [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A New Wearable System for Sensing Outdoor Environmental Conditions for Monitoring Hyper-Microclimate

Cureau

Pigliautile

Pisello

2022

Sensors

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The rapid urbanization process brings consequences to urban environments, such poor air quality and the urban heat island issues. Due to these effects, environmental monitoring is gaining attention with the aim of identifying local risks and improving cities’ liveability and resilience. However, these environments are very heterogeneous, and high-spatial-resolution data are needed to identify the intra-urban variations of physical parameters. Recently, wearable sensing techniques have been used to perform microscale monitoring, but they usually focus on one environmental physics domain. This paper presents a new wearable system developed to monitor key multidomain parameters related to the air quality, thermal, and visual domains, on a hyperlocal scale from a pedestrian’s perspective. The system consisted of a set of sensors connected to a control unit settled on a backpack and could be connected via Wi-Fi to any portable equipment. The device was prototyped to guarantee the easy sensors maintenance, and a user-friendly dashboard facilitated a real-time monitoring overview. Several tests were conducted to confirm the reliability of the sensors. The new device will allow comprehensive environmental monitoring and multidomain comfort investigations to be carried out, which can support urban planners to face the negative effects of urbanization and to crowd data sourcing in smart cities.

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“…Mkrtchyan et al [ 12 ] addressed insurability risk assessment using Bayesian Belief Networks in order to identify refineries that are at the risk of fire and explosion and estimated the associated risk levels. Perez et al [ 13 ] studied environmental health risk assessment using BN in order to identify the correlation between air pollution and pathologies in an industrial area. According to the sustainable development objectives, industrial processes should be designed in such a way that the environmental impacts of the processes through the efficient waste treatment systems are solved effectively.…”

Section: Introductionmentioning

confidence: 99%

Contribution of Bayesian networks as a robust tool in risk assessment under sustainability considerations, a case study of Bandarabbas refinery

Saeedi¹,

Malekmohammadi²

2023

Heliyon

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Using Bayesian networks for environmental health risk assessment

Cited by 14 publications

References 49 publications

Capturing and quantifying the aggregate effects of multi‐source factors affecting miners' health and well‐being: Construction of Bayesian belief networks

Capturing and quantifying the aggregate effects of multi‐source factors affecting miners' health and well‐being: Construction of Bayesian belief networks

A New Wearable System for Sensing Outdoor Environmental Conditions for Monitoring Hyper-Microclimate

Contribution of Bayesian networks as a robust tool in risk assessment under sustainability considerations, a case study of Bandarabbas refinery

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