Table 1 Bibliography related to exposure modeling for a multilevel approach
From: Characterizing environmental geographic inequalities using an integrated exposure assessment
References | Type of assessment | Main input data | Model | Major outcomes / breakthroughs |
|---|---|---|---|---|
Bulle et al, 2019 [5] | Life cycle impact assessment | Emission and exposure data | IMPACT World+ | Novel framework that includes recent methodological advances in multiple impact categories in a consistent way by implementing the same modeling structure of fate, exposure, exposure response, and severity across ecosystem quality and human health-related impact categories. |
Ciffroy et al, 2015 [6] | Integrated Risk Assessment | Emission and exposure data | MERLIN-Expo: fate and exposure model, non-spatial model | Key points for integration across the human and environmental disciplines is the move from environmental fate and exposure estimations to the internal dose in the exposure assessment |
Nieuwenhuijsen et al, 2019 [7] | Environmental epidemiology; exposure-wide association study | Built environment, air pollution, road traffic noise, meteorology, natural space, and road traffic | Proximity models, interpolation models, Land Use Regression models, dispersion models | First large urban exposome study of birth weight that tests many environmental urban exposures. It confirmed previously reported associations for green space exposure and generated new hypotheses for a number of built-environment exposures. |
Vrijheid et al, 2020 [8] | Environmental epidemiology; exposure-wide association study | Indoor and outdoor air pollutants, built environment, green spaces, tobacco smoking, and biomarkers of chemical pollutants | Proximity models, interpolation models, Land Use Regression models, dispersion models | First comprehensive and systematic analysis of many suspected environmental obesogens strengthens evidence for an association of smoking, air pollution exposure, and characteristics of the built environment with childhood obesity risk. |
Juarez et al, 2014 [9] | Spatio-temporal and multilevel approach for examining exogenous and endogenous source-exposure-disease relationships | Natural, built, social and policy environment variables | Spatial and multi-level statistic approach | Retrospective and prospective systems theory modeling and methods, including advanced and complex multi-level, spatial, Bayesian, and high throughput mathematical designs. Use of data-driven, graph theory/combinatorial techniques and analytics from computational biology to identify relationships among the myriad of environmental exposure and population health data points. |
Teeguarden et al, 2016 [10] | Aggregate exposure assessments | Emission, environmental concentration, population behavior and physiology | Aggregate Exposure Pathway | Development of the Aggregate Exposure Pathway concept as the organizational framework for exposure science, builds on the long history of aggregate exposure assessments as a key feature of the field and recent technological advances in computational exposure modeling and informatics. |
Bravo et al, 2012 [11] | Data sampling and data reprensentativeness | Monitoring data, emission and meteorological data | Community Multi-Scale Air Quality (CMAQ) modeling system | Spatial and temporal resolution improvement and uncertainty reduction |
Malherbe et al, 2002 [12] | Data sampling and data reprensentativeness | Topsoil concentration data | Statistical (probabilistic) vs. non-statistical (directed) approaches | Procedure that could be followed to design a soil sampling strategy for human health risk assessment |
Caudeville et al, 2012 [13] | Spatial human exposure | Topsoil concentration data | Geostatistic and Modul’ERS model | Complex geostatical method used for human exposure assessment |
Chakraborty et al, 2011 [14] | Environmental justice and health risk disparities | Air concentration data, ethnicities, cancer rate | Simultaneous autoregressive (SAR) models | Spatial regression models for assessing environmental justice and health risk disparities |
Goovaerts, 2001 [15] | Spatial environmental contamination | Topsoil and parental material data | Several kriging models | Modelling of uncertainty for single continuous soil attributes. The issue of assessing the goodness of such models has rarely been addressed and criteria similar to the ones introduced here could be developed. |
Jerrett et al, 2005 [16] | Spatial environmental contamination | Emission, topology, meteorological, air concentation | Proximity models, interpolation models, Land Use Regression models, dispersion models | Review of the current state of knowledge for intraurban air pollution exposure assessment. |
Cattle et al, 2002 [17] | Spatial environmental contamination | Topsoil concentration data | Kriging model | Comparison of different inteprolation methods applied for air pollution |
Kanevski et al, 2009 [18] | Spatial environmental contamination | Spatial environmental data | Machine learning models | Application of machine learning methods for solving the problems of spatial dimension. Most machine learning literatures address on algorithms and models for solving non-spatial problems. |
Van de Kassteele et al, 2009 [19] | Spatial environmental contamination | Emission, topology, meteorological, air concentation | External drift kriging method | Combination of observations and a deterministic dispersion modeldescription to propose a model-based geostatistical interpolation procedure. |
Breiman, 2001 [20] | Spatial environmental contamination | 14 variables about physicochemical soil properties | Hybrid regression-kriging fitted using Random Forest models | Application of machine learning methods for solving the problems of spatial dimension on environmental thematic |
Ioannidou et al, 2018 [21] | Integrated spatial human exposure | Water, air, soil, food, behavorial data | PLAINE and Modul’ERS | Proposition of an aggregated exposure assessment approach based on on modeling and monitoring network at a national scale. Adapted method for each environmental compartment are adapted for existing monitoring networks |
Guerreiro et al, 2016 [22] | Health impact | Emission, topology, meteorological, air concentation | Chimere and kriging model | Combining observations and chemical transport models through the use of spatial interpolation methods at a continental scale |
Ratola et Jiménez-Guerrero, 2015 [23] | Spatial environmental contamination | Emission, topology, meteorological, air concentation | Chimere and vegetation transfer model | Combining venegetation concentration observations and chemical transport models through the use of transfer model |
Pennington et al, 2005 [24] | Spatial human exposure | Emission, topology, meteorological, air concentation | IMPACT Western Europe | The model facilitates estimation of concentration profiles of dispersed contaminants and human intake at the population level. The results are presented in the form of intake fractions, the fraction of an emission that will be taken in by the entire population. |
Gerlowski et Jain, 1983 [25] | Toxicokinetic modeling and internal exposure | Physiological and exposure data | Toxicokinetic model | First review of physiologically based pharmacokinetics to increase the use of this modeling technique. |
Quindroit et al, 2019 [26] | Toxicokinetic modeling and internal exposure | Physiologicaln ingestion, inhlation and dermal exposure data | Toxicokinetic model | Global model for pyrethroids in humans using in vivo, in vitro and in silico data. |