title: Integrating modelling and smart sensors for environmental and human health.
abstract: Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing 'Big Data' and, more importantly, make the vital step from 'Big Data' to 'Big Information'. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.
authors.affiliation: Natural Environment Research Council, Centre for Ecology & Hydrology, Bush Estate, Penicuik, EH26 0QB, United Kingdom ; University of Exeter Medical School, Knowledge Spa, Truro, TR1 3HD, United Kingdom.
authors.forename: Stefan
authors.initials: S
authors.lastname: Reis
authors.affiliation: Department of Occupational & Environmental Health Sciences, School of Public Health, University of Washington, 1959 Pacific Street, Seattle, WA 98195, USA.
authors.forename: Edmund
authors.initials: E
authors.lastname: Seto
authors.affiliation: Department of Environmental and Occupational Health, George Washington University, 950 New Hampshire Ave. NW, Washington, DC 20052, USA.
authors.forename: Amanda
authors.initials: A
authors.lastname: Northcross
authors.affiliation: HydroEcological Engineering Advanced Decision Support, Berkeley National Laboratory, 1 Cyclotron Road Bld 64-209, Berkeley, CA 94720, USA.
authors.forename: Nigel W T
authors.initials: NW
authors.lastname: Quinn
authors.affiliation: HumNat Lab, School of Public Health - Division of Environmental Health Sciences & Health Informatics Program, Institute on the Environment and Institute for Engineering in Medicine University of Minnesota, Twin-Cities USA.
authors.forename: Matteo
authors.initials: M
authors.lastname: Convertino
authors.affiliation: Department of Chemistry, Cambridge University, United Kingdom.
authors.forename: Rod L
authors.initials: RL
authors.lastname: Jones
authors.affiliation: School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide SA 5005, Australia.
authors.forename: Holger R
authors.initials: HR
authors.lastname: Maier
authors.affiliation: UFZ Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany.
authors.forename: Uwe
authors.initials: U
authors.lastname: Schlink
authors.affiliation: Institute of Occupational Medicine, Research Avenue North, Riccarton, Edinburgh, EH14 4AP, United Kingdom.
authors.forename: Susanne
authors.initials: S
authors.lastname: Steinle
authors.affiliation: Natural Environment Research Council, Centre for Ecology & Hydrology, Bush Estate, Penicuik, EH26 0QB, United Kingdom.
authors.forename: Massimo
authors.initials: M
authors.lastname: Vieno
authors.affiliation: Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007-3510, USA.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Wimberly
keywords: big data; environmental health; environmental sensors; integrated modelling; population health