Integrating modelling and smart sensors for environmental and human health. (December 2015)

• Record Type: Journal Article
• Authors: Reis, Stefan; Seto, Edmund; Northcross, Amanda; Quinn, Nigel W.T.; Convertino, Matteo; Jones, Rod L.; Maier, Holger R.; Schlink, Uwe; Steinle, Susanne; Vieno, Massimo; Wimberly, Michael C.
• Is Part Of: Environmental modelling & software. Volume 74(2015:Dec.)
• Page Start: 238
• Page End: 246
• Publication Date: 2015-12
• Subjects: Integrated modelling -- Environmental sensors -- Population health -- Environmental health -- Big data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.06.003
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Sensors and models play vital roles in harnessing 'Big Data' to extract information. Data analytics can help to diminish monitoring burden and support locating sensors. Exploring 'Big Data' is essential to detect universal associations across space and time. Ethical challenges and issues of standards and harmonisation need to be addressed. Citizen science needs robust sensors and models to crowd-source and interpret data.