Integrating modelling and smart sensors for environmental and human health

被引:79
|
作者
Reis, Stefan [1 ,12 ]
Seto, Edmund [2 ]
Northcross, Amanda [3 ]
Quinn, Nigel W. T. [4 ]
Convertino, Matteo [5 ,6 ,7 ]
Jones, Rod L. [8 ]
Maier, Holger R. [9 ]
Schlink, Uwe [10 ]
Steinle, Susanne [13 ]
Vieno, Massimo [1 ]
Wimberly, Michael C. [11 ]
机构
[1] NERC, Ctr Ecol & Hydrol, Penicuik EH26 0QB, Midlothian, Scotland
[2] Univ Washington, Sch Publ Hlth, Dept Occupat & Environm Hlth Sci, Seattle, WA 98195 USA
[3] George Washington Univ, Dept Environm & Occupat Hlth, Washington, DC 20052 USA
[4] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, HydroEcol Engn Adv Decis Support, Berkeley, CA 94720 USA
[5] Univ Minnesota, Inst Environm, HumNat Lab, Sch Publ Hlth,Div Environm Hlth Sci, St Paul, MN USA
[6] Univ Minnesota, Inst Environm, Hlth Informat Program, St Paul, MN USA
[7] Univ Minnesota, Inst Engn Med, St Paul, MN USA
[8] Univ Cambridge, Dept Chem, Cambridge CB2 1TN, England
[9] Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA 5005, Australia
[10] UFZ Helmholtz Ctr Environm Res, D-04318 Leipzig, Germany
[11] S Dakota State Univ, Geospatial Sci Ctr Excellence, Brookings, SD 57007 USA
[12] Univ Exeter, Sch Med, Knowledge Spa, Truro TR1 3HD, England
[13] Inst Occupat Med, Edinburgh EH14 4AP, Midlothian, Scotland
基金
英国自然环境研究理事会;
关键词
Integrated modelling; Environmental sensors; Population health; Environmental health; Big data; PERSONAL EXPOSURE; BIG DATA; VARIABLE SELECTION; HUMAN MOBILITY; WATER-QUALITY; POLLUTION; FRAMEWORK; FUSION; IMPACT; WEB;
D O I
10.1016/j.envsoft.2015.06.003
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. Crown Copyright (C) 2015 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
引用
收藏
页码:238 / 246
页数:9
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