Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory

Shi, Bin1; Jiang, Jiping2; Sivakumar, Bellie3,4,5; Zheng, Yi2; Wang, Peng1,6

2018-05

10.1016/j.watres.2018.01.057

WATER RESEARCH   影响因子和分区

0043-1354

Field monitoring strategy is critical for disaster preparedness and watershed emergency environmental management. However, development of such is also highly challenging. Despite the efforts and progress thus far, no definitive guidelines or solutions are available worldwide for quantitatively designing a monitoring network in response to river chemical spill incidents, except general rules based on administrative divisions or arbitrary interpolation on routine monitoring sections. To address this gap, a novel framework for spatial-temporal network design was proposed in this study. The framework combines contaminant transport modelling with discrete entropy theory and spectral analysis. The water quality model was applied to forecast the spatio-temporal distribution of contaminant after spills and then corresponding information transfer. indexes (ITIs) and Fourier approximation periodic functions were estimated as critical measures for setting sampling locations and times. The results indicate that the framework can produce scientific preparedness plans of emergency monitoring based on scenario analysis of spill risks as well as rapid design as soon as the incident happened but not prepared. The framework was applied to a hypothetical spill case based on tracer experiment and a real nitrobenzene spill incident case to demonstrate its suitability and effectiveness. The newly-designed temporal-spatial monitoring network captured major pollution information at relatively low costs. It showed obvious benefits for follow-up early-warning and treatment as well as for aftermath recovery and assessment. The underlying drivers of ITIs as well as the limitations and uncertainty of the approach were analyzed based on the case studies. Comparison with existing monitoring network design approaches, management implications, and generalized applicability were also discussed. (C) 2018 Elsevier Ltd. All rights reserved.

Chemical spills Emergency monitoring network Discrete entropy theory Information transfer index Contaminant transport model Fourier approximation

SCI ; EI

英语

NI期刊

通讯

Southern University of Science and Technology[G01296001]

Engineering ; Environmental Sciences & Ecology ; Water Resources

Engineering, Environmental ; Environmental Sciences ; Water Resources

WOS:000427334200014

PERGAMON-ELSEVIER SCIENCE LTD

20180604775915

Disaster prevention ; Entropy ; Environmental management ; Monitoring ; Oil spills ; Pollution detection ; Risk assessment ; Risk perception ; Spectrum analysis ; Uncertainty analysis ; Water quality

Water Analysis:445.2 ; Water Pollution:453 ; Water Pollution Sources:453.1 ; Environmental Engineering, General:454.1 ; Thermodynamics:641.1 ; Accidents and Accident Prevention:914.1 ; Probability Theory:922.1

ENVIRONMENT/ECOLOGY

Web of Science

1.Harbin Inst Technol, Sch Environm, Harbin 150090, Heilongjiang, Peoples R China
2.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
3.Univ New South Wales, Sch Civil & Environm Engn, Sydney, NSW 2052, Australia
4.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA
5.Indian Inst Technol, Dept Civil Engn, Powai 400076, India
6.Harbin Inst Technol, State Key Lab Urban Water Resource & Environm, Harbin 150090, Heilongjiang, Peoples R China

Shi, Bin,Jiang, Jiping,Sivakumar, Bellie,et al. Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory[J]. WATER RESEARCH,2018,134:140-152.

Shi, Bin,Jiang, Jiping,Sivakumar, Bellie,Zheng, Yi,&Wang, Peng.(2018).Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory.WATER RESEARCH,134,140-152.

Shi, Bin,et al."Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory".WATER RESEARCH 134(2018):140-152.