Toward real-time management of bathing water quality a tale of two smart cities. Hanne Kaas, Stephen Flood, Matthew Easton, DHI Ltd.

Transcription

1 Toward real-time management of bathing water quality a tale of two smart cities Hanne Kaas, Stephen Flood, Matthew Easton, DHI Ltd.

2 In Denmark, it all started in the capital Copenhagen Pollution: Sewage water Ship trafic Industries Since 1990th: ca. 130 mio investments to improve quality

3 Second largest city Aarhus Also major investments to get better management and better water quality

4 Since 2002 in Copenhagen - Since 2010 in Aarhus Requested: smart management system for early warning of bathing water quality People should feel safe The traditional monitoring is not sufficient

5 The challenge TIME Outfalls Sampling FREQUENCY

6 The smart solution Bathing Water Forecast service Aarhus Real-time subscription service Copenhagen Informing about bathing water quality (and other water and weather conditions) 2017: 95 beaches in Denmark Similar services in Sweden and New Zealand DHI Working 24/7

7 DHI A new city facility in 2015 Havnevigen

8 Water Forecast rain in Copenhagen 14. August 2010

9 Water forecast Triathlon 15 August 2010 Start kl. 6 UTC tid (lokaltid = UTC+2)

10 In total 778 (59 %) answered the questionaire send out by the SSI iron men was swimming in sewer water (Politikken, 20. Aug) Out of these 428 (55 %) confirmed symptoms of gastroenteritis. Ref.: SSI a lot of debate, and people getting sick, but the Model was spot on

11 The Bathing Water Service Three main components Input data Machine room Publication Discharges Weather SQL databases Water boundary HD ECO WEB DHI 2012

12 The core of the machine room Dynamic hydrodynamic-bacterial model complexes simulating the water quality of the recipient waters to-day and 3 days ahead 2 times a day or more at overflow DHI 2012 software: MIKE 3D FM HD ECO Lab solver

13 Water Forecast Centre Watch operation 24/7 Automatic surveys Watchkeeper staff Bathing water models M11 stream model log Communication with water utility

14 Water boundary forecast from DHIs Water Forecast Weather forecast from Met data provider (StormGeo) Online high frequency load data DHI

15 Inventory of important pollution sources > identify the right tools Sensors DHI DIMS.CORE Data Integration Management System

16 Workflow for load data at Water Utility MIKE 11 + MIKE Urban Aarhus DIMS.CORE BW SQL MIKE 11 CPH MIKE 3D FM ECO Lab DHI DHI Monitoring Integration and control tool Web API at BW Water Service

17 Public website Web API Public bathing water APP Signs at the beaches Managers website sms/ alerts Click on a flag DHI bathing water APP

18 Manager website providing more info Animations Look up latest sewage water discharges Overrule Evaluation DHI

19 And yearly evaluations CPH conclusion season measurements Considering uncertainty in measurements and model Agreement E. coli 88% Agreement enterococci 86% 142 agreements: quality is good (E.coli 500, enterococci 200) 2 agreements: quality is poor (>500, >200) 1 case disagreement; model good, measurement poor => 99% of matchups agreement in resulting colour of flag DHI

20 MIKE OPERATIONS Desktop MIKE OPERATIONS Web Tailored web solutions MIKE Workbench dbase Time-series Spreadsheets GIS Scripts Jobs DHI

21 MIKE OPERATIONS Forecasting workflow Generic workflow components shared by forecasting systems Data retrival Data storage QA/QC Model Execution Postprocess output Publish DHI

22 Another benefit Having a BW model makes it possible to test scenarios Amager Strand Importance of different outfalls Importance of rain water outfalls Best measures to implement Length of sea pipelines Storage capacity Etc. DHI

23 Own work load High In house installation Management of system Professional insight Various internediate solutions Modelling by service provider Publication in house Minor Complete, subscribed service DHI Work load at service provider

24 THANKS YOU Hanne Kaas Steve Flood