16
«(Big) Data for Env. Monitoring, Public Health and Verifiable Risk Assessments- New technologies with innovative handling with data gaps» 1. Five Cases of big handling in the past (1854), 2. State-of-the-art uses, 3. The prospects ahead. Andreas N. Skouloudis [email protected]

(Big) data for env. monitoring, public health and verifiable risk assessment-new technologies with innovative handling with data gaps

Embed Size (px)

Citation preview

«(Big) Data for Env. Monitoring, Public Health and Verifiable

Risk Assessments- New technologies with innovative handling

with data gaps»

1. Five Cases of big handling in the past (1854), 2. State-of-the-art uses, 3. The prospects ahead.

Andreas N. Skouloudis [email protected]

Mapping example of near (real)-time process

• Cartography identified the origins of the cholera during the London Broad Street epidemic in 1854.

• The containment of the epidemic was effective when the water pump was sealed at the Soho Broad Street.

Long-term temporal Hazards

Short-term acute events

Pre and Post tsunami image 26 Dec 2004 at the Malacca village.

Dr RAMESH C DHIMAN National Institute of Malaria Research

Short-term very acute events

Pre and Post Fukushima tsunami images 11 Mar 2011

http://wn.com/fukushima_before_and_after_explosions_satellite_photos

Future needs involve Several Disciplines (health)

2d AutoOil Programme

1. Select modelling periods for annual mean and episodes;

2. Input data on land use, topography, meteorology (multi-layer), and emissions

(PiG) in order to characterise each modelling domain;

3. Definition of three dimensional wind patterns using meteorological models with

two-way nesting;

4. Calculation of concentrations of different pollutants using full photochemistry;

5. Validation of the modelling results;

6. Adjustment of 1995 emission inventories to 2010;

7. Simulation runs for 2010 and comparison with objectives;

8. Development of emission reduction targets and simplified emission/air quality

relationships (source apportionment);

9. Investigation of alternative emission scenarios (sensitivity tests);

10. Generalisation for all cities in the 10 domains

(1065 towns, or 46% of EU15 urban population or 27% of all EU25 pop).

Actual measurements in 2010

BIG Data analysed 91,980,000 hourly records (2years*365days*24hours*5species*1050geo-locations)

Environmental Monitoring …

• Satellite and UAVs for

covered areas:

High resolution of affected areas.

High revisit periods are essential.

• In-situ climate sensors:

Real-time datasets compact (weather) stations.

Not rely only on synoptic observations.

Climate advancements

Examples … 1. GISS ModelE2. 2. Ron L. Miller et.al. measurements since 1850 AGU at the

J. of Advances in Modelling Earth Systems.

Big data for regulatory applications

• Real-time 10min met data

Examples … 1. Eliminate the use/uncertainity of questionnaires. 2. Harmonize highly heterogeneous data, fill data gaps

and verification of population effects. 3. Deal with questions of society and ethics.

• Personal activity data

Population density maps from mob telephones

Francesco Pantisano EUR Report 27361 and http://opencellid.org

… sensors and underwater robots

•…divers collect and sending samples back to the lab to

be tested,

•This FP7 robot makes this process real-time with

chemical sensors that makes these tests in-situ.

•…3000 buoys deployed at seas for conventional data

(GEOSS)

… sensors for citizen needs TrackR

• …ideal for practical applications but,

• Essential for real-time security and

vital intervention in emergencies in-

situ (earthquakes).

Final remarks Areas and Specific Efforts • Environmental monitoring per sec has

consequences for proliferation of data and for pushing research to a new generation of tools;

• There is always a temporal lag in integrating layers of information for environmental monitoring & health and this can effect cumulative population exposure;

• Regulatory applications can significantly advance in

combination with new monitoring tools (telematic use, citizens, traffic counts, RS etc);

• Big data are already available for several areas applications and for assessing specific occupational hazards. It is the handling that redressing.

• Big data are useless if not aiming to resolve

problems that remain unsolved until now.