Can global observation data reveal local social adaptations … · Can global observation data reveal local social adaptations towards sustainability? Chiho WATANABE Department of

Can global observation data reveal local social adaptations towards sustainability?

Chiho WATANABEDepartment of Human Ecology

School of International Health/Global Health Sciences programGrad School of Medicine, University of Tokyo

ICSS-Asia 2011 @VNU-Hanoi 2011.3.3.

outline

• Global observation

• Social Adaptation

• use of GO on SA taking health endpoints as examples

• infectious diseases (GEO health tasks)

• temperature and mortality

• air pollution and CVD

• Local scale prediction and local scale adaptation(SALSA project)

• Conclusions

Global observation data

• remote (satellite) and/or on site (ground) data

• available/feasible data include –temperature, precipitation, humidity, cloud, fire,GHG, aerosol,air pollution (O3, NO2, CO, SO2, PM) land use and land cover

4

Social adaptation• notes on “Adaptation”

• In “climate change” talk, Mitigation vs ----

• In human biological/ecological context, “responses to changes”

• biological (genetic, physiological-ontogenic) changing self

• cultural changing environment

-- Social adaptation ~ cultural … as behavior, aggregated behavior

… is mostly problem driven, and may be short sited.

-- consequence of adaptation will be sum of biological and cultural adaptation.

• environmental changes social responses to

minimizing the risks in various endpoints including;

• food/water, energy, natural resources, biodiversity, ecosystem; health, QOL; climate/weather, disaster

• Academia - Providing “better” information for decision-making

Social adaptation• “Adaptation”





- Social adaptation ~ cultural … as behavior, aggregated behavior







Climate Change Impacts on Flood Control Plan

in Indonesia

10year Probable flood Current Climate

10year Probable flood 50 years later

























Utilizing GO for SA – Examples from GEO (Group of Earth Observations)

under GEOSS (Global Earth Observation System of Systems)

• showing some examples of how GO can be utilized for SA.

•GEO Health Tasks 2009-2011 (www.earthobservations.org)

• providing/improving information systems ;

Global Health Observatory (by WHO) (www.who.int/gho/en/)

• monitoring and prediction- aerosol impacts: Sand & dust storm Warning Advisory & alert systems

(SDS-WAS. By WMO)

-POPs monitoring, air Hg monitoring

- Air Quality Monitoring/forecasting/informingAirnow: real-time, 5 pollutants,

>300 cities, 4,000 stations

Utilizing GO for SA (continued)

– Examples from GEO Health Tasks 2009-2011 (www.earthobservations.org)

•Helping decision making – Infectious diseases

trans-disciplinary in nature

• JAXA: Sistosomiasis Japonica ….. Flood, human behavior

Meningitis Climate

(“dry winter”)

Agentbacteria

Lime disease

Land use/cover

Forest connected ness

Host mammals

(mice, squirrels)

Vector: Tick

Human contact

(ground data)

MalariaTemperature precipitation

Vegetation (NDVI – in JAXA)

VectorMosquito

Actual incidence (ground data)

http://www.earthobservations.org

Utilizing GO for SA (continued)

– Examples from GEO Health Tasks 2009-2011 (www.earthobservations.org)

•Helping decision making – Infectious diseases

• JAXA: Sistosomiasis Japonica ….. Flood, human behavior

Meningitis Climate

(“dry winter”)

Agentbacteria

Lime disease

Land use/cover

Forest connected ness

Host mammals

(mice, squirrels)

Vector: Tick

Human contact

(ground data)

MalariaTemperature precipitation

Vegetation (NDVI – in JAXA)

VectorMosquito

Actual incidence (ground data)

http://www.earthobservations.org

Ground data-human activity

Jiang （2009）

土地利用と住民の

活動強度の空間分布

0.0

5.0

10.0

15.0

20.0

HP IB IP NC

NK

NP

PK

VH VP

M F

Mean traveled distancekm/daytime (6-19）

Contact with vector/agent

contact with pollutant

exposure to heat/sunlight

accerelometer

GPS

Utilizing GO for SA –Climate change/air pollution and healthnot only limited for infectious diseases

• previous studies

• temperature vs. daily mortality

• Different “optima” of the temperature in different area

• Changing profile of temperature vs mortality• “Seasonal diseases Calendar” by Dr. Momiyama

• London – change in a century

• Air pollution and CVD

Climate <change> and health: previous studies

Hashizume et al. (2009)

Cause-specific mortality in Bangladesh

Curriero et al. (2002) 13 cities in USA


Hashizume et al. (2009)

Cause-specific mortality in Bangladesh

Curriero et al. (2002) 13 cities in USA

Such examples demonstrated regional differences


Implication of U-shape for prediction is not straightforward

Honda and Ono (2009)

Winter months

Summer months


Carson et al. (2006)

Historical change in temp-mortality relationship




Identification of the background factors of the changes

(social, environmental, or behavioral factors)

suggestions for “adaptation” strategies




We have been “adapting”!

Identification of the background factors of the changes

(social, environmental, or behavioral factors)

suggestions for “adaptation” strategies

Utilizing GO for SA –Climate change/air pollution and health

• previous studies

• temperature vs. daily mortality

• Different “optima” of the temperature in different area

• Changing profile of temperature vs mortality• “Seasonal diseases Calendar” by Dr. Momiyama

• London – change in a century

• Air pollution and CVD

Utilizing GO for SA – Air pollution and CVD –

USA, 36 cities

N=65,000, 6 years

Miller (2007)

Based on 36 epidemiological studies,

Meta-analysis applied.

Nowrot(2011)

Revealing overlooked importance.

Local prediction and local adaptation–

Climate change/air pollution and health: SALSA project(Development of Seamless Chemical AssimiLation System and its Application for

Atmospheric Environmental Materials; Prof. Nakajima)

• enhanced resolution of GO-based prediction/simulation (in space/ in time)

• enables the analyses at local level

• enable to inform decision-makes at prefecture, city level.

• what is local level analyses?- what will become possible?

locally relevant variables • examples – migration/commute, SES, demography

locally existing conditions

SALSA project “locality” could be revealed by whole –

comparison with outside of the locality is importantin evaluating vulnerability of a particular locality

-A. unpredictability for an “out of range” events; comparison with outside world (which might have experienced the “out of range”)

-B. A change of a variable would not have equal effect on different region and/or different population

SALSA project “locality” could be revealed by whole –

comparison with outside of the locality is importantin evaluating vulnerability of a particular locality

-A. unpredictability for an “out of range” events; comparison with outside world (which might have experienced the “out of range”)

-B. A change of a variable would not have equal effect on different region and/or different population

Climate change and health –With a given change of key variable, impact will be different

The most impoverished region in Nepal

Situation particularly severe in higher hills and mountains in terms of basic infrastructure (roads, communication, health, education, etc)

High prevalence of food insecurity and malnutrition

Mountain ecosystem increasingly fragile due to growing population pressure

Also the region most affected by the past conflict

Problem further aggravated due to impacts of climate change

Heightened expectations after recent political changes

Pahari (2009)

⇒ compounded effects –

might be sensitive for a given change of climate

SALSA project With a given change of key variable, impact may be different ?

Standardized Mortality

Rates (SMR)

--

CEREBROVASCULAR

DISEASES

Food, life style, climate, etc. underlie such differences

An increment of environmental change > different impacts?

something similar to the effect of “drizzling rain” on flood

(Prof. Koike’s presentation).

Conclusions

• Data in GO could/should be utilized in SA

•as a source of information, method of information archiving, trigger of behavioral changes.

In doing so ……,

• Combining with “endpoint” data is essential

• Social adaptation – local

• locally relevant endpoint data– could be important in future prediction (even in other regions such a “local context” is important)

• social science – economics, new technology: transportation, medical, agricultural, etc. relevant for prediction

• comparable data should be collected across regions/populations

• mode of data collection – participation of citizens, utilizing IT

Conclusions



In doing so ……,







Conclusions



In doing so ……,







Thanks for your attention!

Documents

Can global observation data reveal local social adaptations … · Can global observation data reveal local social adaptations towards sustainability? Chiho WATANABE Department of