can global observation data reveal local social adaptations … · can global observation data...
TRANSCRIPT
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
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”
• 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
Climate Change Impacts on Flood Control Plan
in Indonesia
10year Probable flood Current Climate
10year Probable flood 50 years later
Social adaptation• “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”
• 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
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)
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)
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
Climate <change> and health: previous studies
Hashizume et al. (2009)
Cause-specific mortality in Bangladesh
Curriero et al. (2002) 13 cities in USA
Such examples demonstrated regional differences
Climate <change> and health: previous studies
Implication of U-shape for prediction is not straightforward
Honda and Ono (2009)
Winter months
Summer months
Climate <change> and health: previous studies
Carson et al. (2006)
Historical change in temp-mortality relationship
Climate <change> and health: previous studies
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
Climate <change> and health: previous studies
Carson et al. (2006)
Historical change in temp-mortality relationship
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
• 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
• 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