11-12 June 2015, Bari-Italy Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations.

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Presentation transcript:

11-12 June 2015, Bari-Italy Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations YOUR LOGO Societal Benefit Area: Health Name: Simon Hales Institution: World Health Organization

 Is your community developing a set of area- specific essential variables (EV)s?  (yes … but note that “area specific” is not a useful concept in the global health context)  If not, is the community planning to start this in the near future? Have you attended previous meeting? No Are you considering reference documents from other domains? Yes

 What criteria, methodology, and process should be used to identify EVs? ◦ Top down: start with health significance ◦ Describe relevant causal pathways / networks ◦ Consider how EO can contribute useful information  Do you have a template to document a EV? ◦ No, but … good examples of earth observation contributing to health planning for extreme climate events, (cyclones, heatwaves, air pollution), ◦ …Emerging examples for other issues, water, food security, some communicable diseases

 Global “burden of disease”: ◦ (biomedical model, focus on downstream “risk factors” = limited usefulness in this context) ◦ Poor countries: effects of poverty via lack of access to food, water, shelter, clean energy: high mortality rates, low life expectancy, high burden of communicable diseases, unplanned urban settlements = poor infrastructure (WASH, shelter vulnerable to disasters, dirty energy) ◦ Rich countries: diseases of overconsumption (non communicable disease: cardiovascular diseases, cancers, psychological problems).  Threat to global ecological sustainability is the most important (and relevant) health issue

 EVs (from other domains) are more or less relevant to health

 Health is an integrating factor  Paradox ~ health is improving (on average)  Prediction: this will not last  Fundamental determinants of health: ◦ Little to do with health services (the biomedical model is not very useful in this context) ◦ More to do with long term availability of ecosystem services and the social distribution of health-giving, food, water, shelter, security, (life purpose)

 Meeting current human needs...  … without compromising the health of future generations  We need to live within planetary boundaries  (… and social boundaries)  The most important essential variables for health should be included in “upstream” social benefit areas  BUT the social distribution of positive (health-giving) factors and negative (unhealthy) exposures is not necessarily captured  This requires additional analysis (eg. geographic overlay of physical/earth observation variables with socio-demographic variables)?

 There are examples of direct “toxicological” effects on health  BUT most impacts, including the most important impacts, occur via complex pathways

K.R. Hayes et al. / Ecological Indicators 57 (2015) 409–419

Air quality

climate extremes, heat, UVR, cyclones (NRT data) Air quality

climate extremes, heat, UVR, cyclones (NRT data) Air quality Social factors

(long term) ecosystem services: Productive, recycling and regulating… food, water

Consumption, population, social distribution of resources

Primary observations: births, deaths, disease patterns Scenarios of future development (eg. SDGs) Essential variables: Weather/climate, food, water, energy: availability (transport, housing, waste infrastructure, within and between countries) + distribution (socio-economic data) of Indicators: population level

Primary observations: births, deaths, disease patterns Scenarios of future development Essential variables: Weather/climate, food, water, energy: availability (transport, housing, waste infrastructure, within and between countries) + distribution (socio-economic data) Earth system models Ecosystem services Disciplinary models (eg. air quality) Social system: (migration, conflict, resources) Indicators: eg. proportion of population with “healthy” living conditions Observations of policy and management

 Overlapping with EVs in other domains (SBA) ◦ Yes, very substantially, especially climate, disasters, water, food, energy, biodiversity  Priorities for EVs operational monitoring ◦ Contribution to understanding and validating assessment models (medium to long term scenarios, rather than short term forecasts)  Recommendations for GEO/GEOSS ◦ Inform global policy questions about sustainability rather than specific “biomedical” health issues ◦ Extend engagement with global health community  Future work ◦ Sustainable development goals, UN conventions (FCCC, CBD) planetary boundaries, safe pathways of development, extreme events, food security, water and sanitation, ◦ Integration with socio-economic variables (social inequality, population) ◦ Human impacts on earth system (production, consumption)

Primary observations: births, deaths, disease patterns Scenarios of future development Essential variables: Weather/climate, food, water, energy: availability (transport, housing, waste infrastructure, within and between countries) + distribution (socio-economic data) Ecosystem services Social system: (migration, conflict, resources) Earth system models human impacts on earth system

 Weather/climate, “near real time data”, forecasting of extreme events  Famine early warning  Short term forecasting of communicable diseases (under development, generally not yet operationally useful)

 To what extent these EVs (if any) are validated and used ◦ Limited validation and use (as far as I know)  Are the EVs linked to applications and users? ◦ (not efficiently, see examples)  How is a community agreement reached? ◦ Not clear (at least, not to me – community is fragmented and not well established)  Is a community review process in place? ◦ Are the EVs linked to an international body ◦ Yes (SDGs, Future Earth, CBD, Montreal protocol, UNFCCC, UNEP, UNDP, UNICEF, WMO, WHO, IFRCRCS)  Involved in accepting the EVs?  Not clear

 Do you have a database with information on the EVs? ◦ No – relevant data are scattered and mostly not linked to health issues (due to complex, indirect causal pathways for the most important impacts)  Do you know network currently operational for medium-term/long-term monitoring? ◦ All existing networks are potentially relevant  Are the current operational networks operated by your community measuring the EVs? ◦ Health community relies on other to do this

 For some Use Case, have you already focused on EVs’ features: Temporal frequency Spatial resolution Accuracy etc. Yes, (example of air pollution, others below)  Challenges and how these are addressed (if any)  Thinking in terms of global sustainability, not biomedical effects

 Long term (multi-annual) average ambient fine particle concentration at the surface ◦ Method: remote sensing plus atmospheric model plus epidemiological model Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application Aaron van Donkelaar et al,

EO can provide water storage in groundwater and surface stores (lakes) and measures of wetland and ecosystem extent

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