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SPACE DATA PROCESSING : FOREST FIRES AND HUMAN HEALTH

Explore combining remote sensing, health risk assessment, and internet technologies for forest fires and human health. Learn how EHIPS from Moscow's Space Research Institute aids in risk assessment, data processing, and global projects. Utilize EHIPS to model pollutant dispersion, exposure scenarios, and health outcomes. Enhance fire control, health hazard forecasts, and monitoring through EHIPS-based cycles and P2P networks. Optimally manage risks with centralized and distributed information exchanges. Embrace the global P2P configuration for sustainable health and effective feedback loops.

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SPACE DATA PROCESSING : FOREST FIRES AND HUMAN HEALTH

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  1. SPACE DATA PROCESSING: FOREST FIRESAND HUMANHEALTH • Bring together three advanced but separate technologies: • REMOTE SENSING OF FOREST FIRES • HEALTH RISK ASSESSMENT • INTERNET Moscow Russia Space Research Institute Boris Balter Maria Stalnaya Victor Egorov

  2. CONTENTS FROM EHIPS EXPERIENCE TO GLOBAL P2P PROJECT HEALTH RISK: EHIPS APPROACH FIRES: RISK SEEN FROM SPACE P2P: RISK INFO FOR/FROM ALL CYCLING: RISK MANAGEMENT GLOBAL CHANGE

  3. RISK ASSESSMENT DATAFLOW • INPUTDATA: • SOURCE EMISSION • PLUME-DEFINING PARAMETERS • METEOROLOGY • POPULATION EXPOSURE SCENARIOS • POLLUTANT TOXICITIES • PERSONAL SENSITIVITIES • OUTPUTDATA: • RISK UNFOLDING BY: • COHORTS (RISK GROUPS) • TIME (FORECAST) • SPACE (MAPPING)

  4. RISK ASSESSMENT LOGIC

  5. EHIPS ENVIRONMENTALHEALTHINFORMATIONPROCESSINGSYSTEM NOW: USED FOR INDUSTRIAL POLLUTION IN CITIES CAN BE: USED FOR HEALTH EFFECTS OF FOREST FIRES NOW: WORKS IN THE HANDS OF EXPERTS ONLY CAN: EMBED EXPERTISE INTO PROCESSING PIPELINE

  6. EHIPS: INFORMATION FLOWS

  7. EHIPS: RESEARCH TOOL FOR RISK • FEATURES • EXPERT MODE SETS PARAMS FOR PIPELINE PROCESSING • RISK ASSESSMENT CONTINUED TO HEALTH OUTCOMES • ALTERNATIVE MODELS FOR POLLUTANT DISPERSION • FLEXIBLE MODELS FOR EXPOSURE SCENARIO • HARMONIZING MODELED AND OBSERVED VALUES • MULTI-CYCLE, NOT SINGLE CALCULATION www.iki.rssi.ru/ehips/welcome.htm

  8. HARMONIZE MODEL & OBSERVED Model Data 1-year series raw Station 1 Station 2 2-day series Fit good for 1 station only Data Model

  9. FIRE: USE MODEL LESSONS LEARNED MODELLED CONCENTRATIONS FIT DATA ON THE AVERAGE ONLY, NOT IN SHORT-TIME EVENTS GAUSSIAN DISPERSION MODELS INSUFFICIENT, MESOSCALE MODELS ARE NEEDED MAIN SOURCE OF ERROR IS PLUME NOT SOURCE (SOLUTION: DIRECT OBSERVATION OF PLUMES)

  10. FIRE: EHIPS LOGIC+SPACE DATA Model Data

  11. SPACE-FED FEHIPS: DATAFLOW ADDING NEW INPUTS: REMOTE SENSING DATA HUBS: HUGE FIRES METEO DATA HUBS: VERTICAL PROFILES GIS DATA STORES: TERRAIN, POPULATION INDIVIDUALS: EXPOSURE SCENARIOS INDIVIDUALS: HEALTH EFFECT FEEDBACK GETTING NEW OUTPUTS: SELF-CORRECTING MODEL FORECASTS HEALTH HAZARD SPINOFF TO FIRE CONTROL LOCKED-IN MONITORING-CONTROL CYCLES

  12. FIRE EHIPS-BASED CYCLES Cycles EHIPS cycle Fire control Behavior control

  13. P2P • THREE TIERS OF THE SYSTEM • Remote detection and classification of forest fires (FMS) • Assessment of health risk from air pollution by smoke plume (EHIPS) • 3) Information exchange in peer-to-peer networks (P2P)

  14. CENTRALIZED vs. DISTRIBUTED

  15. P2P: INFORMATION FEEDBACKS INTELLECTUAL CENTER (I-CENTER) COORDINATES ALL FEEDBACK CYCLES: INSTITUTIONAL P2P NODES FEED WITH FIRE DATA FROM FMS AND PROVIDE RISK ESTIMATES IN RETURN INDIVIDUALS PROVIDE EXPOSURE / HEALTH INFORMATION TO GET THEIR OWN RISK FROM CONCENTRATIONS INDIVIDUALS INDIRECTLY INDICATE TO THE SYSTEM THE EFFECTIVENSS OF BEHAVIOR CONTROL

  16. EHIPS-BASED P2P SYSTEM Centralized information Decentralized information Network nodes Hardware Software Peer-to-peer operations

  17. OPTIMAL CONTROL ASSESSMENT  MANAGEMENT RISK = OPTIMAL CONTROL OBSERVATION  CONTROLLING BENEFITS OF USING OPTIMAL CONTROL ALGORITHMS TRACKING THE EFFECT OF BEHAVIOR ADVICE MAKING EXPLICIT THE CRITERIA OF SUCCESS QUANTIFYNG THE VALUE OF INFORMATION

  18. I-CENTER: MEDIATOR HUMANS Criteria Scenarios I-CENTER Monitoring Control NATURE

  19. DISTRIBUTING THE I-CENTER I-CENTER DELEGATES THE OPTIMAL CONTROL ‘IN THE SMALL’ TO ELEMENTARY CYCLES IN P2P DISTRIBUTED OPTIMAL CONTROL IS AS (OR MORE) EFFECTIVE AS THE CENTRALIZED ELEMENTARY CYCLE IS BUILT UPON PREDICTOR-CORRECTOR ALGORITHMS BUILT INTO EHIPS I-CENTER USES MORE COMPLICATED DUAL CONTROL ALGORITHMS FOR GLOBAL P2P-FMS COOPERATION

  20. NETWORK OF FEEDBACK CYCLES

  21. GLOBAL HEALTH: THE CORE OF SUSTAINABILITY HEALTH INVOLVES ALL SOCIETAL FACTORS HEALTH: A ‘THERMOMETER’ FOR MONITORING GLOBAL CHANGE P2P HEALTH NETWORK: PART OF DYNAMIC BALANCE NETWORK FOR EARTH FEEDBACK AND OPTIMAL CONTROL EMBEDDED INTO P2P NETWORK

  22. GLOBAL P2P CONFIGURATION IC EHIPS FMS Health data ___ P2P lines ___ FMS lines

  23. GLOBAL CHANGE: A P2P ANSWER WHAT IS COMMON FIRES – AN IMPORTANT LARGE-SCALE TEST LESSONS FROM FIRES USABLE FOR GLOBAL CHANGE WHAT CAN MAKE DIFFERENCE UNCONTROLLABLE SOURCES - THEN BEHAVIOR CONTROL CYCLE RUNS ONLY SOURCES CONTROLLABLE INDIVIDUALLY - THAT’S THE ‘MISSING LINK’ OF FIRE-ORIENTED P2P SOURCES DISTRIBUTED – USE P2P NETWORK ONLY

  24. TIMELINE EHIPS FIRE EHIPS I- CENTER HEALTH P2P GLOBAL P2P

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