Environmental Variabilities and Health Impacts
Pai-Yei Whung, Ph.D.Office of Research and DevelopmentUS Environmental Protection Agency
Asia-Pacific Economic Cooperation Typhoon Symposium
May 2, 2017, Chinese Taipei
United States Environmental Protection Agency
Our Mission: Protect human health and the environment
A number of laws serve as EPA's foundation for protecting the environment and public health (Clean Air Act, Clean Water Act, Toxic Substances Control Act, Resource Conservation and Recovery Act, etc.)
“We do not have to choose between environmental protection and economic development. Protecting human health and the environment can be achieved while working together towards economic prosperity ….” - US EPA Chief of Staff, Ryan Jackson
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EnviroAtlas – Geospatial Data, ToolsSystems Approach for Problem Solving
Serving data around a common theme:
• Geospatial indicators/indices of EGS
• Supplemental data (e.g., boundaries, land cover, soils, hydrography, impaired water bodies, wetlands, demographics, built infrastructure, roads)
• Analytic and interpretive tools
• Ecosystem marketplace data
USEPA EnviroAtlas3
EnviroAtlas Data Access
• Access data via published web services:
No download required, users always using most current data.
EcoINFORMA
Data Basin
ESRI
Community mapping portals,
e.g., Durham, NC
EPA Geoplatform
• Access data via interactive map:
Interactive analysis tools
Additional context
• Download the data and run
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Subwatershed
(12-digit HUC)
National CommunityCensus Block Group + non-summarized
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Status and Trends of Wetlands, NOAA and FS
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EnviroAtlas Eco-Health
• Eco-Health Relationship Browser illustrates scientific evidence for linkages between human health and ecosystem services.
• This interactive tool provides information about several US major ecosystems, the services they provide, and how those services, or their degradation and loss, may affect people.
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EnviroAtlas Eco-Health
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Flood Swipe Map 10
Floodplain Map
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Potential Benefits
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• Reducing vulnerable populations’ exposure to combined sewer overflow events
• Designing built and natural environments for flood-resilient cities and communities
• Reducing vulnerability of agricultural lands to flooding
• Guiding floodplain, riparian, and wetland restoration
Jeremy Baynes, Stephanie Panlasigui, Sean Woznicki
Big Data, Big Computing Power
Source: National Hydrography Dataset
Jeremy Baynes, Stephanie Panlasigui, Sean Woznicki
Over 9 billion pixels for each dataset - 99 billion pixels total
Build models in geographic pieces
Apply model to 30 m resolution pixels from subsets of data
Preliminary map Preliminary map
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Machine Learning Modeling Approach
Python Scipy/scikit-learn
Random forest model
Source: USGS NED
Source: MRLC NLCD
Source: NRCS Soils
Landscape variables
Jeremy Baynes, Stephanie Panlasigui, Sean Woznicki14
Random Forest Modeling Results
FEMA map Modeled map (preliminary)
Combined map (preliminary)
Jeremy Baynes, Stephanie Panlasigui, Sean Woznicki15
Quantitative Microbial Risk Assessment Modeling Tool
Epidemiology
Studies
Risk
Quantification
Field and
LaboratoryMonitoring
Sampling
Measurements
Sorption studies
Inactivation and die-off rates
Mortality kinetics
Impacts: Sunlight, Temp
Data Access,
Retrieval, and Processing
Met data
Soils &Topography
Land use/cover
Watershed/Stream Delineations
Data Access, Retrieval, and Processing Policy-related IssuesRisk target(s)
QMRA Investigations
Site characteristics and pathogens
Model scale and resolution
Risks by varying hydrologic conditions
Source apportionment
Risk by pathogen, fecal source, water type
Sensitivity/Uncertainty
Integrated Modeling Framework
Receiving
Waters
Water body
Network
Watershed
Hydrology
Source-term
Loadings
Sim
ula
tio
ns/S
am
plin
g
Risk
Intake
Exposure
Dose-
Response
Relationship
Health
Impacts
Fate & Transport
Iterate on sources, watersheds, water bodies and receptor locations
(at Receptor
Locations)
Problem DefinitionPathogens
Identification
Etiologies
Properties
ScenariosBaseline
Alternatives
Gene Whelan et al.16
QMRA Flow Diagram
• SDMProjectBuilder – Automatically accesses data sources on the web, retrieves data, analyzes data, and creates the input files for HSPF
• HSPF – Determine spatial and temporal distributions of flow and microbial densities in a mixed-use watershed
• BASINS – View results with graphical and/or tabular viewers
• MRA-IT – Compute risk of infection to pathogens
Data Sources
NLCDNHD
PlusBASINS STORET NWIS <…>
SWAT
HSPF<….>
ModelWASP 3MRA
Models
SDMProjectBulider (D4EM)(Data Collection Software)
BASINS(Used as a Viewer and Editor)
Risk Assessment
(MRA-IT)
FRAMES
MRA-IT(Risk Assessment Computations)
https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=276890
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Manitowoc River Basin, Wisconsin
• What are the major microbial sources?
• What practices contribute to contamination at the pour point?
• What land-use types contribute to contamination at the pour point?
• Under what conditions do these sources manifest themselves?
https://www.google.com/maps/place/Manitowoc,+WI+54220/@44.0938921,-87.7257913,12612m/data=!3m2!1e3!4b1!4m5!3m4!1s0x880337b464eafe91:0x1d27c3ba913f1419!8m2!3d44.0886059!4d-87.657584?hl=en 18
Example Simulated Risk of Infectionto Pathogen (Cryptosporidium) Exposure
Infections per 100,000 4
2012 Rec Water Criterion Illlness rate per 100,000 for Enterochocci3600
RESULTS
Simulated Risk of Infection to Cryptosporidium exposure can be compared with Recreation Water Quality Criteria Rate
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Meter-scale Urban Land CoverCity of Brownsville, TX
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Risk Maps of Mosquito Vector
Distribution Risk map of infective Cx. Nigripalpus Sallam et al.
Preliminary Risk Map (10 m buffer zone) for Aedes mosquitos
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Topography - LiDAR Opportunities
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David Rogers, GFDRR and Chen Baode, Shanghai Met Service
Multi Hazard Impact-based Forecast and Warning Services, Workshop
Implementing Impact-Based Forecast and Warning Services
“WMO Guidelines on Multi-hazard Impact-
based Forecast and Warning Services”
publication (WMO-No. 1150)
Elements of an Impact-based Forecast and Warning Service
1. Partnership
2. Joint Development of Information and Services
3. Develop Capacity
4. Validation
Event Organizer
Police
Ambulance
Search and Rescue
Media
Local Community
Cross train on
requirements and
procedures
Identify required
competencies and skills
Educate users on impact
information
Agreed upon by all
partners
Systematic for
significant events
Regular meetings with
stakeholders to analyze events
Plan, trial and operationalizeimprovements
Objective verification and
assess performance
Example: coping with Tropical Cyclones
Weather and climate events
Weather analyses& forecasts
Hurricane track,size, & intensity
Implementation ofevacuation &
recovery plans
Reducing risk &response scenarios
Mitigation strategies
Affected population & infrastructure,
disruption of services,damages due to wind
& water, etc.
Placing intosituational
context
Storm surge, flooding,inundated areas
WeatherTranslation to
hazards
Extraction ofrelevant information
to predict hazards
ImpactEstimation
EnviroAtlas Eco-Health
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Systems Science and Thinking
27Thomas A. Burke, et al, EHP 2017 Mar
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