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

2

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

4

Subwatershed

(12-digit HUC)

National CommunityCensus Block Group + non-summarized

5

6

Status and Trends of Wetlands, NOAA and FS

7

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.

8

EnviroAtlas Eco-Health

9

Flood Swipe Map 10

Floodplain Map

11

Potential Benefits

12

• 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

13

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

17

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

19

Meter-scale Urban Land CoverCity of Brownsville, TX

20

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

21

Topography - LiDAR Opportunities

22

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

26

Systems Science and Thinking

27Thomas A. Burke, et al, EHP 2017 Mar