# Presentation: 948833

## EMMMA: a web-based system for Environmental Mercury Mapping, Modeling, and Analysis

- Steve Wente, Paul Hearn, David Donato, and John Aguinaldo

- U.S.* *Department of the Interior   U.S. Geological Survey

## Samples in Fish-Hg Compilation

## NDMMF1 / EMMMA2 Progress & Plans

- NDMMF1 / EMMMA2 Progress & Plans

*Modeling Reports*

*Interpretive Reports*

*Improve Model*

*Model Data*

*Collect New Data*

*Data/Predictions on Web*

*Original Fish-Hg Data*

- 1. EPA CAMR RIA
- 2. St. Croix fish

- 1. Model description
- 2. Error assessment
- 3. Geospatial model

*Project Information*

- 1. EMMMA website
- 2. Project article
- 3. User’s guide

**Data Collection**

**Modeling**

**Visualization**

**Publication**

**Future: Collaborative Research Tools, Uncertainty Estimation, ...**

- 1 National Descriptive Model of Mercury in Fish
- 2 Environmental Mercury Mapping, Modeling, & Analysis Website (_[http://](http://emmma.usgs.gov/)__[emmma.usgs.gov](http://emmma.usgs.gov/)_)

**(NDMMF)**

**(EMMMA)**

**Notes:**
Potential Deficiencies
Marine fish data
Additional models
Human Exposure Surface with uncertainty
Health effects models
Automatic model recalibration
Tools for collaborative contaminant distribution and health research

## National Descriptive Model of Mercury in Fish (NDMMF) (http://pubs.usgs.gov/sir/2004/5199/pdf/2004-5199.pdf)

- Statistical model (analysis of covariance)
- Objectively partitions Hg variation between:
- Sample characteristics (species, tissues sampled, & fish length); and
- Spatiotemporal variation – differences among sampling events (specific site & time)
- Calibrated to national dataset (n* *> 58,000)

**Notes:**
Why is fish-tissue Hg data collected? 
Development of fish consumption advisories
Identify spatial and temporal trends
Modeling issues
Species (Hg increases with trophic position)
Tissues (skin-off fillet > whole fish, etc.)
Fish length (Hg increases with length)
Confounding

## NDMMF Accuracy – St. Croix Study (http://pubs.usgs.gov/sir/2006/5063/pdf/2006-5063.pdf)

| Site # (n) | Range (μg/kg) | R2 | Prediction Error |
| --- | --- | --- | --- |
| 1 (14) | 22 – 403 | 0.94 | 22.5 |
| 2 (14) | 27 – 224 | .78 | 21.5 |
| 3 (14) | 23 – 830 | .78 | 41.7 |
| 4 (14) | 19 – 299 | .94 | 20.1 |
| 5 (11) | 13 – 299 | .85 | 35.6 |
| 6 (14) | 12 – 414 | .88 | 33.0 |
| 7 (14) | 24 – 209 | .70 | 39.7 |
| 8 (14) | 21 – 269 | .75 | 34.2 |
| 9 (14) | 22 – 414 | .91 | 28.2 |
| 10 (14) | 21 – 611 | .96 | 35.2 |
| 11 (14) | 22 – 506 | .89 | 34.7 |
| 12 (14) | 8.2 – 243 | .96 | 25.2 |
| 13 (14) | 7.3 – 95 | .94 | 36.1 |
| 14 (14) | 7.8 – 374 | .95 | 26.7 |
| Overall | 7.3 – 830 | .89 | 31.6 |

## NDMMF Accuracy Issue

## Applications: Comprehensive Fish Advisories

- Applications: Comprehensive Fish Advisories

## Applications: Assessing Spatiotemporal Variation

- Applications: Assessing Spatiotemporal Variation

**Predict “Standardized” Fish-tissue Hg Concentrations**

## Temporal Trends

## Spatial Variation (Raw Data) 

- Spatial Variation (Raw Data)

## Spatial Variation (Standardized) 

- Spatial Variation (Standardized)

## Value of EMMMA

- Planning of monitoring projects
- Visualizing fish-mercury variation
- Across sample characteristics (assess model fit, fish consumption advisories)
- Spatiotemporal variation (maps & time trends)
- ‘Eternal’ QA/QC & peer-review
- Easy access to & comparison of data & models
- Rapid improvement in ‘mercury science’

## Questions?

- Steve Wente _[spwente@usgs.gov](mailto:spwente@usgs.gov)_
- Paul Hearn _[phearn@usgs.gov](mailto:phearn@usgs.gov)_
- EMMMA _[http://emmma.usgs.gov](http://emmma.usgs.gov/)_