# Presentation: 066991

## ORD’s Environmental Monitoring and Assessment Program (EMAP)

**Sound Science for Measuring Ecological Condition**

- www.epa.gov/emap

## Key EPA Monitoring Questions

- What are the current conditions of our ecosystems?
- Where are the conditions                    improving or declining?
- What stresses are associated                    with declines?
- Are our management                         programs and policies                       working?

## What’s at Stake?

- >$1B/y spent on monitoring
- Condition of estuaries, coastlines, streams, rivers, wetlands and lakes are still unknown.
- Effectiveness of protection and restoration programs and policies are often unknown

## GOALS of EMAP

- Develop the scientific basis for consistent, unbiased, cost-effective measurement of the condition of the Nation’s aquatic ecosystems
- Status
- Trends
- Build state and tribal capacity for                  monitoring condition and                             transfer our technology
- Make data generally available to all stakeholders (STORET)

## Integrated Monitoring 

**Sampling (continue to Monitor as part of **
**5-year cycle) **

**Condition**

**Waterbody has high Probability of**
** ****Impairment **

**305(b)**
**Reports**

**Waterbody**
**Impairment **
**Confirmed**

**303(d) **
**List**

**TMDL Development**

**States conduct**
**Probability survey **
**With suite of indicators**

**Remediation**

**Intensive sampling **
**to confirm impairment**

**State of the**
**Environment**
**Reports**

**Associated **
**Stressors**

**Point **
**Source**

**Non-point**
**Source**

**Likelihood **
**Criteria**

**Dose -  **
**Response**

**Comparison of # of **
**Expected 303(d)**
**Sites to known sites**

**< or >**

**=**

**Accept State **
**303(d) list**

**Probability of **
**Impairment**
**Assessment**
**Models**

**Waterbody has low Probability of Impairment **

**De-list**

**Waterbody**
**Not impaired**

**Diagnosis**

**Integrated Monitoring **

**Waterbody has Moderate Probability of impairment **

**Standards**

## +

- Population Identification

- Variable Density Approaches

- Designs

- Partnerships

- Analysis

- Data

- Field Sampling

- Training

## CWA:  Resource Monitoring Needs

**National Implementation**

**National Demonstration**

**Regional Demonstration**

**R-EMAP/Small Scale Tests**

**Implementation**

**Land cover/use**

**Indicators**

**Design**

**Classification**
**Strata**

**Reference**
**Conditions**

**Nationally**
**Consistent**
**Design**

**Science Barriers**

**Coastal**

**Streams**

**Large Rivers**

**Great Lakes**

**Lakes**

**Wetlands**

**Resource Areas**

## EMAP Extramural Research Areas

- Coastal Initiative – 60% to State Co-ops
- Western Pilot – 60% to State Co-ops
- GRI – 60% to State and other Co-ops
- R-EMAP – 100% to EPA Regions
- STAR – 100% to Academic Research Institutions

- STAR Grants

- Western Pilot

- R-EMAP

- Coastal Initiative

- GRI

## EMAP Design Approach

- Probabilistic Design Framework** **– Randomized statistical designs that allow interpretation of monitoring data with known uncertainty, extrapolation to the entire population of interest with a small sample size, and the ability to statistically aggregate similar data to larger geographic areas
- Classification - meaningful groupings within resource types and/or ecosystem types to allow better statistical design and analysis
- Biological Indicators - Direct measures of aquatic ecosystem condition, integrates stressors, and the public can relate to them
- Streams, rivers, estuaries, lakes, reservoirs, wetlands

## Probabilistic Survey Design Advantages

- Representative and allows inference to system of interest
- Adaptable to resource characteristics
- Adjusts sample sizes to meet precision requirements
- Adaptable to temporal and spatial scales of interest
- Unbiased
- Cost-effective

**Condition of streams**

## EMAP Uses Biological Indicators

- Historic Aquatic Indicators – Measured physical/chemical characteristics and related them to the biological condition of an aquatic system
- Aquatic Biological Indicators – Direct measure of condition of aquatic ecosystem, integrates stressors, and the public can relate

## Effectiveness of Design

-  Eutrophication of NE US lakes
-  4219 mostly problem lakes sampled by states for 305(b)
-  2756 non-random lakes censused (Rohm et al. 1995)
-  344 lakes with EMAP probability design (11,076 lakes total)
-  Alabama reduced the cost of estuarine monitoring by ~33%, and can now report on all estuarine waters

## Stream Conditions in MAHA

## Estuarine Conditions

## Statistical Change Detection

**0**

**10**

**20**

**30**

**40**

**50**

**1991-93**

**1997-98**

**% area with impaired benthos**

- Change in Percent Area of Chesapeake Bay with Impaired Benthic Community

- *

## EMAP National Demonstrations

- Estuaries – All 24 marine coastal states monitoring with core EMAP design and indicators
- Streams – Mid-Atlantic States and 12 Western States
- Great Rivers – Mississippi River Basin

## Integrated Monitoring and Assessment

**Sampling (continue to Monitor as part of **
**5-year cycle) **

**Condition**

**Waterbody has high Probability of**
** ****Impairment **

**305(b)**
**Reports**

**Waterbody**
**Impairment **
**Confirmed**

**303(d) **
**List**

**TMDL Development**

**States conduct**
**Probability survey **
**With suite of indicators**

**Remediation**

**Intensive sampling **
**to confirm impairment**

**State of the**
**Environment**
**Reports**

**Associated **
**Stressors**

**Point **
**Source**

**Non-point**
**Source**

**Thresholds of **
**Impairment**

**Dose -  **
**Response**

**Comparison of # of **
**Expected 303(d)**
**Sites to known sites**

**< or >**

**=**

**Accept State **
**303(d) list**

**Probability of **
**Impairment**
**Assessment**
**Models (2 levels)**

**Waterbody has low Probability of Impairment **

**De-list**

**Waterbody**
**Not impaired**

**Diagnosis**

**Integrated Monitoring and Assessment**

**Waterbody has Moderate Probability of impairment **

**Standards**

- 1

- 2

- 3

- 5

- 8

- 4

- 6,7

- 9,10

- 10

- 10

- 10

- 10

- 10

- 10

## Example of Integrated Monitoring and Assessment 

**with Maryland Biological Stream Survey Data**
- 	MBSS probability survey for benthic IBI and fish IBI measures of 		stream condition (impairment for BIBI < 3, FIBI < 3), 			chemical and physical measurements taken, land cover data 		available
- 	Analysis:
- 		cumulative distribution functions (cdfs)
- 		conditional probabilities
- 		conditional cdfs

- 1

## Condition of Streams in Maryland

- 54% of 1st order stream miles are impaired (BIBI < 3)
- 40% of 2nd order stream miles are impaired (BIBI < 3)
- 47% of 1st order stream miles are impaired (FIBI < 3)
- 24% of 2nd order stream miles are impaired (FIBI < 3)

- 2

## 3

## 4

## Associated Stressors

- 5

## Thresholds of Impairment

- • pH < 5
- • ANC < 200 μeq/l
- • Nitrate-nitrogen > 2 mg/l
- • DO < 5 ppm
- • Sulfate > 24 mg/l
- • DOC > 8.0 ppm
**Conditional probability thresholds of impairment:**
- 1st order steams: 	DO < 5, DO > 12
- 		 	pH < 6, pH > 8
- 			NO3 < 5, <15
- 			SO4 < 40-50
- 			Temp <5, temp > 25
- 			Hilsenoff <1, Hilsenhoff > 6
- 2nd  order steams: 	DO < 3, DO > 11
- 		 	pH < 5, pH > 8.5
- 			NO3 < ?
- 			SO4 < 75
- 			Temp <10, temp > 28
- 			Hilsenoff <2,

- 6

- 7

## Percent Fines in Substrate

**Percent Fines in Substrate**

- 7

## Impaired Streams in Maryland

- 66% 1st  order  - 5808
- 17% 2nd  order

- 7304 miles in 1st and 2nd
- order streams

- 3725 miles of 1st and 2nd order
- streams should be on 303(d) List
- based on benthic impairment

- 8

## Probability of Impairment Models

- Agriculture on >3% Slopes

**Combine condition information with land cover **
**data to predict probability of impairment**

- 9

- Spatial Models
- for Probability
-  of Impairment

- 10

## Probability of Stream Benthic Impairment for Exceeding Levels of Catchment Urbanization

- 10

**Notes:**
Maryland Biological Stream Survey, 1995-97 second order streams
90% confidence interval about mean value

Curve fit: 

Y ~ if Xo < X1, Co + (Do - Co)/(1 + exp(Bo * (xo - X1))), 
      if Xo > X1, Co + (Do - Co)/(1 + exp(B1 * (xo - X1))))
Do = 1 (fixed)
Co         Bo       xo        B1 
 0.1853756 0.07712671 3.600263 0.1741542

Comparing this model with model for Bo = B1 using Bayesian Information Criterion indicates that there is strong to very strong evidence that this model (B1 and Bo different) is better