Cover. The historical stream gage at the Northeast Branch Anacostia River at Riverdale, Maryland (USGS Station 01649500). 

(Photograph by U.S. Geological Survey)
Water Quality in the Upper Anacostia 

River, Maryland: Continuous and Discrete 

Monitoring with Simulations to Estimate 

Concentrations and Yields, 2003–05

By Cherie V. Miller, Angélica L. Gutiérrez-Magness, Brenda L. Feit Majedi, and 

Gregory D. Foster

Prepared in cooperation with

Prince George’s County Department of Environmental Resources, 

the Maryland Department of the Environment,

the U.S. Environmental Protection Agency, and George Mason University

Scientific Investigations Report 2007–5142

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2007

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Miller, C.V., Gutiérrez-Magness, A.L., Feit Majedi, B.L., and Foster, G.D., 2007, Water quality in the Upper Anacostia River, Maryland: Continuous and discrete monitoring with simulations to estimate concentrations and yields, 

2003–05: U.S. Geological Survey Scientific Investigations Report 2007–5142, 43 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Purpose and Scope ..............................................................................................................................2

Description of the Anacostia River and Watershed .......................................................................2

Contaminants in the Anacostia River................................................................................................7

Methods of Study.........................................................................................................................................11

Measurements of Continuous Flow and Water Quality................................................................11

Field Collection of Water-Quality Samples.....................................................................................11

Laboratory Methods...........................................................................................................................12

Evaluation of Data Integrity...............................................................................................................13

Calculation of Particulate Metal Concentrations and Enrichment Factors ..............................13

Regression Models to Predict Water Quality and Loads .............................................................14

Estimates of Loads and Yields ..........................................................................................................16

Water Quality in the Upper Anacostia River ...........................................................................................16

Flow and Physical Parameters.........................................................................................................17

Nutrients, Suspended Sediment, and Total Organic Carbon.......................................................18

Trace Metals........................................................................................................................................21

Metal Enrichment Factors.................................................................................................................24

Hydrologic and Seasonal Effects on Water Quality...............................................................................24

Anatomy of a Storm—Patterns of Water Quality ...................................................................................26

Estimation of Continuous Concentrations of Nutrients and Suspended Sediment..........................30

Estimation of Contaminant Loads and Yields ..........................................................................................34

Information Learned for the Anacostia River and Urban Streams......................................................36

Summary........................................................................................................................................................38

Acknowledgments.......................................................................................................................................39

References Cited..........................................................................................................................................39

Appendix........................................................................................................................................................43
iv

Figures

1A–C. Maps showing—

1A. Location of the Anacostia River watershed, which includes the lower 

watershed which is partially tidal within Washington, D.C., and the upper 

watershed in Maryland.......................................................................................................3

1B. Detailed view of the Anacostia River watershed...........................................................4

1C. Location of the Anacostia River watershed and the dominant hydrogeomorphic regions in the upper watersheds of the Northeast and Northwest 

Branches (USGS stations 01649500 and 01651000, respectively)................................5

2–3. Photographs showing—

2. The historical stream gage (at left end of bridge) at the Northeast Branch 

Anacostia River at Riverdale, Maryland (USGS station 01649500)..............................6

3. View upstream of the water-quality sampling station at the Northwest Branch 

Anacostia River at Hyattsville, Maryland (USGS station 01651000)............................6

4–5. Maps showing—

4. Land cover in the Anacostia River watershed................................................................8

5. Percent of impervious surface in the Anacostia River watershed .............................9

6–7. Photographs showing—

6. Stream-restoration project by the Maryland State Highway Administration 

on the Northwest Branch Anacostia River (USGS station 01651000), 

August 7, 2003.....................................................................................................................10

7. Automatic sampler with CR10X data logger and modem to record field 

parameters, upload data in near-real time to the world wide web, and to 

trigger the sampler during storm events .......................................................................12

8–11. Graphs showing—

8. Total nitrogen compared to (A) discharge, (B) turbidity, and (C) specific 

conductance. (D) Log-transformed variables for total nitrogen compared to 

turbidity at the Northeast Branch Anacostia River (USGS station 01649500), 

2003–05 ................................................................................................................................15

9. Mean-monthly discharge in cubic feet per second for 2003–05 compared to 

long-term mean-monthly discharge at the (A) Northeast Branch and the 

(B) Northwest Branch Anacostia River (USGS stations 01649500 and 01651000, 

respectively).......................................................................................................................18

10. Diurnal variations in pH at the Northeast Branch Anacostia River at 

Riverdale, Maryland (USGS station 01649500), and on a small tributary that 

enters just above the station ...........................................................................................18

11. Short-term variations in turbidity during base flow at the Northeast Branch 

Anacostia River at Riverdale, Maryland (USGS station 01649500)............................18

12. Boxplots comparing concentrations of selected parameters between the Northwest Branch and Northeast Branch Anacostia River (USGS stations 01651000 and 

01649500, respectively), the Susquehanna River (USGS station 01578310), and the 

Potomac River (USGS station 01646580).................................................................................20

13. Graphs showing major species of nitrogen and mean daily discharge in 2004 

and 2005 at the (A) Susquehanna River at Conowingo, Maryland (USGS station 

01578310), (B) Northeast Branch Anacostia River (USGS station 01649500), and 

(C) Northwest Branch Anacostia River (USGS station 01651000) ......................................21
v

14–15. Boxplots comparing—

14. Statistical distributions of enrichment factors (EF) for trace metals at 

(A) Northeast Branch, and (B) Northwest Branch Anacostia River (USGS 

stations 01649500 and 01651000, respectively).............................................................25

15. Monthly variations in the concentrations of (A) total nitrogen, (B) total 

phosphorus, and (C) total-recoverable zinc..................................................................27

16–26. Graphs showing—

16. Physical parameters—discharge, water temperature, pH, concentration 

of dissolved oxygen, specific conductance, and turbidity for a series of 

storms in March and April 2005 on the Northeast Branch Anacostia River 

(USGS station 01649500)...................................................................................................28

17. Discharge, turbidity, and concentration of suspended sediment during a 

storm in March 2005 on the Northeast Branch Anacostia River (USGS 

station 01649500)................................................................................................................28

18. Discharge, specific conductance, and concentrations of nitrogen species 

during a storm in March 2005 on the Northeast Branch Anacostia River 

(USGS station 01649500)...................................................................................................28

19. Discharge and concentrations of total and dissolved phosphorus during a 

storm in March 2005 on the Northeast Branch Anacostia River (USGS 

station 01649500)................................................................................................................29

20. Discharge and phases of (A) zinc (Zn), (B) lead (Pb), (C) copper (Cu), and 

(D) chromium (Cr) during a storm in March 2005 on the Northeast Branch 

Anacostia River (USGS station 01649500)......................................................................29

21. Overlay of discharge with estimated and observed concentrations of 

suspended sediment on the Northwest Branch Anacostia River (USGS station 

01651000). (A) Traces of three storms in spring 2005, and (B) greater detail of 

one storm in March 2005 ..................................................................................................32

22. Overlay of discharge with estimated and observed concentrations of 

suspended sediment on the Northeast Branch Anacostia River (USGS station 

01649500). (A) Traces of a spring storm in 2004, (B) a summer storm in 

July 2005, and (C) a fall storm in October 2005 .............................................................32

23. Overlay of discharge with estimated and observed concentrations of total 

phosphorus on the Northwest Branch Anacostia River (USGS station 

01651000). Trace of a series of spring and summer storms in 2005...........................33

24. Overlay of discharge with estimated and observed concentrations of total 

phosphorus on the Northeast Branch Anacostia River (USGS station 

01649500). Trace of a series of winter storms in 2004..................................................33

25. Overlay of discharge with estimated and observed concentrations of total 

nitrogen on the Northwest Branch Anacostia River (USGS station 01651000). 

Trace of a series of low-intensity storm events in spring 2005..................................33

26. Overlay of discharge with estimated and observed concentrations of total 

nitrogen on the Northeast Branch Anacostia River (USGS station 01649500). 

Trace of a series of low-intensity storm events in winter and spring 2004..............33

27–28. Photographs showing—

27. Exposed sewer manhole stacks in Sligo Creek, a small tributary on the 

Northwest Branch Anacostia River................................................................................37

28. Storm event at Northeast Branch Anacostia River in winter 2003. High 

turbidity and suspended-sediment concentrations occur during stormflow. 

Suspended sediment is a carrier for many particle-reactive contaminants 

such as trace metals and nutrients ................................................................................38
vi

Tables

1. Statistical summaries of water chemistry at the Northeast and Northwest 

Branches of the Anacostia River (USGS stations 01649500 and 01651000, 

respectively)–physical parameters .........................................................................................17

2. Statistical summaries of water chemistry at the Northeast and Northwest 

Branches of the Anacostia River (USGS stations 01649500 and 01651000, 

respectively), from July 2003 to December 2005–nutrients, total organic carbon 

(TOC) and suspended sediment (SS).......................................................................................19

3. Statistical summaries of water chemistry at the Northeast and Northwest 

Branches of the Anacostia River (USGS stations 01649500 and 01651000, 

respectively) from July 2003 to December 2005–trace metals ...........................................22

4. Median values for concentrations of metals on particulates and enrichment 

factors for the Northeast and Northwest Branches of the Anacostia River 

(USGS stations 01649500 and 01651000, respectively) for data collected from 

July 2003 through December 2005, grouped by flow regime...............................................26

5. Regression equations and statistics for the estimation of suspended sediment, 

total phosphorus, and total nitrogen at the Northeast and Northwest Branches 

of the Anacostia River (USGS stations 01649500 and 01651000, respectively).................31

6. Estimates of annual yields (load per square area in kilograms per year per 

square kilometer) for suspended sediment, total nitrogen, and total phosphorus 

at the Northeast and Northwest Branches of the Anacostia River (USGS stations 

01649500 and 01651000, respectively) for data collected in 2004 and 2005.......................35
vii

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 33.82 ounce, fluid (fl. oz)

liter (L) 0.2642 gallon (gal)

liter (L) 61.02 cubic inch (in3

) 

cubic foot (ft3) 0.02832 cubic meter (m3) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

cubic meter per second (m3/s) 70.07 acre-foot per day (acre-ft/d) 

cubic meter per second (m3/s) 22.83 million gallons per day (Mgal/d) 

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound, avoirdupois (lb)

kilogram (kg) 0.0011 ton (short, 2,000 lb)

Yield rate

kilogram per year per square kilometer 

(kg/yr/km2) 0.00286 ton per year per square mile (tn/yr/mi2)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

 °F=(1.8×°C)+32

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm 

at 25°C).

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

Concentrations of chemical constituents on suspended particulates are given in milligrams per 

kilogram (mg/kg) of dry weight of the suspended particles.

Units of turbidity are given in formazin nephelometric units (FNU), which are nephelometric 

turbidity units based on a formazin standard.

Water year is the 12-month period starting October 1, and ending September 30 of the following 

year.

Abstract

From 2003 through 2005, continuous and discrete waterquality data were collected at two stations on the Anacostia 

River in Maryland: Northeast Branch at Riverdale, Maryland 

(U.S. Geological Survey Station 01649500) and Northwest 

Branch near Hyattsville, Maryland (Station 01651000). Both 

stations are above the heads of tide for the river, and measurements approximately represent contributions of chemicals 

from the nontidal watersheds in the Anacostia River. This 

study was a cooperative effort between the U.S. Geological 

Survey, the Prince George’s County Department of Environmental Resources, the Maryland Department of the Environment, the U.S. Environmental Protection Agency, and George 

Mason University. Samples were collected for suspended 

sediment, nutrients, and trace metals; data were used to calculate loads of selected chemical parameters, and to evaluate 

the sources and transport processes of contaminants. Enrichment factors were calculated for some trace metals and used 

to interpret patterns of occurrence over different flow regimes. 

Some metals, such as cadmium, lead, and zinc, were slightly 

enriched as compared to global averages for shales; overall, median values of enrichment factors for all metals were 

approximately 15 to 35.

Stepwise linear regression models were developed on 

log-transformed concentrations to estimate the concentrations 

of suspended sediment, total nitrogen, and total phosphorus 

from continuous data of discharge and turbidity. The use 

of multiple explanatory variables improved the predictions 

over traditional rating curves that use only streamflow as the 

explanatory variable, because other variables such as turbidity measure the hysteretic effects of fine-grained suspended 

sediment over storm hydrographs. Estimates of the concentrations of suspended sediment from continuous discharge and 

turbidity showed coefficients of determination for the predictions (multiple R2

) of 0.95 and biases of less than 4 percent. 

Models to estimate the concentrations of total phosphorus and 

total nitrogen had lower values of multiple R2 than suspended 

sediment, but the estimated bias for all the models was similar. 

The models for total nitrogen and total phosphorus tended 

to under-predict high concentrations and to over-predict low 

concentrations as compared to measured values.

Annual yields (loads per square area in kilograms per 

year per square kilometer) were estimated for suspended 

sediment, total nitrogen, and total phosphorus using the 

U.S. Geological Survey models ESTIMATOR and LOADEST. 

The model LOADEST used hourly time steps and allowed the 

use of turbidity, which is strongly correlated to concentrations 

of suspended sediment, as a predictor variable. Annual yields 

for total nitrogen and total phosphorus were slightly higher 

but similar to previous estimates for other watersheds of the 

Chesapeake Bay, but annual yields for suspended sediment 

were higher by an order of magnitude for the two Anacostia 

River stations. Annual yields of suspended sediment at 

the two Anacostia River stations ranged from 131,000 to 

248,000 kilograms per year per square kilometer for 2004 and 

2005. LOADEST estimates were similar to those determined 

with ESTIMATOR, but had reduced errors associated with 

the estimates. 

Introduction

Urban streams are particularly complex systems with 

physical and chemical stressors that often overshadow natural stream processes and functions. Some typical physical 

stressors in urban streams include increases in impervious 

surface in the watershed, trash, and in-stream structures such 

as dams, weirs, and riprap that alter the physical form of the 

stream. Chemicals can enter streams from sewage-treatment 

outfalls, leaking underground sewer infrastructure, industrial 

outfalls, landfills, nutrient and pesticide applications for lawns 

and right-of-ways, residues that wash off paved surfaces, 

and the atmosphere. The effects of elevated chemicals in 

urban streams are often detrimental. Some chemicals such as 

pesticides and metals can cause toxicity, and excess nutrients 

can enhance productivity to the point of eutrophication in the 

stream. Nevertheless, urban streams survive and may even 

flourish, albeit often in states that are altered from natural conditions. The benefits of restoration and preservation of urban 

Water Quality in the Upper Anacostia River, Maryland: 

Continuous and Discrete Monitoring with Simulations to 

Estimate Concentrations and Yields, 2003–05

By Cherie V. Miller, Angélica L. Gutiérrez-Magness, Brenda L. Feit Majedi, and Gregory D. Foster
2 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

streams are not just for aesthetics; these environments support 

an ecology that extends well beyond the local urban area. The 

Chesapeake Bay Basin is a good example, where years of 

study have demonstrated the importance of contributions from 

the nontidal tributaries to a healthy estuary (U.S. Geological 

Survey, 2006).

The Anacostia River is an important urban stream in 

Washington, D.C. and Maryland, and is a tributary to the 

Chesapeake Bay. The lower reaches of the Anacostia River 

are within the city limits of Washington, D.C. Most of the 

lower reaches are tidally influenced, and are surrounded by 

dense urban land use. The tidal Anacostia River is listed as 

one of three Regions of Concern by the U.S. Environmental 

Protection Agency’s Chesapeake Bay Program (CBP) 

(Chesapeake Bay Program, 1999). The upper watershed is 

situated in Montgomery and Prince George’s Counties in 

Maryland. Although these counties are still experiencing rapid 

growth and development, they are less densely urbanized than 

the tidal reaches in Washington, D.C. Local governments in 

Washington, D.C. and the surrounding counties are working 

to improve conditions in the Anacostia River and submit an 

annual plan for improvement to the CBP.

Purpose and Scope

The purpose of this report is to summarize the results of a 

study by U.S. Geological Survey (USGS) on the water quality 

in the upper Anacostia River watershed. The USGS study has 

quantified the contributions of suspended sediment (SS) and 

chemicals from the upper watersheds in Maryland, and used 

these data to interpret transport processes and the magnitudes 

of loadings to the more heavily urbanized tidal area. USGS 

has developed a cooperative program to work on the Anacostia 

River with other scientific agencies and jurisdictions including 

the Prince George’s County Department of Environmental 

Resources, the Maryland Department of the Environment, 

the U.S. Environmental Protection Agency, and George 

Mason University.

This study focused on two monitoring stations near the 

Maryland/Washington, D.C. border that are immediately 

upstream of the head of tide in the Anacostia River. Waterquality data were collected to measure: (1) discrete 

concentrations of nutrients, dissolved and total-recoverable 

trace metals, and SS, and (2) continuous water temperature, 

pH, specific conductance, concentration of dissolved oxygen, 

and turbidity over a 2-year period. The discrete data were 

evaluated to determine the primary phases of nutrients and 

metals in transport, the behavior of SS and chemicals over 

time and varying flow regimes, and enrichment factors (EFs) 

for trace metals in SS. Unbiased regression models were 

developed to estimate annual yields of SS, total nitrogen (TN), 

and total phosphorus (TP) at each station. Regression models 

were used to estimate the concentrations of SS, TN, and TP 

from continuous parameters.

The data from this study have provided insights 

into the contributions of potential contaminants from the 

upper watershed of the Anacostia River. These data may 

be compared to data collected in the lower tidal Region of 

Concern in the Anacostia River as well as to other tributaries 

in the Chesapeake Bay watershed, and urban streams across 

the United States. Estimation of the quantities and comparative 

magnitudes of contaminants within the context of transport 

processes in the river system will aid water-resource managers 

in focusing management strategies where improvements may 

be most beneficial and cost-effective. The following objectives 

outline the goals of this study:

1. Water-quality data were collected, summarized, and 

described over a 2-year period at the Northeast and 

Northwest Branches of the Anacostia River (USGS waterquality and gaging stations 01649500 and 01651000, 

respectively), and included nutrients, trace metals, and 

SS. Samples for organic compounds and bacteria were 

collected concurrently for this study but are not described 

in this report.

2. Annual yields (loads per square area) for SS, TN, and TP 

for both Anacostia River stations for calendar years 2004 

and 2005 were estimated.

3. Statistical relations from continuous water-quality and 

-quantity measurements (discharge, water temperature, 

pH, specific conductance, turbidity, and concentration of 

dissolved oxygen) were developed to estimate the concentrations of SS, TN, and TP.

4. Observations of water chemistry in terms of fluvial and 

urban processes in the upper nontidal Anacostia River are 

presented.

Description of the Anacostia River and 

Watershed

The Anacostia River flows through an urban corridor in 

central Maryland and Washington, D.C. (figs. 1A–C). Climate 

in this area is temperate with an average annual rainfall in 

Maryland of approximately 45 inches (National Oceanic and 

Atmospheric Administration, 2002). The Anacostia River 

watershed extends into Montgomery and Prince George’s 

Counties in Maryland and down to the Potomac River. The 

tidal portion of the Anacostia River is mainly within the 

boundaries of Washington, D.C. The stations selected for this 

study are the Northeast Branch Anacostia River at Riverdale, 

Maryland (USGS Station 01649500; fig. 2) and the Northwest 

Branch Anacostia River near Hyattsville, Maryland (USGS 

Station 01651000; fig. 3). The two stations are just above 

the highest points of tidal influence in the Anacostia River, 

which occurs near the boundary between Maryland and 

Washington, D.C. The two branches reach a confluence in the 
Introduction 3

Figure 1A. Location of the Anacostia River watershed, which includes the lower watershed which is partially tidal 

within Washington, D.C., and the upper watershed in Maryland.
4 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Figure 1B. Detailed view of the Anacostia River watershed. The upper watershed of the river includes the Northeast Branch 

and the Northwest Branch of the Anacostia River.
Introduction 5

Figure 1C. Location of the Anacostia River watershed and the dominant hydrogeomorphic regions in the upper 

watersheds of the Northeast and Northwest Branches (USGS stations 01649500 and 01651000, respectively). 

(Modified from Reger and Cleaves, 2003.)
6 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Figure 2. The historical stream 

gage (at left end of bridge) at 

the Northeast Branch Anacostia 

River at Riverdale, Maryland 

(USGS station 01649500). 

(Photograph by U.S. Geological 

Survey)

Figure 3. View upstream of 

the water-quality sampling 

station at the Northwest 

Branch Anacostia River at 

Hyattsville, Maryland 

(USGS station 01651000). 

(Photograph by U.S. Geological 

Survey)
Introduction 7

Anacostia River at Bladensburg Maryland, just below the two 

sampling stations. Bladensburg served as a major seaport for 

Washington, D.C. until the mid-1800s, when sediment from 

erosion due to agriculture and development made most of the 

Anacostia River non-navigable (Williams, 1977; Gottschalk, 

1945). Many changes, particularly to the tidal channels, have 

occurred since then, and the modern-day Anacostia River is 

very different from colonial times. From the latter part of the 

1900s to the present, urbanization has increased throughout 

the watershed and is the most dominant influence on the 

river. Since about 1987, efforts have been made to restore and 

improve conditions on the Anacostia River.

The two main physiographic provinces of the Anacostia 

River Basin are the Piedmont Plateau and the Atlantic Coastal 

Plain (fig. 1C). The upper part of the Anacostia watershed lies 

in the Piedmont Upland Section, specifically in the Hampstead Upland District, which is within the Harford Plateaus 

and Gorges Region (Reger and Cleaves, 2003). The Piedmont 

Upland is comprised of rolling hilly terrain with distinctive 

ridges, hills, and valleys and moderately thin soil zones. The 

Atlantic Coastal Plain consists mainly of alluvium, with generally flatter topography of sedimentary beds and thicker soil 

zones than that of the Piedmont Plateau. Slopes in the Atlantic 

Coastal Plain are usually less than 8 degrees. Lithologies in 

this area of the Atlantic Coastal Plain are mainly quartzitic and 

sometimes micaceous sands, gravels, silts, and clays. River 

valleys are incised into the Atlantic Coastal Plain alluvium; 

delineated in the Anacostia Valley Area in figure 1C. The river 

valleys consist mainly of gently dipping quartzitic and micaceous sediments with gently dipping beds, and locally there 

are Tertiary terraces on either side of the main channels. The 

Fall Zone is a region of the Piedmont Plateau, a transition area 

between the Piedmont Plateau and the Atlantic Coastal Plain, 

where there is often an abrupt change in slope. In the study 

area, the Fall Zone is described by Reger and Cleaves (2003) 

as the Perry Hall Upland District. Bedrock in the Fall Zone is 

a mixture of metagabbros and granites unconformably overlain 

by alluvium. The hilltops are often capped with Cretaceous 

sediments, mainly gravels and sands; rivers may incise down 

to the bedrock (Reger and Cleaves, 2003).

Land cover in the watersheds above the two sampling stations is shown in figure 4. The land-cover classes are grouped 

so that the developed category includes all urban and suburban 

development; commercial, industrial, and residential areas of 

all densities and areas with and without trees and(or) ground 

cover. Major point-source dischargers that are permitted 

through the National Pollutant Discharge Elimination System 

(NPDES) Permitting Program are shown in figure 4 (Frank 

Siano, MDE, written commun., 2006). Two of the NPDES 

sites are municipal wastewater-treatment plants operated by 

the U.S. Department of Agriculture. The other point sources 

in the study area include light industrial waste (some sources 

of metals such as paint pigments and metal electroplating), 

petroleum hydrocarbons from parking and other commercial facilities, and mixed waste from military facilities. The 

land-use category of agriculture includes row and grain crops, 

pasture, and orchards. Forested areas include all undeveloped 

land, and barren areas include quarries and other areas without 

ground cover.

There is a strong influence from various types of development in both watersheds (fig. 4)—the percent of impervious 

surface throughout the watershed areas further emphasizes 

this point (fig. 5). Impervious surfaces are defined as manmade areas such as paved areas and roofs that do not readily 

absorb or retain water. These areas can have a negative effect 

on the biotic integrity of streams because of increases in the 

velocity and quantity of flow during storms and subsequent 

deterioration of channel stability and aquatic habitat. Arnold 

and Gibbons (1996) proposed a threshold of about 10-percent 

impervious surface for observable declines in stream integrity. 

The concept of a threshold is actually variable and not always 

clearly defined, but the percent of impervious surface can be 

an important overall indicator of urban influences. Whereas 

the average percentage of impervious surface in the Anacostia 

River watershed is about 20 percent, higher percentages are 

concentrated in the lower parts of the watershed. Impervious 

surfaces influence flow, as well as the quantities of chemicals 

that are transported. Stormwater-management controls can 

alleviate some of the impacts of impervious surfaces, but it 

is often difficult to implement these controls at or above the 

usual rate of urban and suburban development. 

Several consortia with many local and Federal partners 

have been formed to develop and implement a plan for the restoration of the Anacostia River; the Anacostia Watershed Toxics Alliance (AWTA) (Anacostia Watershed Toxics Alliance, 

2001) and the Anacostia Watershed Restoration Committee 

(AWRC) (Metropolitan Washington Council of Governments, 

1998, 2003) continue to report progress on these efforts. 

Several major stream-restoration projects, both upstream and 

downstream of the sampling station on the Northwest Branch, 

were ongoing during the current study (2003–05) and had the 

potential to influence the amount of sediment that was transported (fig. 6). Stream-restoration projects in the Northwest 

Branch Anacostia River were completed by December 2004.

Hydrology in the nontidal portion of the Anacostia River 

can be described as flashy with a quick, relatively short storm 

response and a lot of energy during high-flow events. Conversely, the tidal Anacostia River within Washington, D.C. 

is more sluggish, and a lot of the sediment that is transported 

from Maryland is deposited in the upper tidal river and mudflats where flow is slower, allowing settling and deposition of 

the suspended sediment. Tidal waters of the Anacostia River 

below Bladensburg have a mean residence time on the order of 

35 days (Velinsky and others, 1994).

Contaminants in the Anacostia River

Most of the contaminants in the Anacostia River occur 

in the lower tidal portion within Washington, D.C., which 

is listed as one of three Regions of Concern by the CBP. 

Segments of the river in this area are listed on Maryland’s 
8 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Figure 4. Land cover in the Anacostia River watershed (Hitt, 2006). Industrial and municipal discharge sites are 

permitted through the National Pollutant Discharge Elimination System (NPDES).
Introduction 9

Figure 5. Percent of impervious surface in the Anacostia River watershed (Smith and others, 2002).
10 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

303(d) list for a number of contaminant issues, including 

bacteria, biological integrity, polychlorinated biphenyls 

(PCBs), and the pesticide heptachlor epoxide. Since the 

late 1980s, a number of organizations such as the AWRC 

and AWTA have been formed to facilitate the rehabilitation 

of the Anacostia River and watershed. The membership 

of these organizations includes Federal, State, and County 

governments, as well as a number of citizens groups. 

Organizations such as the “Anacostia Watershed Society” 

and the “Friends of Sligo Creek” use volunteers to conduct 

clean-up and monitoring efforts and to educate the public 

about the status of their local streams. Recent improvements 

in the Anacostia River include wetland and riparian-zone 

restoration, trash and debris reduction, replacement of leaking 

sewer infrastructure, and Low-Impact Development (LID) 

that uses passive engineering improvements to redirect and 

process storm runoff (Metropolitan Washington Council 

of Governments, 1998, 2003; Anacostia Watershed Toxics 

Alliance, 2001).

Recent studies by Pinkney and others (2001, 2004) 

have reported severe chemical effects on brown bullhead in 

the lower tidal Anacostia River. They observed metabolites 

of polycyclic aromatic hydrocarbons (PAHs) in bile, DNA 

adducts of PAHs in liver tissue, PCBs, and organochlorine 

pesticides in muscle tissue, and used cytochrome P450 

(ethoxyresorufin-O-deethylase or EROD assay) in liver to link 

observations of barbel malformations and tumors to exposure 

to organic chemicals. They concluded that the likely causative 

chemicals for the observed diseases in brown bullhead in the 

Anacostia River were PAHs, dichloro-diphenyl-trichloroethane 

(DDT), PCBs, and(or) chlordanes. 

Sedimentation has been a continuous problem in the 

Anacostia River over the last 200 years as the land surface 

in the watershed has been cleared for development. Williams 

(1977) documented significant narrowing of the channel from 

Bladensburg, Maryland, down to the Washington, D.C. border 

during this period, with increasing rates of sedimentation 

during the 1900s. Yorke and Herb (1978) documented large 

increases in land clearing in the upper Northwest Branch 

of the Anacostia during the 1960s and 1970s with a large 

shift in land use to suburban and urban. An important effect 

of urbanization is the increase in peak stormflows due to 

increases in impervious surface in the watershed and hard 

channelization of the streambeds. Increased peak storm 

velocities can accelerate the rates of erosion of streambanks, 

thus augmenting sediment loads. Yorke and Herb (1978) 

documented increased sediment loads in the more urbanized 

areas of the watershed and showed that most of the load was 

transported during storm events—73 percent in 2.2 percent of 

the time, and 94 percent in 5.7 percent of the time. Loads of 

sediment were variable between storms and were correlated 

to the volume and peak discharge of the storm runoff. York 

and Herbe (1978) determined that major sources of sediment 

in the watersheds were cropland, urban land, and construction 

sites, although more recent laws governing sediment-control 

structures at construction sites would be expected to reduce 

Figure 6. Stream-restoration 

project by the Maryland State 

Highway Administration on the 

Northwest Branch Anacostia 

River (USGS station 01651000), 

August 7, 2003. (Photograph by 

U.S. Geological Survey)
Methods of Study 11

some of these effects. Studies such as Horowitz and others 

(in press) have shown that antecedent conditions such as the 

length and intensity of dry periods between storms as well 

as the strength of preceding storms affect the quantities of 

sediment and chemicals that are mobilized during each storm. 

Methods of Study

Two types of water-quality and quantity data were 

collected at the sampling stations on the Northeast and 

Northwest Branches of the Anacostia River: (1) continuous 

measurements for discharge, pH, water temperature, 

specific conductance, concentration of dissolved oxygen, 

and turbidity were collected from January or February 2004 

through December 2005; and (2) discrete measurements of 

the concentrations of nutrients, SS, and trace metals were 

collected from July 2003 through December 2005. All data 

were archived in the USGS National Water Information 

System (NWIS) database and are available upon request from 

the USGS Maryland-Delaware-D.C. Water Science Center 

(MD-DE-DC WSC) in Baltimore, Maryland.

Field methods described in this report are documented in 

the USGS National Field Manual (NFM) for the collection of 

water-quality data (U.S. Geological Survey, variously dated) 

and Guidelines and Standard Procedures for ContinuousWater-Quality Monitors (Wagner and others, 2006). Chapters 

of the NFM are updated on a continuous basis and are available on the world wide web.

Measurements of Continuous Flow and 

Water Quality

Continuous-flow measurements were estimated from 

river stage that was recorded using a stilling well and float 

recorder. The gage height was recorded every 15 minutes, 

individual measurements of flow were made throughout the 

range of stages, and a stage-discharge relation was maintained 

to create a record of continuous flow. The stations at both the 

Northeast and Northwest Branches of the Anacostia River 

have operated continuously from 1938 to the present.

Continuous measurements for water temperature, specific conductance, pH, concentration of dissolved oxygen, 

and turbidity was performed using YSI model 6600 multiparameter data sondes that were deployed in the river. The 

data sondes reported readings of each parameter collected at 

15-minute intervals to an onsite electronic CR10X data logger, 

which stored the data for transmission to the USGS at 4-hour 

intervals. Continuous data were reported in near-real time on 

the world wide web at http://waterdata.usgs.gov/md/nwis/

current/?type=quality.

To collect accurate and precise data, equipment at both 

sites was field inspected and calibrated at intervals ranging 

from 1 to 6 weeks as dictated by hydrologic conditions, 

data-transmission quality (fouling and drift), and the known 

functional limitations of the equipment. For cleaning and 

calibration of the monitor and sensors, multiple observations 

of each parameter were made during the entire process, and 

were used to determine separate corrections for both fouling 

and drift. Standards for specific conductance were prepared 

and quality-assured at the USGS National Water-Quality 

Laboratory (NWQL) in Denver, Colorado. Standards for 

pH were manufactured by the Fisher Scientific Company. 

Formazin-based standards for turbidity were manufactured by 

YSI and were used without dilution. Shifts and corrections that 

exceeded acceptable criteria (Wagner and others, 2006) were 

applied to the continuous-data records in the USGS NWIS 

database after each field inspection was completed. Corrected 

(provisional data) were displayed in near-real time on the 

USGS webpage of the MD-DE-DC WSC. After review, data 

were published in the USGS Annual Water-Data Report series 

for water years (October through September) during which 

data were collected.

Field Collection of Water-Quality Samples

Discrete samples for water quality were collected from 

the streams during base-flow conditions using equal-width 

increment (EWI) sampling techniques (U.S. Geological Survey, variously dated). Multiple depth-integrated subsamples 

were collected at equal intervals across the stream (approximately 10 sections) and composited into an acid-washed 

polycarbonate churn splitter to collect a representative sample 

of the entire stream. The samples were processed within the 

housing at the streamflow-gaging station, in a clean USGS 

field van, or in the USGS MD-DE-DC WSC laboratory to 

avoid environmental contamination. Subsamples of whole 

water were dispensed while churning into clean polyethylene 

or glass bottles. After all whole-water samples had been collected, samples for dissolved analysis were collected from the 

churn using a peristaltic pump with an in-line acid-washed 

polycarbonate capsule filter [0.45 μm (micrometer) effective 

pore size]. Samples for total-recoverable and dissolved metals 

were fixed with 1 mL (milliliter) of Nitrix nitric acid to attain 

a pH of less than 2. Samples for whole-water analysis of nutrients were fixed with 1 mL of sulfuric acid. Samples for nutrients and metals were chilled to less than 4°C (degrees Celsius) 

and shipped on ice overnight to the USGS NWQL in Denver, 

Colorado. Samples for SS were stored and shipped later to the 

USGS Sediment Laboratory in Louisville, Kentucky.

An automatic sampler was installed at each station so 

that samples could be collected more frequently during storm 

events (fig. 7). Four to six samples were collected over each 

storm hydrograph, based on changes in flow and precipitation. 

Pressure transducers and rain gages were installed and 

connected directly to the autosamplers to provide information 

on stream conditions that would automatically trigger the 

autosamplers. Time-discrete samples were processed from 

selected points in the storm hydrograph. To compare methods 

for EWI and autosampling, several samples were collected 
12 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

simultaneously using both techniques. Field data for water 

and air temperature, pH, specific conductance, concentration 

of dissolved oxygen, and turbidity were recorded from a 

handheld YSI field meter (Model 600XL) at the time of each 

manual sampling event.

Laboratory Methods

Nutrients were analyzed at the USGS NWQL. Samples 

for total and soluble phosphorus and Kjeldahl nitrogen were 

digested concurrently in block digestors at high temperature 

with a mercury, Hg(II), catalyst. Ammonium ions, both those 

originally present and those generated by the procedure, were 

analyzed colorimetrically by a salicylate-hypochlorite Berthelot-reaction procedure using an air-segmented continuous-flow 

analyzer. Ammonia nitrogen was analyzed separately by the 

same colorimetric procedure, and organic nitrogen was determined by difference. Phosphate was analyzed in a separate aliquot from the same digestate using the ammonium-molybdate 

colorimetric method and automated segmented flow (Fishman, 

1993; Patton and Truitt, 2000). Nitrite-nitrogen was analyzed 

by diazotization and colorimetric detection in automated segmented flow. Nitrate was reduced by cadmium metal and analyzed as nitrite to give total nitrate/nitrite-nitrogen (Fishman, 

1993). Nitrate-nitrogen was determined by difference. Total 

nitrogen was calculated as the sum of total Kjeldahl nitrogen 

and total nitrate/nitrite nitrogen.

Metals were measured in the dissolved phase and as total 

recoverable by partial digestion at the NWQL. Samples for 

total-recoverable metals were prepared by whole-water inbottle digestion with nitric acid (about 3 percent by volume). 

Acidified samples were incubated at 65°C for 8 hours and 

then filtered through a 0.45-μm filter. Samples for aluminum 

(Al), arsenic (As), cadmium (Cd), cobalt (Co), copper (Cu), 

iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc 

(Zn) were analyzed by direct-injection inductively coupled 

plasma-mass spectrometry (ICP-MS). Individual interferences 

for trace metals were controlled (Faires, 1992; Garbarino and 

Struzeski, 1998; Garbarino, 1999a,b; Garbarino and Hoffman, 

1999; Garbarino and others, 2006). Aliquots for both dissolved 

and total-recoverable mercury (Hg) were analyzed separately 

using the cold-vapor method and atomic-fluorescence spectroscopy (Garbarino and Damrau, 2001). The total-recoverable 

digestion method extracts labile metals but does not dissolve 

silicates or other resistant mineral phases. All methods at 

the USGS NWQL are quality-assured by the National Environmental Laboratory Accreditation Program (NELAP), the 

USGS Branch of Quality Systems that conducts blind audits, 

and the USGS Standard Reference Sample (SRS) program.

Concentrations of SS and the percent of sediment that 

was finer than 0.63 μm were analyzed using filtration and 

gravimetric methods at the USGS Sediment Laboratory in 

Louisville, Kentucky. All methods and quality-assurance 

procedures for sediment analyses are documented in the 

laboratory quality-assurance plan (Shreve and Downs, 2005).

Figure 7. Automatic sampler 

with CR10X data logger and 

modem to record physical 

parameters, upload data in 

near-real time to the world 

wide web, and to trigger the 

sampler during storm events. 

(Photograph by U.S. Geological 

Survey)
Methods of Study 13

Evaluation of Data Integrity

Field blanks were collected and analyzed for trace 

metals, nutrients, SS, and total organic carbon. Most field 

blanks had concentrations less than the reporting levels or 

were not detected. In several instances, there were measurable 

concentrations in the field blanks but these values were very 

near the reporting limits and were almost always less than 

10 percent of ambient concentrations. Total organic carbon 

had two blanks with concentrations of E0.3 and 0.5 mg/L 

(milligrams per liter). E-coded values are concentrations that 

are determined to be below the reporting level but above the 

detection level. These samples represent positive detections, 

but the values have a low precision. One blank for trace 

metals had a concentration of 17 μg/L (micrograms per liter) 

for total-recoverable Al, possibly indicating a problem with 

filtration in that sample, but all other values for metals in this 

sample were non-detects or less than the reporting limit. On 

August 24, 2005, the field blank from the Hyattsville site 

had a total-recoverable Zn concentration of 5 μg/L, which 

is above the reporting level of 2 μg/L, and is in the range of 

concentrations found in base-flow samples at both sites. Lowlevel concentrations of As, Fe, Mn, Cu and nutrients also were 

detected in this sample.

Approximately 10 percent of all samples were replicated 

to evaluate the precision of the methods. For most of the 

replicates, a second sample was pulled from the composite 

sample in the churn splitter. Relative percent differences 

for replications were well within plus or minus 3 percent 

for nutrients, 5 percent for metals, and 15 percent for the 

concentrations of SS, except where concentrations were near 

the reporting limits.

Some replicates compared different sampling methods 

such as autosampling from a point on the side of the stream 

with EWI sampling using either a D-81 or DH-95 integrated 

sampler (USGS integrated sampling devices; U.S. Geological 

Survey, variously dated). Replicate comparisons from the 

autosampler and the EWI method were similar during 

base-flow conditions at both stations. For stormflow at the 

Northeast Branch, the autosampler compared well to the 

EWI sampling method. Samples collected at the Northwest 

Branch during stormflow did not compare well because of 

an ephemeral side branch just above the site that contributed 

a lot of sand at higher velocities, and possibly because of 

high temporal variations in flow over the duration of sample 

collection. Discrete samples for SS collected on March 23, 

2005 were variable between the left and right banks. Samples 

from the bank where the autosampler intake is located had 

lower total concentrations of SS and a higher percentage of 

finer materials: 798 mg/L and 68 percent of the material 

was finer than 0.63 μm in diameter. The right bank, which is 

below the ephemeral overflow bank (fig. 3), had 1,350 mg/L 

of total SS and 40 percent fine material. The sample collected 

from the autosampler for comparison had 670 mg/L of SS and 

70 percent fine material. Differences in the time of sample 

collection contributed to some of the variability between these 

measurements, because flow in these urban streams is very 

flashy and variable over short time frames. During the sample 

collection period, flow varied from 1,470 to 1,830 ft3/s (cubic 

feet per second), and a composite sample collected using the 

EWI method at the peak of the storm contained 2,100 mg/L 

of SS. Concentrations of trace metals were replicated in this 

example much better than the total mass of particles (average 

of about plus or minus 15 percent for metals as compared to 

plus or minus 52 percent for total SS). Most trace metals are 

sequestered and transported on the fine materials rather than 

the sand-size fractions (Horowitz, 1991).

To evaluate the recovery efficiency of the total-recoverable methods, nine samples were replicated with splits sent to 

the NWQL for a total-recoverable analysis and to the USGS 

trace-metal laboratory in Reston, Virginia for a total digestion for Zn. During base flow, sediment concentrations were 

low and the methods were very comparable, with Zn values 

all less than or equal to 5 μg/L. For stormflow samples, where 

there was more sediment in transport, the total digestions had 

concentrations that were consistently higher than those found 

in total-recoverable samples; the relative differences between 

the two methods were approximately 8 percent. This is similar 

to the sampling and analytical error, and although the total-recoverable metals data have a small negative bias during storms 

when most of the transport occurs, the fraction of metals that 

are unquantified is in the most refractory forms and least available to the ecosystem.

Calculation of Particulate Metal Concentrations 

and Enrichment Factors

To compare metal enrichments on sediments in the 

Anacostia River to concentrations of metals on sediments at 

other stations, total-recoverable trace-metal concentrations 

were converted to particulate concentrations by subtracting 

the dissolved component of the metals and normalizing to 

the concentration of SS. This provides the concentrations of 

each metal on the particles in transport (Mp in milligrams per 

kilogram (mg/kg), equation 1), essentially normalizing for 

flow. As size fractions shift during changes in flow regime, the 

particulate-metal concentrations may also shift. When values 

were censored (less than the reporting limit), they were set to 

zero for the purposes of this calculation. In some cases where 

values of dissolved metals were greater than total-recoverable 

because the values were small and(or) similar within analytical error, negative values were generated. These also were 

set to zero for the calculation. Hg was not included in this 

calculation because it was frequently not detected. As also had 

a fairly high degree of non-detects and therefore the concentrations of As should be considered a gross estimate. Mp

 is 

calculated by use of the following equation:

M

M M

SS P

TR dis = [ ] −

(1)
14 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

where

Mp = Concentration of trace metals on particles in 

mg/kg (Concentrations of Al and Fe were 

converted to weight percent),

MTR = Concentration of total-recoverable trace 

metals in μg/L,

Mdis = Concentration of dissolved trace metals in 

μg/L,

and

SS = Concentration of suspended sediment in 

mg/L.

Enrichment factors (EFs) of the concentrations of metals 

in SS in the Anacostia River over the average global crustal 

abundance in shales as estimated by Turekian and Wedepohl 

(1961) were calculated for each trace metal (equation 2). 

Crustal shales rather than average global crust were used for 

this calculation, because they approximate the fine materials 

in SS. For this calculation, normalization of the trace-metal 

concentrations to a conservative element such as Al eliminated 

some of the bias in the dataset due to variations in grain-size 

distribution and sources of rock material in the sediment 

(Horowitz, 1991). Ratios of enrichment may be used to infer 

provenance of sediment materials, and possibly contamination 

if the values are high enough to be significant. Because the 

global abundance estimates are gross averages, enrichments 

should be considered significant only for values that are one 

or more orders of magnitude higher than the crustal shales. 

As and Hg were not included in this analysis because the incidence of censoring in these data was too high, thus creating 

artifacts in the ratios. The EF is a unitless ratio.

EF

M Al

M Al

i i

ACA ACA

=

(2)

where

EF = Enrichment factor for the concentration of a 

trace metal on particulates,

Mi = Concentration of trace metal in sample,

Ali = Concentration of Al in sample,

MACA = Concentration (abundance) of the trace 

metal in average crustal shale material,

and

AlACA = Concentration (abundance) of Al in average 

crustal shale material.

Regression Models to Predict Water Quality 

and Loads

Linear stepwise regression on log-transformed variables 

was used to develop statistical relations between continuous 

physical parameters and discrete sampling for the estimation 

of concentrations of nutrients and SS. Model development 

approaches followed the general procedures described in 

Helsel and Hirsch (1992). The first step in this analysis was 

to plot chemical concentrations against in-stream sensor 

measurements, and to visually determine if a potential relation 

existed. For example, a series of plots for TN, which is the 

sum of the concentrations of total Kjeldahl nitrogen and 

nitrate/nitrite, at the Northeast Branch Anacostia River station 

are shown in figures 8A–C. There were fairly strong and 

similar relations to TN for both discharge and turbidity, but 

they were not linear, and there was strong homoscedasticity 

of the residuals (the residuals were not independent of 

concentration). There appeared to be a subtle relation worth 

investigating between TN and specific conductance (SC) 

(fig. 8C). Values of SC originally were skewed because of 

road salt applied in the watershed during the winter. The 

increase in salinity and SC due to road salt had no relation to 

TN. Values influenced by road salt were basically outliers for 

this relation, and one solution was to remove all values over 

400 μS/cm (microsiemens per centimeter), which was a roadsalt threshold determined from the probability distribution 

of these data. The adjusted dataset for SC was tested for 

regression-model development.

Log transformation of these data greatly improved 

the linearity of the relation, but some homoscedasticity of 

the residuals remained (fig. 8D). Other power forms of the 

variables were tested, and in some cases produced better 

linear fits, but those model forms had unacceptable biases 

when the data were transformed back to real space. Without 

physical reasons to explain relations between the variables in 

other power forms, the log transforms were selected based on 

common usage in the literature.

The linear relation between the log-transformed values of 

turbidity and TN is shown in figure 8D. There is more uncertainty in this relation (coefficient of determination, R2

=0.67) 

than is found in the models for SS and TP, but the trend line 

for log-transformed data is more linear than for raw data.

After examination of the potential relations between 

variables, stepwise-regression models were determined using 

the SPlus Statistical Package, version 7.0 (Insightful Corporation). The appropriate form of each model was determined 

by log-transforming all the variables, starting with a formula 

using all variables, and then removing them one at a time, both 

in forward and backward steps. The final model was chosen 

based on several factors. The R2 estimates the proportion of 

the variance in the predicted variable that is explained by the 

multiple independent variables in the regression equation. The 

estimates of R2 should be reviewed with caution, however, 

because they were determined in log space and will overestimate the true variances explained by the model, once the 

data are transformed back into real space (McCuen, 2003). 

To add value to the model, the addition of a variable should 

substantially improve the R2 and decrease the residual standard 

deviation. Other determining factors for variable inclusion in 

the model were regression coefficients that were sufficiently 

different from zero and substantially large enough to define 

a predictive relation, and the overall bias of the predictions 

based on the sum of the residuals. In some cases, the addition 
Methods of Study 15

of another variable can cause a potentially spurious coefficient 

to shrink, thus indicating covariance; this also was taken into 

consideration when refining a model.

Often, there are not clear criteria to evaluate all of the 

observed factors, making the selection of an appropriate model 

somewhat subjective. In the example for TN, the relation indicated by the correlation between log10SC and log10TN (with 

high values of SC filtered out) did not significantly improve 

the model, and the bias of the residuals was the same for both 

forms of the model equations. The final model determined for 

TN at both stations was of the form

Log10TN ~ β1 + β2log10Turbidity (3)

where

TN = concentration of total nitrogen in mg/L, 

Turbidity is in formazin nephelometric units (FNU),

and

	 β1, β2 = regression coefficients.

The bias of each estimation was determined by 

transforming the estimated concentrations back to real space, 

plotting the residuals against the concentrations, and by 

calculating the actual relative bias or systematic error of the 

residuals (equation 4; McCuen, 2003). After transformation 

of the variables back into real space, the residuals were not 

normally distributed with higher residuals for values at 

higher concentrations.

RB e

X nX X X X i

n

= = − i

=

∑1

1

i

^

(4)

where

RBX = the actual relative bias (bias normalized 

to the average concentration of a waterquality parameter),

e = the total bias or average of the residuals, 

predicted minus observed,

Figure 8. Total nitrogen compared to (A) discharge, (B) turbidity, and (C) specific conductance. (D) Log-transformed 

variables for total nitrogen compared to turbidity at the Northeast Branch Anacostia River (USGS station 01649500), 2003–05 

[Outlier values of specific conductance that were influenced by road salt are removed from (A). Log-fitted curves are shown 

on figures A through C, and a linear fit with the coefficient of determination (R2) is shown in figure D.]
16 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

X = the average concentration of all observed 

measurements of the water-quality 

parameter,

Xi

^

= the predicted concentration for the ith

sample,

Xi = the observed concentration for the ith

sample,

and

n = the number of observations or samples.

Estimates of Loads and Yields

Loads are the product of the concentration of a specific 

water-quality constituent and the volume flux of streamflow 

over a designated unit of time. For the current study, annual 

loads were calculated for SS, TN, and TP at each of the two 

Anacostia River stations for calendar years 2004 and 2005. 

Loads were calculated two different ways to compare (1) 

results from the more traditional methods that are based on 

a rating curve between the concentration of the parameter 

of interest and flow and time (Cohn, 1988; Cohn and others, 1989), and (2) estimates of concentrations from data-rich 

continuous in-stream measurements. Models developed in 

LOADEST (Runkel and others, 2004) for the current study 

were based on hourly time steps of flow and turbidity.

The model ESTIMATOR was developed by the USGS 

and employs a 7-parameter log-linear regression model with 

refinements to correct for bias (Cohn, 1988). In this model, 

measured concentrations are regressed against mean daily discharge and time, including terms for seasonality. A Minimum 

Variance Unbiased Estimator (MVUE) is used to correct for 

bias that occurs when the predicted data are transformed back 

from log space into real space (Bradu and Mundlak, 1970). 

ESTIMATOR can handle a large percentage of data below the 

reporting limit because it uses the Adjusted Maximum Likelihood Estimator (AMLE) to assign values of concentration 

to these censored data (Cohn, 1988; Cohn and others, 1992; 

Cohn, 2005). Multicollinearity, or non-independence between 

explanatory variables, is reduced or eliminated in ESTIMATOR by using centering variables. To accomplish this, the 

center or mean of the variable is subtracted from each occurrence and these centered variables are used in the regression 

(Cohn and others, 1992). ESTIMATOR is calibrated using 

mean daily flow, so the time step is 24 hours.

The second modeling approach used the model 

LOADEST, which is based on the same basic statistical 

design (AMLE) as the original ESTIMATOR model to provide unbiased estimates of chemical loads. The advantages of 

LOADEST include more flexibility to include other userdefined predictor variables such as turbidity, and smaller time 

steps (hourly in the current study) for short-term variability 

that occurs during storms and diurnal cycles. The LOADEST 

model may be further improved by the Least Absolute Deviation (LAD) method to correct for the non-normal distribution 

of the residuals using a clustering approach called “smearing” 

(Runkel and others, 2004). A disadvantage of the LAD method 

is that it would require a bootstrap simulation to calculate the 

standard error of the prediction (SEP), which is not practical 

given the size of the continuous datasets.

Uncertainties associated with each estimate of mean 

annual load are expressed in terms of the standard error (SE), 

which represents the variability that may be attributed to the 

parameter uncertainty. The AMLE method also calculates the 

SEP from which a 95-percent confidence interval for each 

annual-load estimate can be developed. In the current study, 

LOADEST and ESTIMATOR were applied and compared 

using the water-quality datasets from the Northeast and Northwest Branches of the Anacostia River. Loads using the LAD 

in LOADEST were compared to those determined with the 

AMLE method and were very similar (within 5 percent), so it 

was assumed that normality was not an important bias on the 

annual loads, and the AMLE loads reported herein incorporate 

the 95-percent confidence intervals. 

A limitation on load estimates for the two Anacostia 

River stations is the short time span of the dataset (2 years). 

More accurate load estimates would require more years of data 

collection. Cohn (1988) recommended a minimum of 3 years 

of data for annual-load estimates and Yochum (2000) determined that a 9-year sliding window optimizes the regression 

approach used by the ESTIMATOR model. This has most 

likely increased the size of the confidence intervals in the current study and future modeling estimates of load and concentration would benefit from more data to refine the model.

Finally, annual load estimates were normalized to basin 

area to determine yields for each basin that are independent 

of the size of the watershed. Annual yields in kilograms per 

year per square kilometer for SS, TN, and TP at both of the 

Anacostia River stations are presented in later sections of 

this report.

Water Quality in the Upper 

Anacostia River

Two types of water-quality data were collected at the 

Northeast and Northwest Branches of the Anacostia River: 

(1) continuous measurements for discharge, pH, water 

temperature, SC, concentration of dissolved oxygen, and 

turbidity were collected from January or February 2004 

through December 2005; and (2) discrete measurements of 

the concentrations of nutrients, SS, and trace metals were 

collected from July 2003 through December 2005. Hydrologic 

conditions in the local area during this period were average, 

without large extremes of wet or dry; however, the region was 

recovering from a severe drought that had ended the previous 

summer. Concentrations and loads or yields determined during 

the study period are assumed to be representative of average 

hydrologic conditions at these sites. All water-quality data are 

publicly available on the USGS NWISWEB website at 

http://waterdata.usgs.gov/nwis.
Water Quality in the Upper Anacostia River 17

Flow and Physical Parameters

Data are summarized to show the median tendencies 

and extremes of each measurement of field conditions 

(table 1). Physical parameters were measured each time a 

discrete water-quality sample was collected and continuously 

with data sondes. Statistics for these data show that discrete 

samples were collected over a representative range of 

conditions at the two stations. Annual statistics for continuousflow data may be compared to the long-term record for both 

stations, which extends back to 1938. Time-series plots of 

mean-monthly discharge with an overlay of the long-term 

mean-monthly discharge at each station are shown in 

figures 9A–B. Flow during the period of study was relatively 

average, but 2003 was a wetter year than 2004 or 2005 

(James and others, 2003).

Median values of SC ranged from approximately 200 to 

400 μS/cm. Anomalously high values of SC occurred sporadically during the winter months due to salting of roads in the 

watershed for deicing purposes.

Supersaturation of dissolved oxygen occurred on afternoons when biological productivity was high. The pH was in 

the neutral to alkaline range and also peaked each day in the 

afternoon during periods of increased productivity. Diurnal 

shifts in pH during base flow were generally up to 2 or more 

units of pH. These patterns in pH occurred year round, even 

during the winter months when biological activity is expected 

to be low. A multiparameter data sonde deployed in a tributary just upstream from the station at the Northeast Branch 

recorded even larger diurnal swings in pH, with values as high 

as 10 on some afternoons (fig. 10). Some of this variability 

can be explained by diurnal changes in productivity. No other 

source of alkalinity was identified upstream of these stations 

during the study, but the peak values of pH were higher than 

what might be expected based on productivity alone.

Turbidity generally was low during base-flow conditions, 

usually much less than 100 FNU. Maximum turbidity 

values occurred during storm events, and were as high as 

approximately 1,000 FNU. An unexplained phenomenon in 

turbidity was occasionally observed during base flow at both 

Table 1. Statistical summaries of water chemistry at the Northeast and Northwest Branches of the Anacostia River (USGS stations 

01649500 and 01651000, respectively)–physical parameters.

[Values presented are the median and range (in parentheses, from minimum to maximum) for the 2-year period. Measurements of physical parameters were 

recorded each time a water-quality sample was collected. Continuous measurements were collected in 15-minute intervals. Period of record for continuous 

flow data at Northeast Branch is from 1938 to the current year. Period of record for continuous flow data at Northwest Branch is from 1938 to the current year. 

Continuous measurements for water temperature, pH, specific conductance, turbidity, and dissolved oxygen at both stations were recorded from January 2003 

through December 2005; ft3/s, cubic feet per second; °C, degrees Celsius; μS/cm, microsiemens per centimeter at 25 degrees Celsius; FNU, formazin nephelometric units; mg/L, milligrams per liter; <, less than; —, not available; Dec, December]

Discharge,

ft3/s pH

Water 

temperature,

°C

Specific 

conductance,

μS/cm at 25°C

Turbidity, 

FNU

Dissolved 

oxygen,

mg/L

Northeast Branch

Discrete data for July 2003–Dec 2005 496

(13.0–4,570)

7.2

(6.5–8.9)

12.9

(0.8–29.4)

227

(70.0–3,209)

91

(1.0–581)

10.3

(7.9–17.3)

Continuous data for water year 2004 132

(20–8,000)

7.6

(6.6–9.5)

14.0

(0.0–28.7)

362

(42–7,000)

17.9

(<1–1,000) —

Continuous data for water year 2005 89.5

(9.4–5,130)

7.4

(6.7–9.7)

14.1

(0.0–32.0)

419

(55–6,100)

14.7

(<1–980)

11.2

(4.1–20.2)

Long-term record for 1938–2005* 175

(1.4–12,000) — — — — —

Northwest Branch

Discrete data for July 2003–Dec 2005 250

(6.7–3,950)

7.4

(6.3–8.3)

14.8

(0.8–26.7)

287

(75.0–2,450)

100

(<1–760)

11.0

(7.8–16.3)

Continuous data for water year 2004 80.2

(8.24–9,310)

7.4

(6.7–8.6)

17.7

(0.5–27.7)

335

(57–4,270)

21.5

(<1–850) —

Continuous data for water year 2005 57.8

(5.1–5,000)

7.4

(6.9–9.4)

15.1

(0.0–30.5)

408

(75–6,960)

15.4

(<1–880)

10.8

(4.0–19.3)

Long-term record for 1938–2005* 49.6

(0.2–18,000) — — — — —

*

 Statistics based on 1938–2005 data.
18 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

stations. For a short period of time, at a similar time each day, 

turbidity increased for several hours by about 20–30 FNU 

without any observed change in flow or other conditions, and 

then receded to previous base-flow levels. The time of day for 

these observations changed over the course of the study, but 

for the time period of each event, it was regularly observed at 

approximately the same time each day. An example of peaks 

in turbidity that occurred each day in November 2005 at 

approximately 19:00 Eastern Standard Time is shown in 

figure 11. In this example, the peaks do not occur on the 

weekend days (November 12 and 13), indicating that the 

observed peaks are related to weekday activities in the 

watershed. No known cause for the observed behavior was 

discovered during the project, but on several occasions, 

observations of larger changes in turbidity and SC were traced 

to broken sewer lines. 

Nutrients, Suspended Sediment, and 

Total Organic Carbon

Statistical summaries of concentrations of nutrients, 

total organic carbon, and SS for the two Anacostia River 

stations are presented in table 2. Boxplots comparing the 

concentrations of (a) ammonia nitrogen, (b) nitrate nitrogen, 

(c) dissolved Kjeldahl nitrogen, (d) total Kjeldahl nitrogen, 

(e) total phosphorus, and (f) total organic carbon at the two 

Anacostia River stations and two larger integrated tributaries 

to Chesapeake Bay, the Susquehanna River at Conowingo, 

Maryland (USGS station 01578310) and the Potomac River 

at Washington, D.C. (USGS station 01646580) are shown 

in figures 12A–F. Data from the Susquehanna and Potomac 

Rivers were collected during the same period as part of the 

Chesapeake Bay River Input Monitoring (RIM) Program 

Figure 10. Diurnal variations in pH at the Northeast Branch 

Anacostia River at Riverdale, Maryland (USGS station 

01649500), and on a small tributary that enters just above the 

station.

Figure 9. Mean-monthly discharge in cubic feet per 

second for 2003–05 compared to long-term meanmonthly discharge at the (A) Northeast Branch and the 

(B) Northwest Branch Anacostia River (USGS stations 

01649500 and 01651000, respectively).

Figure 11. Short-term variations in turbidity during base flow 

at the Northeast Branch Anacostia River at Riverdale, Maryland 

(USGS station 01649500).
Water Quality in the Upper Anacostia River 19

Table 2. Statistical summaries of water chemistry at the Northeast and Northwest Branches of the Anacostia River (USGS stations 01649500 and 01651000, respectively), from July 2003 to December 2005–nutrients, total organic carbon (TOC), and suspended sediment (SS).[Values presented are the median and range (in parentheses, from minimum to maximum) for the 2.5-year period. The complete dataset is available on the world wide web at http://waterdata.usgs.gov/nwis. For comparison, summaries of values for the Susquehanna and Potomac Rivers from the same period (U.S. Geological Survey, 2006) are presented; mg/L, milligrams per liter; μm, micrometers; LTMDL, long-term method detection limit; MRL, method reporting level; in some cases more than one reporting level is given because these changed over the term of the project; total nitrogen is the sum of total Kjeldahl nitrogen and nitrate/nitrite; however, values may not add up exactly to 100 percent due to rounding error; <, less than; —, not available; n, number of observations]

Nitrate-N,

mg/L

Nitrite-N,

mg/L

Ammonia-N,

mg/L

Ortho phosphate-P,

mg/L

Soluble 

phosphorus,

mg/L

Dissolved 

Kjeldahl nitrogen,

mg/L

Total Kjeldahl 

nitrogen,

mg/L

Total 

phosphorus,

mg/L

Total 

nitrogen,

mg/L

LTMDL 0.008 0.004/0.001 0.008/0.005 0.004/0.003 0.002 0.05 0.05 0.002 —MRL 0.16 0.008/0.002 0.015/0.010 0.007/0.006 0.004 0.10 0.10 0.004 —Northeast Branch

(n=83)

0.75

(0.33–1.4)

0.009

(0.002–0.036)

0.074

(<0.01–0.45)

0.018

(<0.006–0.089)

0.033

(0.006–0.12)

0.51

(0.14–1.2)

1.3

(0.18–3.1)

0.25

(0.018–0.67)

2.0

(0.8–3.5)

Northwest Branch

(n=81)

0.90

(0.36–2.0)

0.011

(0.003–0.08)

0.064

(<0.01–0.50)

0.12

(<0.006–0.08)

0.27

(0.002–0.11)

0.45

(0.13–2.7)

1.6

(0.16–5.1)

0.33

(0.010–0.93)

2.4

(0.91–5.9)

Susquehanna River

(n=45)

1.3

(0.72–2.1)

0.009

(0.004–0.14)

0.04

(<0.04–0.16)

0.0075

(<0.006–0.08)

0.011

(<0.01–0.1)

0.24

(0.12–0.69)

0.34

(0.19–0.89)

0.05

(0.015–0.23)

1.6

(1.1–3.1)

Potomac River

(n=62)

1.3

(0.53–2.1)

0.008

(<0.008–0.16)

<0.04

(<0.04–0.08)

0.012

(<0.006–0.11)

0.024

(<0.01–0.13)

0.21

(0.11–0.48)

0.47

(0.16–2.2)

0.051

(0.007–0.70)

2.0

(1.4–3.5)

Total organic 

carbon,

mg/L

Suspended 

sediment,

mg/L

Percent of 

sediment finer 

than 0.63 μm

LTMDL 0.2 — —MRL 0.4 1 —Northeast Branch

(n=83)

4.8

(1.8–24)

180

(1–1,975)

73

(36–95)

Northwest Branch

(n=81)

4.8

(1.4–19)

373

(2–1,700)

77

(43–94)

Susquehanna River

(n=45)

3.1

(1.9–11)

14

(2–173)

—

Potomac River

(n=62)

5.2

(2.2–26)

14

(1–788)

—
20 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Figure 12. Boxplots comparing concentrations of selected parameters between the Northwest Branch and Northeast Branch 

Anacostia River (USGS stations 01651000 and 01649500, respectively), the Susquehanna River (USGS station 01578310), and the Potomac 

River (USGS station 01646580). The data were collected during the same time period, and both studies emphasized storm sampling.
Water Quality in the Upper Anacostia River 21

(U.S. Geological Survey, 2006). Although there were no 

statistically significant differences in the concentrations of 

SS and nutrients between the two Anacostia River sites 

(table 2), the concentrations at both of these stations differ 

from those at the RIM sites. Differences in the median 

concentrations of ammonia, nitrate, total Kjeldahl nitrogen, 

and total phosphorus among the four basins were determined 

by the Kruskal Wallis Rank Sum test and were significant for 

each set of boxplots (figs. 12A–E). Differences in the median 

concentrations of total organic carbon were not significant 

between sites (fig. 12F). Chi square and p values for the 

comparisons are reported with each plot.

There are basic differences in nutrient chemistry in small 

urban streams as compared to larger river systems with a 

majority of agricultural, forested, or more integrated land use 

in their basins. For example, nitrate is typically the dominant 

form of nitrogen in many streams. Maximum concentrations 

of nitrate in streams commonly occur during base flow when 

ground water is the major component of flow, and are lower 

during storms, when the stream is diluted by rain and surface 

runoff. Time-series plots of the concentrations of nitrogen 

species in figure 13A demonstrate this typical behavior for 

the Susquehanna River, where the dominant form of nitrogen consistently was nitrate. Concentrations of nutrients in 

the Potomac River at Chain Bridge in Washington, D.C. are 

not plotted here, but were similar. The same major species 

of nitrogen plotted over time for the Anacostia River stations 

(figs. 13B–C) showed a very different pattern that probably 

is more typical of small urban streams. At these stations, the 

dominant form of nitrogen was organic particles that were 

transported mainly during storm events. Ammonia was a small 

component of total nitrogen in all the rivers, but in the urban 

settings, ammonia was almost always present and the concentrations increased slightly rather than being diluted during 

storm events. A major source of ammonia in the Anacostia 

River is likely the degradation of organic matter. It has not 

been determined what portion of organic matter was stored in 

bed sediment that was resuspended during storm events, and 

how much was washed in from the watershed. Both dissolved 

and total Kjeldahl nitrogen consistently increased during storm 

events, and it is likely that a mixture of sources of organic 

nitrogen were responsible.

Trace Metals

Concentrations of trace metals are summarized in table 3. 

For comparison, ranges of concentrations from other regional 

and national studies are provided. Concentrations from the 

USGS Fall Line Toxics Program (Chesapeake Bay Program, 

1996) are from two base-flow synoptic surveys (fall and spring 

1994) at the limits of tidal effects in nine major tributaries of 

the Chesapeake Bay, and a fixed-station study from 1990–93 

on the Susquehanna River (USGS Station 01578310). Methods 

used for the Fall Line Toxics Program were generally the same 

as those used in the current study except that detection limits 

Figure 13. Major species of nitrogen and mean daily discharge 

in 2004 and 2005 at the (A) Susquehanna River at Conowingo, 

Maryland (USGS station 01578310), (B) Northeast Branch 

Anacostia River (USGS station 01649500), and (C) Northwest 

Branch Anacostia River (USGS station 01651000). [Scales vary 

on y-axes in each plot.]
22 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Table 3. Statistical summaries of water chemistry at the Northeast and Northwest Branches of the Anacostia River (USGS stations 

01649500 and 01651000, respectively) from July 2003 to December 2005–trace metals.—Continued

[Values presented are the median and the range (in parentheses, from minimum to maximum) for the 2-year period. The complete dataset is available on the 

world wide web at http://waterdata.usgs.gov/nwis. Concentration data from other studies are included for comparison: Chesapeake Bay synoptic summaries 

of nine major tributaries sampled twice during 1994 (Chesapeake Bay Program, 1996)a; dissolved and total-recoverable metal concentrations measured in the 

Susquehanna River from 1990–93 by the USGS Fall Line Toxics Program (data available on request from USGS MD-DE-DC WSC)b; typical metal concentration in streams from Drever (1999)c

; ranges of concentrations of trace metals in river and basin bed sediment in the tidal Anacostia River, Washington, 

D.C. (Velinsky and others, 1994)d; crustal abundances of the elements in average shales from Turekian and Wedepohl (1961)e; and ranges of particulate metal 

concentrations from National Water-Quality Assessment (NAWQA) Program data summaries for sieved bed sediment (<0.63 μm fraction) from Rice (1999)f; 

summaries of metal concentrations in urban lake sediments from Mahler and others (2006)g; units for dissolved and total-recoverable metals are in micrograms 

per liter (μg/L); units for particulate metal concentrations are in milligrams per kilogram (mg/kg) of dry weight of the suspended particles, except that aluminum and iron are presented in mass percent; LTMDL, long-term method detection limit; MRL, method reporting level; E, value is greater than the LTMDL but 

less than the MRL and is coded as estimated because of lower precision in this value; <, less than; —, not available]

Aluminum Arsenic Cadmium Chromium Cobalt Copper

Dissolved concentrations

LTMDL 0.8 0.1 0.018 0.4/0.02 0.02 0.2

MRL 1.6 0.2 0.04 0.8/0.04 0.04 0.4

Northeast Branch 32

(3.6–380)

0.55

(<0.2–1.1)

0.043

(<0.04–0.29)

E0.73

(<0.8–9.4)

1.2

(0.15–4.3)

3.9

(1.1–8.2)

Northwest Branch 21

(4.1–220)

0.46

(<0.2–2.7)

E0.033

(<0.04–0.38)

0.55

(<0.8–2.7)

0.48

(0.07–1.4)

3.6

(0.44–7.1)

Fall Line tributaries (1994)a 8.4–115 0.08–0.47 0.01–0.24 0.03–0.28 — 0.11–2.3

Susquehanna River 1990–93b 30

(<10–230)

<1

(<0.6–1.2)

<1

(<1–1.2)

<1

(<0.2–5.4) <3 2(<1–8)

Typical Streamc 50 2 — 1 0.2 7

Total-recoverable concentrations

LTMDL 1 0.9/0.06 0.018 0.4/0.02 0.02 0.3

MRL 2 1.9/0.12 0.04 0.8/0.04 0.04 0.6

Northeast Branch 960

(27–11,000)

E1.4

(E0.22–3.5)

0.16

(<0.04–0.55)

8.6

(0.54–28)

5.0

(0.43–28)

13

(1.5–40)

Northwest Branch 3,700

(41–13,000)

E1.4

(E0.18–5.7)

0.16

(0.02–1.4)

10

(<0.8–33)

6.7

(0.66–24)

19

(1.3–85)

Susquehanna River 1990–93b

 — <1

(<1–1)

<1

(<1–1)

2

(<1–10) — 3(<1–11)

Particulate concentrations

Northeast Branch 0.63

(0.17–3.7)

1.3

(<1–89)

0.50

(<1–13)

34

(<1–600)

18

(<1–129)

35

(<1–200)

Northwest Branch 1.0

(0.49–4.6)

0.63

(<1–270)

0.48

(<1–8.3)

25

(<1–250)

16

(<1–205)

50

(<1–400)

Fall Line tributaries (1994)a 0.09–17.9 — 0.52–33 20–320 — 2.6–281

Tidal Anacostiad — — 0.45–3.2 51-155 — 33–126

Crustal Abundance (Shale)e 8.0 13 0.3 90 19 45

NAWQAf 6.4

(1.4–14)

6.3

(1–200)

0.4

(<0.1–56)

64

(<1–700) — 27(6–620)

NAWQA(Lake sediments, 1990s: 

dense urban/light urban/reference)g — — 1.60/0.49/0.64 75/65/47 — 83/68/34
Water Quality in the Upper Anacostia River 23

Table 3. Statistical summaries of water chemistry at the Northeast and Northwest Branches of the Anacostia River (USGS stations 

01649500 and 01651000, respectively) from July 2003 to December 2005–trace metals.—Continued

Iron Lead Manganese Mercury Nickel Zinc

Dissolved concentrations

1 0.04 0.1 0.009 0.03 0.3 LTMDL

2 0.08 0.2 0.02 0.06 0.6 MRL

190

(51–480)

0.46

(0.067–1.7)

59

(1.8–230) <0.02 4.0(1.9–6.5)

12

(1.6–55)

Northeast Branch

135

(<0.6–467)

0.35

(E0.046–1.8)

60

(2.3–197) <0.02 2.7(0.66–4.5)

6.1

(1.5–29)

Northwest Branch

21–1,495 <0.03–0.39 1.4–135 <0.1 <0.1–3.2 0.3–19 Fall Line tributaries (1994)a

21.5

(<3–810)

<1

(<0.06–5)

40

(2.0–170)

0.021

(<0.1–0.20)

3

(<1–9)

1.6

(<3–22)

Susquehanna River 1990–93b

40 1 8 0.07 2 30 Typical Streamc

Total-recoverable concentrations

3/1 0.1 0.3 0.005 0.08 1.0 LTMDL

6/2 0.2 0.6 0.018 0.16 2.0 MRL

3,050

(30–20,000)

14

(0.36–57)

200

(25–1,500)

0.026

(<0.018–0.11)

9.8

(2.8–42)

68

(2.9–460)

Northeast Branch

5,900

(360–22,000)

20

(E0.12–150)

430

(52–1,900)

0.039

(<0.018–0.14)

14

(2.4–58)

70

(3.0–380)

Northwest Branch

640

(160–3,000)

2

(<1–17)

120

(25–190)

<0.1

(<0.1–1)

5.5

(2.0–23)

10

(<10–50)

Susquehanna River 1990–93b

Particulate concentrations

1.8

(0.27–24)

79

(14–203)

560

(<1–5,600) — 24(<1–205)

260

(<1–3,900)

Northeast Branch

1.9

(0.94–12)

64

(20–440)

940

(<1–22,000) — 35(<1–580)

200

(<1–1,900)

Northwest Branch

0.02–18.4 <3.0–130 29–16,830 — 0.7–331 <3.0–1,095 Fall Line tributaries (1994)a

— 32–409 — 0.2–1.0 — 137–512 Tidal Anacostiad

4.7 20 850 0.4 68 95 Crustal Abundance (Shale)e

3.5

(0.7–19)

27

(<0.7–19)

— — 27

(6–530)

110

(<4–9,000)

NAWQAf

— 214/56/48 — — — 343/203/134 NAWQA(Lake sediments, 1990s: 

dense urban/light urban/reference)g
24 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

and analytical precision have improved since the early 1990s. 

Typical stream values for dissolved metals in the United States 

are presented from Drever (1999).

Dissolved concentrations for metals sampled in the 

Anacostia River were similar between the two Anacostia 

River stations and to those in the Susquehanna River and in 

other streams across the United States. This indicates that the 

concentrations of dissolved trace metals in these typical urban 

streams may be controlled by solubility, and are probably 

more regulated by physical parameters such as temperature, 

pH, and redox than by variations in sources and hydrology.

Concentrations of dissolved trace metals at the Anacostia River stations were compared to guidelines and criteria 

established by the U.S. Environmental Protection Agency 

(USEPA) (2006) and the Canadian Council of Ministers of the 

Environment (CCME) (2006) for the protection of aquatic life 

in freshwater (Appendix). The toxicity of most of these metals 

is a function of hardness and(or) alkalinity, which were not 

determined during the current study, so comparisons are made 

relative to a general description of water quality and should be 

considered with caution. In some instances, dissolved concentrations of Cd, Pb, Cu, Ni, and(or) Zn exceeded the CCME 

guidelines, which are the more stringent of the two, but with 

the exception of a few values of dissolved Cd at both stations 

that were just above chronic levels, most values did not exceed 

the USEPA criteria.

A positive correlation was found between the dissolved 

concentrations of three trace metals: Mn, Co, and Ni. This is a 

natural geochemical association found commonly in a number 

of environmental settings and may be compared to redox 

processes that form Mn nodules on the ocean floor (Burdige, 

1993). The correlation is not likely to be directly related to 

urban processes, but may be indirectly related to periodic 

anoxic conditions in bed sediment.

Total-recoverable metals in the Anacostia River, except 

for Cd, had concentrations that were significantly higher 

than those found in the Susquehanna River (Chesapeake Bay 

Program, 1996) (table 3; p < 0.05 for the Kruskal Wallis Rank 

Sum Test). Total-recoverable metals include metals that are 

transported in the particulate fraction, where metals naturally 

partition, so they are good indicators of the total metal load in 

each river.

To facilitate comparison of metal enrichments on sediment in the Anacostia River to sediment from other stations, 

concentrations of total-recoverable trace metals were converted to particulate concentrations (equation 1; table 3). 

Calculated concentrations of particulate metals were compared 

to estimates of their global crustal abundance in average shale 

materials (Turekian and Wedepohl, 1961) and to two national 

summaries (samples collected across the conterminous United 

States) from the USGS National Water-Quality Assessment 

(NAWQA) Program. NAWQA summarized results of metal 

concentrations on streambed sediments (Rice, 1999) and in 

urban-lake bed sediments (Mahler and others, 2006). Three 

different values for the median concentrations in recent (1990) 

lake-bed sediments are shown from the study by Mahler and 

others in table 3: dense urban (greater than 52 percent urban 

land use in the watershed), light urban (6–43 percent urban 

land use), and reference (less than 1.5 percent urban land 

use). The concentrations of metals associated with SS at both 

Anacostia River stations are highly variable, but median values 

are similar to crustal shales and fall in the range of other light 

urban streams. Particulate concentrations also compared well 

to values measured during 1995–96 on the Anacostia River by 

Gruessner and others (1997), although the total suspended solids observed in that study were much lower than totals found 

during most of the storms in the current study.

Metal Enrichment Factors

Enrichment factors over the global crustal abundance in 

shales were calculated for each trace metal (equation 2). These 

factors are ratios that normalize the observed concentrations of 

metals on SS in the Anacostia River to average baseline concentrations in crustal shales. Boxplots comparing the statistical distributions of the calculated EFs for each trace metal at 

the two Anacostia River stations are shown in figures 14A–B. 

For all of the metals in this analysis, there appears to be some 

enrichment over the concentrations in average shales, but most 

of the EFs were substantially lower than a factor of 100, and 

the median factors were usually less than 10. The median EFs 

for Cr, Co, Cu, and Ni appeared to be very close to unity with 

the exception of some outliers in the datasets. Values for Cd, 

Pb, and Zn were higher than those found for the other metals, 

and may be related to urban sources; however, these values 

are not large enough to define major enrichments or to provide 

evidence of significant local sources of contamination. Results 

of the EF calculations confirm the earlier comparison to the 

national dataset of Mahler and others (2006)—that the upper 

watershed of the Anacostia River falls into the category of 

light urban land use.

Hydrologic and Seasonal Effects on 

Water Quality

For many of the metals, higher particulate concentrations 

were found during base flow as compared to the metals on 

SS in transport during storm events. The median values of the 

calculated particulate-metal concentrations and EFs grouped 

according to flow for the two Anacostia River stations are 

shown in table 4. Again, As and Hg were not included in this 

analysis because of the high rate of censoring in these data. 

The appropriate cutoffs between base flow and stormflow 

were determined using probability plots of the distributions 

of discharge, and found to be at approximately 100 ft3/s at 

both sites. Flow greater than 100 ft3/s includes all instances of 

excess flow, including high base flow. When differences 

were significant, as determined with a Wilcoxon Rank Sum 

Test (p < 0.05), the cells in table 4 are shaded and bolded. 
Hydrologic and Seasonal Effects on Water Quality 25

Figure 14. Statistical distributions of enrichment factors (EF) 

for trace metals at (A) Northeast Branch, and (B) Northwest 

Branch Anacostia River (USGS stations 01649500 and 

01651000, respectively). [EF are unitless ratios.]

A likely explanation for the observed differences is that the 

size of the particles changes during different flow regimes, 

with coarser sand-size particles being mobilized during 

higher-energy events. When the same analysis was done on the 

EFs (table 4), differences between the two flow regimes were 

smaller and less often significant, supporting the observation 

that the shift in particulate concentrations is related to variations in particle size rather than a shift in sources of sediment 

that have different enrichments. As was found for nitrogen 

and phosphorus, metals are transported mainly on particulate 

phases, particularly in the smaller size fractions due to their 

higher surface areas, more charged surfaces, and better scavenging potentials (Horowitz, 1991).

To test for seasonal or other time-related variability in 

water quality, selected parameters were plotted with groupings 

by month (figs. 15A–C). Boxplots are presented for (a) TN, 

(b) TP, and (c) total-recoverable Zn, and values that are plotted 

are total recoverable in whole-water concentrations rather than 

particulate. In all three cases (figs. 15A–C), the differences 

between months were not large, but did show a relation to 

flow, with the highest concentrations occurring during months 

with high flows. Because many of the water-quality parameters that were measured are very strongly particle-reactive, 

their volume-based concentrations (in micrograms per liter) 

increased during storm events when flows were greater and 

more SS was in transport. This is in contrast to the mass-based 

particulate concentrations (in milligrams per kilogram), which 

generally decrease during the course of a storm as more sandsize particles are transported.

Velinsky and others (1994) compared metals in SS and 

bed sediment in the tidal Anacostia River Basin to metals in 

sediment at outfalls and in sewers. They concluded that the 

major sources of trace metals, particularly Pb, were local 

for the tidal reaches, coming from both stormwater and the 

combined sewer systems. They calculated EFs for river and 

basin bed sediment using different normalization calculations 

than those used during the current study, normalizing to Fe 

and to average concentrations in the Chesapeake Bay drainage 

area, which they assumed to be “unimpacted” local sediment. 

Results of their enrichment calculations, however, were similar 

to those found in SS at the Northeast and Northwest Branches 

by the current study, indicating possibly similar sources and 

loads for metals in both the tidal and nontidal reaches of the 

Anacostia River.

In a later study, Velinsky and others (1999) found consistently higher concentrations of both dissolved and totalrecoverable trace metals at the Northeast Branch station than 

at the Northwest Branch station. Comparisons of results from 

both of these stations and the lower tidal stations were variable, but Velinsky and others (1999) concluded that there was 

a significant flux of trace metals and nutrients from the nontidal watershed to the upper tidal waters. In the current study, 

concentrations of dissolved and total-recoverable metals were 

similar between the Northwest and Northeast Branch stations.
26 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

As rainfall diluted the water in stream base flow, chemical and physical parameters shifted accordingly (fig. 16). 

Temperature decreased slightly, and SC decreased as cooler 

low-ionic-strength rainwater entered the stream. The diurnal patterns in pH and dissolved oxygen that are produced 

by biological productivity and respiration during base-flow 

conditions were dampened during each storm. Rainwater 

is not strongly buffered, but large volumes, combined with 

increasing turbulence in the stream, temporarily overshadowed 

the biological processes and the background alkalinity of the 

stream. Similarly, the concentration of dissolved oxygen was 

closer to equilibrium with the atmosphere from the addition of 

rainwater and from increased reaeration with turbulent flow.

The relation of turbidity to flow was closely correlated, 

but was hysteretic, with peaks for discharge and turbidity 

occurring at different times during the events. There are a 

number of processes that contribute to the suspension and 

mobility of sediment during a storm event. First, there is an 

initial flush from overland flow that washes sediment into the 

stream, and an initial surge in energy within the stream that 

mobilizes flocculent (fine-grained) and loosely held sediment 

from the streambed. As energy in the stream increases, coarser 

material is brought into transport, and at the peak of a storm 

hydrograph, sand-size material becomes an important component of the suspended load. This was often observed in the 

water-quality data during storms, as increases in the concentration of total SS early in the storm hydrographs, and decreases 

in the percent of sediment finer than 0.63 μm as the storms 

progressed.

Table 4. Median values for concentrations of metals on particulates and enrichment factors for the Northeast and Northwest 

Branches of the Anacostia River (USGS stations 01649500 and 01651000, respectively) for data collected from July 2003 through 

December 2005, grouped by flow regime.

[Values that are significantly different for base flow compared to flow greater than 100 cubic feet per second (ft3/s) are in bold with a shaded cell (p < 0.05 for 

the Wilcoxon Rank Sum Test); <, greater than; ft3/s, cubic feet per second; μg/L, micrograms per liter]

Northeast Branch Northwest Branch

Particulate metal 

concentration, μg/L Enrichment factor, unitless Particulate metal concentration, μg/L Enrichment factor, unitless

Base flow Flow > 100 ft3/s Base flow Flow > 100 ft3/s Base flow Flow > 100 ft3/s Base flow Flow > 100 ft3/s

Cadmium 0.6 0.5 15 23 0.9 0.5 16 14

Chromium 140 27 10 4 48 23 3 2

Cobalt 22 18 7 12 25 15 8 7

Copper 67 30 9 8 100 39 13 8

Lead 97 53 27 38 71 62 16 28

Nickel 20 24 2 4 60 32 5 4

Zinc 420 220 25 27 320 170 19 16

Anatomy of a Storm—Patterns of 

Water Quality

One way to better understand the dynamics of contaminant transport behavior in surface water is to examine the 

shifts in physical and chemical parameters over the course of 

one or more individual storm events. The use of an autosampler enabled this analysis with the collection of multiple discrete samples of water quality over storm hydrographs. These 

samples, combined with continuous water-quality monitoring 

data, provide some insight into the transport processes that 

occur during storm events. Several storms that occurred in 

March and April 2005 on the Northeast Branch of the Anacostia River were used for this analysis.

Time-series plots of physical parameters (fig. 16) measured in the river over the three storms followed predictable 

patterns. Stream discharge was flashy; the stream increased 

in response to precipitation within 1 to 3 hours of the onset of 

each storm. Discharge increased quickly, reached peak flow, 

and started to recede within about 12 hours after the end of 

each rainfall event. The hydrograph showed multiple peaks 

and plateaus as rainfall intensity shifted up and down, as different parts of the upstream watershed responded to the storm, 

and as the ground-water component of flow increased from 

recharge to the shallow water table. Rapid response is typical 

of urban streams where the channels have been straightened 

and stabilized with riprap and where impervious surfaces 

retard shallow infiltration of recharge to ground water.
Anatomy of a Storm—Patterns of Water Quality 27

A time-series plot of the concentration of suspended 

sediment over the first March storm in 2005 on the 

Northeast Branch Anacostia River is shown in figure 17 and 

demonstrates the early flush or resuspension of sediment. A 

sample was not collected immediately preceding the storm, 

but a base-flow sample was collected about 2 weeks prior 

to the storm; the concentrations of SS and other parameters 

in that sample are shown on this and subsequent plots for 

reference. A second peak in SS occurred as discharge neared 

its peak and was likely related to increases in the transport of 

sand at the highest stream velocities. The fraction of sediment 

finer than 0.63 μm dropped to 52 percent at the peak of the 

storm; average base-flow fractions of fine sediment were 

approximately 90 percent.

The understanding of physical processes in the stream 

forms a basis for interpretation of the water and sediment 

chemistry. A time-series plot of the concentrations of nitrogen 

species over the first March storm in 2005 is shown in 

figure 18. As already noted, the major portion of nitrogen in 

these two urban streams was transported as particulate organic 

nitrogen. As was the case with SS, the curve of total Kjeldahl 

nitrogen increased sharply at the beginning of the storm and 

peaked early, well before the final peak of flow. There was no 

second nitrogen peak as found with SS, because sand particles 

are chemically cleaner as compared to finer particles and, as 

previously discussed, sand becomes an important component 

of the sediment at high-flow velocities. Ammonia nitrogen 

also increased slightly at the onset of the storm and then 

slowly receded as the storm progressed. Nitrate and nitrite 

forms of nitrogen decreased slightly during the storm and 

increased slowly again towards the end when ground water 

from base flow and interflow were reestablished as a large 

component of flow. The dilution effect on nitrate-nitrogen 

was much less pronounced than in other streams such as the 

Susquehanna River, however (fig. 13).

The concentration of TP behaved predictably during 

storm events, being influenced by the transport of SS (fig. 19). 

TP increased quickly at the onset of the storm, peaking well 

before the peak in flow, likely on a combination of the first 

flush of runoff and disturbance of the more flocculent (finegrained) bed sediment. TP remained high during the storm, 

and after flow had peaked, did not decrease until well into the 

receding limb of the storm. Conversely, dissolved phosphorus 

behaved similarly to nitrate/nitrite, showing a slight dilution 

effect during the storm that recovered as flow ebbed.

The concentrations of total-recoverable and dissolved 

trace metals during the storm events were plotted with the 

particulate concentrations and EFs. The different phases of 

Zn, Pb, Cu, and Cr are shown in figures 20A–D, respectively. 

Zn is an insoluble and strongly particle-reactive trace metal. 

Concentrations of total-recoverable Zn (fig. 20A) increased 

quickly at the onset of the storm, and then slowly started to 

decline even before the peak of the hydrograph, as the size 

fractions of the sediment shifted toward coarser particles. 

Total-recoverable Zn continued to decline as the storm ebbed, 

until it returned to base-flow concentrations. By calculation, 

Figure 15. Monthly variations in the concentrations of (A) total 

nitrogen, (B) total phosphorus, and (C) total-recoverable zinc. 

[Data are grouped by month and cover the period of 2004–05]
28 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Figure 18. Discharge, specific 

conductance, and concentrations of 

nitrogen species during a storm in March 

2005 on the Northeast Branch Anacostia 

River (USGS station 01649500). [X-axis is 

broken during the 2-week period between 

the base-flow sample on March 9 and the 

storm event of March 23–25, 2005.]

Figure 17. Discharge, turbidity, and 

concentration of suspended sediment 

during a storm in March 2005 on the 

Northeast Branch Anacostia River (USGS 

station 01649500). [X-axis is broken during 

the 2-week period between the base-flow 

sample on March 9 and the storm event of 

March 23–25, 2005.]

Figure 16. Physical parameters—discharge, water temperature, pH, concentration of dissolved oxygen, specific conductance, and 

turbidity for a series of storms in March and April 2005 on the Northeast Branch Anacostia River (USGS station 01649500).
Anatomy of a Storm—Patterns of Water Quality 29

Figure 20. Discharge and phases of (A) zinc (Zn), (B) lead (Pb), (C) copper (Cu), and (D) chromium (Cr) during a storm in March 2005 

on the Northeast Branch Anacostia River (USGS station 01649500). [Traces are plotted as dissolved concentrations in micrograms per 

liter (μg/L), total-recoverable concentrations in μg/L, particulate concentrations in milligrams per kilogram (mg/kg), and the ratio of 

the enrichment factor (unitless); x-axis is broken during the 2-week period between the base-flow sample on March 9 and the storm 

event of March 23–25, 2005.]

Figure 19. Discharge and concentrations 

of total and dissolved phosphorus during a 

storm in March 2005 on the Northeast Branch 

Anacostia River (USGS station 01649500). 

[X-axis is broken during the 2-week period 

between the base-flow sample on March 9 

and the storm event of March 23–25, 2005.]
30 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

the particulate concentrations of Zn are independent of the 

quantity of sediment, but are reflective of the shifts in relative 

abundance of small clay-size particles that strongly sorb 

the metals. Particles are expected to have higher average 

concentrations of metals when flows are slower and finer 

particles dominate. There was a small rise in the concentration 

of particulate Zn at the very beginning of the storm, as 

materials were transported in the first flush, but it was diluted 

quickly by sand-size particles that are more typically metal 

poor. EFs, being unitless ratios that are normalized to Al, 

reduce grain-size effects, and compare particulate metal 

concentrations to background levels. There was an initial 

rise in the enrichment of Zn at the beginning of the storm, 

indicating a shift in the sources of the fine particulates, but 

this recovered quickly to base-flow enrichments very early 

in the storm. This may indicate an early effect from the first 

flush of local runoff into the river that shifted to more regional 

background ratios for this urban stream.

Pb, which also is very particle-reactive, behaved similarly 

to Zn during the storm, with some exceptions (fig. 20B). EFs 

for Pb were higher than those calculated for Zn, which is consistent with earlier observations by Velinsky and others (1994). 

Dissolved phases for both Zn and Pb were not affected by the 

storm process. Because solubilities for both Zn and Pb are 

very low, they are most likely saturated in the dissolved phase, 

and do not change with shifts in flow.

The geochemistry of Cu during the storm (fig. 20C) 

varied somewhat from that of Zn and Pb. Cu readily chelates, 

and often has a significant dissolved phase when there are 

dissolved organic compounds available for complexation. 

The dissolved phase of Cu increased during the storm, and 

remained elevated throughout the event until the river returned 

to base-flow conditions. Particulate concentrations and enrichments of Cu were low until the onset of the storm. Cu rose 

quickly at the beginning of the storm, as runoff contributed the 

first flush, and then receded quickly as particle sizes increased, 

and as sources possibly shifted from the initial runoff. Cu is 

a common urban contaminant, sometimes enhanced from the 

passage of water through copper plumbing systems and from 

brake-pad wear residuals in road runoff.

Cr (fig. 20D) exhibited a somewhat different pattern during the storm, with particulate concentrations declining at the 

very beginning of the storm. Total-recoverable Cr increased 

slightly during the storm as overall particle loads increased, 

but the Cr EFs were almost unaffected by the storm, indicating 

that runoff from local sources in the Anacostia River watershed was not an important contributor of Cr.

Although analysis of a single storm provides insight, the 

patterns in concentrations of SS, nutrients, and metals vary 

between individual storms. Concentrations and behavior vary 

due to: (1) differences in the intensity of each storm; (2) the 

antecedent conditions, such as the length of dry period prior 

to the storm and the intensity of previous storms; and (3) the 

amount of material in storage in bed sediment.

Shiller (1997) observed seasonal and spatial variations 

in dissolved metals in the Mississippi River, a large and 

well-integrated river system. The Mississippi River has three 

major tributaries that vary in their contribution of flow, and 

Shiller (1997) observed non-conservative behavior in most 

of the metals; he attributed this in part to seasonality, shifts 

in redox, and to temperature (biological activity). Concentrations of solid-phase metals in the Mississippi River were fairly 

stable, indicating more regional sources. Concentrations of 

ammonium were in phase with temperature, likely mirroring shifts in seasonal productivity. Water chemistry in larger, 

more integrated streams with less urban development than the 

Anacostia River is controlled primarily by variations in redox, 

temperature, and biological activity. Large rivers also tend to 

be more homogeneous as it takes more massive quantities of 

water and(or) sediment to shift the concentrations of chemicals 

in transport. In contrast, urban streams appear to show shifts 

in stream chemistry during storm events that are more likely 

related to local shifts in sources.

Estimation of Continuous 

Concentrations of Nutrients and 

Suspended Sediment

The collection of continuous water-quality data 

at 15-minute intervals can provide a detailed picture of 

hydrologic processes, but is generally limited to physical 

parameters that are easily measured with water-quality data 

sondes. In some cases, it is possible to deploy instruments that 

can do micro-chemical tests, such as selected colorimetric 

analyses of nutrients, but these are costly and require a 

higher degree of maintenance with chemical reagents and 

frequent calibration. Ion-selective electrodes (ISEs) for some 

parameters such as ammonia and nitrate also are options. 

Measurements using ISEs are prone, however, to matrix 

effects and are sometimes inaccurate. Recently, the USGS 

has used multiple-regression models to predict water-quality 

concentrations and loads for nutrients, pesticides, metals, and 

bacteria from continuous physical parameters such as turbidity 

and flow (Christensen and others, 2000; Christensen, 2001; 

Rasmussen and Ziegler, 2003; Rasmussen and others, 2005).

In the current study, linear regression relations on logtransformed variables were determined to estimate SS, TN, 

and TP from continuous physical parameters at both Anacostia 

River stations. Final equations with statistics on the estimates 

are presented in table 5. Continuous variables—discharge, 

water temperature, pH, SC, and turbidity—were tested against 

the concentrations of nutrients and SS in a stepwise-regression 

model. Concentration of dissolved oxygen was not used 

because of gaps in the dataset. Turbidity was a significant 

explanatory variable for the concentrations of SS and nutrients. Turbidity is an indicator of the suspended material in the 

water column. It is determined by shining an incident beam of 

light into a parcel of water and measuring the reflected light at 

an angle of 90 degrees to the incident light. It is less sensitive 
Estimation of Continuous Concentrations of Nutrients and Suspended Sediment 31

to sand-size and larger particles because these do no stay suspended within the instrument, and they do not scatter light as 

effectively as finer particles (Anderson, 2005). Thus, turbidity 

is an excellent indicator of the fine particles in the water column, which include clays, silts, mineral flocs, finely divided 

organic matter, and microscopic organisms. Contaminants 

such as metals and nutrients tend to adhere to the surfaces of 

these finer materials, and thus, turbidity may correlate well 

with their concentrations.

For SS, the use of a second variable, discharge, is 

physically supported, because turbidity does not quantify the 

transport of sand-size particles. The addition of discharge to 

the multiple-regression model refined the estimates with some 

explanation of the variability due to changes in energy in the 

system at peak flows. As discussed in the previous section on 

the water and sediment patterns of chemistry during storms, 

however, there are pronounced hysteretic effects over the 

course of each hydrograph, and shifts in the distributions of 

particle sizes.

This line of reasoning would be expected to be less true 

for TP, which is sequestered to charged surfaces, particularly 

to iron-sesquioxide flocs, and is thus predominantly transported on the finer particles. As expected, the inclusion of 

discharge in the TP equations did not significantly increase 

the coefficient of determination (multiple R2), but it did 

decrease the average relative bias of the estimate by about one 

half, making the inclusion of both turbidity and flow a better 

model choice.

For both river stations, there was a weak relation between 

TN and SC when the spurious values for high SC due to road 

salt were removed. When this adjusted SC was included in the 

equation, it increased the overall multiple R2, but not enough 

to compensate for the exclusion of sample data with high SC 

values. The relative average bias for both model versions was 

virtually the same. On the basis of these observations and the 

principle of parsimony, the adjusted SC was not included in 

the model for TN at either site.

The values of multiple R2 should be considered carefully 

because these were determined in log space. When the concentrations were retransformed back to real space, the variance 

was increased, particularly at high concentrations.

Comparisons of estimated concentrations to observed 

values within the context of stream hydrology provide some 

insight into the accuracy of these models. Time-series overlays 

of discharge and the predicted and observed concentrations 

of SS, TN, and TP at both of the Anacostia River stations are 

shown in figures 21–26. The traces for three storms in spring 

2005 for the Northwest Branch Anacostia River are shown in 

figure 21A, and a closer look at the March storm is shown in 

figure 21B. Residuals (the difference between observed and 

estimated values) overall were generally within 10 percent 

or less of the ambient concentrations. Both the estimated and 

observed concentrations showed a hysteresis with their relation to discharge over the course of the storm. In this example, 

the concentration of SS remained elevated well after the 

flow had peaked, and even beyond the point when conditions 

returned to base flow. This plot also shows that the use of realtime estimation provided more details about the short-term 

shifts in the concentration over time. The initial rise in the 

hydrograph was not completely smooth. Changes in the levels 

of SS reflected the shifts in flow and energy on the rising limb 

and possibly shifts in the sources of sediment over time.

Table 5. Regression equations and statistics for the estimation of suspended sediment, total phosphorus, and total nitrogen at the 

Northeast and Northwest Branches of the Anacostia River (USGS stations 01649500 and 01651000, respectively).

[mg/L, milligrams per liter; SS, concentration of suspended sediment in mg/L; TP, concentration of total phosphorus in mg/L; TN, concentration of total nitrogen in mg/L; Q, discharge in cubic feet per second; Turb, Turbidity in formazin nephelometric units (FNU); %, percent; multiple R2

, coefficient of determination; actual relative bias, relative bias of the residuals]

Regression equation Multiple R2 Actual relative 

bias

Degrees of 

freedom

Northeast Branch Anacostia River (Station 01649500)

Suspended sediment log10SS = -0.8655 + 0.4228log10Turb + 0.8201log10Q 0.95 -5.5% 70

Total phosphorus log10TP = -2.0342 + 0.2798log10Turb + 0.2924log10Q 0.88 -3.7% 61

Total nitrogen log10TN = 0.0419 + 0.1427log10Turb 0.67 -3.8% 68

Northwest Branch Anacostia River (Station 01651000)

Suspended sediment log10SS = -0.4754 + 0.6827log10Turb + 0.5252log10Q 0.95 -3.4% 67

Total phosphorus log10TP = -2.1355 + 0.4591log10Turb + 0.227log10Q 0.92 -3.8% 58

Total nitrogen log10TN = 0.0755 + 0.1646log10Turb 0.65 -3.8% 64
32 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Figure 21. Time-series overlay of discharge with estimated 

and observed concentrations of suspended sediment on the 

Northwest Branch Anacostia River (USGS station 01651000). 

(A) Traces of three storms in spring 2005, and (B) greater detail 

of one storm in March 2005.

Figure 22. Time-series overlay of discharge with estimated 

and observed concentrations of suspended sediment on the 

Northeast Branch Anacostia River (USGS station 01649500). 

(A) Traces of a spring storm in 2004, (B) a summer storm in 

July 2005, and (C) a fall storm in October 2005.
Estimation of Continuous Concentrations of Nutrients and Suspended Sediment 33

Figure 23. Time-series overlay of discharge with estimated 

and observed concentrations of total phosphorus on the 

Northwest Branch Anacostia River (USGS station 01651000). 

Trace of a series of spring and summer storms in 2005.

Figure 24. Time-series overlay of discharge with estimated 

and observed concentrations of total phosphorus on the 

Northeast Branch Anacostia River (USGS station 01649500). 

Trace of a series of winter storms in 2004.

Figure 25. Time-series overlay of discharge with estimated 

and observed concentrations of total nitrogen on the 

Northwest Branch Anacostia River (USGS station 01651000). 

Trace of a series of low-intensity storm events in spring 2005.

Figure 26. Time-series overlay of discharge with estimated 

and observed concentrations of total nitrogen on the Northeast 

Branch Anacostia River (USGS station 01649500). Trace of a 

series of low-intensity storm events in winter and spring 2004.
34 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

Comparisons between estimated and observed concentrations of SS during three different seasons at the Northeast Branch station are shown in figures 22A–C. In the first 

example (spring 2004, fig. 22A), the model estimations for 

the first storm are quite good, again capturing the hysteretic 

behavior of SS with residuals that are generally much less than 

10 percent, but in subsequent storms, estimated values vary 

by as much as 50 percent, particularly during the event on 

April 14. In the second example (summer 2005, fig. 22B), 

modeled and observed concentrations tracked very closely, 

within 10 percent, but in the third example (fall 2005, 

fig. 22C) the residuals were off by more than 100 percent for 

two measurements during the peak of the storm. Peaks in turbidity and flow were 470 FNU and 6,210 ft3

/sec, respectively, 

which indicates very high flows, but lower than normal peak 

values in turbidity. At these high flows, it is expected that a 

large fraction of the SS would be sand-size, and it is possible 

that this error was due to the inefficiency of the autosampler 

to draw large-grained materials up through the lines, rather 

than to the multiple-regression model. It was not determined 

whether seasonal differences in the relation were part of the 

errors or if the discrepancies were due to random error. Generally over the 2-year period of the current study, the model 

was a very good estimator of the concentrations of SS, with 

residuals of much less than 10 percent, but with some exceptions. It is apparent from the different hydrographs that the 

hysteretic effects were variable between storms, emphasizing 

again the limitations of discharge rating curves to estimate SS 

that do not include turbidity (Horowitz, 2003). Rating-curve 

analyses based on flow and(or) turbidity are best statistical fits 

to a dataset, so over- and under-predictions are expected for 

individual value estimates.

Similar time-series plots for TP and TN are shown in 

figures 23–26. For TP (figs. 23 and 24), the model predictions 

are still fairly good with some consistent over-estimation at 

base-flow concentrations and under-estimation for the high 

outliers. Some events seem to fit the models better than others, 

and residuals on average are around 10 percent of the observed 

values. For the February 7, 2004 storm on the Northeast 

Branch (fig. 24), the concentrations of TP were estimated 

fairly well on the rising limb of the hydrograph; however, for a 

storm that followed several days later, the concentrations were 

over-predicted by about 50 percent more than the observed 

value on the falling limb.

The plots for the prediction of TN (figs. 25–26) show 

that the model for TN was not as good as for SS and TP, as 

evidenced by the lower R2 values (table 5). The models for 

predicted TN appear to be less accurate on the falling limbs 

of the hydrographs, and occasionally for extreme values 

where estimated values were under-predicted by as much as 

60 percent, obviously missing some important predictor of 

the variance in TN. The biases for the predictions of TN were 

similar to SS and TP, so errors may be expected to average out 

somewhat over time, such as when these data would be used to 

calculate annual loads and yields.

The sampling regime in the current study was weighted 

towards base-flow sample collection in the morning to early 

afternoon hours, which is a weakness in the dataset. Shortterm variability observed in pH, temperature, turbidity, and 

the concentration of dissolved oxygen indicates that there 

are important shifts in the stream chemistry over the diurnal 

cycle during base flow. Water-quality samples collected more 

frequently over the entire diurnal cycle would improve the 

understanding of hydrologic and biologic processes in the 

Anacostia River.

Estimation of Contaminant Loads 

and Yields

Estimates of annual loads (converted to yields) of SS, 

TN, and TP were calculated for the Northeast and Northwest 

Branches of the Anacostia River using two versions of the 

USGS AMLE unbiased log-linear load models, ESTIMATOR 

and LOADEST. ESTIMATOR uses mean daily flow and time 

as predictors of concentration, and in the current application, 

LOADEST used hourly steps of discharge and turbidity as 

predictors. Flow was left out of the LOADEST calculations 

for TN, because the regression analysis did not find this to be 

a significant predictor. LOADEST estimates for 2004 at the 

Northwest Branch were not possible because measurement 

of continuous turbidity was not started until February of 

that year.

Results for each parameter were mostly similar 

between the two methods (table 6), as would be expected 

from the similar forms of the models, but there were some 

notable exceptions. Yields determined by LOADEST were 

consistently higher for each parameter and at each station than 

by ESTIMATOR. However, differences were not large and 

values were within the 95-percent confidence intervals of each 

other. The most notable difference between the two estimates 

was that the SEP, expressed as confidence intervals for 

LOADEST, were much smaller than those from ESTIMATOR 

(table 6). Given the greater detail of information with hourly 

time steps in the predictors, and observations that turbidity 

explains more of the variance in concentrations, LOADEST 

would be expected to produce more accurate and precise yield 

estimates than ESTIMATOR. Rasmussen and others (2005) 

compared different modeling approaches to predict loads 

and found that: (1) load estimates based on discrete samples 

that do not target storm events under-estimate the true loads, 

(2) loads based solely on discharge over-estimate loads, and 

(3) loads based on continuous turbidity are in between the 

other two estimates and have much smaller errors associated 

with them. These differences between the models for the 

load estimates were not observed in the current study, but the 

confidence intervals were smaller with the LOADEST models.

For perspective, annual yields for the stations on the 

Northeast and Northwest Branches of the Anacostia River 

are compared with estimates from other studies for these and 
Estimation of Contaminant Loads and Yields 35

Table 6. Estimates of annual yields (load per square area in kilograms per year per square kilometer) for suspended sediment, total 

nitrogen, and total phosphorus at the Northeast and Northwest Branches of the Anacostia River (USGS stations 01649500 and 01651000, 

respectively) for data collected in 2004 and 2005.

[Loads were estimated using two modeling approaches—ESTIMATOR, using mean daily flow and time as predictors of concentration, and LOADEST, using 

hourly time steps of flow and turbidity as predictors. For comparison to other rivers, yields estimated by the Metropolitan Washington Council of Governments 

(1998) at the same two river stations, and yields estimated for the Chesapeake Bay River Input Monitoring Program (U.S. Geological Survey, 2006) for the 

Susquehanna River (USGS station 01578310), Potomac River (USGS station 01646580), Patuxent River (USGS station 01594440), and James River (USGS 

station 02035000) also are presented. Values in parentheses are the 95-percent confidence intervals based upon the standard errors of the predictions. All loads 

are calculated for the calendar years (January through December); kg/year/km2, kilograms per year per square kilometer; km2, square kilometers; ft3/s, cubic 

feet per second; MWCOG, Metropolitan Washington Council of Governments]

Watershed area, 

km2

Mean annual flow, 

ft3/s

Suspended sediment,

kg/year/km2

Total nitrogen,

kg/year/km2

Total phosphorus,

kg/year/km2

2004

Northeast Branch (ESTIMATOR) 189 104.4 131,000

(51,300–277,000)

959

(873–1,050)

97

(71–130)

Northeast Branch (LOADEST) 189 104.4 224,000

(157,000–310,000)

962

(891–1,040)

110

(89–134)

Northwest Branch (ESTIMATOR) 128 61.3 215,000

(65,700–531,000)

1,080

(936–1,240)

126

(70–209)

Northwest Branch (LOADEST) 128 61.3 — — —

Susquehanna 70,189 65,540 37,300 1,390 68

Potomac 29,940 15,470 42,800 952 60

Patuxent 901 431.6 24,000 801 56

James 16,193 8,093 29,800 333 47

2005

Northeast Branch (ESTIMATOR) 189 104.7 176,000

(76,800–347,000)

976

(867–1,090)

109

(78–148)

Northeast Branch (LOADEST) 189 104.7 219,000

(156,000–300,000)

1,010

(925–1,090)

117

(94–144)

Northwest Branch (ESTIMATOR) 128 71.3 223,000

(60,500–587,000)

1,200

(1,000–1,430)

161

(80–1,430)

Northwest Branch (LOADEST) 128 71.3 248,000

(164,000–361,000)

1,360

(1,210–1,530)

161

(126–203)

Susquehanna 70,189 45,810 21,100 1,050 40

Potomac 29,940 10,580 23,400 608 24

Patuxent 901 437.7 27,000 787 53

James 16,193 6,239 16,500 227 27

1998

Northeast Branch (MWCOG) 189 100.9 81,793 1,636 236

Northwest Branch (MWCOG) 128 58.3 106,356 1,702 248
36 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

other Chesapeake Bay tributaries (table 6). Yields at these 

Anacostia River stations were estimated in the early 1990s by 

the Metropolitan Washington Council of Governments (1998), 

and yields for the Susquehanna River at Conowingo, Maryland 

(USGS station 01578310), the Potomac River at Washington, D.C. (USGS station 01646580), the Patuxent River at 

Bowie, Maryland (USGS station 01594440), and the James 

River at Cartersville, Virginia (USGS station 02035000) were 

estimated for 2004 and 2005 by the U.S. Geological Survey 

(2006). The Patuxent River station compares well to the Anacostia River stations because it is similarly a small watershed 

in the Baltimore-Washington, D.C. corridor that is quickly 

becoming urbanized. The other three stations have much larger 

watersheds and have more mixed land use, but are still useful 

for comparison. 

Senus and others (2004) compared yields and trends for 

all of the Chesapeake Bay RIM stations from 1997–2001; the 

yields of TN, TP, and SS were similar across all of the larger 

integrated river basins. For 2004, yields for TN on the Susquehanna River were higher than those at any of the other stations, 

but this can be attributed to a higher than average rainfall and 

corresponding higher streamflow in this watershed. When the 

yields were calculated as flow-weighted, they were more similar at all of the RIM stations. According to Langland and others (2006), freshwater flow entering the Bay from the Susquehanna River watershed was particularly high in 2004, with 

flow during the summer season the highest during a 3-month 

period recorded since 1937. TN yields in the Susquehanna 

River were the highest since 1990, and TP and SS yields were 

the fifth highest. Annual flow-weighted concentrations actually showed significant downward trends for many of the sites 

for all three parameters.

Yields of TN and TP for the Anacostia River were generally higher than those estimated for the other Fall Line tributaries (table 6), but the differences were not large. Variations 

in hydrologic conditions between the stations may account for 

some of the observed differences in yields, but the effects of 

urban land use also are important.

Yields of SS were higher by an order of magnitude at 

both of the Anacostia River stations than those at any of the 

RIM tributaries. The Anacostia River watersheds are within 

the Atlantic Coastal Plain and Piedmont Plateau Physiographic 

Provinces as opposed to all of the RIM watersheds, which are 

above the Fall Line, so comparisons should be made carefully. However, urbanization is an important factor for elevated 

yields of SS. Increases in impervious surface in the watershed 

and restructuring of stream channels can destabilize stream 

banks and increase export of sediment from a watershed.

Gellis and others (2005) used historical data to estimate 

similar average annual yields for SS from 1963–1975 at the 

Northwest Branch Anacostia River near Colesville, Maryland 

(USGS station 01650500) - 246,000 kilograms per year per 

square kilometer (kg/yr/km2). The Colesville station is a short 

distance upstream from the station monitored in the current 

study near Hyattsville. Some of the high yields of SS during 

the current study could be attributed to stream-restoration 

projects in the Northwest Branch, but as high yields were 

found at both stations and are consistent with earlier estimates 

at another station in the Northwest Branch, other factors also 

must be considered.

Information Learned for the 

Anacostia River and Urban Streams

This report has summarized data collected from 

July 2003 through December 2005 at two stations on the 

Anacostia River. The stations are above the head of tide on 

the Northeast and Northwest Branches of the Anacostia River, 

and data collected at these sites encompass the majority of 

the nontidal, upper watersheds of the Anacostia River. The 

sampling strategy used in this study is novel for this watershed, in that real-time measurements of physical parameters 

with water-quality monitors were collected concurrently with 

discrete water-quality sampling for SS, nutrients, and metals, 

and combinations of these measurements were used to infer 

information about hydrologic and chemical processes in the 

streams. Significant statistical relations were found between 

the continuous data and discrete water chemistry.

The information gained by monitoring over short 

time scales has allowed more detailed interpretation of the 

processes in these streams. Observations of large diurnal 

shifts in parameters such as pH, concentration of dissolved 

oxygen, temperature, and sometimes turbidity during base 

flow have provided information on biological productivity 

in the river ecosystem, as well as a glimpse into periodic 

perturbations in the urban environment. There are numerous 

sources of variation in stream chemistry, particularly in an 

urban environment, and additional study is needed to break 

down loads of chemical contaminants into their component 

sources (fig. 27). Some immediate benefits of real-time 

data collection included: (1) rapid identification of broken 

sewer lines based on measurements of turbidity and SC, 

(2) observations of daily rises in chemical parameters such 

as turbidity that record unidentified activities in the urban 

watershed, (3) observations of productivity based on dissolved 

oxygen and pH dynamics that may be enhanced by other urban 

processes, and (4) more accurate yield estimates of SS and 

nutrients. Future applications of these data could include more 

quantitative estimates of productivity from diurnal variations 

in the concentration of dissolved oxygen, which may have 

important applications for the management of nutrient-induced 

eutrophication in the Anacostia River.

Another important result from continuous-data collection 

(turbidity and streamflow) has been more accurate estimates 

of loads and yields. Comparisons of estimates with the model 

ESTIMATOR, which uses traditional rating curves based on 

mean daily flow to estimates with the model LOADEST, that 

are based on hourly interval measurements of flow and turbidity, have shown significantly improved loading estimates with 

the smaller time steps. The results provided annual estimates 
Information Learned for the Anacostia River and Urban Streams 37

of yields of SS, TP, and TN at both stations, and concentrations of these three parameters have been estimated in real 

time on a USGS website, providing current and continuous 

information on water quality in the Anacostia River.

The current study demonstrated the need to collect more 

data over shorter diurnal time cycles to capture new, shorterterm sources of variance, and the need to collect a higher 

density of samples for parameters that have a high overall 

variance in concentration. For example, continuous monitoring recorded large diurnal changes in some of the basic field 

parameters such as temperature, concentration of dissolved 

oxygen, and pH. These parameters can have substantial effects 

on the concentrations, partitioning, and bioavailability of metals and nutrients.

The importance of targeting sampling designs on storm 

events and collecting sufficient data also is evident from the 

results of this and other studies (fig. 28). Horowitz (2003) 

presented a summary of the USGS NASQAN work and 

evaluated the use of rating curves based on flow (turbidity was 

measured in that study but was not used in the interpretation). 

Stations with daily sediment collection were used to compare 

different sampling strategies. Sediment rating curves were 

found to over-predict low concentrations of SS and to underpredict the high values. Thus, Horowitz (2003) showed that 

short timeframes for load estimates are undesirable because 

the biases do not have a chance to balance out. He concluded 

that the best approach for load estimation would be to use 

a monthly sampling strategy that was hydrologically based, 

that sampling plans must include a sufficient number of storm 

events, and that individual annual rating curves were better 

than long-term curves because of natural shifts in the ratings 

over time. There are inherent errors from hysteretic effects in 

the concentration of SS during storm events, when using only 

discharge as a predictor. All of these findings are in agreement 

with the results of the current study.

The Anacostia River is not used for drinking water, but 

it is an important tributary to the Chesapeake Bay, and an 

important environment for wildlife in an urban watershed. 

Yields of sediment and nutrients for the nontidal Anacostia 

River were estimated for 2004–05 and compared to yields 

from other local or regional studies. Yields of nitrogen and 

phosphorus were slightly higher but similar to those found in 

other watersheds of the Chesapeake Bay, but the yields of SS 

were higher by an order of magnitude in the Anacostia River. 

Urbanization has been a factor in the mobilization of sediments in the Anacostia watershed, as evidenced by ongoing 

development disturbance of the land surface and changes in 

stream channels that increase the energy during storms (figs. 2 

and 6; Gellis, 2005).

Other studies have observed high sedimentation rates in 

the lower tidal portions of the Anacostia River where velocities are slower and high sediment loads from the nontidal 

watershed accumulate (Velinsky and others, 1999). Comparisons of yield estimates between the two Anacostia River stations in the current study showed very similar results to those 

of Velinsky and others (1999). As observed in this and other 

studies, contaminants such as nutrients and trace metals are 

usually associated with the particulate phases and an understanding of the fluxes and deposition patterns of SS would be 

important to evaluate transport processes.

The upper Anacostia River watershed is similar to urban 

watersheds across the United States (Mahler and others, 2006). 

The geochemistry of nutrients and trace metals in streams is 

very complex, with processes that transfer chemical constituents between phases in the water column and bed sediments, 

as well as in and out of the river system. These processes have 

been described as “material spiraling” (Hart and Hines, 1995), 

whereby there is a dynamic interaction between the biogeochemical cycling of chemicals and downstream advection. In 

this model, contaminants within the stream will typically be 

distributed within 3 compartments: the water compartment, 

wherein metals and nutrients are present as dissolved or colloidal phases; the seston compartment of suspended biological 

or abiotic particulate materials; and the benthic compartment, 

wherein metals and(or) nutrients may be temporarily or permanently stored or at least retarded in transport. Within each 

Figure 27. Exposed sewer manhole stacks in Sligo Creek, 

a small tributary on the Northwest Branch Anacostia River. 

(Photograph courtesy of the Anacostia Watershed Society.)
38 Water Quality in the Upper Anacostia River, Maryland: Monitoring to Estimate Concentrations and Yields, 2003–05

stream segment, many biogeochemical processes can modulate the transfer of contaminants between the compartments, 

including chemical transformations, adsorption and desorption, and biological uptake and release. Superimposed on all of 

these processes is the net downstream flux of water, sediment, 

and associated contaminants, as well as fluxes to and from 

the river from external sources. The complex shifts in particle 

size and composition observed during storms in the Anacostia 

River demonstrate these dynamic interactions between physical and chemical processes. The time scales for variations in 

constituent concentrations span short diurnal cycles to longer 

annual and interannual time frames with climatic changes. 

Understanding the dynamics of these processes requires the 

measurement of water quality on the same time scales and in 

dissolved as well as particulate phases. Data programs must 

be designed to best capture the sources of variability. Understanding of the interactive processes that modulate the concentrations and loads or yields of contaminants and the spatial 

and temporal scales of variability is important to waterresources managers in developing strategies to improve 

river ecosystems.

Summary

In 2003–05, the U.S. Geological Survey partnered with 

the Prince George’s County Department of Environmental 

Resources, the Maryland Department of the Environment, the 

U.S. Environmental Protection Agency, and George Mason 

University to monitor water quality in the Anacostia River. 

Two stations, both with long historical records of discharge, 

were upgraded with automated sampling equipment to collect 

data for suspended sediment, nutrients, trace metals, and continuous physical parameters. These stations are the Northeast 

Branch Anacostia River at Riverdale, Maryland (U.S. Geological Survey station 01649500) and the Northwest Branch 

Anacostia River near Hyattsville, Maryland (U.S. Geological 

Survey station 01651000). Some of the data collected at these 

stations have been used to quantify the contributions of sediment and nutrients from the upper watershed of the Anacostia 

River in Maryland to the more heavily urbanized tidal area 

within Washington, D.C., and to interpret transport processes 

and sources of contamination for suspended sediment, nutrients, and trace metals.

Figure 28. Storm event at Northeast Branch Anacostia River in winter 2003. High turbidity and 

suspended-sediment concentrations occur during stormflow. Suspended sediment is a carrier for many 

particle-reactive contaminants such as trace metals and nutrients. (Photograph courtesy of Al Dombroski, 

Jr., Riverdale, Maryland.)
References Cited 39

Data were evaluated to determine the primary phases of 

nutrients and metals in transport, the behavior of suspended 

sediment and chemicals over time and varying flow regimes, 

and enrichment factors for trace metals. Particulate Kjeldahl 

nitrogen was found to be an important transport phase for 

nitrogen in both streams during storm events. This pattern is 

likely typical of urban streams as compared to streams that are 

more dominated by agriculture or undeveloped land. During storm events, particulate phases were important carriers 

for nutrients and metals. Detailed analysis of a single storm 

showed that concentrations of nutrients and metals were 

highest during the leading edge of the storm, decreased early 

as more sand-size material was incorporated in the sediment 

load, and further decreased during storm recession as the concentration of total suspended sediment decreased.

Unbiased multiple-regression models were developed to 

estimate annual yields of suspended sediment, total nitrogen, 

and total phosphorus at each station in 2004 and 2005. These 

yields were compared to similar calculations for other tributaries within the Chesapeake Bay watershed, including the 

Susquehanna, Potomac, Patuxent, and James Rivers. Yields 

for total nitrogen and total phosphorus in the upper Anacostia 

River watershed were generally higher than in most of the 

other tributaries, except for the Susquehanna River, which 

experienced a higher than normal flow during that time period. 

Suspended-sediment yields were higher at both branches of 

the Anacostia River, by approximately a factor of 10, than 

at any of the other Chesapeake Bay tributaries. Some of the 

differences in yields may be due to differences in surficial 

geology between the watersheds, but urban development is an 

important influence on the Anacostia River.

Linear regression relations on log-transformed variables 

were determined to estimate concentrations of suspended 

sediment, total nitrogen, and total phosphorus from continuous field parameters at both Anacostia River stations. Turbidity was the most significant explanatory variable for all three 

parameters, highlighting the importance of fine particulate 

matter as a carrier for contaminants in river systems. In some 

cases, the use of discharge as a second variable was significant 

and physically supported, because turbidity does not measure 

sand-size particles. Discharge is a good indicator of changes 

in energy in a river system, particularly at peak flows. There 

were, however, pronounced hysteretic effects over the course 

of each hydrograph and estimates of concentration were better during some storms than others. Factors that affected the 

statistical relations include antecedent conditions such as the 

size and timing of previous storms, seasonality, and shifts in 

sediment sources over the course of each storm. Estimated 

concentrations of suspended sediment, total nitrogen, and total 

phosphorus in near-real time for the Northeast and Northwest 

Branches of the Anacostia River may be viewed on the world 

wide web at http://md.water.usgs.gov/rtqwmodeling/.

The current study has provided information on contributions of potential contaminants from the upper watershed of 

the Anacostia River. Data from the upper watershed were compared to conditions in the lower tidal Region of Concern in the 

Anacostia River as well as to other tributaries in the Chesapeake Bay watershed, and to urban streams across the United 

States. The upper Anacostia River may be characterized as a 

light-urban stream with moderate concentrations of nutrients 

and trace metals in transport. Estimation of the quantities and 

comparative magnitudes of contaminants within the context of 

transport processes of the river system will aid water-resources 

managers in focusing strategies where improvements may be 

most beneficial and cost-effective.

Acknowledgments

The authors wish to thank the following people for 

their assistance and contributions to this investigation and 

report. Dr. Mow-Soung Cheng of the Prince George’s County 

Department of Environmental Resources and Narendra 

Panday and George Harman of the Maryland Department of 

the Environment have provided support and encouragement 

for developing new study approaches on this project. USGS 

hydrologic technicians David Brower, George Zynjuk, Jon 

Evans, and Deborah Bringman provided support in helping 

to design the field efforts and in collecting data. Jonathan 

Dillow and James Jeffries provided review and quality 

assurance of the data. Maps were created by Andrew LaMotte 

and Timothy Auer of the USGS. The authors thank Arthur 

Horowitz, Andrew Ziegler, Bradley Garner, James Gerhart, 

Robert Shedlock, Valerie Gaine, Jonathan Dillow, and Dorothy 

Tepper, all of USGS, for thoughtful reviews that enhanced the 

quality of this report.

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Appendix 43

Appendix. Water-quality criteria and guidelines for dissolved trace metals in freshwater.

[Recommended guidelines and criteria for the protection of aquatic life in freshwater. Toxicity for most of these metals varies as a function of hardness in the water column. Canadian Council of Ministers of the Environment values 

for copper, lead, and nickel are presented as ranges that vary as a function of alkalinity. These values are based on a 

value of 100 milligrams per liter of hardness; ammonia is not included in this table because the toxicity of ammonia 

is strongly pH-dependent; all units are in micrograms per liter; CMC, Criteria Maximum Concentration (acute); 

CCC, criterion continuous concentration (chronic); —, not available]

USEPAa CCMEb

CMC CCC Guidelines

Aluminum 750 87 —

Arsenic 340 150 5.0

Cadmium 2.0 0.25 0.017

Chromium (III) 570 74 8.9

Chromium (VI) 16 11 1.0

Copper 13 9.0 2–4

Lead 65 2.5 1–7

Nickel 470 52 25–150

Zinc 120 120 30

a

U.S. Environmental Protection Agency (USEPA, 2006)

b

Canadian Council of Ministers of the Environment (CCME, 2006).

Prepared by Publishing Service Centers 3 and 1.

Edited by Valerie M. Gaine.

Graphics by Timothy W. Auer.

Layout by Ann Marie Squillacci.

For additional information, contact:

Director, MD-DE-DC Water Science Center

U.S. Geological Survey

5522 Research Park Drive

Baltimore, MD 21228

or visit our Web site at:

http://md.water.usgs.gov