Characterization of Stormwater Runoff From 

Bridge Decks in Eastern Massachusetts, 2014–16

Scientific Investigations Report 2018–5033

U.S. Department of the Interior

U.S. Geological Survey
Cover. Photograph of bridge-deck scuppers draining into the Charles River on State Route 2A in Boston near U.S. Geological Survey 

bridge-deck-monitoring station 422108071052501 during a rain storm.
Characterization of Stormwater 

Runoff From Bridge Decks in 

Eastern Massachusetts, 2014–16

By Kirk P. Smith, Jason R. Sorenson, and Gregory E. Granato 

Scientific Investigations Report 2018–5033

U.S. Department of the Interior

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

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Deputy Director

exercising the authority of the Director

U.S. Geological Survey, Reston, Virginia: 2018

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Suggested citation:

Smith, K.P., Sorenson, J.R., and Granato, G.E., 2018, Characterization of stormwater runoff from bridge decks in eastern Massachusetts, 2014–16: U.S. Geological Survey Scientific Investigations Report 2018–5033, 73 p., 

https://doi.org/10.3133/sir20185033.

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

ISBN 978-1-4113-4222-4 
iii

Acknowledgments

The Jacobs Engineering Group Inc., a contractor for the Massachusetts Department of Transportation (MassDOT), provided funding for this study. The authors express their gratitude to Henry 

L. Barbaro, Stormwater Program Supervisor, MassDOT, and Bryan Cordeiro, stormwater analyst, 

MassDOT, for providing logistical support and periodic traffic control.

v

Contents

Acknowledgments........................................................................................................................................iii

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

Introduction.....................................................................................................................................................2

Site Selection..................................................................................................................................................3

Data Collection Methods and Results of Quality-Assurance Sampling ...............................................3

Continuous Monitoring of Bridge-Deck Runoff ...............................................................................3

Design of Bridge-Deck-Monitoring Systems ..........................................................................3

Calculation of Runoff Coefficients .....................................................................................................4

Collection and Analysis of Samples ..................................................................................................7

Samples of Bridge-Deck Runoff................................................................................................7

Selection of Storms ............................................................................................................7

Sample Collection...............................................................................................................8

Sample Processing...........................................................................................................13

Sample Analysis................................................................................................................13

Samples of Bridge-Deck Sediment.........................................................................................13

Sample Processing...........................................................................................................13

Sample Analysis................................................................................................................13

Distribution of Bridge-Deck Sediment ............................................................................................14

Sample Collection and Processing................................................................................14

Sample Analysis................................................................................................................14

Data Quality...................................................................................................................................................14

Bridge-Deck Flow ...............................................................................................................................14

Sediment and Chemical Quality .......................................................................................................16

Field Blank Samples ..................................................................................................................16

Concurrent Replicate-Split Runoff Samples .........................................................................18

Replicate Samples of Bridge-Deck Sediment.......................................................................20

Analysis Methods ........................................................................................................................................20

Bridge-Deck Runoff Simulations...............................................................................................................22

Simulating Bridge-Deck Hydrology ........................................................................................22

Simulating Bridge-Deck Runoff Concentrations ..................................................................22

Simulating Runoff Treatment....................................................................................................24

Quality of Bridge-Deck Runoff...................................................................................................................25

Suspended Sediment .........................................................................................................................25

Loss on Ignition of Suspended Solids and Particulate Carbon...................................................25

Nutrients...............................................................................................................................................29

Correlation Between Constituents...................................................................................................30

Interbridge Comparison of Constituent Concentrations .......................................................................30

Spatial Distribution of Bridge-Deck Sediment........................................................................................33

Chemical Analysis of Sediment.................................................................................................................37

Comparisons of Highway and Bridge-Deck Constituent Concentrations ..........................................39

Suspended Sediment .........................................................................................................................40

Nutrients...............................................................................................................................................41

Sediment Quality.................................................................................................................................42
vi

Example Bridge-Deck Runoff Simulations...............................................................................................43

Representativeness of Selected Statistics ....................................................................................45

Runoff-Quality Risk Analysis.............................................................................................................55

Runoff-Quality Annual Yield Analyses.............................................................................................58

Runoff Treatment Analyses ...............................................................................................................59

Summary........................................................................................................................................................60

References Cited..........................................................................................................................................63

Figures

1. Maps showing locations of bridge-deck-monitoring sites in eastern

Massachusetts, 2014–16. ...........................................................................................................4

2. Photographs of monitoring equipment at U.S. Geological Survey

bridge-deck-monitoring stations on A, State Route 2A in Boston (422108071052501);

B, Interstate 90 near Weston (422025071154501); and C, State Route 20 near

Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16. ...............8

3. Photograph of an H flume at U.S. Geological Survey bridge-deck-monitoring

station on State Route 20 near Quinsigamond Village (421247071470201) in eastern

Massachusetts, 2014–16 .............................................................................................................8

4. Photographs of road surface conditions that affect the runoff coefficient values...........9

5. Boxplots showing distribution of all antecedent dry periods and the antecedent

dry periods for runoff events during which runoff samples were collected at U.S.

Geological Survey monitoring stations on State Route 2A in Boston

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route

20 near Quinsigamond Village (421247071470201) in eastern Massachusetts,

2014–16 .........................................................................................................................................11

6. Boxplots showing distribution of storm totals equal to or greater than 0.05 inch

recorded at U.S. Geological Survey station 01104430 during November 2001

through July 2014 and precipitation totals for storms during which samples were

collected at U.S. Geological Survey bridge-deck-monitoring stations on State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, August 2014 through September 2016...12

7. Graph showing example of automated flow-proportional collection of stormflow

subsamples at U.S. Geological Survey bridge-deck-monitoring station at State

Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts,

December 17, 2015......................................................................................................................12

8. Photograph of a static mixer assembly at U.S. Geological Survey

bridge-deck-monitoring station on State Route 2A in Boston (422108071052501) in

eastern Massachusetts, 2014–16.............................................................................................13

9. Photographs of A, collection of bridge-deck sediment sample and B, vacuumed

strip adjacent to 3-foot ruler after collection of sediment sample on Interstate 90

near Weston (422025071154501)...............................................................................................16

10. Plot showing discrete measurements of pump flow in relation to the theoretical

water-level/flow relation for the H flume compared to the distribution of peak flows

recorded during sampled events at U.S. Geological Survey bridge-deck-monitoring

station on State Route 20 near Quinsigamond Village (421247071470201), eastern

Massachusetts, 2015–16. .........................................................................................................18
vii

11. Boxplots showing distribution of concentrations of constituents measured in

composite samples of bridge-deck runoff and in concurrent replicate-split samples

collected at U.S. Geological Survey bridge-deck-monitoring stations on State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16. .....................................................19

12. Boxplots showing distribution of absolute relative percent difference values

between replicate-split samples and composite samples of bridge-deck runoff

collected at U.S. Geological Survey bridge-deck-monitoring stations on State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16.......................................................20

13. Graph showing an example of the estimation of the average and standard deviation

of total phosphorus concentrations in runoff composite samples collected from

U.S. Geological Survey bridge-deck-monitoring station State Route 20 near

Quinsigamond Village (421247071470201) by using the robust regression on order

statistics method. .......................................................................................................................24

14. Boxplots showing distribution of three particle-size classes for suspended

sediment in composite samples of bridge-deck runoff collected at U.S.

Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route

20 near Quinsigamond Village (421247071470201) in eastern Massachusetts,

2014–16 .........................................................................................................................................28

15. Scatterplot showing the relation between particles size less than 0.0625 millimeter

in diameter and particles size less than 0.25 millimeter in diameter to

concentrations of suspended sediment in composite samples of bridge-deck runoff

collected at U.S. Geological Survey bridge-deck-monitoring stations on State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16. .....................................................28

16. Boxplots showing distribution of concentrations of loss on ignition, particulate

carbon, particulate organic carbon, and particulate inorganic carbon in composite

samples of bridge-deck runoff collected at U.S. Geological Survey

bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501),

Interstate 90 near Weston (422025071154501), and State Route 20 near

Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16. .............29

17. Boxplots showing distribution of concentrations of total phosphorus, particulate

nitrogen, and total dissolved nitrogen in composite samples of bridge-deck runoff

collected at U.S. Geological Survey bridge-deck-monitoring stations on State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16.......................................................30

18. Photographs of sediment sources from deterioration of the A, bridge-deck surface;

B, median barrier; and C, shoulder area that contributed to the sediment yield on

the Interstate 90 bridge near Weston, Massachusetts, 2015–16. .....................................35

19. Graphs showing normalized bridge-deck sediment yields at five fixed locations on

three bridges monitored by the U.S. Geological Survey on A, State Route 2A in

Boston (422108071052501); B, Interstate 90 near Weston (422025071154501);

and C, State Route 20 near Quinsigamond Village (421247071470201) in eastern

Massachusetts, 2015–16 ...........................................................................................................36
viii

20. Photograph of sediment buildup propagating westward onto the State Route 20

bridge from the upgradient roadway, near Quinsigamond Village, Massachusetts,

 2016. ..............................................................................................................................................37

21. Bar chart showing the percentage of the concentration estimated for selected

constituents associated with the mean particle size of suspended sediment

collected from three bridges monitored by the U.S. Geological Survey on State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16. .....................................................40

22. Boxplots showing distribution of concentrations of suspended sediment and the

distribution of concentrations of three particle-size classes for suspended

sediment in composite samples of highway runoff collected at USGS stations

423027071291301, 423027071291302, 423016071431501, 422821071332001,

422716071343901, 422420071153302, 422620071153301, and 421647071024703 in

eastern Massachusetts, 2005–7 (Smith and Granato, 2010), and from three

bridge-deck-monitoring stations (422108071052501, 422025071154501, and

421247071470201) in this study, eastern Massachusetts, 2014–16.....................................42

23. Boxplots showing distribution of total phosphorus and total nitrogen in composite

samples of highway runoff collected at USGS stations 423027071291301,

423027071291302, 423016071431501, 422821071332001, 422716071343901,

422420071153302, 422620071153301, and 421647071024703 in eastern

Massachusetts, 2005–7 (Smith and Granato, 2010), and from three

bridge-deck-monitoring stations (422108071052501, 422025071154501, and

421247071470201) in this study, eastern Massachusetts, 2014–16.....................................43

24. Chart showing concentrations of selected elements associated with sediment A,

less than 0.0625 millimeter (mm) in diameter; B, greater than or equal to 0.0625 mm

to 0.25 mm in diameter; and C, greater than 0.25 mm in diameter in three

bridge-deck composite samples collected on State Route 2A in Boston

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route

20 near Quinsigamond Village (421247071470201) in eastern Massachusetts,

2015–16, in relation to the range of concentrations of selected elements

associated with the same grain sizes in highway-runoff sediment from USGS

stations 423027071291301, 423027071291302, 422821071332001, 422716071343901,

422420071153302, and 422620071153301 in eastern Massachusetts, 2005–7 (Smith

and Granato, 2010). ....................................................................................................................44

25. Chart showing the distribution of concentrations of selected constituents in 1

kilogram of sediment estimated on the basis of the mean concentration of each

constituent associated with three particle-size ranges in highway sediment

collected from USGS stations 423027071291301, 423027071291302, 422821071332001,

422716071343901, 422420071153302, and 422620071153301 in eastern Massachusetts

in 2005–7 (Smith and Granato, 2010) and from bridges on State Route 2A

(422108071052501) in Boston, Interstate 90 near Weston (422025071154501), and

State Route 20 near Quinsigamond Village (421247071470201) in eastern

Massachusetts, 2015–16, and from the mean particle-size distribution of

suspended sediment in composite samples of runoff collected at each location..........45

26. Graphs showing measured total nitrogen concentrations and concentrations

simulated by using A, traditional statistics and B, statistics calculated by using

robust alternative methods .......................................................................................................51

27. Graphs showing measured total phosphorus concentrations and concentrations

simulated by using A, traditional statistics and B, statistics calculated by using

robust alternative methods .......................................................................................................53
ix

28. Graphs showing measured suspended sediment concentrations and

concentrations simulated by using A, traditional statistics and B, statistics

calculated by using robust alternative methods ...................................................................54

29. Graphs showing concentrations simulated by using L-moments and measured A,

total nitrogen; B, total phosphorus; and C, suspended sediment concentrations...........56

30. Graphs showing probability plots of A, total nitrogen and B, total phosphorus,

including measured concentrations, concentrations simulated with the median of

robust statistics (table 18), and concentrations simulated by modifying the

simulated highway runoff by using the generic best-management practice

statistics (table 10)......................................................................................................................57

31. Graphs showing yields of A, total nitrogen; B, total phosphorus; and C, suspended

sediment in pounds per acre per year for each simulated year by percentile................58

Tables

1. Names, locations, and other bridge attributes for U.S. Geological Survey

bridge-deck-monitoring stations in eastern Massachusetts, 2014–16. ..............................6

2. Land-use characteristics for a one-half mile radius around the

bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501),

Interstate 90 near Weston (422025071154501), and State Route 20 near

Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.................7

3. Constituents and physical properties measured in composite samples of

bridge-deck runoff, reporting levels, analytical techniques, and parameter codes,

2014–16 .........................................................................................................................................10

4. Constituents measured in samples of bridge-deck sediment in milligrams per

kilogram, reporting levels, analytical techniques, and parameter codes, 2014–16.........11

5. Sample location, in percent of total bridge span, and mass of bridge-deck sediment

samples in grams collected at five bridge locations on State Route 2A in Boston

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route

20 near Quinsigamond Village (421247071470201) in eastern Massachusetts,

2015–16 .........................................................................................................................................15

6. Bridge-deck sediment yields, in pounds per curb-mile, for three bridge locations

for bridges on State Route 2A in Boston (422108071052501), Interstate 90 near

Weston (422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2015–16.......................................................17

7. Quality-control samples collected at U.S. Geological Survey

bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501),

Interstate 90 near Weston (422025071154501), and State Route 20 near

Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16...............68

8. Summary of field-blank data and comparison to composite bridge-deck runoff samples, 

in milligrams per liter, collected at U.S. Geological Survey bridge-deck-monitoring 

stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16.......................................................18

9. Concentrations and absolute relative percent difference between three

replicate-split and environmental composite samples of bridge-deck sediment

collected from U.S. Geological Survey bridge-deck-monitoring stations on State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16.......................................................21
x

10. Stormwater control measure best-management practice performance statistics

for flow and concentration treatment used in the Stochastic Empirical Loading and

Dilution Model. ...........................................................................................................................26

11. Summary statistics for concentrations of constituents in composite samples of

bridge-deck runoff collected from State Route 2A in Boston (422108071052501),

Interstate 90 near Weston (422025071154501), and State Route 20 near

Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16...............27

12. Results and attained significance levels for Anderson-Darling tests for

concentrations of selected constituents in composite samples of bridge-deck

runoff collected at U.S. Geological Survey bridge-deck-monitoring stations on

State Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16.......................................................31

13. Results and attained significance levels for Spearman rho tests for concentrations

of selected constituents in composite samples of bridge-deck runoff collected at

U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in

Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State

Route 20 near Quinsigamond Village(421247071470201) in eastern Massachusetts,

2014–16 .........................................................................................................................................32

14. Attained F statistics, significance levels, predicted R-squared value, and relevant

statistics from one-way analysis of variance tests and Tukey pairwise comparisons

performed on the ranks of selected concentration and physical parameter values

in composite samples of bridge-deck runoff collected at U.S. Geological Survey

bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501),

Interstate 90 near Weston (422025071154501), and State Route 20 near

Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16...............34

15. Mean proportion of suspended sediment in three particle-size classes in

composite samples of bridge-deck and highway runoff and dry weight

total-recoverable concentrations by sediment-size class for selected elements in

three composite samples of bridge-deck sediments collected from bridges on

State Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2016, and from highways in eastern

Massachusetts, 2005–7 .............................................................................................................38

16. Results and attained significance levels (p-values) from Mann-Whitney tests for

paired sample sets of constituent concentrations in bridge-deck runoff collected at

U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in

Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State

Route 20 near Quinsigamond Village (421247071470201) during 2014–16 and in

highway runoff collected from U.S. Geological Survey monitoring stations on

State Route 2 (USGS stations 423027071291301, 423027071291302), Interstate 190

(USGS station 423016071431501), Interstate 495 (USGS stations 422821071332001,

422716071343901), Interstate 95 (USGS stations 422420071153302, 422620071153301),

and Interstate 93 (USGS station 421647071024703) in eastern Massachusetts,

2005–7 ...........................................................................................................................................41

17. Examples of maximum or typical concentrations of phosphorus, nitrogen, and

sediment species in environmental samples recorded in the literature...........................46
xi

18. Average, standard deviation, and skew of the common logarithms of event-mean

concentrations in composite samples of bridge-deck runoff collected from State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16, as calculated by using

traditional and robust methods ................................................................................................50

19. Average, standard deviation, and skew of the common logarithms of event-mean

concentrations in composite samples of bridge-deck runoff collected from State

Route 2A in Boston (422108071052501), Interstate 90 near Weston

(422025071154501), and State Route 20 near Quinsigamond Village

(421247071470201) in eastern Massachusetts, 2014–16, as calculated by using

L-moment methods.....................................................................................................................55

20. Simulated long-term average-annual bridge-deck runoff and stormwater control 

measure best-management practice discharge yields, in pounds per acre per year for 

total nitrogen, total phosphorus, and suspended sediment................................................59
xii

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Length

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.004047 square kilometer (km2

)

square foot (ft2

) 0.09290 square meter (m2)

square inch (in2

) 6.452 square centimeter (cm2)

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2)

Volume

gallon (gal) 3.785 liter (L)

gallon (gal) 0.003785 cubic meter (m3)

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 foot per day (ft3

/d) 0.02832 cubic meter per day (m3/d)

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3

/s)

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg)

ton, short (2,000 lb) 0.9072 metric ton (t)

ton, long (2,240 lb) 1.016 metric ton (t)

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

°F = (1.8 × °C) + 32.

Datum

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Supplemental Information

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 in milligrams per liter (mg/L).
xiii

Abbreviations

AADT annual average daily traffic

Al aluminum

ANOVA one-way analysis of variance

As arsenic

Ba barium

BMP best-management practice

Cd cadmium

Cr chromium

Cu copper

DN total dissolved nitrogen

EPA U.S. Environmental Protection Agency

HRDB Highway-Runoff Database

LOI loss on ignition of suspended solids

MassDOT Massachusetts Department of Transportation

MIC minimum irreducible concentration

Mn manganese

Ni nickel

NWIS National Water Information System

p attained significance level

Pb lead

PC particulate carbon

PIC particulate inorganic carbon

PN particulate nitrogen

POC particulate organic carbon

RPD relative percent difference

SD standard deviation

SELDM Stochastic Empirical Loading and Dilution Model

SS suspended sediment

TKN total Kjeldahl nitrogen

TN total nitrogen

TP total phosphorus

USGS U.S. Geological Survey

WWTP wastewater treatment plant

Zn zinc

Characterization of Stormwater Runoff From Bridge Decks 

in Eastern Massachusetts, 2014–16

By Kirk P. Smith, Jason R. Sorenson, and Gregory E. Granato

Abstract

The quality of stormwater runoff from bridge decks 

(hereafter referred to as “bridge-deck runoff”) was characterized in a field study from August 2014 through August 2016 

in which concentrations of suspended sediment (SS) and 

total nutrients were monitored. These new data were collected to supplement existing highway-runoff data collected 

in Massachusetts which were deficient in bridge-deck runoff 

concentration data. Monitoring stations were installed at 

three bridges maintained by the Massachusetts Department of 

Transportation in eastern Massachusetts (State Route 2A in the 

city of Boston, Interstate 90 in the town of Weston, and State 

Route 20 near Quinsigamond Village in the city of Worcester). 

The bridges had annual average daily traffic volumes from 

21,200 to 124,000 vehicles per day; the land use surrounding 

the monitoring stations was 25 to 67 percent impervious.

Automatic-monitoring techniques were used to collect 

more than 160 flow-proportional composite samples of bridgedeck runoff. Samples were analyzed for concentrations of SS, 

loss on ignition of suspended solids (LOI), particulate carbon 

(PC), total phosphorus (TP), total dissolved nitrogen (DN), 

and particulate nitrogen (PN). The distribution of particle size 

of SS also was determined for composite samples. Samples 

of bridge-deck runoff were collected year round during rain, 

mixed precipitation, and snowmelt runoff and with different 

dry antecedent periods throughout the 2-year sampling period. 

At the three bridge-deck-monitoring stations, median 

concentrations of SS in composite samples of bridge-deck 

runoff ranged from 1,490 to 2,020 milligrams per liter (mg/L); 

however, the range of SS in individual composites was vast at 

44 to 142,000 mg/L. Median concentrations of SS were similar in composite samples collected from the State Route 2A 

and Interstate 90 bridge (2,010 and 2,020 mg/L, respectively), 

and lowest at the State Route 20 bridge (1,490 mg/L). Concentrations of coarse sediment (greater than 0.25 millimeters 

in diameter) dominated the SS matrix by more than an 

order of magnitude. Concentrations of LOI and PC in composite samples ranged from 15 to 1,740 mg/L and 6.68 to 

1,360 mg/L, respectively, and generally represented less than 

10 and 3 percent of the median mass of SS, respectively. Concentrations of TP in composite samples ranged from 0.09 to 

7.02 mg/L; median concentrations of TP ranged from 0.505 to 

0.69 mg/L and were highest on the bridge on State Route 2A 

in Boston. Concentrations of total nitrogen (TN) (sum DN and 

PN) in composite samples were variable (0.36 to 29 mg/L). 

Median DN (0.64 to 0.90 mg/L) concentrations generally 

represented about 40 percent of the TN concentration at each 

bridge and were similar to annual volume-weighted mean concentrations of nitrogen in precipitation in Massachusetts. 

Nonparametric statistical methods were used to test for 

differences between sample constituent concentrations among 

the three bridges. These results indicated that there are no 

statistically significant differences for concentrations of SS, 

LOI, PC, and TP among the three bridges (one-way analysis of 

variance test on rank-transformed data, 95-percent confidence 

level). Test results for concentrations of TN in composite 

samples indicated that concentrations of TN collected on State 

Route 20 near Quinsigamond Village were significantly higher 

than those concentrations collected on State Route 2A in 

Boston and Interstate 90 near Weston. Median concentrations 

of TN were about 93 and 55 percent lower at State Route 2A 

and at Interstate 90, respectively, compared to the median 

concentrations of TN at State Route 20. 

Samples of sediment were collected from five fixed locations on each bridge on three occasions during dry weather to 

calculate semiquantitative distributions of sediment yields on 

the bridge surface relative to the monitoring location. Mean 

yields of bridge-deck sediment during this study for State 

Route 2A in Boston, Interstate 90 near Weston, and State 

Route 20 near Quinsigamond Village were 1,500, 250, and 

5,700 pounds per curb-mile, respectively. Sediment yields at 

each sampling location varied widely (26 to 25,000 pounds 

per curb-mile) but were similar to yields reported elsewhere 

in Massachusetts and the United States. Yields calculated for 

each sampling location indicated that the sediment was not 

evenly distributed across each bridge in this study for plausible reasons such as bridge slope, vehicular tracking, and 

bridge deterioration.
2 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Bridge-deck sediment quality was largely affected by the 

distribution of sediment particle size. Concentrations of TP 

in the fine sediment-size fraction (less than 0.0625 millimeter 

in diameter) of samples of bridge-deck sediment were about 

6 times greater than in the coarse size fraction. Concentrations 

for many total-recoverable metals were 2 to 17 times greater 

in the fine size fraction compared to concentrations in the 

coarse size fraction (greater than or equal to 0.25 millimeter 

in diameter), and concentrations of total-recoverable copper 

and lead in the fine size fraction were 2 to 65 times higher 

compared to concentrations in the intermediate (greater than 

or equal to 0.0625 to 0.25 millimeter in diameter) or the coarse 

size fraction. However, the proportion of sediment particles 

less than 0.0625 millimeter in diameter in composite samples 

of bridge-deck runoff was small (median values range from 

4 to 8 percent at each bridge) compared to the larger sediment 

particle-size mass. As a result, more than 50 percent of the 

sediment-associated TP, aluminum, chromium, manganese, 

and nickel was estimated to be associated with the coarse size 

fraction of the SS load. In contrast, about 95 percent of the 

estimated sediment-associated copper concentration was associated with the fine size fraction of the SS load.

Version 1.0.2 of the Stochastic Empirical Loading and 

Dilution Model was used to simulate long-term (29–30-year) 

concentrations and annual yields of SS, TP, and TN in bridgedeck runoff and in discharges from a hypothetical stormwater 

treatment best-management practice structure. Three methods 

(traditional statistics, robust statistics, and L-moments) were 

used to calculate statistics for stochastic simulations because 

the high variability in measured concentration values during 

the field study resulted in extreme simulated concentrations. 

Statistics of each dataset, including the average, standard 

deviation, and skew of the common (base 10) logarithms, 

for each of the three bridges, and for a lumped dataset, were 

calculated and used for simulations; statistics representing the 

median of statistics calculated for the three bridges also were 

used for simulations. These median statistics were selected 

for the interpretive simulations so that the simulations could 

be used to estimate concentrations and yields from other, 

unmonitored bridges in Massachusetts. Comparisons of the 

standard and robust statistics indicated that simulation results 

with either method would be similar, which indicated that the 

large variability in simulated results was not caused by a few 

outliers. Comparison to statistics calculated by the L-moments 

methods indicated that L-moments do not produce extreme 

concentrations; however, they also do not produce results that 

represent the bulk of concentration data. 

The runoff-quality risk analysis indicated that bridgedeck runoff would exceed discharge standards commonly 

used for large, advanced wastewater treatment plants, but that 

commonly used stormwater best-management practices may 

reduce the percentage of exceedances by one-half. Results 

of simulations indicated that long-term average yields of 

TN, TP, and SS may be about 21.4, 6.44, and 40,600 pounds 

per acre per year, respectively. These yields are about 1.3, 

3.4, and 16 times simulated ultra-urban highway yields in 

Massachusetts; however, simulations indicated that use of 

a best-management practice structure to treat bridge-deck 

runoff may reduce discharge yields to about 10, 2.8, and 

4,300, pounds per acre per year, respectively.

Introduction

Stormwater discharges from Massachusetts roadways 

can adversely affect the quality of receiving water and may 

result in the failure of a water body to meet Massachusetts 

surface-water-quality standards. Many studies have shown 

that highway runoff can be a source of suspended sediment 

(SS) and nutrients (Breault and Granato, 2000; Smith, 2002; 

Kayhanian and others, 2003; Smith and Granato, 2010). Data 

from focused bridge studies (Malina and others, 2005; Wagner 

and others, 2011) also indicate that concentrations of total 

phosphorus (TP) and SS in samples of stormwater runoff from 

bridge decks (hereafter referred to as “bridge-deck runoff”) 

are similar to concentrations in samples of runoff collected 

from open highways. In Massachusetts, bridge-deck runoff 

is collected in cast iron drainage inlets called “scuppers.” 

Scuppers typically drain to trunk-line conveyance systems, 

where the runoff is discharged to a structural source control 

near the bridge embankment. For some bridges, bridge-deck 

runoff is discharged directly to the underlying land surface 

or water body. The Massachusetts Department of Transportation (MassDOT) owns about 4,800 bridges, and 38 percent 

are over water (Henry Barbaro, Massachusetts Department 

of Transportation, written commun., 2014). Bridge-deck 

scuppers do not provide water-quality treatment of stormwater runoff, and thus discharge from bridges is of concern to 

regulating officials.

Data on the quality of bridge-deck runoff is limited in the 

northeastern United States, and the transferability of existing 

highway-runoff data to bridge decks is not well documented. 

To address this data gap, the U.S. Geological Survey (USGS) 

piloted a field study where composite samples of bridgedeck runoff were collected between August 2014 through 

August 2016 to document the quality of bridge-deck runoff 

for concentrations of SS, total nutrients, loss on ignition of 

suspended solids (LOI), and particulate carbon (PC) at three 

bridges maintained by the MassDOT in eastern Massachusetts. The integration of these data with a technically sound 

highway-runoff model can be used to guide, substantiate, and 

support highway planning, design, and maintenance decisions. 

The study findings aid in the interpretation of local, regional, 

and national bridge-deck runoff data including concentrations, 

loads, potential effects on receiving waters, and the potential 

effectiveness of various best-management practices (BMPs). 

This study is also a component of the implementation of 

MassDOT’s National Pollutant Discharge Elimination System, 

Small Municipal Separate Storm Sewer Systems general 

permit.
Data Collection Methods and Results of Quality-Assurance Sampling 3

This report documents concentrations of SS, SS particle 

size, total nutrients, LOI, and PC measured in flow-weighted 

composite samples of bridge-deck runoff collected from 

bridge scuppers for three bridges in eastern Massachusetts 

during a 2-year monitoring period (2014–16). It also describes 

the physiochemical characteristics of samples of bridge-deck 

sediment and documents the monitoring and sample collection methods for all data contained within the report. The 

report discusses the relation between concentration data for 

the respective constituents among the three bridge-deck stations and compares the composition and quality of sediment in 

samples collected from bridge decks and highways in Massachusetts. The data presented in this report are integrated in the 

Highway-Runoff Database (HRDB; Version 1.0.0b) (Granato 

and Cazenas, 2009), which serves as a preprocessor for the 

Stochastic Empirical Loading and Dilution Model (SELDM) 

(Granato, 2013). SELDM yields of SS and total nutrients and 

examples of BMP analysis also are presented in this report.

Site Selection

Sections of bridge deck on three highways in eastern 

Massachusetts were selected for this study because they represented varying traffic volumes and surrounding impervious 

density, factors that previously were determined to affect constituent concentrations (Smith and Granato, 2010). The USGS 

bridge-deck-monitoring stations were on State Route 2A 

(Massachusetts Avenue) in the city of Boston and over the 

Charles River spanning between the cities of Boston and Cambridge (station number 422108071052501; hereafter referred 

to as “State Route 2A monitoring station”), Interstate 90 (Massachusetts Turnpike) over the Charles River near the town of 

Weston (station number 422025071154501; hereafter referred 

to as “Interstate 90 monitoring station”), and State Route 20 

near Quinsigamond Village in the city of Worcester over the 

Blackstone River (station number 421247071470201; hereafter referred to as “State Route 20 monitoring station”) (fig. 1). 

The areas that drain to the monitoring stations range from 

2,300 to 10,100 square feet (ft2) and are 100 percent impervious. These values represent the area between scupper inlets to 

the crown of the roadway or to the opposite roadway shoulder. Posted speed limits range from 30 to 55 miles per hour 

and the annual average daily traffic (AADT) volumes range 

from about 21,200 to about 124,000 vehicles per day (Massachusetts Department of Transportation, 2017a) (table 1). 

The wearing bridge surface is asphalt, except for Interstate 90, 

which is concrete. The land use surrounding each monitoring station is primarily developed land (43.1 to 78.8 percent) 

(Homer and others, 2015) with accompanying high impervious area (25 to 67 percent; Massachusetts Office of Geographic Information System, 2007) (table 2). The Charles 

River, the largest water body near any of the bridges, accounts 

for 16 percent of the surrounding area at the State Route 2A 

monitoring station in Boston. The amount of development 

near each bridge decreases from east to west (table 2).

Prior to the monitoring period, bridge scuppers at each 

location were cleaned by MassDOT, except for the Interstate 90 bridge where the necessary maintenance equipment 

was not available at the beginning of the study. The Interstate 90 bridge scuppers were cleaned in October 2015. There 

were no major construction activities at the three bridges 

during the study period. Street sweeping was done once a 

week, weather permitting, on the State Route 2A bridge in 

Boston and as necessary on the other bridges. The application 

of winter maintenance materials was limited to salt. A sand or 

sand-salt mixture was not applied to the bridges. 

Data Collection Methods and Results 

of Quality-Assurance Sampling

The methodology described herein includes a description of the design of each bridge-deck-monitoring station 

(herein referred to as “monitoring station”) and the collection 

and analysis methods for samples of bridge-deck runoff and 

bridge-deck sediment. At each bridge, a continuous monitoring and sampling system was installed and operated from 

August 2014 through August 2016. Samples of bridge-deck 

sediment were collected at five locations across each bridge 

following three dry antecedent periods during the study. 

Continuous Monitoring of Bridge-Deck Runoff

Automatic-monitoring techniques were used to collect 

continuous measurements of water level in the runoff collection system and rainfall and to collect composite samples of 

bridge-deck runoff. Continuous monitoring data were collected from August 2014 through September 2016 at each 

bridge; composite samples of bridge-deck runoff were collected from August 2014 through August 2016 at each bridge. 

Runoff coefficients were calculated, in part, to determine 

practical flow thresholds for triggering the automatic samplers. 

Samples of bridge-deck runoff were collected by the automatic 

samplers on a flow-proportional basis.

Design of Bridge-Deck-Monitoring Systems

At each monitoring station, the outlet of a bridge-deck 

scupper was modified to divert runoff from the scupper outfall 

to a shelter containing an H flume rated for 2 cubic feet per 

second (ft3/s) where flow data were collected (fig. 2). The 

H flume combines the sensitivity and accuracy of a sharpcrested weir and the self-cleaning features of a flume (Kilpatrick and Schneider, 1983). The capacity of the flumes was 

sufficient to characterize all flow rates at each monitoring 

station. Water level in the flume was measured by a gas-purge 

bubbler system and a National Institute of Standards and 
4 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Technology traceable pressure sensor. Redundant measurements of water level were made with an ultrasonic sensor at 

the same measurement location (fig. 3). These secondary level 

measurements were useful to detect debris or ice in the flume 

that caused the pressure sensor to measure erroneously high 

water levels. Continuous measurements of water level were 

converted to continuous flow values by programming the dataloggers with the stage-flow relation for the flume. Rainfall data 

were measured at the monitoring stations on Interstate 90 and 

State Route 20 with an unheated 8-inch (in.) diameter tippingbucket sensor mounted about 7 feet (ft) above the ground surface. The area near the State Route 2A monitoring station was 

not suitable for the measurement of rainfall; instead rainfall 

data were available at USGS station 01104683 (Muddy River 

at Brookline, Massachusetts) about 1.5 miles southwest of the 

bridge-deck-monitoring station. Precipitation data (inclusive 

of mixed precipitation and snowmelt water) also were available at USGS station 422302071083801 (Fresh Pond in Gate 

House at Cambridge, Mass.), about 3.5 miles northwest of the 

bridge-deck-monitoring station but only used as a redundant 

data source. Rainfall was measured to estimate the total runoff 

for the drainage area for each station to estimate flow-proportional sampling thresholds. Each station was equipped with 

telemetry to transmit stored data and to enable the dataloggers 

to be remotely programmed. 

Calculation of Runoff Coefficients

A runoff coefficient is the ratio of the volume of runoff 

to the volume of rainfall. Runoff coefficients for each rainfall 

event in this study were calculated by dividing the runoff 

total by the product of the measured rain total and the drainage area for each respective monitoring station. These runoff 

coefficients were used to select appropriate flow thresholds for 

triggering the automatic samplers, to identify potential errors 

related to measurements of rainfall or water level, and to identify changes in the contributing area. The ratio of the volume 

of runoff to the volume of rainfall for a given area will range 

from zero (no runoff) to one (100 percent of the precipitation 

is measured in the runoff); however, if a runoff coefficient 

exceeds a value of one, there likely is an error in the measurement of precipitation, flow, and (or) contributing area (Church 

and others, 1999). Changes in the contributing area can result 

when flow from an upgradient drainage area is diverted into 

the drainage area of interest, such as when the inlet of one or 

A

71°

72° 73°

42°30

42°

41°30'

70°30'

70°

NANTUCKET

 SOUND

MASSACHUSETTS

0 50 KILOMETERS

0 25 50 MILES

25

Vermont

New Hampshire

Massachusetts

Rhode Island

Connecticut Atlantic Ocean

New York

Study area

 2

423016071431501 423027071291302

421647071024703

422716071343901

90

495

190

93

95

290

290

93

395

95

90

495

95

422821071332001

01104430

01104683

423027071291301

421247071470201

422025071154501

422108071052501

422302071083801

422420071153302

422620071153301

Figure 1. Locations of bridge-deck-monitoring sites in eastern Massachusetts, 2014–16. A, U.S. Geological Survey stations in 

this study. B, State Route 2A in Boston (422108071052501). C, Interstate 90 near Weston (422025071154501). D, State Route 20 near 

Quinsigamond Village (421247071470201).
Data Collection Methods and Results of Quality-Assurance Sampling 5

Charles River

Blackstone

 River

Boston

Weston

Quinsigamond Village

Cambridge

Charles

River

#

71°47'30" 71°47' 71°46'30"

42°12'45"

42°12'30"

#

71°16' 71°15'30"

42°20'30"

42°20'15"

#

71°06' 71°05'30" 71°05'

42°21'15"

42°21'

422025071154501

422108071052501

421247071470201

B

C

D

90

95

128

90

90

20 146

90

20

2A

90

30

30

146

20

Bridge-deckmonitoring station

EXPLANATION

U.S. Geological Survey

precipitation station 

U.S. Geological Survey

station number

U.S. Geological Survey

highway-monitoring

station (Smith and

Granato, 2010)

01104430

Base from Massachusetts Office of Geographic Information (MassGIS) orthophotographs, 2012–14

Lambert conformal conic projection, North American Datum of 1983

0.25 0.5 MILE

0 0.25 0.5 KILOMETER

0

Figure 1. Locations of bridge-deck-monitoring sites in eastern Massachusetts, 2014–16. A, U.S. Geological Survey stations in 

this study. B, State Route 2A in Boston (422108071052501). C, Interstate 90 near Weston (422025071154501). D, State Route 20 near 

Quinsigamond Village (421247071470201).—Continued
6 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 1. Names, locations, and other bridge attributes for U.S. Geological Survey bridge-deck-monitoring stations in eastern Massachusetts, 2014–16. [Locations of stations are shown in figure 1. USGS, U.S. Geological Survey; ADT, average daily traffic (value in parenthesis represents year of measurement); traffic volume source: Massachusetts Department of Transportation (2017a)] USGS station number Highway Town or city Annual ADT Annual ADT per lane Number of the to- tal lanes moni- tored Latitude (decimal degrees) Longitude (decimal degrees) Drain- age area contributing runoff to scuppers (square feet) Median barrier Pavement type Posted speed limit (miles per hour) Approxi- mate bridge length (feet) Approxi- mate bridge span area (acre) River crossing 422108071052501 State Route 2A Boston 36,900 (2010) 9,230 2 of 4 42.352 -71.0903 2,400 No Asphalt, concrete walkway 30 2,200 2.6 Charles River 422025071154501 Interstate 90 Weston 124,000 (2013) 20,700 3 of 6 42.3402 -71.2626 2,300 Yes Concrete 55 reducing to 30 1,030 1.9 Charles River 421247071470201 State Route 20 Quinsigamond Village in the city of Worcester 21,200 (2015) 5,300 2 of 4 42.2129 -71.7841 10,100 Yes Asphalt 55 1,000 1.7 Blackstone River
Data Collection Methods and Results of Quality-Assurance Sampling 7

Table 2. Land-use characteristics for a one-half mile radius 

around the bridge-deck-monitoring stations on State Route 

2A in Boston (422108071052501), Interstate 90 near Weston 

(422025071154501), and State Route 20 near Quinsigamond 

Village (421247071470201) in eastern Massachusetts, 2014–16.

[Locations of stations are shown in figure 1. Land use and total impervious 

area are expressed as percentages of the total area. Land-use data source: 

Homer and others (2015). Impervious area data source: Massachusetts Office 

of Geographic Information (2007)]

Land-use 

category

Percentage of area in a one-half mile radius 

around each of the U.S. Geological Survey 

bridge-deck-monitoring stations

State Route 2A Interstate 90 State Route 20

Open water 16.3 4.7 0.1

Developed open 

space

1.5 28.3 15.5

Developed land 78.8 43.1 46.3

Forest and shrub 0.4 18.9 28.8

Grassland and 

cropland

0.5 0.9 4.4

Wetland 2.5 4.1 4.9

Impervious area 67 25 26

more upgradient scuppers is partially or completely blocked 

by deposits of sediment, debris, or slush around the scupper 

grate (fig. 4A). In such cases, some or all of the water from a 

neighboring drainage area is diverted to a downgradient drainage system. Similarly, runoff that leaks through expansion 

joints in the bridge deck (fig. 4B) or obstructions to the inlet of 

scuppers in the monitored drainage area results in low runoff 

coefficients. Runoff coefficients also can vary during the 

winter months on the basis of available snowmelt water and 

scupper inlets that are frozen or blocked by snow (fig. 4C). 

Measurements of rainfall were not made locally at the State 

Route 2A monitoring station in Boston, and therefore the rainfall totals acquired from the nearby USGS stations 01104683 

and 422302071083801 (fig. 1) may not always accurately 

reflect conditions at the bridge location.

There was a large amount of variability in the runoff 

coefficients during this study because the drainage areas are 

relativity small and blockages of scupper inlets were common; therefore, estimated constituent yields from storm runoff 

volume are not presented, and reported drainage area may not 

reflect actual contributing areas during different runoff events 

during the study period.

Collection and Analysis of Samples

Flow-proportional composite samples of bridge-deck 

runoff were collected automatically during storms. Samples 

for each runoff event were generally collected the following 

day, processed, and shipped overnight to the laboratory for 

analysis of concentrations of SS, TP, total dissolved nitrogen 

(DN), particulate nitrogen (PN), LOI, PC, particulate inorganic 

carbon (PIC), and particulate organic carbon (POC) (table 3). 

Samples of sediment in highway runoff also were sieved into 

specific particle-size ranges and analyzed for concentrations of 

TP and 10 total-recoverable metals (table 4). 

Samples of Bridge-Deck Runoff

A range of 54–56 flow-proportional composite samples 

of bridge-deck runoff were collected automatically during 

rainfall, mixed precipitation, and snowmelt events between 

August 2014 and August 2016 at each of the monitoring 

stations. Composite samples of runoff were collected during 

runoff events that were characteristic of the range of antecedent dry periods and event rain totals (fig. 5) and a range of 

precipitation events (fig. 6) throughout the study period. Storm 

precipitation volumes during the study period were similar to 

storm precipitation volumes recorded during the prior 13-year 

period (November 2001 through July 2014) at U.S. Geological Survey station 01104430 (Hobbs Brook below Cambridge Reservoir near Kendall Green, Mass.) (fig. 6), which 

is approximately geographically centered among the three 

bridges and about 4 miles north of the Interstate 90 monitoring 

station (fig. 1).

Selection of Storms

Storm-event samples were selected to reflect seasonal and 

antecedent dry variations throughout the study period. Most of 

the storm precipitation volumes for sampled storms (predominately rainfall and mixed precipitation event) during this study 

were within the interquartile range of all storm precipitation 

volumes (rainfall, mixed precipitation, and snowmelt) greater 

than or equal to 0.05 in. recorded at each monitoring station 

(fig. 6). The distribution of storm precipitation volumes for 

sampled storms at State Route 2A monitoring station tended 

to be skewed higher than the distribution of storms during the 

study period; however, precipitation for this monitoring station 

was measured at USGS station 01104683 and may not always 

accurately reflect site conditions at the bridge. 

In this study, runoff events are defined as a function of 

flow where sequential measurements of discharge greater than 

or equal to 0.005 ft3/s are separated by 6 hours or more of 

discharge less than 0.005 ft3/s. Runoff events consist of any 

form of runoff including rainfall, mixed precipitation, and 

snowmelt runoff. A discharge of 0.005 ft3/s was chosen as a 

cutoff because it was the minimum value that was discernable 

between the presence of flow and no flow on the basis of the 

resolution of level sensors and the stage-discharge relation for 

each monitoring station.
8 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

A

B

C

Figure 2. Monitoring equipment at U.S. Geological Survey 

bridge-deck-monitoring stations

on A, State Route 2A in Boston (422108071052501); 

B, Interstate 90 near Weston (422025071154501); and 

C, State Route 20 near Quinsigamond Village (421247071470201)

in eastern Massachusetts, 2014–16. Locations of stations are 

shown on figure 1.

Sample Collection

Samples of bridge-deck runoff for the analysis of SS and 

water chemistry were collected immediately upstream from 

the H flumes in the pipe by using an unrefrigerated automatic 

sampler controlled by a datalogger. Each autosampler was 

configured to hold four 3.75-liter (L) sample bottles that were 

pretreated with 4 milliliters (mL) of dilute sulfuric acid. The 

acid was added to maintain low pH in the samples and prevent 

dissolved phosphorus from partitioning to the bottle walls 

prior to sample collection by field crews. The first sample was 

collected when flow exceeded a minimum threshold (typically 

0.005 to 0.008 ft3/s), and subsequent samples were collected 

at flow-proportional intervals (samples collected at equal 

volumes of runoff) (fig. 7). After a runoff event was sampled 

and flow subsided below 0.005 ft3/s for a minimum period 

of 6 hours, the datalogger was programmed to instruct the 

sampler to move the distributor arm to the next sample bottle. 

This method allowed for the collection of additional samples 

for subsequent runoff events without compromising the previously collected composite sample.

Approximately 50 subsamples of bridge-deck runoff were collected for an equivalent runoff of 1 in. of rain. 

Figure 3. An H flume at U.S. Geological Survey bridge-deckmonitoring station on State Route 20 near Quinsigamond Village 

(421247071470201) in eastern Massachusetts, 2014–16. Location of 

station is shown on figure 1.
Data Collection Methods and Results of Quality-Assurance Sampling 9

A

B

C

Figure 4. Road surface conditions that affect the runoff coefficient values. A, 

pair of blocked scupper inlets on Interstate 90 upgradient from the U.S. Geological 

Survey bridge-deck-monitoring station 422025071154501; B, expansion joint on State 

Route 20 upgradient from the U.S. Geological Survey bridge-deck-monitoring station 

421247071470201; and C, partially blocked scupper inlet on State Route 2A near U.S. 

Geological Survey bridge-deck-monitoring station 422108071052501, Massachusetts, 

2014–16. Locations of stations are shown on figure 1.

Flow-proportional thresholds, at which point the datalogger 

triggered the automatic water sampler, were fixed at each site 

throughout the study irrespective of expected storm characteristics; only the volume for each subsample was altered. Generally, a composite of bridge-deck runoff consisted of multiple 

200-mL aliquots; however, the aliquot volume for the subsamples was reduced for storms with forecasted rain amounts 

greater than 1.0 in. to increase the total number of potential 

samples, so that the 3.75-L sample bottle would be adequate in 

size to represent the entire storm. In some cases, the forecasted 

rainfall volume was underestimated and resulted in a composite of two or more bottles. For small storms with forecasted 

rainfall amounts less than 0.5 in., the aliquot volume for each 

subsample was increased to ensure that sufficient water volume was collected to satisfy analytical requirements. Because 

the frequency for the collection of the subsamples was a function of a flow threshold derived from an equivalent runoff of 

1 in. of rain from each site, the density of subsamples forming 

the composite of bridge-deck runoff was comparable from 

storm to storm and from site to site.

Various factors were considered during the selection and 

construction of the sites to ensure the best possible performance of the automatic samplers. Vertical distances from 

fixed sampling points to the sampler-pump heads were only 

about 2 ft and within optimal suction limits (Bent and others, 

2001). All sampler lines were mounted in a sloping manner to 

allow for the complete purging and draining of sample water 

between samples. Sampler intakes were fixed to static mixers 

designed specifically for this project at each sampling point for 

all sampling locations (fig. 8). The purpose of the static mixer 

was to provide a secure and consistent mount for the sampler 

intake, reduce transport velocity, and provide agitation to 

produce a sample that represented the average concentration of 

SS (Smith, 2002; Smith and Granato, 2010). Sampler intakes 

were oriented in a horizontal and downstream direction. This 

configuration minimizes debris accumulation by forming a 

small eddy that captures sand particles at the intake and thus 
10 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 3. Constituents and physical properties measured in composite samples of bridge-deck runoff, reporting levels, analytical techniques, and parameter codes, 2014–16. [Total dissolved nitrogen processed through a 0.3-micrometer glass fiber filter. mg/L, milligram per liter; USGS, U.S. Geological Survey; EPA, U.S. Environmental Protection Agency; NWQL, National Water Quality Laboratory; KY, USGS Kentucky Water Science Center Sediment Laboratory] Physical property or constituent Reporting level (mg/L) Analytical technique Analyzing laboratory Reference USGS parameter code Total phosphorus 0.01 Alkaline persulfate digestion NWQL Patton and Kryskalla, 2003 00655 Total dissolved nitrogen 0.03 Alkaline persulfate digestion NWQL Patton and Kryskalla, 2003 62854 Particulate nitrogen 0.03 EPA 440.0 NWQL Zimmerman and others, 1997 49570 Particulate organic carbon 0.05 EPA 440.0 NWQL Zimmerman and others, 1997 00689 Particulate inorganic carbon 0.03 EPA 440.0 NWQL Zimmerman and others, 1997 00688 Particulate carbon [inorganic plus organic] 0.05 EPA 440.0 NWQL Zimmerman and others, 1997 00694 Suspended sediment 0.5 Filtration/gravimetry KY Guy, 1969; Shreve and Downs, 2005 80154 Suspended sediment, percent finer percent smaller than 0.0625 millimeter in diameter Percent Filtration/sieving/gravimetry KY Guy, 1969; Shreve and Downs, 2005 70331 Suspended sediment, percent finer percent smaller than 0.25 millimeter in diameter Percent Filtration/sieving/gravimetry KY Guy, 1969; Shreve and Downs, 2005 70333 Loss on ignition of suspended solids 0.5 Method I-3765 KY Fishman and Friedman, 1989 00535
Data Collection Methods and Results of Quality-Assurance Sampling 11

Table 4. Constituents measured in samples of bridge-deck sediment in milligrams per kilogram, reporting levels, analytical techniques, 

and parameter codes, 2014–16.

[Samples analyzed by RTI Laboratories, Inc., in Livonia, Michigan. USGS, U.S. Geological Survey; SM, standard method]

Constituent

Minimum 

detection 

level

Analytical technique Reference

USGS 

parameter 

code

Phosphorus as 

phosphorus 0.8 SM 4500-P F Clesceri and others, 1998 68075

Aluminum 10

Digestion method 3050B; inductively 

coupled plasma-atomic emission 

spectrometry, method 6010C

U.S. Environmental Protection Agency, 1996;

U.S. Environmental Protection Agency, 2000

65196

Arsenic 4 67876

Barium 4.6 67877

Cadmium 3.4 67880

Chromium 7.6 67882

Copper 5.3 67884

Lead 3.1 64181

Manganese 21 67888

Nickel 13 67890

Zinc 19 64180

allows the sampler to collect a more representative sample of 

the course load (Edwards and Glysson, 1999). The static mixers were constructed from a 0.75-in. low-density polyethylene. 

Each automatic sampler was configured to hold four 

3.75-L polyethylene sample bottles. Sample bottles were 

cleaned with phosphate-free, laboratory-grade soap and tap 

water; then immersed in a 5-percent solution of hydrochloric 

acid for a period of at least 6 hours; and finally rinsed with 

deionized water until the specific conductance of the waste 

rinse water was less than 1 microsiemens per centimeter. The 

sampler’s intake lines consisted of 0.5-in. polyethylene tubing 

attached to silicon pump-head tubing and a discharge tube. 

Prior to the initial installation of the sampling equipment, the 

various tubing was cleaned as described above; however, the 

tubing was only cleaned with phosphate-free, laboratory-grade 

soap and deionized water between sample collection. As part 

1

10

0.1

50

(54)(184)

Antecedent period, in days

(100) (54) (134) (56)

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Count

State Route 2A Interstate 90 State Route 20

Mean

(56)

All antecedent periods

Antecedent periods prior

to sampled events

Bridge-deck-monitoring stations 

Figure 5. Distribution of 

all antecedent dry periods 

and the antecedent dry 

periods for runoff events 

during which runoff samples 

were collected at U.S. 

Geological Survey monitoring 

stations on State Route 2A 

in Boston (422108071052501), 

Interstate 90 near Weston 

(422025071154501), and State 

Route 20 near Quinsigamond 

Village (421247071470201) 

in eastern Massachusetts, 

2014–16. Locations of stations 

are shown on figure 1.
12 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

0.1

1

10

0.05

(133) (50)

Storm precipitation volume, in inches

(1,079) (100) (52) (134) (53)

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Count

Interstate 90

Mean

(52)

All storm totals for period

Storm totals for sampled

events

State Route 2A State Route 20

Bridge-deck-monitoring stations 

U.S. Geological

Survey station

01104430

Figure 6. Distribution of storm totals equal to or greater than 0.05 inch recorded at U.S. Geological 

Survey station 01104430 during November 2001 through July 2014 and precipitation totals for storms 

during which samples were collected at U.S. Geological Survey bridge-deck-monitoring stations on State 

Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 

near Quinsigamond Village (421247071470201) in eastern Massachusetts, August 2014 through September 

2016. Precipitation totals for State Route 2A in Boston were recorded at U.S. Geological Survey station 

01104683. Locations of stations are shown on figure 1.

Time between samples

Subsample

Flow

Time

EXPLANATION

13:00

0

5

10

15

20

25

30

Time between subsamples, in minutes

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Flow, in cubic feet per second

15:00 17:00 19:00 21:00 23:00

Figure 7. Example of automated flowproportional collection of stormflow 

subsamples at U.S. Geological Survey bridgedeck-monitoring station at State Route 20 near 

Quinsigamond Village (421247071470201) in 

eastern Massachusetts, December 17, 2015.

Location of station is shown on figure 1.
Data Collection Methods and Results of Quality-Assurance Sampling 13

Figure 8. A static mixer assembly at U.S. Geological Survey 

bridge-deck-monitoring station on State Route 2A in Boston 

(422108071052501) in eastern Massachusetts, 2014–16. Location of 

station is shown on figure 1.

of the routine cleaning practice, lint-free wipes were forced 

hydraulically through the sampler’s tubing to remove internal 

deposits or films that were difficult to remove by circulating 

solution alone. Afterward, the sample tubing was purged with 

2 L of deionized water. The sampler’s pump-head tubing was 

routinely replaced with new tubing throughout the project as 

the tubing performance deteriorated.

Sample Processing

Water samples were processed in the USGS New England Water Science Center laboratory in Northborough, Mass., 

typically during the day following the conclusion of the runoff 

events. The pH of the entire sample volume was adjusted to 

be between 1.6 and 1.95 prior to processing to meet preservation requirements for nutrient analysis. Subsamples for the 

analysis of SS, TP, DN, PN, LOI, and PC were split by pouring the contents of each composite sample bottle fitted with 

a funnel cap into a Deka port cone splitter (Capel and others, 

1995) modified to accept 0.5-in. tubes. The modification of 

the cone splitter to accept tubes of the same diameter as the 

sampler was necessary to ensure that all particles in the runoff 

samples would pass through the splitting device. Results of 

experimental tests by the USGS (Capel and others, 1995; 

Capel and Larson, 1996) indicate that subsamples containing coarse sediment are more precisely processed with a cone 

splitter compared to other splitting devices. In many cases, it 

was necessary to split subsamples multiple times to reduce the 

volume sufficiently to satisfy analytical requirements when the 

sample volume was large. Sample-water particulates were processed by passing a known volume of sample water through 

a TeflonTM filter assembly and a 25-millimeter (mm) glassmicrofiber filter with a 0.3-micrometer (μm) pore size (U.S. 

Geological Survey, variously dated). The filters were analyzed 

for PN, PC, PIC, and POC. The filtrate from this procedure 

was analyzed for DN.

Sample Analysis

Samples for analysis of nutrients and carbon constituents 

were preserved with Optima-grade sulfuric acid 4.5-normal 

solution and chilled. Other samples only required refrigeration. Samples for the analysis of nutrients and carbon constituents were double bagged after processing and stored on ice for 

overnight delivery to the USGS National Water Quality Laboratory in Denver, Colorado, where they were analyzed (table 

3). Samples were analyzed for SS, distribution of particle size, 

and LOI at the USGS Kentucky Water Science Center Sediment Laboratory (table 3). Constituent concentrations for the 

composite samples of bridge-deck runoff are available through 

the USGS National Water Information System (NWIS; 

U.S. Geological Survey, 2016).

Samples of Bridge-Deck Sediment

A composite sample of bridge-deck sediment for analysis of sediment quality was collected directly from the bridge 

surface, scuppers, or flumes on each bridge. These composite 

samples were wet sieved into three particle-size ranges (less 

than 0.0625 mm in diameter, greater than or equal to 0.0625 to 

0.25 mm in diameter, and greater than 0.25 mm in diameter), 

and each size range was analyzed for concentrations of TP and 

10 total-recoverable metals (table 4).

Sample Processing

Samples of bridge-deck sediment for analysis of sediment quality were wet sieved with bridge-deck runoff water 

specifically collected for this purpose through precleaned 

0.25-mm and 0.0625-mm nylon-mesh sieves. The sieves, and 

polyethylene settling bags, were cleaned by immersing them 

in a 5-percent solution of hydrochloric acid for a period of 

about 6 hours and thoroughly rinsing them with deionized 

water. Sediment greater than or equal to 0.0625 to 0.25 mm in 

diameter and sediment greater than 0.25 mm in diameter for 

each bridge was set aside in separate clean polyethylene bags. 

Sediment particles less than 0.0625 mm in diameter were 

collected in polyethylene bags with native water and allowed 

to settle undisturbed in a laboratory refrigerator in the USGS 

New England Water Science Center laboratory in Northborough, Mass., for at least a week. After the sediment settled, the 

supernatant was decanted and discarded, and the sediment was 

retained for chemical analysis.

Sample Analysis

Samples for sediment quality containing particles less 

than 0.0625 mm in diameter, greater than or equal to 0.0625 

to 0.25 mm in diameter, and greater than 0.25 mm in diameter 

were submitted to RTI Laboratories, Incorporated in Livonia, 

Michigan, for analysis of TP and 10 total-recoverable metals 

(table 4). Concentrations of TP were determined by automated 

ascorbic acid reduction (Clesceri and others, 1998). Concentrations of total-recoverable metals were determined with the 
14 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

use of U.S. Environmental Protection Agency (EPA) digestion 

method 3050B (U.S. Environmental Protection Agency, 1996) 

and inductively coupled plasma-atomic emission spectrometry 

(U.S. Environmental Protection Agency, 2000). Bridge-deck 

sediment quality data are available through the NWIS database 

(U.S. Geological Survey, 2016).

Distribution of Bridge-Deck Sediment

The bridges contain numerous scuppers, and most scupper outlets are over water or are too high to access. As a result, 

bridge-deck runoff monitoring was limited to a single scupper 

on each bridge. Characterizing the distribution of the mass of 

bridge-deck sediment on the surface of the roadway at different points across each bridge is necessary to qualify the results 

obtained at the monitored scupper.

To determine the distribution of sediment on the bridge 

decks, samples of sediment along the curb were collected with 

portable vacuums at five evenly distributed locations across 

each of the bridges. The nozzle width of the vacuum and the 

number and lengths of the vacuumed sections were recorded, 

and samples were returned to the New England Water Science 

Center laboratory in Northborough, Mass., where they were 

extracted from the vacuums and a mass per curb length was 

determined for each set of samples. 

Sample Collection and Processing

Three sampling events were conducted on each bridge 

deck between April 2015 and September 2016. Samples of 

bridge-deck sediment were collected at five evenly distributed locations across each bridge deck (table 5). Dedicated 

hand-held battery-powered vacuums were used to collect 

bridge-deck sediment samples at each of the five sampling 

locations. Fixed sampling locations included the center of 

each bridge and adjacent locations that represent about 10 

and 30 percent of the total length of each bridge span. Each 

of the five samples was a composite of two to five vacuumed 

strips that were the width of the vacuum nozzle (0.22 ft), 3 ft 

long, and perpendicular to direction of traffic flow from the 

rightmost curb edge (fig. 9). The number of strips vacuumed 

at each location was determined by the amount of sediment 

available on the bridge deck at the time of sample collection. 

Sediment was removed from pre-tared vacuum tanks and each 

filter was dried in pre-tared stainless-steel trays at 105 degrees 

Celsius (°C) to a constant weight, or until the weight change 

from the previous recorded weight was less than 0.5 milligram 

(mg) (Clesceri and others, 1998) at the USGS New England 

Water Science Center laboratory in Northborough, Mass. The 

resultant bridge-deck sediment mass, in grams, for each bridge 

and sampling event is listed in table 5. Previous studies have 

shown that this method of street sediment collection provides 

good precision and that these sample-collection techniques 

do not likely introduce additional variability (Pitt, 1979; 

Bannerman and others, 1983; Selbig and Bannerman, 2007; 

Sorenson, 2013).

Sample Analysis

Sediment yield in pounds per curb-mile was estimated 

from the dry mass of bridge-deck sediment at each sampling 

location (eq. 1). These estimated yields provide a semiquantitative account of the mass of sediment at fixed points across 

each bridge. The mass of sediment used to estimate the linear 

yield was collected within 3 ft of the curb. Data from several 

studies indicate that between 75 and 90 percent of the street 

sediment is near the curb (Sartor and Boyd, 1972; Pitt, 1979; 

Selbig and Bannerman, 2007), and this also was observed on 

the bridge sites through visual observations during routine 

sample collection (fig. 9B). Sample composite yields for each 

bridge are summarized in table 6. 

 P g

W N × M = ×0 0022.

× (1)

where

P is the mass of the sediment for a bridgedeck sampling location, in pounds per 

curb-mile;

g is the total dry mass of sampled bridge-deck 

sediment, in grams;

0.0022 is the unit conversion factor between grams 

and pounds;

W is the width vacuumed for each sample strip 

(vacuum nozzle width), in feet;

N is the total number of sample strips at each of 

the bridge-deck sampling location; and

M is the conversion from the width vacuumed, in 

feet, to miles.

Data Quality

The accuracy and precision of the data collected in this 

study were evaluated by making quality-control measurements 

at each of the monitoring stations and collecting various types 

of quality-control samples. Quality-control samples include 

field blanks and replicate-split samples. The identification of 

random error and systematic bias can be achieved through the 

collection and analysis of quality-control data. These analyses provided the basis for the interpretation of sediment and 

chemical data collected in this study.

Bridge-Deck Flow

Quality-assurance data were collected at each monitoring station to evaluate the accuracy of the theoretical waterlevel/flow relation of each flume. This relation was tested by 

simultaneously measuring flow from a 9,600-gallon-per-hour 

centrifugal pump with an in-line flowmeter and measuring 

the water level in the flume. Pump and theoretical flow values 

were recorded after pump flow and flume water level were 

stable across a range of pumping rates. This process was 
Data Quality 15

Table 5. Sample location, in percent of total bridge span, and mass of bridge-deck sediment samples in grams collected at five bridge locations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2015–16.[Locations of stations are shown in figure 1. Shaded areas indicate bridge sample location nearest to scupper where runoff samples were collected. Bridge-deck sample location expressed as a percent of total bridge span length measured from nearest bridge abutment from east to west. mm, month; dd, day; yyyy, year; °, decimal degrees]

Collection date

(mm/dd/yyyy)

Number of strips 

vacuumed

(0.22 foot wide)

Sample mass of bridge-deck sediment in grams for five equidistant locations

10 percent 30 percent Center 70 percent 90 percent

Latitude Longitude Latitude Longitude Latitude Longitude Latitude Longitude Latitude Longitude

State Route 2A in Boston, Massachusetts

42.35245° -71.09032° 42.35347° -71.09081° 42.35447° -71.09132° 42.35547° -71.09182° 42.35645° -71.09232°

04/02/2015 3 89.6 198 342 274 79.5

09/01/2015 3 5.70 33.3 6.60 49.0 37.3

11/23/2015 3 19.6 18.0 18.6 69.7 48.8

Interstate 90 near Weston, Massachusetts

42.34172° -71.25998° 42.34138° -71.26073° 42.34093° -71.26152° 42.34088° -71.26202° 42.34027° -71.26247°

09/03/2015 3 2.10 7.10 5.90 5.40 6.10

12/15/2015 5 5.02 78.2 58.8 83.5 42.6

09/20/2016 3 1.48 5.05 5.89 6.93 8.43

State Route 20 near Quinsigamond Village, Massachusetts

42.2141° -71.78323° 42.21365° -71.78358° 42.21322° -71.78392° 42.21277° -71.78428° 42.21235° -71.7847°

04/01/2015 2 934 725 405 605 198

8/27/2015 3 63.9 67.7 45.1 55.9 45.4

11/27/2015 3 58.7 86.0 47.1 75.8 59.5
16 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

A B

Figure 9. A, collection of bridge-deck sediment sample and 

B, vacuumed strip adjacent to 3-foot ruler after collection of 

sediment sample on Interstate 90 near Weston (422025071154501).

Location of station is shown on figure 1.

repeated until the pump reached its maximum rate of flow. 

Pump flow values typically were within the range of flow 

estimated from a water level of plus or minus 0.01 ft around 

the theoretical water level-flow relation (fig. 10). This range 

(plus or minus 0.01 ft) in water level represents the typical 

error for the measurement of water levels in the flume. Errors 

in flow at the low end of the relation may be larger as a result 

of minor physical imperfections in the flume than that in the 

theoretical water-level/flow relation. Maximum pump flow 

values exceeded all peak flows measured during sampled 

events, except for the monitoring station at State Route 20, 

which had the largest drainage area. At State Route 20, about 

80 percent of the flows measured during sampled events were 

within the range of flows tested with the pump (fig. 10). Nevertheless, there was no indication that flow greater than the test 

range deviated from the theoretical level-flow relation at this 

monitoring station. These water level-flow pump tests indicate 

that the theoretical relation for each flume provided accurate 

estimates for flow.

Sediment and Chemical Quality

Quality-control samples, including field blank and replicate-split samples, were collected to identify potential bias in 

sampling and processing methods and contamination resulting 

from the sampling equipment and from the sample-collection, 

processing, and analysis processes. These quality-control 

samples are listed in table 7 in the back of the report.

Field Blank Samples

A field blank is used to test for positive bias that can 

result from contamination at any stage of sample collection, processing, or analysis, as well as from the sampling 

equipment itself. Source-solution blanks were prepared 

from deionized water produced by a laboratory-grade waterpurification system that uses ion-exchange packs and reverse 

osmosis. The source-solution water was transported to the 

monitoring stations in precleaned polyethylene cubits. Field 

blanks were collected throughout the study period and at every 

monitoring station. These samples were collected by pumping 

blank water through the automatic sampler tubing and into the 

collection bottle, and processing it in a manner consistent with 

the collection of environmental samples of bridge-deck runoff.

During the study period, 10 field blanks were collected 

and submitted for chemical and sediment analysis. Measurable concentrations for each constituent in field-blank samples 

were compared to the USGS National Water Quality Laboratory and sediment laboratory reporting limit and environmental concentration data collected during the study (table 8). 

For analytes not detected in samples, such as PIC, PN, and 

TP, a concentration equal to the laboratory reporting limit is 

reported with a “less than” (<) remark code in all data tables in 

this report. Concentrations of SS in field blanks were slightly 

greater than the laboratory reporting limit in 7 of 10 field 

blank samples, with the maximum concentration in the field 

blanks being less than 2 percent of the minimum environmental concentration. Concentrations of LOI, PC, and POC were 

detected less often in field blank samples, and the maximum 

concentrations in the field blanks were 11, 11, and 2 percent 

of minimum environmental concentrations in samples of 

bridge-deck runoff respectively. Only 1 of the 10 field blanks 

had a detection of DN at a level that was below the minimum 

environmental concentration for that constituent. Low levels 

of contamination may be acceptable if the level of contamination is within the measurement error of the analytical method 

or is well below the concentrations of the constituent in the 

environmental samples. For example, the maximum concentration for SS in the field blanks was more than an order of 
Data Quality 17

Table 6. Bridge-deck sediment yields, in pounds per curb-mile, for five bridge locations for bridges on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2015–16.[Locations of stations are shown in figure 1. Shaded areas indicate bridge sample location nearest to scupper where runoff samples were collected. Bridge-deck sample location expressed as a percent of total bridge span length measured from nearest bridge abutment from east to west. mm, month; dd, day; yyyy, year; °, decimal degrees]

Collection date 

(mm/dd/yyyy)

Bridge-deck sediment yields, in pounds per curb-mile

10 percent 30 percent Center 70 percent 90 percent Mean

Latitude Longitude Latitude Longitude Latitude Longitude Latitude Longitude Latitude Longitude

State Route 2A in Boston, Massachusetts

42.35245° -71.09032° 42.35347° -71.09081° 42.35447° -71.09132° 42.35547° -71.09182° 42.35645° -71.09232°

04/02/2015 1,600 3,500 6,000 4,800 1,400 3,500

09/01/2015 100 590 120 860 660 470

11/23/2015 350 320 330 1,200 860 610

Mean 680 1,500 2,200 2,300 970 1,500

Interstate 90 near Weston, Massachusetts

42.34172° -71.25998° 42.34138° -71.26073° 42.34093° -71.26152° 42.34088° -71.26202° 42.34027° -71.26247°

09/03/2015 37 130 100 95 110 90

12/15/2015 53 830 620 880 450 570

09/20/2016 26 89 100 120 150 100

Mean 39 350 270 370 240 250

State Route 20 near Quinsigamond Village, Massachusetts

42.2141° -71.78323° 42.21365° -71.78358° 42.21322° -71.78392° 42.21277° -71.78428° 42.21235° -71.7847°

04/01/2015 25,000 19,000 10,700 16,000 5,200 15,000

8/27/2015 1,100 1,200 800 990 800 980

11/27/2015 1,000 1,500 830 1,300 1,000 1,100

Mean 9,000 7,200 4,100 6,100 2,300 5,700
18 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

0.0

Measured calibration flow

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Mean

Theoretical flow plus and

minus 0.01 feet around

theoretical level-flow

relation

0.80.70.60.50.40.30.20.1

0.01

0.1

1

0.001

Flow, cubic feet per second

Water level, in feet

 Distribution of

peak event flows

Figure 10. Discrete 

measurements of pump flow 

in relation to the theoretical 

water-level/flow relation (offset 

by plus or minus 0.01 feet) 

for the H flume compared to 

the distribution of peak flows 

recorded during sampled events 

at U.S. Geological Survey bridgedeck-monitoring station on State 

Route 20 near Quinsigamond 

Village (421247071470201), 

eastern Massachusetts, 2015–16. 

Location of station is shown on 

figure 1.

magnitude lower than the minimum concentration measured in 

samples of bridge-deck runoff. In general, contamination bias 

was low for all constituents.

Concurrent Replicate-Split Runoff Samples

Concurrent replicate-split samples are thought to be 

identical in composition to the environmental samples and are 

collected simultaneously during sample processing. Replicatesplit samples provide a measure of bias and variability for the 

method of sample processing (splitting, filtering, and preservation), laboratory analysis, and effects such as analyte degradation that can happen prior to laboratory analysis. Concurrent 

replicate-split samples were collected throughout the year and 

over a range of varying concentrations that occurred during 

the study (fig. 11).

Concurrent replicate-split samples were collected from 

33 bridge-deck composite samples. Bridge-deck composite 

runoff samples were split into smaller representative aliquots, 

as described previously, to satisfy the different analytical 

Table 8. Summary of field-blank data and comparison to composite bridge-deck runoff samples, in milligrams per liter, collected at 

U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston 

(422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.

[Locations of stations are shown in figure 1. USGS, U.S. Geological Survey; NWQL, National Water Quality Laboratory; <, less than]

Constituent

USGS 

parameter 

code

Sample 

count

NWQL 

reporting 

limit

Number of 

detections 

in field 

blanks

Maximum 

concentration 

in field blank

Minimum 

concentration 

in bridgedeck runoff

Maximum 

concentration 

in bridgedeck runoff

Loss on ignition of suspended solids 00535 7 0.5 4 2 19 1,740

Particulate carbon [inorganic plus organic] 00694 10 0.05 4 0.76 6.68 1,360

Particulate organic carbon 00689 8 0.05 2 0.11 6.57 1,100

Particulate inorganic carbon 00688 8 0.03 0 <0.03 <0.03 255

Particulate nitrogen 49570 10 0.030 0 <0.030 0.179 26.7

Total phosphorus 00665 10 0.01 0 <0.01 0.09 7.02

Total dissolved nitrogen 62854 10 0.05 1 0.07 0.18 5.63

Suspended sediment 70331 10 0.5 7 2 44 142,000
Data Quality 19

0.01

0.1

1

10

100

1,000

10,000

100,000

200,000

Concentration, in milligrams per liter

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Loss on ignition

of suspended solids

Particulate carbon

Particulate 

inorganic carbon

Particulate 

organic carbon

Total phosphorus

Total dissolved

nitrogen

Particulate nitrogen

Suspended sediment

Replicate-split sample

Mean

(33) (28) (32) (33) (32) (33) (33)

(32) Count of replicate-split sample

(33)

Figure 11. Distribution of concentrations of constituents measured in composite samples of bridge-deck runoff and in 

concurrent replicate-split samples collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 

2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond 

Village (421247071470201) in eastern Massachusetts, 2014–16. Locations of stations are shown on figure 1.

methods. The absolute relative percent difference (RPD) was 

calculated for each sample pair (concurrent replicate-split 

sample and composite runoff sample). The median RPD for 

concentrations of SS- and particle-associated constituents 

ranged from 10 to 25 percent, except for the median RPD for 

concentrations of PIC, which was about 65 percent (fig. 12). 

The maximum RPD for SS and LOI was 59 and 45 percent, 

respectively; about 75 percent of the RPDs for each constituent were less than 20 percent. The maximum RPD for PC, 

POC, TP, and PN was about 100 percent for each constituent; 

about 75 percent of the RPDs for each constituent were less 

than 42 percent. The RPD for PIC was much greater compared 

to the RPD for other carbon constituents. Concentrations of 

PIC were an order of magnitude lower than concentrations of 

PC and POC (fig. 11) and may explain the higher variability 

in the RPD values. Dissolved constituents generally are not 

affected by splitting; however, the low RPD for DN (less 

than 8 percent for all sample pairs; median less than 2 percent 

for all sample pairs) also is an indication that the analytical 

method performance was generally precise. 

The collection of representative aliquots from composite 

samples containing high concentrations of sand-size particles 

is difficult, and the results are often imprecise (Selbig and 

others, 2007; Smith and Granato, 2010). Under experimental 

conditions, the relative standard deviation for concentrations 

of sand-size particles in aliquots obtained by processing two 

artificial samples containing known concentrations of SS (50 

and 200 milligrams per liter [mg/L]) through a Deka port cone 

splitter ranged from 12 to 45 percent (Capel and others, 1995). 

The relative standard deviation for the RPDs of 33 SS pairs 

in this study was higher at 99 percent. Concentrations of SS 

in composite samples collected in this study varied by more 

about three orders of magnitude (fig. 11), and the sediment 

matrix was dominated by sand-size particles. The lower precision for SS and particle-associated constituent concentrations 

observed in concurrent replicate-split sample aliquots in this 

study may be explained, in part, by the variability and high 

concentrations of SS in composite samples of runoff collected 

from the bridge decks where the concentration of SS in 96 percent of the sample pairs was substantially greater than the concentrations of SS in the two artificial samples described in the 

Deka port cone splitter experiment (Capel and others, 1995). 

Furthermore, sample composite volumes in this study ranged 

from 1 to 16 L; thus composite samples often were split two or 

three times to reduce the aliquot volume sufficiently, and this 

also could have attributed to lower sample splitting precision 

for SS and particle-associated constituent concentrations. The 

relation between the RPDs of sample pairs and the associated 
20 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Absolute relative percent difference Loss on ignition of suspended solids Particulate carbon Particulate inorganic carbon Particulate organic carbon Total phosphorus Total dissolved nitrogen Particulate nitrogen Suspended sediment

20

40

60

80

100

120

140

160

180

200

0

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Mean

Count of replicatesplit samples(32)

(33) (33) (28) (32) (33) (32) (33) (33) Figure 12. Distribution of 

absolute relative percent 

difference values between 

replicate-split samples 

and composite samples of 

bridge-deck runoff collected 

at U.S. Geological Survey 

bridge-deck-monitoring 

stations on State Route 2A 

in Boston (422108071052501), 

Interstate 90 near Weston 

(422025071154501), and State 

Route 20 near Quinsigamond 

Village (421247071470201) 

in eastern Massachusetts, 

2014–16. Locations of stations 

are shown on figure 1.

concentrations of SS was poor, indicating the precision was 

random across the range of concentrations measured in composite samples of bridge-deck runoff. 

Replicate Samples of Bridge-Deck Sediment

One replicate sample, representing one of three particlesize classes of bridge-deck sediment, was collected at each 

bridge site to determine the precision of the concentration data 

associated with the particle-size classes less than 0.0625 mm 

in diameter, greater than or equal to 0.0625 to 0.25 mm in 

diameter, and greater than 0.25 mm in diameter. The RPD 

between concentrations of TP and total-recoverable metals 

in many replicate-split samples and bridge-deck sediment 

samples tended to be lower (less than 12 percent) in the finesediment fraction and increased with particle-size diameter 

(table 9). In general, many constituents affiliated with SS 

tend to be concentrated on the particles less than 0.0625 mm 

in diameter, and the relation becomes less homogenous as 

sediment diameter increases (Breault and others, 2005; Smith, 

2005; Smith and Granato, 2010). Aside from arsenic (As) and 

copper (Cu), the RPD for the other metals in the coarser size 

fractions ranged from 0 to 64 percent. The RPDs for concentrations of As and Cu were the least precise in the two larger 

size fractions. The RPD for concentrations of chromium (Cr) 

was the most precise in all three particle-size ranges.

Analysis Methods

The Anderson-Darling test, which compares the fit of 

an observed cumulative distribution function to an expected 

cumulative distribution function, was used to test for normality of each dataset (Minitab, Inc., 2010). The Spearman rho 

test was used to evaluate monotonic relations between concentrations of constituents collected at the bridges (Helsel and 

Hirsch, 2002). The Spearman rho test is based on the ranks of 

the data and, therefore, is resistant to effects caused by outliers 

(Helsel and Hirsch, 2002). Test results range from −1 (negative relation) through 0 (no relation) to +1 (positive relation). 

A negative coefficient indicates that one variable tends to 

increase as the other decreases, and a positive coefficient indicates that the two variables tend to increase together; however, 

the variables may not necessarily change at a constant rate 

in a monotonic relation. The absolute value of the correlation coefficient indicates the strength of the relation between 

variables. For the Anderson-Darling and the Spearman rho 

test, the significance level (α) was equal to 0.05. When the 

attained significance level of the test (p-value) was less than 

0.05, the null hypothesis of “data is normally distributed” 

(Anderson-Darling) or “no correlation in ranked data” (Spearman) was rejected.

Data collected in this study were analyzed to determine 

if the quality of bridge runoff differed significantly from 

bridge to bridge and if the quality of bridge runoff differed 
Analysis Methods 21

Table 9. Concentrations and absolute relative percent difference between three replicate-split and environmental composite samples of bridge-deck sediment collected from U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.[Locations of stations are shown in figure 1. Values calculated from a single pair of composite samples for each particle-size class. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. USGS, U.S. Geological Survey; Composite, composite runoff sample; Replicate, concurrent replicate-split sample; RPD, absolute relative percent difference; <, less than; ND, not detected in particle-size range]

Sample type Particle-size range

Concentration, dry weight, in milligrams per kilogram

Phosphorus 

(p68075)

Aluminum 

(p65196)

Arsenic 

(p67876)

Barium 

(p67877)

Cadmium 

(p67880)

Chromium 

(p67882)

Copper 

(p67884)

 Lead 

(p64181)

Manganese 

(p67888)

Nickel 

(p67890)

Zinc 

(p64180)

USGS station 421247071470201

Composite Greater than

0.25 millimeter

in diameter

44 7,400 2 37 0.11 22 <4.5 8.8 220 23 73

Replicate 44 3,800 9 28 0.11 22 <4.0 4.8 330 14 58

RPD 0 64 127 28 0 0 ND 59 40 49 23

USGS station 422025071154501

Composite Greater than or equal to 

0.0625 to 0.25 millimeter in diameter

35 6,200 6.8 100 0.4 56 5 120 290 34 300

Replicate 45 5,600 17 66 0.56 59 190 94 340 31 380

RPD 25 10 86 41 33 5 190 24 16 9 24

USGS station 422108071052501

Composite Less than

0.0625 millimeterin diameter

280 22,000 32 190 1.5 120 140 100 560 57 1,100

Replicate 310 23,000 32 170 1.5 120 140 92 560 55 1,100

RPD 10 4 0 11 0 0 0 8 0 4 0
22 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

significantly from the quality of highway runoff collected 

in eastern Massachusetts (Smith and Granato, 2010). Nonparametric rank-based methods were used for comparison of 

constituent concentrations collected on bridges or highways. 

Analysis of rank-transformed data is more robust compared to 

the analysis of raw data that are not normally distributed, and 

the mean rank represents an estimate of the median (Helsel 

and Hirsch, 2002). Bridge-deck runoff concentration data were 

analyzed by using the one-way analysis of variance (ANOVA) 

test on the ranks of the data (Minitab, Inc., 2010). Subsequently, the Tukey pairwise comparison method test was used 

to identify statistically significant differences (p-value less 

than 0.05) between datasets from each bridge (Minitab, Inc., 

2010). 

The Tukey method controls the overall specified error 

rate (95-percent joint confidence level) for all pairwise comparisons, and it is applicable to the uneven sample sizes (Helsel and Hirsch, 2002). The Mann-Whitney test, also referred to 

as the rank-sum test, was used to determine whether constituent concentrations collected from bridges differed statistically 

(p-value less than 0.05) from paired constituent concentrations collected from highways (Helsel and Hirsch, 2002). The 

Mann-Whitney test is a nonparametric method that makes 

no assumptions about the distribution of data and is used to 

determine if groups of data come from the same population 

or, alternatively, if the median values are different (Helsel and 

Hirsch, 2002). 

Bridge-Deck Runoff Simulations

Model simulations of runoff quality can provide important information for engineers and managers when choosing 

BMPs that are most effective in consistently reducing the 

target. The Stochastic Empirical Loading and Dilution Model 

(SELDM) was used to simulate bridge-deck runoff quality 

(Granato, 2013, 2016). These simulations demonstrate waterquality risk analysis and simulate annual bridge-runoff yields 

with and without use of stormwater control measure BMPs. 

Standard methods and variables described by Granato (2013) 

and statistics from the National Weather Service long-term 

precipitation monitoring sites in ecoregion 59 (the Northeastern Coastal Zone) were used for the hydrologic simulations. 

Version 1.0.2 of SELDM was used to perform longterm (29–30-year) simulations of bridge-deck runoff quality. 

SELDM can be used to model sites with different land uses 

and sites in different settings because it is a lumped parameter model that simulates runoff by using representative input 

values for conditions at a site of interest. Although SELDM 

is primarily designed to indicate the risk for stormwater 

event-mean composites, flows, and loads to be above userselected water-quality goals for individual storm events, it also 

calculates annual yields. SELDM is not calibrated by fitting 

input values to a historical record; it is calibrated by selecting statistics for runoff-quality variables and BMP-treatment 

variables from robust and representative datasets (Granato, 

2013, 2014; Granato and Jones, 2015, 2016, 2017). The input 

statistics that are selected can have a substantial effect on the 

potential number of water-quality exceedances in a simulation 

and the estimated annual yields. 

Simulating Bridge-Deck Hydrology

Although standard methods and variables described by 

Granato (2013) were used for the simulations in this study, 

information about the timing of runoff from the highway is not 

needed for annual-loads analyses. Therefore, the only bridgesite hydraulic variables needed for these analyses were the 

drainage area and the impervious fraction. A drainage area of 

1 acre and an impervious fraction of one (100 percent) were 

selected so the loads would be computed as annual yields in 

pounds per acre of pavement per year. The primary hydrologic 

variables used by SELDM to simulate runoff are the volume, 

duration, and the number of hours between event midpoints 

for runoff-generating storm events. Storm events are generated stochastically by using the two-parameter exponential 

distribution. The numbers of hours between event midpoints 

for runoff-generating storm events are summed from event 

to event until the total elapsed time exceeds either 365 or 

366 days, and then all the events within this period are lumped 

into an annual load accounting year. This process continues 

until the minimum threshold is exceeded and the last full year 

is simulated (Granato, 2013). The average of statistics from 

the 31 National Weather Service long-term precipitation monitoring sites in ecoregion 59 (the Northeastern Coastal Zone) 

were used for the simulations. These average values for stormevent depth, duration, and the number of hours between event 

midpoints are 0.704 in., 9.69 hours, and 155 hours, respectively (Granato, 2010, 2013). Stochastic variations in precipitation-event values resulted in 8 simulations with 29 annualload accounting years and 3 simulations with 30 annual-load 

accounting years. SELDM simulates runoff from precipitation 

by using stochastic runoff coefficients simulated with the 

Pearson type III distribution (Interagency Advisory Committee 

on Water Data, 1982; Chow and others, 1988). The stochasticrunoff coefficient statistics used in the analysis were calculated 

by using the standard SELDM values for the average (0.785), 

standard deviation (SD; 0.1917), and skew (−1.19) of runoff 

coefficients for sites that are fully impervious; these statistics 

were calculated by using rainfall-runoff data from 58 highway-runoff monitoring sites (Granato, 2013). 

Simulating Bridge-Deck Runoff Concentrations

SELDM was used to simulate long-term concentrations 

and yields of SS, TP, and total nitrogen (TN) in bridge runoff. 

These values were simulated by using the log-Pearson type 

III distribution with the average, SD, and skew of the common (base 10) logarithms of SS, TP, and TN concentrations. 

In this study, these constituents were simulated by using the 
Bridge-Deck Runoff Simulations 23

logarithms of concentrations because such data commonly fit a 

lognormal or log-Pearson type III distribution (Di Toro, 1984; 

Novotny, 2004; Granato and others, 2009; Granato, 2013). If 

data were simulated as lognormal, the skew was set equal to 

zero, which linearized the distribution of generated data with 

respect to the logarithmic and probability axes. In comparison, datasets with negative skews were concave down, which 

resulted in lower values at both ends of the distribution than 

would be produced with a lognormal distribution. Datasets 

with positive skews were concave up, which resulted in higher 

values at both ends of the distribution than would be produced 

with a lognormal distribution. Large positive skew values, 

when coupled with large SD values, may produce unrealistic concentrations, flows, and loads if an extreme random 

number is generated. Because monitoring data were limited, 

with many datasets commonly having fewer than 20 events 

per site, generating a long-term record set of many events 

(usually more than 1,600 in the Northeastern Coastal Zone) 

required extrapolation beyond the percentiles of the original 

data; therefore, careful selection of representative statistics 

was warranted. 

Three methods for estimating sample statistics were used 

to evaluate input values for these long-term simulations. The 

first method was to use the traditional statistics for estimating 

the average, SD, and skew because they are most commonly 

used in hydrology and statistics (Haan, 1977; Interagency 

Advisory Committee on Water Data, 1982; Chow and others, 

1988; Hosking, 1990; Stedinger and others, 1993; Helsel and 

Hirsch, 2002). The second method was to use robust regression on order statistics to estimate the average and SD and 

Pearson’s second skew as a robust estimate of skew (Haan, 

1977; Helsel and Hirsch, 2002). The third method was to use 

L-moments, which also are considered robust estimates of 

sample statistics (Hosking, 1990; Stedinger and others, 1993).

The traditional methods for estimating the average, SD, 

and skew have some limitations when these statistics calculated from limited datasets are used to simulate more values 

than are available (Haan, 1977; Interagency Advisory Committee on Water Data, 1982; Chow and others, 1988; Hosking, 

1990; Stedinger and others, 1993; Helsel and Hirsch, 2002). 

Although the average is the best estimate of the center of the 

distribution, the value of the average can be substantially 

biased by either high or low outliers in small datasets. Estimates of the average values tend to stabilize more rapidly with 

each additional sample and are less biased by outliers than the 

SD or skew because these statistics are calculated by using the 

sum of squared differences between each value and the average and the sum of cubed differences between each value and 

the average, respectively. 

Robust regression on order statistics and Pearson’s 

second skew were used to estimate sample statistics because 

values calculated by using these methods may be less biased 

by outliers than values calculated by using traditional methods (Haan, 1977; Helsel and Hirsch, 2002). If the logarithms 

of data are plotted against the normal quantiles of the plotting position of each data point, then the intercept and slope 

of a regression line based on these points are estimates of 

the average and SD of a lognormal distribution of these data 

(Haan, 1977; Helsel, 2004; National Institute of Standards and 

Technology, 2012). For this study, the Kendall-Theil Robust 

Line software (Granato, 2006) was used to obtain the estimates of the average and SD with the composite sample runoff 

data because this regression method is resistant to outliers. The 

TP concentrations from composite runoff samples from the 

State Route 20 bridge-deck-monitoring station are presented 

in figure 13 as an example. The average was estimated by 

adding the median error to the intercept because the intercept 

of the Kendall-Theil Robust Line is an estimate of the median 

not the average (Granato, 2006). Pearson’s second skew was 

used to estimate the value of skew because it is less affected 

by outliers than the traditional measure of skewness. The 

traditional equation for skewness is calculated by using the 

sum of cubed differences from the mean and the cubed SD; 

therefore, a few extreme values can have a substantial effect 

on this estimate of skew (Haan, 1977; Stedinger and others, 

1993; Helsel and Hirsch, 2002; National Institute of Standards 

and Technology, 2012). Pearson’s second skew, however, is 

calculated by subtracting the median from the mean, multiplying the difference by 3, and dividing by the SD, which avoids 

the use of cubed values (Haan, 1977; Stedinger and others, 

1993; Helsel and Hirsch, 2002; National Institute of Standards 

and Technology, 2012).

L-moments also were used to estimate sample statistics 

for potential use in SELDM simulations. L-moments are linear 

combinations of probability-weighted moments (Hosking, 

1990; Stedinger and others, 1993). The average L-moment 

statistic is calculated by using the same equation as the traditional average; however, the L-moments statistics which are 

analogous to the SD and skew statistics are considered unbiased estimators of sample statistics because these L-moment 

statistics are not calculated by using the squared or cubed differences from the average value. Calculation of these statistics 

is described by Stedinger and others (1993).

Selecting runoff-quality statistics from monitored sites to 

represent runoff quality at an unmonitored site is not a welldefined process. Although AADT counts have been reported 

as a predictive variable, such relations commonly are categorical, and the differences among sites may be attributable to the 

increasing development of surrounding areas that is associated with increasing traffic counts (Driscoll and others, 1990; 

Smith and Granato, 2010; Wagner and others, 2011; Taylor 

and others, 2014). Differences in monitoring statistics among 

sites may not be meaningful without availability of extremely 

large datasets (for example, 25 to 100 events) because of the 

variability of highway and urban runoff quality (Burton and 

Pitt, 2002; California Department of Transportation, 2009; 

Granato, 2013). 

Robust methods are needed to use available data from 

monitored sites to estimate potential effects of runoff at 

unmonitored sites. In this study, the three monitored bridges 

constitute only about 0.16 percent of MassDOT-owned bridges 

over water in Massachusetts. Similarly, the 15 bridge decks 
24 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

-2.5

0.01

0.1

1

10

EXPLANATION

Total phosphorus, in milligrams per liter

Total phosphorus = 10(-0.22915 + 0.39499 * K)

Average = 10(-0.22915+0.0175)

Estimated logarithmic values:

Median: -0.229

Average: -0.212

Standard deviation: 0.395

Phosphorus, water, unfiltered, in milligrams per liter

Normal quantile (K)

2.01.51.00.50.0-0.5-1.0-1.5-2.0 2.5

Figure 13. An example of the estimation of the average and 

standard deviation of total phosphorus concentrations in runoff 

composite samples collected from U.S. Geological Survey 

bridge-deck-monitoring station State Route 20 near Quinsigamond 

Village (421247071470201) by using the robust regression on order 

statistics method. Location of station is shown on figure 1.

in North Carolina in a larger study by Wagner and others 

(2011) constitute only about 0.1 percent of the 13,500 bridges 

maintained by North Carolina Department of Transportation 

(2017). Granato and Jones (2015) examined use of statistics 

from the same 15 bridge-runoff monitoring sites to estimate 

the risks for adverse effects of total phosphorus in receiving streams from bridge-deck runoff in North Carolina. They 

determined that correlations of concentrations to AADT counts 

(using both Pearson’s R and Spearman’s rho) were small and 

not statistically significant at the 95-percent confidence limits. 

Granato and Jones (2015) also determined a lack of significant correlation among statistics indicating that the average, 

SD, and skew of the common logarithms of concentration do 

not covary. They concluded that statistics may vary randomly 

from site to site, and the median of each concentration statistic 

may be representative for simulating runoff quality at unmonitored sites. Similarly, Risley and Granato (2014) examined 

potential effects of runoff-quality statistics on simulated 

concentrations and the potential for simulated water-quality 

excursions in receiving waters in Oregon. They concluded 

that using the median of statistics from hydrologically similar 

sites provided robust concentration estimates. Therefore, the 

median of statistics from the three bridge sites were used 

to simulate planning-level yield estimates for all bridges in 

eastern Massachusetts.

Simulating Runoff Treatment

Runoff treatment was analyzed to examine the potential 

effects of flow reductions and concentration reductions by 

stormwater BMPs on concentrations and annual yields from 

bridges. The BMP effluent concentrations and discharge 

volumes were simulated by using the BMP-treatment module in SELDM (Granato, 2013, 2014). The SELDM BMPtreatment module has provisions for stochastic modeling of 

three stormwater treatments: volume reduction, hydrograph 

extension, and water-quality treatment. Hydrograph extension 

is an important variable for dilution analyses, but the durations 

of BMP discharges do not substantially affect the total-annual 

yield from the highway. The SELDM BMP module uses the 

trapezoidal distribution and the rank correlation with the 

associated highway-runoff variable to provide a stochastic 

transfer function to approximate the quantity and quality of 

BMP effluent given the associated inflow values in a simulation. SELDM uses rank correlation to preserve the structure 

of inflow and outflow data commonly present in BMP studies. 

Correlations between the ratio of outflow to inflow volumes 

and the magnitude of inflows commonly are positive because 

it would be difficult for BMPs built with commonly used 

designs to retain or infiltrate a large proportion of flow from 

a large runoff event. The small positive correlation between 

highway inflow volumes and the outflow ratios reduces the 

average effectiveness of flow reduction by the BMP. Correlations between the concentration ratio and inflow concentrations are negative because BMP-monitoring datasets indicate 

that BMPs are more effective for substantially reducing large 
Quality of Bridge-Deck Runoff 25

inflow concentrations than small inflow concentrations. The 

negative correlation between highway inflow concentrations 

and the outflow ratios increases the average effectiveness of 

concentration reduction by the BMP. In many studies, BMP 

outflow concentrations can exceed low inflow concentrations 

(Granato, 2014; Taylor and others, 2014). To represent this 

phenomenon, SELDM simulates the effect of the minimum 

irreducible concentration (MIC), which is the lowest expected 

BMP effluent concentration (Granato, 2013, 2014). SELDM 

substitutes the MIC for BMP effluent concentrations that are 

less than the MIC.

For these analyses, a generic BMP was simulated by 

using the median of treatment statistics for flow reductions, 

concentration reductions, and MICs from nine BMP categories 

with data from the 2012 International BMP database (Granato, 

2014). The BMP categories and associated performance 

statistics for flow and concentration treatment from which the 

median values were derived and used for simulations in these 

analyses are shown in table 10. The categories bioretention, 

composite BMPs, detention basin, biofilter (swale), media 

filter, retention pond, wetland basin, and wetland channel 

were selected because flow statistics, concentration statistics, 

and MIC statistics were available from multiple BMP monitoring sites for these categories (Granato, 2014). The MIC 

values that were chosen for these simulations were based on 

the 25th percentile of MIC estimates from available sites for 

each category. 

Quality of Bridge-Deck Runoff

Concentrations of SS, sediment particle size, nutrients, 

LOI, and PC were measured in more than 160 flow-proportional composite samples (about 50 or more per monitoring station) of bridge-deck runoff collected from the three 

bridge-deck-monitoring stations between 2014 and 2016 (U.S.

Geological Survey, 2016). In addition to NWIS, the data also 

are available in the HRDB (version 1.0.0b) (Granato, 2017) 

that was developed by USGS in cooperation with the Federal 

Highway Administration (Granato and Cazenas, 2009). Summary statistics and statistical tests are presented to characterize and contrast the datasets for each bridge (table 11). The 

concentrations of constituents in composite samples of bridgedeck runoff varied considerably from storm to storm; however, 

concentration statistics for many constituents were similar for 

each bridge.

Suspended Sediment

Suspended sediment, especially the fine sediment fraction, is an important transport mechanism for many constituents in stormwater runoff. Several recent studies in Massachusetts have documented the affiliation of phosphorus and metals 

with sediment and the corresponding effect of concentration 

with specific particle sizes from samples of sediment collected 

from oil-grit separators (Smith, 2002), streambeds (Smith, 

2005), street sweepings (Breault and others, 2005; Sorenson, 

2013), and highway runoff (Smith and Granato, 2010). Metal 

concentrations associated with sediment tend to increase 

with a decrease in sediment particle size because as sediment 

particle size decreases the sediment surface area increases, 

allowing metals to bond to the sediment surface. Understanding the relation between sediment quality and particle size is 

important for the selection and implementation of structural 

and nonstructural BMPs to improve the quality of water discharged from bridge decks.

Concentrations of SS in composite samples of bridgedeck runoff ranged from 44 to 142,000 mg/L; however, most 

concentrations were less than 23,000 mg/L (maximum value 

for the 90th percentile for the three stations; table 11). Median 

concentrations of SS were nearly identical in samples collected from State Route 2A and Interstate 90 bridge-deckmonitoring stations (2,010 and 2,020 mg/L, respectively); the 

median concentration of SS in samples from State Route 20 

bridge-deck-monitoring station was lower at 1,490 mg/L. The 

particle-size distribution of SS at each monitoring station was 

highly skewed towards the coarse size fraction (size fraction 

greater than 0.25 mm in diameter) (fig. 14). At each bridgedeck-monitoring station, the median concentration for the 

coarse size fraction was about an order of magnitude greater 

than the finer two size fractions (0.0625 to 0.25 mm and less 

than 0.0625 mm in diameter). The median distribution of particles in the fine size fraction (less than 0.0625 mm in diameter) 

ranged from 4 to 8 percent at each monitoring station (table 

11). The median distribution of particles less than 0.25 mm in 

diameter was less than or equal to 26 percent for each bridge. 

The percentage of finer particles tended to increase with a 

decrease in concentration of SS (fig. 15), which is likely attributed to dissimilar buildup rates or roadway surface load capacity for the different particle sizes; that is, silt and clay size 

particles are easily dispersed by wind or vehicular turbulence, 

whereas coarse sand deposits tend to not disperse easily (less 

mobile) and increase in place between runoff events.

Loss on Ignition of Suspended Solids and 

Particulate Carbon

Concentrations of SS include organic particles in addition to mineral sediments. The organic fraction of the SS 

concentration is represented by LOI (or volatile suspended 

solids) and PC concentration data. Concentrations of LOI are 

generally considered a crude estimate of PC because other 

constituents, such as volatile salts, organic compounds, sulfide 

oxidation, inorganic carbon, and hydroxide compounds, also 

may be volatilized or otherwise diminished during the analytical method (Brown and Dykstra, 1995; Veres, 2002) and result 

in concentrations that are greater than those determined by 

methods for PC. Concentrations of LOI in composite samples 

of bridge-deck runoff generally were about 2.5 times greater 

than concentrations of PC in the same samples (fig. 16). 
26 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Table 10. Stormwater control measure best-management practice performance statistics for flow and concentration treatment used 

in the Stochastic Empirical Loading and Dilution Model. 

[The concentration-reduction and flow-reduction statistics are for the trapezoidal distribution of the ratio of outflow to inflow concentration or flow volume. 

The Spearman’s rho correlation coefficients are calculated by using the ranks of the inflow concentrations or flows and the associated ratios of outflow to 

inflow concentrations or flows. The minimum irreducible concentration (MIC) estimates for the suspended sediment concentrations were developed with total 

suspended solids concentrations but are considered applicable for estimating the MIC of suspended sediment concentrations because differences in the results 

of these analytical methods are small once the large grain-size fractions are removed within the best-management practice. LBMPV, lower bound of the most 

probable value; UBMPV, upper bound of the most probable value; Spearman’s rho, Spearman’s correlation coefficient; N, nitrogen; P, phosphorus; --, insufficient data; NA, not applicable]

Best-management 

practice type Minimum LBMPV UBMPV Maximum Spearman’s rho MIC, in milligrams per liter

Flow reduction

Bioretention 0 0.019 0.152 0.947 0.61 NA

Composite -- -- -- -- -- NA

Detention basin 0.147 0.147 0.657 1.232 0.07 NA

Biofilter (swale) 0.06 0.306 0.495 1.085 0.29 NA

Infiltration basin -- -- -- -- -- NA

Media filter 0.113 0.742 0.742 1.262 0 NA

Retention pond 0.208 0.665 0.903 1.832 0 NA

Wetland basin 0.136 0.934 0.934 1.233 0.21 NA

Wetland channel 0.116 0.548 0.548 1.849 0.27 NA

Median 0.116 0.548 0.657 1.233 0.21 NA

Total nitrogen as N

Bioretention 0.148 0.4 0.593 2.01 -0.636 0.09

Composite 0.222 0.372 0.372 1.088 -0.081 0.06

Detention basin 0.141 0.417 1.998 3.121 -0.548 0.1

Biofilter (swale) 0.174 0.642 0.642 2.27 -0.552 0.17

Infiltration basin 0.052 0.052 0.158 2.598 -0.6 0.38

Media filter 0.126 0.391 0.536 1.703 -0.318 0.05

Retention pond 0.332 0.693 0.693 1.522 -0.508 0.14

Wetland basin 0.272 0.394 0.394 2.181 -0.437 0.04

Wetland channel 0.346 0.367 0.539 1.705 -0.595 0.11

Median 0.174 0.394 0.539 2.01 -0.548 0.1

Total phosphorus as P

Bioretention 0.013 0.176 0.325 2.339 -0.42 0.01

Composite 0 0.126 0.17 1.562 -0.571 0.005

Detention basin 0.24 0.415 0.561 1.55 -0.498 0.03

Biofilter (swale) 0.105 0.669 0.827 3.556 -0.669 0.01

Infiltration basin 0.002 0.002 0.031 3.649 -0.292 0.002

Media filter 0.161 0.21 0.228 1.597 -0.555 0.005

Retention pond 0.053 0.199 0.38 1.653 -0.606 0.006

Wetland basin 0.056 0.512 0.88 2.158 -0.517 0.008

Wetland channel 0.171 0.226 0.623 2.203 -0.401 0.007

Median 0.056 0.21 0.38 2.158 -0.517 0.007

Suspended sediment concentration

Bioretention 0 0 0 0.885 -0.635 0.06

Composite 0 0 0 0.791 -0.626 0.2

Detention basin 0 0 0 1.158 -0.631 0.89

Biofilter (swale) 0 0 0 1.545 -0.569 1

Infiltration basin 0 0 0 0.902 -0.738 1.9

Media filter 0 0 0 0.652 -0.604 0.43

Retention pond 0 0 0 0.822 -0.721 0.74

Wetland basin 0 0 0 1.681 -0.759 0.28

Wetland channel 0 0 0 2.21 -0.446 0.2

Median 0 0 0 0.902 -0.631 0.43
Quality of Bridge-Deck Runoff 27

Table 11. Summary statistics for concentrations of constituents in composite samples of bridge-deck runoff collected from State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.[Locations of stations are shown in figure 1. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. mg/L, milligram per liter; N, nitrogen; P, phosphorus; <, less than]

Statistic

Suspended 

sediment, 

in mg/L 

(p80154)

Suspended 

sediment, 

percent 

smaller than 

0.25 millimeter 

(p70333)

Suspended 

sediment, 

percent 

smaller than 

0.0625 millimeter 

(p70331)

Loss on 

ignition of 

suspended 

solids,

in mg/L 

(p00535)

Particulate 

carbon 

[inorganic 

plus organic],

in mg/L 

(p00694)

Particulate 

organic 

carbon, 

in mg/L 

(p00689)

Particulate 

inorganic 

carbon, 

in mg/L 

(p00688)

Particulate 

nitrogen, 

in mg/L as N 

(p49570)

Total 

phosphorus, 

in mg/L as P 

(p00655)

Total 

dissolved 

nitrogen, 

in mg/L as N 

(p62854)

Total nitrogen 

(particulate 

nitrogen plus dissolved nitrogen), 

in mg/L as N 

(p00600)

State Route 2A in Boston, Massachusetts

Count 54 54 54 54 53 51 51 53 53 52 52

Minimum 253 3 0 16.0 6.78 6.78 <0.03 0.179 0.200 0.18 0.360

10th 589 6 1 31.3 11.3 10.2 <0.03 0.298 0.292 0.31 0.678

25th 996 10 2 47.5 18.0 17.6 <0.03 0.449 0.480 0.41 1.08

Median 2,010 14 4 83.0 38.5 38.1 0.63 0.772 0.690 0.64 1.45

Mean 6,800 19 8 148 71.0 66.0 6.24 2.00 1.10 0.83 2.84

75th 5,020 23 9 141 69.6 69.5 1.28 1.64 1.10 0.95 2.45

90th 13,100 38 20 295 108 102 3.27 2.76 2.24 1.54 3.99

Maximun 142,000 68 45 1,740 1,360 1,100 255 26.7 7.02 3.37 29

Interstate 90 near Weston, Massachusetts

Count 54 54 54 54 46 44 44 46 46 46 46

Minimum 78 7 1 19.0 7.31 7.11 <0.03 0.204 0.110 0.220 0.520

10th 589 12 2 43.4 13.9 13.0 0.20 0.420 0.270 0.345 0.925

25th 1,020 17 4 67.3 27.5 26.4 0.26 0.743 0.373 0.47 1.33

Median 2,020 26 8 94.5 38.4 35.4 0.77 1.11 0.505 0.74 1.8

Mean 5,250 28 9 187 60.6 57.4 3.68 1.72 0.660 1.06 2.78

75th 4,970 38 13 191 67.1 61.1 3.25 2.27 0.670 1.22 2.98

90th 11,400 45 18 476 119 116 8.86 3.49 1.09 1.45 5.25

Maximun 38,700 58 34 1,060 392 377 51.6 13.5 3.16 5.63 17

State Route 20 near Quinsigamond Village, Massachusetts

Count 55 55 55 55 51 48 48 51 51 51 51

Minimum 44 6 1 15.0 6.68 6.57 <0.03 0.212 0.090 0.28 0.580

10th 132 8 2 29.8 12.6 11.6 0.03 0.448 0.200 0.51 1.200

25th 352 11 3 67.0 25.1 24.4 0.14 0.770 0.360 0.72 1.75

Median 1,490 21 7 139 54.4 51.5 0.57 1.89 0.590 0.90 2.8

Mean 6,390 31 18 218 88.2 80.6 8.81 2.81 0.974 1.20 4.01

75th 6,250 42 24 232 107 98.6 3.37 3.74 1.14 1.32 5.2

90th 22,900 75 59 622 213 199 12.1 6.21 1.78 1.92 7.9

Maximun 60,000 96 89 1,140 490 378 174 15.9 6.02 4.69 18
28 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

1

10

100

1,000

10,000

100,000

200,000

Suspended sediment concentration,

 in milligrams per liter

Greater than 0.25 millimeter in diameter 

Concentration of suspended sediment 

Greater than or equal to 0.0625 and 0.25 millimeter

in diameter 

Less than 0.0625 millimeter in diameter 

State Route 2A Interstate 90 State Route 20

Bridge-deck-monitoring stations 

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Mean

(55) Count of samples

(54) (54) (55)

Minimum

value of 0

Figure 14. Distribution of three particle-size classes for suspended sediment in composite samples of bridge-deck runoff collected 

at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston 

(422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16. Locations 

of stations are shown on figure 1.

10

0

20

40

60

80

100

Suspended sediment, percent smaller in diameter

Suspended sediment concentration, in milligrams per liter

100 1,000 10,000 100,000 300,000

EXPLANATION

Particles less than 0.25 millimeter

in diameter 

Particles less than 0.0625 millimeter

in diameter 

Figure 15. The relation between particles 

size less than 0.0625 millimeter in diameter 

and particles size less than 0.25 millimeter 

in diameter to concentrations of suspended 

sediment in composite samples of bridgedeck runoff collected at U.S. Geological 

Survey bridge-deck-monitoring stations on 

State Route 2A in Boston (422108071052501), 

Interstate 90 near Weston (422025071154501), 

and State Route 20 near Quinsigamond Village 

(421247071470201) in eastern Massachusetts, 

2014–16. Locations of stations are shown on 

figure 1.
Quality of Bridge-Deck Runoff 29

0.01

0.1

1

10

100

1,000

10,000

Concentration, in milligrams per liter

State Route 2A Interstate 90 State Route 20

Bridge-deck-monitoring stations 

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Mean

(55) Count of samples

(54)

Loss on ignition of suspended solids

Nutrient concentration 

Particulate carbon

Particulate organic carbon

Particulate inorganic carbon

(53) (51) (51) (54) (46) (44) (44) (55) (52) (49) (52)

Figure 16. Distribution of concentrations of loss on ignition of suspended solids, particulate carbon, 

particulate organic carbon, and particulate inorganic carbon in composite samples of bridge-deck 

runoff collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston 

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond 

Village (421247071470201) in eastern Massachusetts, 2014–16. Locations of stations are shown on figure 1.

Concentrations of LOI and PC in composite samples ranged 

from 15.0 to 1,740 mg/L and 6.68 to 1,360 mg/L, respectively 

(table 11). Median values representing the proportion of LOI 

and PC to SS were less than 10 and 3 percent in composite 

samples collected at the three bridge-deck-monitoring stations, 

respectively. Concentrations of PC were primarily organic in 

content (table 11; fig. 16). Less than about 12 percent of the 

total PC was inorganic in 90 percent of the samples collected 

at each bridge-deck-monitoring station.

Nutrients

Many studies have shown that stormwater runoff can be 

a source of nutrients (Davenport, 1990; Breault and Granato, 

2000; Smith, 2002; Kayhanian and others, 2003; Smith and 

Granato, 2010). Phosphorus is somewhat insoluble and tends 

to be associated with sediment, including local soils and even 

maintenance sand used to increase traction on roads during 

winter storms (Smith and Granato, 2010). As previously noted 

in the “Site Selection” section, maintenance sand was not 

applied to any of the bridges in this study but may have been 

used on roadways in the vicinity of the monitoring stations and 

tracked onto the bridge decks. Natural organic material also 

is a source of TP and PN in stormwater runoff (Breault and 

others, 2005; Smith, 2005; Smith and Granato, 2010; Sorenson, 2013). Although each bridge is elevated and contains little 

or no adjacent vegetation, leaves and other natural organic 

matter were frequently observed in water samples. Concentrations of TP and TN in composite samples of bridge-deck 

runoff ranged from 0.09 to 7.02 mg/L and 0.36 to 29.0 mg/L, 

respectively (table 11). The distribution of TP concentrations 

was similar at each bridge-deck-monitoring station, whereas 

median concentrations ranged from 0.505 to 0.690 mg/L 

(fig. 17). Highest observed median concentrations of TP were 

in composite samples of bridge-deck runoff collected on State 

Route 2A in Boston (table 11). The distribution of PN and 

DN concentrations also was somewhat similar at each bridge; 

however, the concentrations of DN and PN were highest at the 

State Route 20 monitoring station. Approximately 40 percent 

of the TN concentration at each monitoring station consisted 

of DN. Annual precipitation-weighted mean concentrations 

of DN (inorganic forms of ammonia plus nitrate) during 2015 

and 2016 were 0.625 and 0.859 mg/L in Boston, respectively 

(National Atmospheric Deposition Program, 2017a), and were 

0.735 and 0.773 mg/L in central Massachusetts, respectively 

(National Atmospheric Deposition Program, 2017b), which 

are near the median DN concentrations in composite samples 

of bridge-deck runoff at each monitoring station (0.64 to 
30 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

0.1

1

10

0.05

50

Concentration, in milligrams per liter

State Route 2A Interstate 90 State Route 20

Bridge-deck-monitoring stations 

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Mean

(55) Count of samples

Total phosphorus

Nutrient concentration 

Particulate nitrogen

Total dissolved nitrogen

(53) (53) (52) (46) (46) (46) (52) (52) (52)

Figure 17. Distribution of concentrations of total phosphorus, particulate nitrogen, and total dissolved 

nitrogen in composite samples of bridge-deck runoff collected at U.S. Geological Survey bridgedeck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston 

(422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern 

Massachusetts, 2014–16. Locations of stations are shown on figure 1.

0.90 mg/L) (table 11). Traffic volume (table 1) and available 

land-use data (table 2) showed no consistent trend that might 

indicate why nitrogen concentrations in samples collected 

from the State Route 20 and Interstate 90 bridge locations 

seemed to differ from nitrogen concentrations in samples collected from the State Route 2A bridge location in Boston.

Correlation Between Constituents

Results for all Anderson-Darling tests were significant 

(p-value less than 0.05) (table 12) for sample sets at each 

bridge, indicating that concentration data for composite 

samples of bridge-deck runoff were not normally distributed. 

Right-skewed distributions for highway and urban runoff data 

are not unusual because concentration data are highly variable 

and often include high-concentration outliers (Driscoll and 

others, 1990; Smith, 2002; Smith and Granato, 2010). Because 

the bridge-deck runoff concentration data were not normally 

distributed, nonparametric statistical methods were used for 

analysis of all data. 

Results for Spearman rho tests on the datasets indicated 

that the concentration data were positively correlated with 

each other (table 13). The relation between concentrations of 

SS and other particulate constituents (LOI, PC, POC, PIC, 

and PN) were significant and in many cases, the relations 

were moderately strong (Spearman rho coefficient greater 

than 0.7). Spearman rho test results indicated that the relations 

between concentrations of PN and LOI (coefficients ranging 

from 0.772 to 0.908) and PC (coefficients ranging from 0.906 

to 0.942) were stronger than the relation between PN and SS 

(coefficients ranging from 0.595 to 0.640). Conversely, relations between concentrations of TP and LOI, PC, and SS were 

similar at each bridge, although the relations were consistently 

weaker at Interstate 90 (table 13). These data indicated that 

concentrations of organic constituents (LOI and PC) were 

better correlated with concentrations of TN compared to concentrations of SS, and these organic constituents seem to have 

relations of similar strength with TP compared to concentrations of SS, implying that natural organic materials are likely 

an important source for total nutrients.

Interbridge Comparison of Constituent 

Concentrations

Concentration data collected from each bridge were 

compared to the respective concentration data collected 

from each of the other bridges to determine if the datasets 

were significantly different. Results for the ANOVA and 
Interbridge Comparison of Constituent Concentrations 31

Table 12. Results and attained significance levels for Anderson-Darling tests for concentrations of selected constituents in 

composite samples of bridge-deck runoff collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A 

in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village 

(421247071470201) in eastern Massachusetts, 2014–16.

[Locations of stations are shown in figure 1. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. p-value, significance level; P, phosphorus; N, nitrogen; <, less than.]

Constituent Anderson-Darling test results

Count Average Standard deviation Anderson-Darling statistic p-value

State Route 2A in Boston, Massachusetts

Total phosphorus as P (p00655) 53 1.096 1.304 6.812 <0.005

Total nitrogen as N (p00600) 52 2.839 4.72 10.020 <0.005

Total dissolved nitrogen as N (p62854) 52 0.829 0.643 3.694 <0.005

Particulate nitrogen as N (p49570) 53 2.003 4.418 11.782 <0.005

Suspended sediment (p80154) 54 6,795 19,486 12.299 <0.005

Particulate carbon (p00694) 53 71.03 184.6 12.616 <0.005

Loss on ignition of suspended solids (p00535) 54 147.4 246.1 8.312 <0.005

Interstate 90 near Weston, Massachusetts

Total phosphorus as P (p00655) 46 0.660 0.571 4.622 <0.005

Total nitrogen as N (p00600) 46 2.785 2.848 4.990 <0.005

Total dissolved nitrogen as N (p62854) 46 1.059 1.088 5.561 <0.005

Particulate nitrogen as N (p49570) 46 1.72 2.097 4.919 <0.005

Suspended sediment (p80154) 54 5,251 8,700 8.777 <0.005

Particulate carbon (p00694) 46 60.62 68.16 4.956 <0.005

Loss on ignition of suspended solids (p00535) 54 186.6 214 6.247 <0.005

State Route 20 near Quinsigamond Village, Massachusetts

Total phosphorus as P (p00655) 51 0.974 1.179 5.809 <0.005

Total nitrogen as N (p00600) 51 4.013 3.395 2.734 <0.005

Total dissolved nitrogen as N (p62854) 51 1.197 0.937 4.719 <0.005

Particulate nitrogen as N (p49570) 51 2.815 3.048 3.873 <0.005

Suspended sediment (p80154) 55 6,393 11,368 8.062 <0.005

Particulate carbon (p00694) 51 88.24 95.84 3.973 <0.005

Loss on ignition of suspended solids (p00535) 55 217.6 237 4.723 <0.005

accompanying Tukey pairwise comparison tests performed on 

the rank-transformed data are reported in table 14. Results for 

the ANOVA test indicate if the differences among the datasets 

are significant. The subsequent Tukey pairwise comparison 

tests are necessary to determine which datasets differ from one 

another; datasets that share the same letter are not significantly 

different (p-value 0.05).

Results of the ANOVA tests indicated that concentrations of SS, LOI, PC, and TP in datasets for each bridge pair 

were not significantly different (table 14). Test results for the 

fine sediment-size fraction (less than 0.0625 mm in diameter), 

intermediate sediment-size fraction (greater than or equal 

to 0.0625 to 0.25 mm in diameter), TN, PN, and DN were 

significant, indicating that the dataset from at least one bridge 

may be different compared to the datasets from the other 

bridges. Results for the post hoc Tukey pairwise comparison 

test, however, did not indicate that there was a significant 

difference between the fine sediment-size fraction in composite samples collected at the three bridges. Test results for the 

Tukey pairwise comparison test did indicate that datasets for 

the intermediate sediment-size fraction, TN, PN, and DN were 

significantly different at one or more bridges. Grouping information indicated that the mean of the rank-transformed data 

for the intermediate sediment-size fraction collected on Interstate 90 was significantly higher than the means of the ranktransformed data for the other bridges (table 14). The higher 
32 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Table 13. Results and attained significance levels for Spearman rho tests for concentrations of selected constituents in composite 

samples of bridge-deck runoff collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston 

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village(421247071470201) in 

eastern Massachusetts, 2014–16.

[Locations of stations are shown in figure 1. Shaded areas indicate values that are significant at a 95-percent confidence interval. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. P, phosphorus; N, nitrogen; p-value, significance level; <, less than]

Constituent Statistic

Loss on 

ignition 

of suspended 

solids 

(p00535)

Particulate 

carbon 

[inorganic plus 

organic] 

(p00694)

Particulate 

organic 

carbon 

(p00689)

Particulate 

inorganic 

carbon 

(p00688)

Total 

dissolved 

nitrogen 

(p62854)

Particulate 

nitrogen 

(p49570)

Total 

phosphorus 

as P 

(p00655)

Total 

nitrogen 

as N 

(p00600)

State Route 2A in Boston, Massachusetts

Particulate carbon [inorganic 

plus organic] (p00694)

Spearman rho 0.897

p-value <0.001

Particulate organic carbon 

(p00689)

Spearman rho 0.884 0.998

p-value <0.001 <0.001

Particulate inorganic carbon 

(p00688)

Spearman rho 0.676 0.511 0.483

p-value <0.001 <0.001 0.001

Total dissolved nitrogen as N 

(p62854)

Spearman rho 0.298 0.283 0.375 0.252

p-value 0.032 0.042 0.01 0.098

Particulate nitrogen as N 

(p49570)

Spearman rho 0.908 0.942 0.938 0.546 0.398

p-value <0.001 <0.001 <0.001 <0.001 0.003

Total phosphorus as P (p00655) Spearman rho 0.780 0.665 0.602 0.563 0.119 0.682

p-value <0.001 <0.001 <0.001 <0.001 0.400 <0.001

Total nitrogen as N (p00600) Spearman rho 0.739 0.748 0.800 0.522 0.737 0.859 0.502

p-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

Suspended sediment (p80154) Spearman rho 0.769 0.658 0.627 0.735 0.045 0.640 0.799 0.417

p-value <0.001 <0.001 <0.001 <0.001 0.752 <0.001 <0.001 0.002

Interstate 90 near Weston, Massachusetts

Particulate carbon [inorganic 

plus organic] (p00694)

Spearman rho 0.845

p-value <0.001

Particulate organic carbon 

(p00689)

Spearman rho 0.852 0.995

p-value <0.001 <0.001

Particulate inorganic carbon 

(p00688)

Spearman rho 0.680 0.660 0.614

p-value <0.001 <0.001 <0.001

Total dissolved nitrogen as N 

(p62854)

Spearman rho 0.434 0.427 0.404 0.203

p-value 0.003 0.003 0.007 0.192

Particulate nitrogen as N 

(p49570)

Spearman rho 0.772 0.906 0.905 0.618 0.507

p-value <0.001 <0.001 <0.001 <0.001 <0.001

Total phosphorus as P (p00655) Spearman rho 0.579 0.497 0.485 0.423 0.251 0.543

p-value <0.001 <0.001 0.001 0.005 0.093 <0.001

Total nitrogen as N (p00600) Spearman rho 0.750 0.818 0.808 0.529 0.748 0.927 0.512

p-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

Suspended sediment (p80154) Spearman rho 0.840 0.739 0.733 0.739 0.250 0.595 0.413 0.557

p-value <0.001 <0.001 <0.001 <0.001 0.094 <0.001 0.004 0.001
Spatial Distribution of Bridge-Deck Sediment 33

Table 13. Results and attained significance levels for Spearman rho tests for concentrations of selected constituents in composite 

samples of bridge-deck runoff collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston 

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village(421247071470201) in 

eastern Massachusetts, 2014–16.—Continued

[Locations of stations are shown in figure 1. Shaded areas indicate values that are significant at a 95-percent confidence interval. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. P, phosphorus; N, nitrogen; p-value, significance level; <, less than]

Constituent Statistic

Loss on 

ignition 

of suspended 

solids 

(p00535)

Particulate 

carbon 

[inorganic plus 

organic] 

(p00694)

Particulate 

organic 

carbon 

(p00689)

Particulate 

inorganic 

carbon 

(p00688)

Total 

dissolved 

nitrogen 

(p62854)

Particulate 

nitrogen 

(p49570)

Total 

phosphorus 

as P 

(p00655)

Total 

nitrogen 

as N 

(p00600)

State Route 20 near Quinsigamond Village, Massachusetts

Particulate carbon [inorganic 

plus organic] (p00694)

Spearman rho 0.874

p-value <0.001

Particulate organic carbon 

(p00689)

Spearman rho 0.830 0.993

p-value <0.001 <0.001

Particulate inorganic carbon 

(p00688)

Spearman rho 0.677 0.659 0.624

p-value <0.001 <0.001 <0.001

Total dissolved nitrogen as N 

(p62854)

Spearman rho 0.343 0.450 0.524 0.152

p-value 0.015 0.001 <0.001 0.330

Particulate nitrogen as N 

(p49570)

Spearman rho 0.834 0.937 0.931 0.558 0.548

p-value <0.001 <0.001 <0.001 <0.001 <0.001

Total phosphorus as P (p00655) Spearman rho 0.762 0.733 0.678 0.725 0.214 0.658

p-value <0.001 <0.001 <0.001 <0.001 0.131 <0.001

Total nitrogen as N (p00600) Spearman rho 0.729 0.843 0.888 0.497 0.750 0.925 0.572

p-value <0.001 <0.001 <0.001 0.001 <0.001 <0.001 <0.001

Suspended sediment (p80154) Spearman rho 0.783 0.735 0.661 0.827 0.082 0.621 0.831 0.484

p-value <0.001 <0.001 <0.001 <0.001 0.572 <0.001 <0.001 <0.001

proportion of sediment in this size fraction on the Interstate 90 

may be related to the effect of concrete particles transported in 

runoff from the degradation of the bridge surface and median 

barriers that were not present at the other bridges (fig. 18). 

The mean of the rank-transformed data for TN collected on 

State Route 20 was significantly higher than the means of the 

rank-transformed data for the other bridges. Median concentrations of TN were about 93 and 55 percent lower at State 

Route 2A in Boston and at Interstate 90 near Weston, respectively, compared to the median concentrations of TN at State 

Route 20 near Quinsigamond Village (table 11). The means 

of the rank-transformed data for DN and PN for bridge pairs 

State Route 2A and Interstate 90, and Interstate 90 and State 

Route 20 were not statistically different; however, test results 

for the bridge pair State Route 2A and State Route 20 were 

significant and indicated that the fractional portions of TN 

were significantly higher at the State Route 20 bridge compared to State Route 2A.

Spatial Distribution of Bridge-Deck 

Sediment

Differences in wind current across the bridge, the slope 

of the bridge, sediment source, and depositional decay of 

vehicle tracking all can affect the distribution of sediment and 

associated constituents across each bridge. Samples of sediment were collected from the deck surface of each bridge to 

determine if sediment transported to the instrumented scupper 

inlets were representative of sediment mass across the entire 

bridge-deck surface. Samples of bridge-deck sediment were 

collected at five evenly distributed locations the entire length 

of each bridge deck three times between April 2015 and September 2016. These data represent a semiquantitative assessment of the distribution of bridge surface sediment on each 

bridge at the time of sampling.

Yields of bridge-deck sediment for each of the five sampling locations (table 6) ranged from 26 to 25,000 pounds per 

curb-mile (lbs/curb-mi). Yields were greatest in April 2015 at 

the bridge on State Route 2A and State Route 20. The bridge 
34 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 14. Attained F statistics, significance levels, predicted R-squared value, and relevant statistics from one-way analysis of variance tests and Tukey pairwise comparisons performed on the ranks of selected concentration and physical parameter values in composite samples of bridge-deck runoff collected at U.S. Geological Survey bridge- deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16. [Locations of stations are shown in figure 1. Shaded areas indicate values that are significant at a 95-percent confidence interval. Constituents or parameters that do not share a letter (A, B) are significantly dif- ferent for Tukey pairwise comparisons. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. Suspended sediment percentage representing particle size class greater than or equal to 0.0625 to 0.25 millimeter (mm) in diameter is the difference between U.S. Geological Survey parameter codes p70333 and p70331. ANOVA, one-way analysis of variance; mg/L, milligram per liter; <, less than; ≤, less than or equal to; >, greater than value shown; P, phosphorus; N, nitrogen; --, test not performed] Constituent or physical parameter ANOVA test results for rank-transfomed data Tukey pairwise compari- sons—grouping informa- tion from Tukey method at 95-percent confidence level F statistic Attained signifi- cance level Predicted R2 of ANOVA test on ranks (percent) Bridge Sample count Average Standard deviation 95-percent confidence interval Suspended sediment (mg/L) (p80154) 0.98 0.379 0.00 State Route 2A 54 86.19 42.3 73.50 to 98.87 -- Interstate 90 54 85.17 42.57 72.48 to 97.85 -- State Route 20 55 74.78 55.39 62.21 to 87.35 -- Suspended sediment <0.0625 mm in diameter (percent) (p70331) 3.34 0.038 0.38 State Route 2A 54 68.71 46.02 56.24 to 81.18

A

Interstate 90 54 87.59 39.41 75.12 to 100.06

A

State Route 20 55 89.55 52.68 77.20 to 101.91

A

Suspended sediment 0.0625≤0.25 mm 

in diameter (percent) 13.2 <0.001 10.92

State Route 2A 54 65 42.27 53.19 to 76.81

B

Interstate 90 54 106.5 42.14 94.69 to 118.31

A

State Route 20 55 74.64 47.2 62.93 to 86.34

B

Loss on ignition of suspended solids 

(p00535) 2.85 0.061 0

State Route 2A 54 70.12 45.53 57.58 to 82.66 --Interstate 90 54 84.82 43.86 72.28 to 97.37 --State Route 20 55 90.89 50.3 78.46 to 103.32 --

Particulate carbon [inorganic plus 

organic] (p00694) 2.2 0.115 2.96

State Route 2A 53 65.89 41.92 54.42 to 77.35 --

Interstate 90 46 73.43 38.58 61.13 to 85.74 --

State Route 20 48 83.5 45.78 71.45 to 95.55 --

Total phosphorus as P (p00655) 3.42 0.078 3.42

State Route 2A 53 84.03 42.5 72.36 to 95.70 --

Interstate 90 46 64.38 38.24 51.86 to 76.90 --

State Route 20 51 76.67 47.29 64.77 to 88.56 --

Total nitrogen as N (p00600) 8.22 <0.001 6.39

State Route 2A 52 59.98 42.36 48.70 to 71.27

B

Interstate 90 46 72.45 39.81 60.45 to 84.44

B

State Route 20 51 92.62 41.15 81.22 to 104.01

A

Particulate nitrogen as N (p49570) 5.21 0.007 4.94

State Route 2A 52 62.57 44.6 51.06 to 74.07

B

Interstate 90 46 73.37 43.98 61.14 to 85.60 A, B

State Route 20 51 89.15 37.08 77.53 to 100.76

A

Total dissolved nitrogen as N (p62854) 6.99 <0.001 2.78

State Route 2A 53 60.79 42.83 49.45 to 72.14

B

Interstate 90 46 74.78 39.09 62.60 to 86.96 A, B

State Route 20 51 91.43 43.05 79.87 to 103.00

A
Spatial Distribution of Bridge-Deck Sediment 35

A B

C

Figure 18. Sediment sources from deterioration of the A, bridge-deck surface; B, median barrier; and C, shoulder area that contributed 

to the sediment yield on the Interstate 90 bridge near Weston, Massachusetts, 2015–16. Location of bridge is shown on figure 1.

on Interstate 90 was not sampled at this time, but the greatest 

yields were measured in December 2015. High fluctuations in 

the sediment mass and yields on the bridge decks were similar 

to those seen on other types of streets and highway surfaces in 

the region (Smith, 2002; Breault and others, 2005; Smith and 

Granato, 2010; Sorenson, 2013). 

The mean yields of bridge-deck sediment were 1,500, 

250, and 5,700 lbs/curb-mi for State Route 2A, Interstate 90, 

and State Route 20, respectively (table 6). The mean yields 

were only calculated from three events and were heavily 

affected by the April and December values. Nevertheless, 

the yields of bridge-deck sediment in this study were similar 

to those reported in other studies. Mean yields of material 

on streets in Cambridge, Mass., ranged from 522 to 740 lbs/

curb-mi, and end-of-winter yields ranged from 2,609 to 

4,788 lbs/curb-mi (Sorenson, 2013). Selbig and Bannerman (2007) reported residential street-dirt yields of 614 and 

569 lbs/curb-mi; Law and others (2008) reported mean streetsolid yields of 645 lbs/curb-mi within an area routinely swept 

and 1,100 lbs/curb-mi for a control area within their study; and 

the Seattle Public Utilities and Herrera Environmental Consultants (2009) reported median yields of street solids that ranged 

from 69 to 2,200 lbs/curb-mi on streets swept twice per month. 

Yields of bridge-deck sediment (table 6) at each sampling 

location were normalized to the yield of bridge-deck sediment 

measured proximate to the monitoring station at each bridge 

(fig. 19) for greater comparison of the sediment distribution 

across each bridge.

The State Route 2A bridge is the only convex bridge in 

this study where the center of the bridge is higher in elevation than either side of approach. Although the bridge-deck 

yields were often variable, normalized bridge-deck sediment 

yields indicated that the sediment mass often was lower at the 

fixed sampling location proximate to the monitoring station 

compared to the other fixed sampling locations (fig. 19A). 

The mean yield of bridge-deck sediment measured proximate 

to the monitoring station (680 lbs/curb-mi) on the eastern 

end (Boston) of the bridge (fig. 1) was 55 percent lower than 

the mean yield of bridge-deck sediment represented by the 

sampling locations (1,500 lbs/curb-mi) (table 6). The lower 
36 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

EXPLANATION

0.1

1

10

Normalized sediment curb yield

 April 2015

 September 2015

 November 2015

A. State Route 2A

Station: location proximate to

bridge-deck-monitoring station

B. Interstate 90

Normalized sediment curb yield

 September 2015

 December 2015

 September 2016

10 percent

0.1

1

5 C. State Route 20

Normalized sediment curb yield

Bridge-deck sample location expressed as a percent of total 

bridge span length measured from nearest bridge abutment

 April 2015

 August 2015

 November 2015

0.1

1

5

Station

Station

Station

East (road sign direction) West

30-percent 50-percent 30-percent 10-percent

EXPLANATION

Station: location proximate to

bridge-deck-monitoring station

EXPLANATION

Station: location proximate to

bridge-deck-monitoring station

Month and year

Month and year

Month and year

Figure 19. Normalized bridge-deck sediment yields at five fixed locations on three bridges monitored 

by the U.S. Geological Survey on A, State Route 2A in Boston (422108071052501); B, Interstate 90 near 

Weston (422025071154501); and C, State Route 20 near Quinsigamond Village (421247071470201) in 

eastern Massachusetts, 2015–16. Locations of stations are shown on figure 1.
Chemical Analysis of Sediment 37

bridge-deck sediment yields measured near the monitoring station might be related to depositional decay of vehicle tracking 

because the traffic flow at the fixed sampling locations is from 

west to east (city of Cambridge to the city of Boston).

Normalized bridge-deck sediment yields for Interstate 

90 were similar for much of the bridge, except for the easternmost location, which was lower in comparison to the 

four other sampling locations (fig. 19B).The mean yield of 

bridge-deck sediment measured proximate to the monitoring 

station on Interstate 90 (240 lbs/curb-mi), located on the most 

western section of the bridge (fig. 1), was similar to the mean 

yield of bridge-deck sediment represented by the sampling 

locations (250 lbs/curb-mi). The distribution of the sediment 

mass across this bridge was largely related to the contribution 

of solids from the deterioration of the bridge-deck surface, 

median barrier, and shoulder area (fig. 18). The elevation of 

the Interstate 90 bridge decreased from east to west causing 

the mass of bridge sediments to migrate westward with traffic and runoff and resulting in higher deposits in the western 

sampling locations. Scupper inlets were often blocked (fig. 

18C) on the bridge, which can contribute to the migration of 

sediments westward.

Normalized bridge-deck sediment yields for State 

Route 20 near Quinsigamond Village seemed to indicate that 

the sediment mass decreased from east to west, especially for 

the April and August 2015 sampling events (fig. 19C). The 

monitoring station was about one-third of the way across the 

bridge from the eastern side (fig. 1). The mean yield of bridgedeck sediment proximate to the monitoring station on State 

Route 20 (7,200 lbs/curb-mi) was about 26 percent higher 

than the mean yield of bridge-deck sediment representing the 

sampling locations (5,700 lbs/curb-mi) (table 6). This bridge 

was constructed in 2005 (Massachusetts Department of Transportation, 2017b) and showed no signs of deterioration that 

might contribute to the sediment mass on the bridge deck. The 

roadway approach and the bridge both decreased in elevation 

from east to west. The distribution of the sediment mass across 

this bridge likely was the result of wash-on from the roadway 

upgradient of the bridge (fig. 20). Unlike the Interstate 90 

bridge, the materials from the State Route 20 bridge did not 

seem to substantially contribute to the sediment mass, but 

sediment yields did diminish westward as the sediment was 

captured in the scuppers. Similar to Interstate 90, the scupper 

inlets often were blocked on State Route 20, which caused 

the sediment mass to propagate farther onto the bridge than it 

might otherwise if the scupper inlets were always open.

The results from this study and from studies in other 

areas of the United States discussed previously in this section indicate that street-surface sediment masses and yields 

can vary widely. The distribution of sediment measured three 

times at each bridge during this study does not represent a 

quantitative assessment; however, plausible explanations are 

described above for the likely causes of the general distribution of the sediment yields measured and observed during this 

study on each bridge.

Figure 20. Sediment buildup propagating westward onto 

the State Route 20 bridge from the upgradient roadway, near 

Quinsigamond Village, Massachusetts, 2016. Location of bridge is 

shown on figure 1.

Chemical Analysis of Sediment

Many trace metals are associated with roadway sediments 

(Gupta and others, 1981; Smith, 2002; Breault and others, 

2005; Smith and Granato, 2010; Sorenson, 2013). An analysis 

of the particulate fraction of SS in samples of bridge-deck 

runoff indicated that PC represents about 3 percent of the SS 

and about 60 percent of TN is associated with the sediment 

fraction (U.S. Geological Survey, 2016). Natural plant matter, 

which is directly associated with the PC, also is a source of TP 

and TN and may contain a greater amount of these nutrients 

per unit mass compared to mineral sediment alone (Smith and 

Granato, 2010).

Results of laboratory analysis of sieved sediment samples 

indicated that bridge-deck sediment contained high concentrations of TP and various metals and that in many cases, 

the two fractions of sediment less than 0.25 mm in diameter 

were more enriched compared to the coarse sediment fraction 

(table 15). Concentrations of TP in the fine sediment fraction 

(less than 0.0625 mm in diameter) were about 6 times greater 

than in the coarse sediment fraction (greater than or equal to 

0.25 mm in diameter), but concentrations of TP in the intermediate sediment fraction (greater than or equal to 0.0625 to 

less than 0.25 mm in diameter) were similar to concentrations 

in the coarse sediment fraction. Total-recoverable Cu was not 

detected at the reporting limit concentration, 7.5 milligrams 
38 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 15. Mean proportion of suspended sediment in three particle-size classes in composite samples of bridge-deck and highway runoff and dry weight total-recoverable concentrations by sediment-size class for selected elements in three composite samples of bridge-deck sediments collected from bridges on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2016, and from highways in eastern Massachusetts, 2005–7. [Locations of stations are shown in figure 1. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. <, less than; ≤, less than or equal to; ≥, greater than or equal to] U.S. Geological Survey station number Highway Sediment particle-size class (millimeter in diameter) Mean proportion of suspended sedi- ment in composite samples of runoff (percent) Constituents measured in composite samples of sediment collected from bridges or highways, in milligrams per kilogram Phosphorus (p65196) Aluminum (p65196) Arsenic (p67876) Barium (p67877) Cadmium (p67880) Chromium (p67882) Copper (p67884) Lead (p64181) Manganese (p67888) Nickel (p67890) Zinc (p64180) 421247071470201 State Route 20 <0.0625 18 280 17,000 17 350 1.5 97 140 110 510 54 770 421247071470201 0.0625≤0.25 13 35 5,500 5.6 63 0.4 120 10 31 340 41 240 421247071470201 ≥0.25 69 44 7,400 2.0 37 0.1 22 <4.5 8.8 220 23 73 422025071154501 Interstate 90 <0.0625 9 180 18,000 32 540 1.4 140 240 230 530 53 1,200 422025071154501 0.0625≤0.25 18 35 6,200 6.8 100 0.4 56 5.0 120 290 34 300 422025071154501 ≥0.25 73 27 8,500 1.7 50 0.2 21 <4.1 3.5 480 19 67 422108071052501 State Route 2A <0.0625 8 280 22,000 32 190 1.5 120 140 100 560 57 1,100 422108071052501 0.0625≤0.25 11 48 5,800 5.3 47 0.4 59 7.0 30 330 31 240 422108071052501 ≥0.25 81 51 8,900 3.1 58 0.3 25 <7.5 11 510 21 350 Mean values Bridges in this study <0.0625 12 250 19,000 27 360 1.5 120 170 150 530 55 1,000 0.0625≤0.25 14 39 5,800 5.9 70 0.37 78 7 60 320 35 260 ≥0.25 74 41 8,300 2.3 48 0.20 23 <7.5 7.8 400 21 160 Highways in eastern Massachusetts (Smith and Granato, 2010) Average <0.0625 60 1,100 23,000 23 270 1.3 380 250 160 760 56 1,500 0.0625≤0.25 23 470 9,900 9.7 110 <1.0 130 160 103 300 30 270 ≥0.25 17 270 6,000 7.3 58 <1.0 510 46 24 290 27 102
Comparisons of Highway and Bridge-Deck Constituent Concentrations 39

per kilogram or less, in the coarse sediment fraction of 

samples of bridge-deck sediment collected from any of the 

bridges. Concentrations of Cu in the fine sediment fraction 

were 13 to 47 times more concentrated than concentrations in 

the intermediate sediment fraction. Total-recoverable concentrations of aluminum (Al), manganese (Mn), and nickel 

(Ni) were only slightly higher in the fine sediment fraction 

(less than 2 times higher) than concentrations in the coarse 

sediment fraction, which were similar or slightly higher than 

concentrations in the intermediate sediment fraction. Totalrecoverable concentrations for arsenic (As), barium (Ba), cadmium (Cd), Cr, and zinc (Zn) were about 2 to 17 times greater 

in the fine sediment fraction than concentrations in the coarse 

sediment fraction. Total-recoverable concentrations of As, Ba, 

Cd, Cr, and Zn were generally higher (about 1 to 4 times) in 

the intermediate sediment fraction than in the coarse sediment fraction. Similar to total-recoverable concentrations of 

Cu, concentrations of lead (Pb) also were highly associated 

with the finer sediment fractions where the concentration of 

Pb in the fine sediment fraction and the intermediate sediment fraction were about 8 to 65 times greater and about 2 to 

33 times greater than the concentration in the coarse sediment 

fraction, respectively.

These data demonstrate that the proportion of sediment 

in each particle-size class and sediment chemistry associated 

with each of these particle-size classes can have a substantial 

effect on the overall concentration of the various constituents, 

including TP, in aggregate samples of bridge-deck sediment 

and in composite samples of bridge-deck runoff containing 

SS. The overall concentration for a given sample can be estimated from the sum of the product of each sediment particlesize class and associated chemistry (eq. 2). On the basis of the 

mean proportion of SS in each particle-size class for all three 

bridges and the mean chemical concentration of each particlesize fraction for the three bridges (table 15), about 54 percent 

of the estimated sediment-associated TP was associated with 

the two larger sediment fractions (fig. 21). The cumulative 

proportion of the concentration of 6 of 10 metals (As, Ba, 

Cd, Cu, Pb, and Zn) in the intermediate and coarse sediment 

fractions was less than 56 percent. About 66 to 84 percent of 

the concentration of the other 4 of 10 metals (Al, Cr, Mn, and 

Ni) was associated with the intermediate and coarse sediment 

fractions. Only about 5 percent of the Cu concentration was 

associated with the intermediate and coarse sediment fraction.

C c p i

n

= ( ) =

∑ * i

1

i

(2)

where

C is the concentration of the selected 

constituent, in milligrams per kilogram;

n is the total number of particle-size classes 

(three in the present study);

i is an index to each particle-size class;

c is the mean concentration of the selected 

constituent associated with the particle-size 

class, in milligrams per kilogram; and

p is the mean proportion of sediment in each 

particle-size class.

Although the fine sediment fraction generally contains 

higher concentrations of TP and metals, the proportion of the 

fine sediment in composite samples of runoff was relatively 

small in comparison to the two larger sediment fractions 

(fig. 14). As a result, more than 50 percent of the estimated 

sediment-associated TP and various metals (8 of the 10) may 

be directly related to the larger mass of the intermediate and 

coarse sediment fractions (fig. 21) and can potentially be 

remediated by nonstructural BMPs (such as street sweeping) 

and by various structural BMPs that operate through settling (Waschbusch, 1999; Smith, 2002). Removal of the finer 

sediment fraction, though often more difficult, also may be 

achieved with other structural BMPs (Shoemaker and others, 

2000) and high-efficiency street sweepers (Breault and others, 

2005; Sorenson, 2013). Unlike structural BMPs that operate during each event, assuming proper maintenance, street 

sweeping is only a periodic method for removing sediment 

from the roadway. The State Route 2A bridge in Boston was 

reported to be swept weekly (weather permitting); however, 

concentrations of SS were not significantly different compared 

to the other two bridges that were swept infrequently. This is 

not an indication that the sweeping practice on the State Route 

2A bridge is ineffective, but perhaps the bridge-deck sediment buildup or recovery occurs quickly. Breault and others 

(2005) estimated a street-sediment accumulation rate of about 

50 pounds per curb-mile per day and indicated that the coarser 

size fractions between 0.25 and 2.0 mm seemed to accumulate 

most rapidly in New Bedford, Mass., which is similar to the 

measured conditions at this bridge. 

Comparisons of Highway and BridgeDeck Constituent Concentrations

The USGS, in cooperation with Federal Highway 

Administration and the MassDOT, conducted a field 

study during 2005–7 to characterize the quality of highway runoff for a wide range of constituents throughout 

Massachusetts (Smith and Granato, 2010). These data 

included concentrations of SS, TP, and TN collected from 

catch-basin outlets on highway segments with 100-percent 

impervious drainage areas. Sample concentrations from 

highway-monitoring stations on State Route 2 (USGS stations 423027071291301, 423027071291302), Interstate 190 

(USGS station 423016071431501), Interstate 495 (USGS 

stations 422821071332001, 422716071343901), Interstate 95 

(USGS stations 422420071153302, 422620071153301), and 

Interstate 93 (USGS station 421647071024703) (fig. 1), which 

are in eastern Massachusetts and have similar AADT volumes, 

were compared to constituent concentrations collected in 
40 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Phosphorus

Aluminum

Barium

Cadmium

Chromium

Copper

Lead

Manganese

Nickel

Zinc

Arsenic

0

10

20

30

40

50

60

70

80

90

100

Percent of constituent associated with the average proportion

of each particle size class of suspended sediment

Constituent

EXPLANATION

 Bridge-deck sediment associated

with particles greater than or equal to

0.25 millimeter in diameter

 Bridge-deck sediment associated

with particles greater than or equal to

0.0625 to 0.25 millimeter in diameter

 Bridge-deck sediment associated

with particles less than

0.0625 millimeter in diameter

Figure 21. Showing the percentage of the concentration estimated for selected constituents associated with the mean 

particle size of suspended sediment collected from three bridges monitored by the U.S. Geological Survey on State Route 

2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond 

Village (421247071470201) in eastern Massachusetts, 2014–16. Locations of stations are shown on figure 1.

the present study. Concentrations of total-recoverable metals 

in samples of sediment collected from highways in Massachusetts and the bridges in this study also were compared 

to determine if the 2005–7 total-recoverable metals data are 

transferable and can be used to expand the number of constituents measured in bridge-deck runoff in Massachusetts.

Suspended Sediment

Concentrations of SS in samples of bridge-deck runoff 

were significantly higher (Mann-Whitney test results p-values 

<0.001) than those collected from the selected highwaymonitoring stations in eastern Massachusetts (table 16). Much 

of this difference was likely the result of the collection of 

runoff downstream from catch basins on highways where 

some portion of the SS was reduced in the runoff, particularly 

coarse sediments (Smith, 2002), whereas the bridge scuppers 

provided no SS reduction within the stormwater flow train. 

Other factors such as high bridge walls adjacent to the roadway (figs. 9A and 20) and median barriers (fig. 18B) may more 

effectively trap sediment on the bridges compared to highways 

with low curbing; deterioration of bridge components (fig. 

18) and bridge wash-on (fig. 20) also may have contributed to 

higher concentrations of SS in samples of bridge-deck runoff. 

The proportion of fine and coarse particle-size fractions also 

was significantly different (p-values <0.001) in samples collected from the bridge-deck-monitoring stations compared to 

the highway-monitoring stations (table 16; fig. 22); however, 

the proportion of the intermediate sediment-size fraction 

in bridge-deck runoff was not statistically different than in 

highway-runoff composite samples. Although the percentage 

of the fine particle-size fraction of SS in composite samples 

of bridge-deck runoff (median of 6 percent) was statistically different than in composite samples of highway runoff 

(median of 60 percent), the range in the concentrations of the 

fine fraction of SS was not substantially higher than that in the 

highway dataset compared to the range in concentrations for 

the two larger sediment-size fractions where the median values 

were separated by an order of magnitude or more (fig. 22).

Concentrations of SS varied widely in the highwayrunoff dataset, but sample sets generally were statistically 

similar from highway to highway (Smith and Granato, 2010). 

Data collected from the bridges in this study indicated that 

concentrations of SS in all particle-size fractions in untreated 

bridge-deck runoff were generally higher, particularly in the 

two larger sediment-size fractions, than concentrations of SS 

for the respective particle-size fractions in samples collected 

from highways with catch-basin treatment. Although treatment 

on par with catch-basin performance may reduce the bridgerunoff SS concentration substantially, such treatment may 

have little effect on the reduction of fine-grained SS. 
Comparisons of Highway and Bridge-Deck Constituent Concentrations 41

Table 16. Results and attained significance levels (p-values) from Mann-Whitney tests for paired sample sets of constituent 

concentrations in bridge-deck runoff collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston 

(422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) 

during 2014–16 and in highway runoff collected from U.S. Geological Survey monitoring stations on State Route 2 (USGS stations 

423027071291301, 423027071291302), Interstate 190 (USGS station 423016071431501), Interstate 495 (USGS stations 422821071332001, 

422716071343901), Interstate 95 (USGS stations 422420071153302, 422620071153301), and Interstate 93 (USGS station 421647071024703) in 

eastern Massachusetts, 2005–7.

[Locations of stations are shown in figure 1. Shaded areas indicate values that are significant at a 95-percent confidence interval. The alpha-numeric identifiers 

starting with “p” are the U.S. Geological Survey parameter codes. Suspended sediment percentage representing particle size class greater than or equal to 0.0625 

to 0.25 millimeter (mm) in diameter is the difference between U.S. Geological Survey parameter codes p70333 and p70331. Suspended sediment percentage 

representing particle size class greater than or equal to 0.25 mm in diameter is the difference greater than U.S. Geological Survey parameter code p70333. mg/L, 

milligram per liter; <, less than; ≤, less than or equal to; ≥, greater than or equal to; P, phosphorus; N, nitrogen]

Constituent Highway Bridge Point 

estimate

95-percent 

confidence interval W statistic p-value

Count Median Count Median

Suspended sediment (mg/L) (p80154) 94 87 163 1,960 -1,810 -2,230 to -1,320 5,442 <0.001

Suspended sediment <0.0625 mm in 

diameter (percent) (p70331)

94 60 163 6 51.0 46.0 to 57.0 18,945 <0.001

Suspended sediment 0.0625≤0.25 

mm in diameter (percent)

94 13.5 163 12.0 1.00 -1.00 to 3.99 12,700 0.318

Suspended sediment ≥0.25 mm in 

diameter (percent)

94 17 163 79 -56.0 -60.9 to -51.0 5,465 <0.001

Total phosphorus (mg/L as P)

(p00655)

96 0.130 150 0.575 0.450 -0.530 to -0.380 5,642 <0.001

Total nitrogen (mg/L as N)1

 (p00600) 96 1.24 103 2.10 -0.720 -1.10 to 0.380 7,777 <0.001

Total nitrogen (mg/L as N)2

 (p00600) 96 1.24 46 1.80 -0.560 -0.910 to -0.250 6,041 <0.001

1

Bridge data inclusive of State Route 2A and Interstate 90.

2

Bridge data inclusive of State Route 20.

Nutrients

Concentrations of TP and TN in samples of bridge-deck 

runoff also were higher compared to the concentrations in 

samples collected from the selected highway-monitoring stations in eastern Massachusetts (fig. 23). Concentrations of TP 

in sample sets collected at each of the bridges were significantly higher (p-values <0.001) than those collected from the 

highway-monitoring stations (table 16). High concentrations 

of TP in bridge-deck runoff samples were not unexpected 

given the high concentrations of SS in the samples. Chemical 

analysis of bridge-deck sediment indicated that TP was closely 

associated with the sediments, particularly the fine sediment 

fraction (table 15).

Concentrations of TN in sample sets collected at each 

bridge were significantly higher (p-values <0.001) than those 

collected from the highway-monitoring stations (table 16). 

The difference in TN concentrations between the highway 

and bridge datasets may, in part, be somewhat overstated and 

affected by analytical method bias. Whole-water concentrations of TN in highway-runoff samples were analyzed by 

alkaline persulfate digestion (Patton and Kryskalla, 2003). 

Experimental evidence has since indicated that a negative bias 

in concentrations of TN is present across a range of sediment 

concentrations and increases as SS concentration increases 

(Rus and others, 2012). As a result, concentrations of TN in 

the prior highway dataset (Smith and Granato, 2010), particularly samples with high SS concentrations, were likely underestimated. Concentrations of TN in this study were calculated 

as the sum of DN and PN (table 3), and therefore the negative 

bias was eliminated. The alkaline persulfate digestion method, 

when used for samples absent of SS (filtered samples), has 

a reported precision of 2.3 percent (Rus and others, 2012). 

The RPDs between concentrations of DN in environment 

and replicate samples in this study (fig. 12) generally were 

within this reported precision, indicating that the performance 

of the method also was acceptable for the sample matrix in 

this study.

Concentrations of TN in the highway dataset may exhibit 

a negative bias as a result of method performance during the 

earlier study (Smith and Granato, 2010); however, this bias 

may not fully explain the difference in TN concentrations for 

each study. The substantial difference between concentrations 

of SS in samples of bridge-deck runoff and highway runoff 

(fig. 22) also may explain part of the difference in TN concentrations. In this study, most of the TN concentration in samples 

of bridge-deck runoff consisted of PN, and thus it is likely 

that the particulate concentration of nitrogen in the highway 
42 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

0.1

1

10

100

1,000

10,000

100,000

200,000

Suspended sediment, in milligrams per liter

Highway sediment

Particles

greater than 0.25

 millimeter in 

diameter

Particles 

greater than or

equal to 0.0625

to 0.25 millimeter

in diameter

Particles 

less than 0.0625 

 millimeter in 

diameter

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

EXPLANATION

Mean

Bridge-deck sediment

Suspended 

sediment

(94)

(94) Count of samples

(163) (94) (163) (94) (163) (94) (163)

Minimum

value of 0

Figure 22. Distribution of concentrations of suspended sediment and the distribution of concentrations 

of three particle-size classes for suspended sediment in composite samples of highway runoff collected 

at USGS stations 423027071291301, 423027071291302, 423016071431501, 422821071332001, 422716071343901, 

422420071153302, 422620071153301, and 421647071024703 in eastern Massachusetts, 2005–7 (Smith and 

Granato, 2010), and from three bridge-deck-monitoring stations (422108071052501, 422025071154501, and 

421247071470201) in this study, eastern Massachusetts, 2014–16. Locations of stations are shown on figure 1.

samples, which have significantly lower concentrations of SS, 

also was proportionally lower than the concentrations of PN 

in the bridge-runoff samples. Therefore, much of the difference between concentrations of TN in the highway and bridge 

datasets may in fact be realistic.

Sediment Quality

The quality of bridge-deck sediment from State 

Route 2A in Boston, Interstate 90 near Weston, and State 

Route 20 near Quinsigamond Village was compared to the 

sediment quality measured in three composite samples of 

highway sediment collected from monitoring stations on 

State Route 2 in Littleton (USGS stations 423027071291301, 

423027071291302), Interstate 495 in Boxborough 

(USGS stations 422821071332001, 422716071343901), 

and Interstate 495 in Waltham, Mass. (USGS stations 422420071153302, 422620071153301) (Smith and 

Granato, 2010) (fig. 1). For each of the three particle-size 

fractions, concentrations of TP in bridge sediment were lower 

than concentrations measured in highway sediment (fig. 24). 

Concentrations of TP in highway sediment were 3 to 5 times 

greater in the fine fraction and 6 to 40 times greater in the 

intermediate and coarse fractions compared to concentrations 

in the same size fractions of bridge-deck sediment. Except 

for concentrations of Cr, concentrations of total-recoverable 

metals in the fine fraction of bridge-deck sediment were similar (within 50 percent) to the concentration in the same size 

fraction of highway sediment (fig. 24A). Concentrations of Cr 

were 5 to 17 times greater in the intermediate and coarse fraction of highway sediment in comparison to concentrations in 

the same respective size fractions in bridge sediment samples 

(figs. 24B–C). Concentrations of Cu were as much as 49 times 

greater in the intermediate fraction of highway sediment in 

comparison to concentrations in the bridge sediment samples 

(fig. 24B); Cu was not detected in samples containing the 

coarse size fraction of bridge sediment.

Although the quality of sediment for each size fraction is 

important, it is necessary to consider the proportion of sediment in each particle-size fraction within the sample. As was 

previously discussed in the “Suspended Sediment” section, the 

concentration of SS, as well as the particle-size distribution, 

in samples of bridge-deck runoff was significantly different 

compared to the SS concentration and particle-size distribution 

in composite samples of highway runoff. To better illustrate 

the relation, a 1-kilogram synthetic sample was estimated on 

the basis of the mean SS particle-size distribution and mean 
Example Bridge-Deck Runoff Simulations 43

0.01

0.1

1

10

30

Nutrient concentration, in milligrams per liter

Highway Bridge Highway Bridge

EXPLANATION

(94)

(96) (150) (96) (149)

Total phosphorus Total nitrogen

Highway sediment

10th percentile

25th percentile

75th percentile

90th percentile

Median

Maximum

Minimum

Mean

Bridge-deck sediment

Count of samples

0.005

Figure 23. Distribution of total phosphorus and total nitrogen in composite samples of highway runoff 

collected at USGS stations 423027071291301, 423027071291302, 423016071431501, 422821071332001, 

422716071343901, 422420071153302, 422620071153301, and 421647071024703 in eastern Massachusetts, 

2005–7 (Smith and Granato, 2010), and from three bridge-deck-monitoring stations (422108071052501, 

422025071154501, and 421247071470201) in this study, eastern Massachusetts, 2014–16. Locations of 

stations are shown on figure 1.

associated constituent concentration for each size fraction 

(eq. 2; table 15) for bridge deck and highway SS (fig. 25). This 

simulated sample indicated that the concentrations of TP and 

trace metals associated with SS in highway runoff were higher 

than concentrations in the same mass of bridge-deck sediment. 

The higher concentrations of TP and trace metals (except for 

Al) associated with the highway sediment were the result 

of a higher proportion of fine to coarse sediment compared 

to that in bridge-deck sediment and the greater affinity for 

phosphorus and trace metals to associate with the fine size 

fraction (fig. 24).

These data demonstrated that the distribution of sediment particle size and associated chemical concentrations 

in bridge-deck sediment was different compared to highway 

sediment quality, and that Massachusetts highway-runoff data 

cannot reliably be used to estimate constituent concentrations 

and yields of nutrients and trace metals from bridge decks. 

Where bridge-deck runoff is treated by catch basins or other 

BMPs that provide similar sediment removal characteristics 

as present in the highway setting, some trace metal yields for 

both highway and bridge decks may be similar. As discussed 

earlier, concentrations associated with specific particle-size 

classes for many constituents, except for TP, Cd, Cr, and Cu, 

were similar in highway and bridge sediments. Therefore, reasonable planning-level concentrations for many trace metals 

in bridge-deck runoff can be derived from known concentrations of specific trace metals associated with sediments from 

both studies (Smith and Granato, 2010) and from the average 

gradated concentrations of SS in the composite samples of 

runoff collected from the bridges in this study in the absence 

of site-specific data.

Example Bridge-Deck Runoff 

Simulations

SELDM was developed to indicate the risk for stormwater concentrations, flows, and loads to be larger than userselected water-quality goals, the potential need for mitigation 

measures, and the potential effectiveness of such measures for 

reducing these risks (Granato, 2013). SELDM is a stochastic model because it uses Monte Carlo methods to produce 

the random combinations of input variable values needed to 

generate the stochastic population of values for each component variable. Results are ranked and plotting positions 

are calculated to indicate the level of risk as a probability 

of occurrence. SELDM is designed to provide long-term 

planning-level estimates, which are defined as the results of 

analyses used to evaluate alternative management measures. 
44 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

0.1

1

10

100

1,000

10,000

15,000

Concentration of constituent in sediment 

less than 0.0625 millimeter in diameter,

in milligrams per kilogram

State Route 20 near Quinsigamond Village

Interstate 90 near Weston

State Route 2A in Boston

A

B

Concentration of constituent in sediment 

greater than or equal to 0.0625 to 0.25 millimeter

in diameter, in milligrams per kilogram

Phosphorus

Aluminum

Barium

Cadmium

Chromium

Copper

Lead

Manganese

Nickel

Zinc

Arsenic

Phosphorus

Aluminum

Barium

Cadmium

Chromium

Copper

Lead

Manganese

Nickel

Zinc

Arsenic

C

EXPLANATION

Median

Maximum

Minimum

Highway sediment

Bridge sediment location

0.1

1.0

10.0

100.0

1,000.0

10,000.0

100,000.0

Concentration of constituent in sediment 

greater than 0.25 millimeter in diameter,

in milligrams per kilogram

0.1

1

10

100

1,000

10,000

15,000

Phosphorus

Aluminum

Barium

Cadmium

Chromium

Copper

Lead

Manganese

Nickel

Zinc

Arsenic

Figure 24. Concentrations of selected elements 

associated with sediment A, less than 0.0625 

millimeter (mm) in diameter; B, greater than or 

equal to 0.0625 mm to 0.25 mm in diameter; and C, 

greater than 0.25 mm in diameter in three bridgedeck composite samples collected on State Route 

2A in Boston (422108071052501), Interstate 90 near 

Weston (422025071154501), and State Route 20 near 

Quinsigamond Village (421247071470201) in eastern 

Massachusetts, 2015–16, in relation to the range 

of concentrations of selected elements associated 

with the same grain sizes in highway-runoff 

sediment from USGS stations 423027071291301, 

423027071291302, 422821071332001, 

422716071343901, 422420071153302, and 

422620071153301 in eastern Massachusetts, 2005–7 

(Smith and Granato, 2010). Locations of stations are 

shown on figure 1.
Example Bridge-Deck Runoff Simulations 45

Highway sediment greater than

0.25 millimeter in diameter

Highway sediment greater than or equal

 to 0.0625 to 0.25 millimeter in diameter

Highway sediment less than 

0.0625 millimeter in diameter

Bridge-deck sediment less than

0.0625 millimeter in diameter

Bridge-deck sediment greater than or equal

 to 0.0625 to 0.25 millimeter in diameter

Bridge-deck sediment greater than

0.25 millimeter in diameter

 EXPLANATION

Phosphorus

Aluminum

Barium

Cadmium

Chromium

Copper

Lead

Manganese

Nickel

Zinc

Arsenic

0.1

1

10

100

1,000

10,000

20,000

Concentration, in milligrams per kilogram

Figure 25. The distribution of concentrations of selected constituents in 1 kilogram of sediment estimated on the 

basis of the mean concentration of each constituent associated with three particle-size ranges in highway sediment 

collected from USGS stations 423027071291301, 423027071291302, 422821071332001, 422716071343901, 422420071153302, 

and 422620071153301 in eastern Massachusetts in 2005–7 (Smith and Granato, 2010) and from bridges on State Route 2A 

(422108071052501) in Boston, Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village 

(421247071470201) in eastern Massachusetts, 2015–16, and from the mean particle-size distribution of suspended sediment 

in composite samples of runoff collected at each location. Locations of stations are shown on figure 1.

Planning-level estimates are recognized to include substantial 

uncertainties, commonly of orders of magnitude (Barnwell 

and Krenkel, 1982; Marsalek, 1991; Granato, 2013). These 

uncertainties are especially applicable to stormwater issues 

because measured flows and concentrations commonly vary 

by several orders of magnitude, even within monitoring studies with few samples. The SELDM analyses for this study 

documents the results of simulations that can be used to assess 

the potential effects of various input concentration statistics on 

simulated concentrations, provide an example concentration 

risk analysis, and produce estimates of long-term yields of SS, 

TP, and TN. 

Representativeness of Selected Statistics

Runoff-concentration statistics are used by SELDM to 

perform the long-term simulations. The dataset collected for 

this study has more event-mean composites than most other 

datasets (Granato and Cazenas, 2009; Smith and Granato, 

2010), but generating the long-term simulated record dataset 

required extrapolation beyond the percentiles of the original 

data. Concentrations of TP, TN, and SS in bridge-deck samples 

collected in this study ranged by about 2 orders of magnitude 

for TP and TN (ranged from 0.09 to 7.02 mg/L and 0.36 to 

29 mg/L, respectively) and almost 4 orders of magnitude for 

SS (ranged from 44 to 142,000 mg/L) (table 11). Examples of 

maximum or typical concentrations of phosphorus, nitrogen, 

and sediment species in environmental samples recorded in the 

literature also are shown in table 17.

Concentrations of various species of nitrogen and sediment are shown in table 17 because these other species are 

more commonly measured in highway- and urban-runoff studies. For example, the National Stormwater Quality Database 

(Pitt and others, 2015) contains 6,156 total Kjeldahl nitrogen 

(TKN) measurements but only 694 TN concentrations. Similarly, the International BMP Database contains 21,964 total 

suspended solids measurements but only 1,384 SS concentrations. The maximum TN and TP concentrations measured in 

this study were comparable to maximum nitrogen and phosphorus concentrations in other highway and urban runoff studies and to weak (low-level) human wastewater concentrations 

(table 17). The maximum SS concentrations, however, were 

2 to 17 times the maximum concentrations measured in other 

highway and urban runoff studies.

In SELDM, concentrations are simulated by using the 

frequency-factor method (eq. 3). The frequency factor is calculated using the following equation:
46 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Table 17. Examples of maximum or typical concentrations of phosphorus, nitrogen, and sediment species in environmental samples 

recorded in the literature.

[The count in the dataset is provided to compare availability of different water-quality parameters. Precise constituent name parameter codes are not provided 

because several of the sources provide commonly used constituent names but do not specify the exact definitions. The alpha-numeric identifiers starting with “p” 

are the U.S. Geological Survey parameter codes. mg/L, milligram per liter; N, nitrogen; total nitrogen (p00600), nitrate plus nitrite (p00631); total Kjeldahl nitrogen 

(p00625); P, phosphorus; total phosphorus (p00665); SSC, suspended sediment concentration (p80154); TSS, total suspended solids (p00530); EPA, U.S. Environmental Protection Agency; NA, not applicable; HRDB, Highway-Runoff Database, version 1.0.0a; BMPDB, International Stormwater Best Management Practices 

Database, version 2016-11-17; NSQD, National Stormwater Quality Database, version 4.02; SWQDM, Surface-Water Quality Data Miner; EPA, U.S. Environmental Protection Agency]

Constituent Water-quality matrix Value type Concentration,

in mg/L

Count in 

Dataset Source of information

Nitrogen as N

Total nitrogen Highway runoff Maximum 

(State Route 2A)

29 52 This study

Total nitrogen Highway runoff Maximum 

(Interstate 90)

17 46 This study

Total nitrogen Highway runoff Maximum 

(State Route 20)

18 51 This study

Total nitrogen Highway runoff Maximum 6.1 164 HRDB, Smith and Granato, 2010

Nitrite plus nitrate Highway runoff Maximum 9 412 HRDB, Smith and Granato, 2010

Total Kjeldahl nitrogen Highway runoff Maximum 36 1,410 HRDB, Smith and Granato, 2010

Total nitrate Highway runoff Maximum 48 1,055 HRDB, Smith and Granato, 2010

Total nitrogen Urban runoff Maximum 53 12,001 BMPDB, http://www.bmpdatabase.org/

Total nitrogen Urban runoff Maximum 90.1 694 NSQD, Pitt and others, 2015

Total Kjeldahl nitrogen Urban runoff Maximum 200 16,195 BMPDB, http://www.bmpdatabase.org/

Total Kjeldahl nitrogen Urban runoff Maximum 940 6,156 NSQD, Pitt and others, 2015

Total nitrogen Human wastewater Typical (weak) 20 NA Peavy and others, 1985

Total nitrogen Human wastewater Typical (medium) 40 NA Peavy and others, 1985

Total nitrogen Human wastewater Typical (strong) 85 NA Peavy and others, 1985

Total nitrogen Human wastewater Typical (weak) 26 NA Gross, 2005

Total nitrogen Human wastewater Typical (medium) 60 NA Gross, 2005

Total nitrogen Human wastewater Typical (strong) 75 NA Gross, 2005

Total nitrogen Livestock waste slurry Typical cattle 3,000 NA Hooda and others, 2000

Total nitrogen Livestock waste slurry Typical pig 5,000 NA Hooda and others, 2000

Total nitrogen Livestock liquid manure Typical hog 2,645 NA Brown, 2013

Total nitrogen Livestock liquid manure Typical dairy 1,601 NA Brown, 2013

Total nitrogen Livestock liquid manure Typical beef 1,543 NA Brown, 2013

Total nitrogen Livestock liquid manure Typical poultry 5,567 NA Brown, 2013

Total nitrogen Receiving water Maximum 1,500 50,160 SWQDM, Granato and others, 2009

Total nitrogen Receiving water Maximum (EPA 

Ecoregion 59)

45 3,932 SWQDM, Granato and others, 2009
Example Bridge-Deck Runoff Simulations 47

Table 17. Examples of maximum or typical concentrations of phosphorus, nitrogen, and sediment species in environmental samples 

recorded in the literature.—Continued

[The count in the dataset is provided to compare availability of different water-quality parameters. Precise constituent name parameter codes are not provided 

because several of the sources provide commonly used constituent names but do not specify the exact definitions. The alpha-numeric identifiers starting with “p” 

are the U.S. Geological Survey parameter codes. mg/L, milligram per liter; N, nitrogen; total nitrogen (p00600), nitrate plus nitrite (p00631); total Kjeldahl nitrogen 

(p00625); P, phosphorus; total phosphorus (p00665); SSC, suspended sediment concentration (p80154); TSS, total suspended solids (p00530); EPA, U.S. Environmental Protection Agency; NA, not applicable; HRDB, Highway-Runoff Database, version 1.0.0a; BMPDB, International Stormwater Best Management Practices 

Database, version 2016-11-17; NSQD, National Stormwater Quality Database, version 4.02; SWQDM, Surface-Water Quality Data Miner; EPA, U.S. Environmental Protection Agency]

Constituent Water-quality matrix Value type Concentration,

in mg/L

Count in 

Dataset Source of information

Phosphorus as P

Total phosphorus Bridge-deck runoff Maximum 

(State Route 2A)

7.02 53 This study

Total phosphorus Bridge-deck runoff Maximum 

(Interstate 90)

3.16 46 This study

Total phosphorus Bridge-deck runoff Maximum 

(State Route 20)

6.02 51 This study

Total phosphorus Highway runoff Maximum 17 1,439 HRDB, Smith and Granato, 2010

Total phosphorus Urban runoff Maximum 80.2 7,232 NSQD, Pitt and others, 2015

Total phosphorus Urban runoff Maximum 80.2 20,258 BMPDB, http://www.bmpdatabase.org/

Total phosphorus Human wastewater Typical (weak) 4 NA Peavy and others, 1985

Total phosphorus Human wastewater Typical (medium) 8 NA Peavy and others, 1985

Total phosphorus Human wastewater Typical (strong) 15 NA Peavy and others, 1985

Total phosphorus Human wastewater Typical (weak) 6 NA Gross, 2005

Total phosphorus Human wastewater Typical (medium) 10 NA Gross, 2005

Total phosphorus Human wastewater Typical (strong) 12 NA Gross, 2005

Total phosphorus Livestock waste slurry Typical cattle 520 NA Hooda and others, 2000

Total phosphorus Livestock waste slurry Typical pig 1,310 NA Hooda and others, 2000

Total phosphorus Livestock liquid manure Typical hog 814 NA Brown, 2013

Total phosphorus Livestock liquid manure Typical dairy 369 NA Brown, 2013

Total phosphorus Livestock liquid manure Typical beef 317 NA Brown, 2013

Total phosphorus Livestock liquid manure Typical poultry 1,924 NA Brown, 2013

Total phosphorus Receiving water Maximum 640 246,403 SWQDM, Granato and others, 2009

Total phosphorus Receiving water Maximum (EPA 

Ecoregion 59)

9 10,644 SWQDM, Granato and others, 2009
48 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Table 17. Examples of maximum or typical concentrations of phosphorus, nitrogen, and sediment species in environmental samples 

recorded in the literature.—Continued

[The count in the dataset is provided to compare availability of different water-quality parameters. Precise constituent name parameter codes are not provided 

because several of the sources provide commonly used constituent names but do not specify the exact definitions. The alpha-numeric identifiers starting with “p” 

are the U.S. Geological Survey parameter codes. mg/L, milligram per liter; N, nitrogen; total nitrogen (p00600), nitrate plus nitrite (p00631); total Kjeldahl nitrogen 

(p00625); P, phosphorus; total phosphorus (p00665); SSC, suspended sediment concentration (p80154); TSS, total suspended solids (p00530); EPA, U.S. Environmental Protection Agency; NA, not applicable; HRDB, Highway-Runoff Database, version 1.0.0a; BMPDB, International Stormwater Best Management Practices 

Database, version 2016-11-17; NSQD, National Stormwater Quality Database, version 4.02; SWQDM, Surface-Water Quality Data Miner; EPA, U.S. Environmental Protection Agency]

Constituent Water-quality matrix Value type Concentration,

in mg/L

Count in 

Dataset Source of information

Suspended sediment and solids

SSC Highway runoff Maximum 

(State Route 2A)

60,000 55 This study

SSC Highway runoff Maximum 

(Interstate 90)

142,000 54 This study

SSC Highway runoff Maximum 

(State Route 20)

38,700 54 This study

SSC Highway runoff Maximum 8,580 431 HRDB, Smith and Granato, 2010

TSS Highway runoff Maximum 5,100 2,253 HRDB, Smith and Granato, 2010

SSC Urban runoff Maximum 8,580 1,384 BMPDB, http://www.bmpdatabase.org/

TSS Urban runoff Maximum 10,505 21,964 BMPDB, http://www.bmpdatabase.org/

TSS Urban runoff Maximum 10,700 6,695 NSQD, Pitt and others, 2015

TSS Human wastewater Typical (weak) 100 NA Peavy and others, 1985

TSS Human wastewater Typical (medium) 200 NA Peavy and others, 1985

TSS Human wastewater Typical (strong) 350 NA Peavy and others, 1985

TSS Human wastewater Typical (weak) 155 NA Gross, 2005

TSS Human wastewater Typical (medium) 250 NA Gross, 2005

TSS Human wastewater Typical (strong) 330 NA Gross, 2005

SSC Receiving water Maximum (post 

volcanic eruption)

1,770,000 2,904 SWQDM, Granato and others, 2009

SSC Receiving water Maximum 

(nonvolcanic)

966,000 273,046 SWQDM, Granato and others, 2009

SSC Receiving water Maximum (EPA 

Ecoregion 59)

640 3,546 SWQDM, Granato and others, 2009
Example Bridge-Deck Runoff Simulations 49

Log(Ci) = Avg + SD × Ki

where

i is the individual simulated value, which 

ranges from one to the number of 

simulated storm events;

Ci is the ith simulated concentration;

Avg is the average of the logarithms of 

concentration;

SD is the standard deviation of the logarithms of 

concentration; and

Ki is the Pearson type III random variate. 

The calculated average value (Avg) sets the magnitude of 

the center of the simulated sample. The magnitude of the SD 

controls the variation of concentrations above and below the 

average; larger SD values will result in a larger range in simulated values. The Pearson type III random variate (Ki

), which 

is a function of the skew (Interagency Advisory Committee on 

Water Data, 1982; Chow and others, 1988; Granato, 2013), is 

the value generated by SELDM. If the skew is equal to zero, 

then Ki

 is a normal random variate. The SELDM simulations 

that use precipitation statistics from ecoregion 59 (the Northeastern Coastal Zone) result in about 1,680 events. In theory, 

the associated range of normal Ki values would be about plus 

or minus 3.24 for this number of events. SELDM, however, 

generates Ki

 values randomly, and more extreme Ki values 

may be generated in any simulation. If the skew is nonzero 

then the Ki

 values will be skewed (Interagency Advisory Committee on Water Data, 1982; Chow and others, 1988), which 

increases the probability that extreme Ki

 values may be generated (Granato, 2013). 

To understand why very extreme outliers may not have 

very extreme percentiles, it is important to understand that the 

plotting positions written to the output files by SELDM are the 

sample statistics, calculated from the ranks of the output values rather than the population statistics, which are the percentiles based on the random number that is generated. However, 

it is the magnitude of SD values that controls the effect of Ki

values on simulated concentrations. Therefore, statistics used 

for simulation data must be carefully selected because these 

high concentrations, measured within a 2-year study, will be 

used to simulate a 29 to 30 year period. If conditions during 

the study period resulted in uncharacteristically high or low 

concentrations, then the probability of occurrence based on 

sample statistics may be inflated in comparison to the actual 

population statistics. For example, if a streamgage is established and operated for 5 years and the 100-year flood (a flood 

magnitude with an actual exceedance risk of about 1 percent) 

occurs, the sample statistics would indicate that that measured 

flood-flow magnitude would have an exceedance risk of about 

17 percent based on the 5 available annual-flood values.

The average, SD, and skew of the common (base 10) logarithms of event-mean concentrations in composite samples of 

bridge-deck runoff calculated by using the traditional statistics 

and the robust statistics are shown in table 18. The medians 

of each statistic, which in theory may be the best estimates of 

(3) runoff quality from any randomly selected unmonitored bridge 

in the State, are also shown in table 18. The median of the 

statistics may include the average from one site, the SD from 

another, and the skew from the third site; therefore, concentrations simulated by using these statistics may not represent the 

data from any particular site. Statistics for the lumped data, 

which may better represent the exceedance risk of the highest and lowest concentrations if the individual site datasets 

come from the same population of bridge-runoff quality (the 

samples from different bridges are not statistically different), 

also are documented in table 18. Concentrations of SS and 

TP in sample sets for the bridge in this study were not significantly different. Concentrations of nitrogen in samples of 

bridge-deck runoff were lowest at State Route 2A in Boston 

and highest at State Route 20 near Quinsigamond Village; concentrations of TN were significantly higher for State Route 20 

than for the other two bridges (table 14). As with the median 

statistics, simulations made by using the lumped statistics may 

not represent values from a particular site, but if lumping the 

data represents the probability of exceedance for the highest 

and lowest concentrations, results may be more realistic than 

statistics from the individual datasets.

Information about the uncertainty in the sample statistics 

is also included in table 18. The standard error of the estimate 

and the 95-percent confidence interval of the average, SD, 

and skew of the traditional statistics were calculated by using 

methods specified by the Interagency Advisory Committee on 

Water Data (1982). The 95-percent confidence intervals for 

each statistic indicate if the statistics calculated by using the 

different methods and by using the lumped data are significantly different from the values calculated by using traditional 

methods. The values of the average and SD calculated by 

using the robust alternative methods were within the 95-percent confidence intervals of the associated statistics calculated 

by using traditional methods. The values of Pearson’s second 

skew calculated by using the robust statistics also were within 

the 95-percent confidence intervals of skews calculated by 

using traditional methods, except for the second skew of TN 

and TP for the State Route 2A monitoring station and TN for 

the lumped data (table 18). Statistics for the median values 

were within the 95-percent confidence intervals of the other 

statistics calculated with data from individual sites (table 18). 

Although the statistics were not significantly different, there 

were substantial differences among the values that affected the 

outcomes of long-term simulations.

The effects of selected statistics (table 18) on simulated 

TN concentrations are shown in figure 26. The effects of the 

large positive skew of the traditional TN statistics for data 

from the monitoring stations on State Routes 20 and 2A are 

evident in the fact that the distribution of simulated concentrations was concave up, which contributed to the high outlier 

values of 130 and 245 mg/L (fig. 26A). These values were 

higher than maximum values of TN in published highway and 

urban runoff datasets (table 17). The simulated values were 

comparable to typical values of “strong” human wastewater 

but were within the maximum values for urban runoff (table 
50 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 18.

Average, standard deviation, and skew of the common logarithms of event-mean concentrations in composite samples of bridge-deck runoff collected from 

State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16, as calculated by using traditional and robust methods.[Locations of stations are shown in figure 1. The standard error of the estimate (SEE) and 95-percent confidence interval (CI) were calculated by using equations in the Interagency Advisory Committee on Water Data (1982) Bulletin 17B. The calculated statistic is not significantly different from zero if the 95-percent CI crosses zero. Numbers have been rounded to three significant figures. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. N, nitrogen; P, phosphorus]

Bridge-monitoring station Count Median Average Average1 Standard deviation Standard deviation1 Skew Skew2 Skew3 Value SEE 95-percent CI Value SEE 95-percent CI Value SEE 95-percent CI

Total nitrogen as N (p00600) (milligrams per liter)

State Route 2A 52 0.161 0.236 0.051 0.134–0.338 0.191 0.370 0.052 0.266–0.474 0.307 1.17 0.330 0.507–1.833 0.608 0.293

Interstate 90 46 0.255 0.317 0.046 0.224–0.410 0.305 0.311 0.038 0.234–0.388 0.305 0.697 0.350 -0.008–1.402 0.598 0.492

State Route 20 51 0.447 0.474 0.048 0.378–0.570 0.472 0.340 0.034 0.272–0.408 0.362 0.064 0.333 -0.605–0.733 0.238 0.207

Median of sites 51 0.255 0.317 0.048 0.221–0.413 0.305 0.340 0.039 0.262–0.418 0.307 0.697 0.333 0.028–1.366 0.547 0.293

Lumped data 149 0.301 0.342 0.029 0.285–0.399 0.311 0.354 0.023 0.309–0.399 0.338 0.569 0.199 0.176–0.962 0.347 0.089

Total phosphorus as P (p00665) (milligrams per liter)

State Route 2A 53 -0.161 -0.123 0.047 -0.217– -0.029 -0.155 0.345 0.041 0.263–0.427 0.322 0.805 0.327 0.149–1.461 0.330 0.056

Interstate 90 46 -0.297 -0.280 0.041 -0.363– -0.197 -0.279 0.280 0.031 0.218–0.342 0.256 0.464 0.350 -0.241–1.169 0.182 0.211

State Route 20 51 -0.229 -0.196 0.054 -0.304– -0.088 -0.212 0.388 0.040 0.308–0.468 0.395 0.299 0.333 -0.370–0.968 0.255 0.129

Median of sites 51 -0.229 -0.196 0.048 -0.292– -0.100 -0.212 0.345 0.037 0.271–0.419 0.322 0.464 0.333 -0.205–1.133 0.287 0.129

Lumped data 150 -0.240 -0.196 0.028 -0.251– -0.141 -0.214 0.346 0.022 0.303–0.389 0.323 0.557 0.198 0.166–0.948 0.382 0.241

Suspended sediment concentration (p80154) (milligrams per liter)

State Route 2A 54 3.30 3.38 0.076 3.228–3.532 3.36 0.555 0.06 0.435–0.675 0.553 0.602 0.325 -0.050–1.254 0.432 0.325

Interstate 90 54 3.31 3.35 0.078 3.194–3.506 3.34 0.571 0.055 0.461–0.681 0.559 0.085 0.325 -0.567–0.737 0.210 0.161

State Route 20 55 3.17 3.19 0.109 2.971–3.409 3.20 0.806 0.077 0.652–0.960 0.871 0.076 0.322 -0.570–0.722 0.074 0.103

Median of sites 54 3.30 3.35 0.078 3.194–3.506 3.34 0.571 0.055 0.461–0.681 0.559 0.085 0.325 -0.567–0.737 0.263 0.161

Lumped data 163 3.29 3.30 0.051 3.199–3.401 3.30 0.656 0.036 0.585–0.727 0.665 0.038 0.091 -0.142–0.218 0.046 0.045

1Estimated by using robust regression on order statistics (Helsel and Hirsch, 2002; Granato, 2006).2Estimated by using the robust Pearson’s second coefficient of skewness (Haan, 1977) with the commonly used average and standard deviation.3Estimated by using the robust Pearson’s second coefficient of skewness (Haan, 1977) with the robust regression on order statistics for the average and standard deviation.
Example Bridge-Deck Runoff Simulations 51

0.1

1

10

100

1,000

A

Total nitrogen, in milligrams per liter

0.03 99.9799.999.59998959080706050403020105210.50.1

0.1

1

10

100

1,000

B.

Total nitrogen, in milligrams per liter

Percentage of storm events in which the event-mean 

concentration was equaled or exceeded

EXPLANATION

 State Route 2A (422108071052501) data

 Interstate 90 (422025071154501) data

 State Route 20 (421247071470201) data

 Lumped data

 Simulation of State Route 2A data 

 Simulation of Interstate 90 data 

 Simulation of State Route 20 data 

 Simulation of runoff quality with the median

 Simulation of lumped data

Figure 26. Measured total nitrogen concentrations and concentrations simulated by using A, traditional statistics and B, 

statistics calculated by using robust alternative methods. The selected statistics are shown in table 18.
52 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

17). These values were, however, well within TKN concentrations measured in urban runoff. In theory TN concentrations 

should be greater than or equal to TKN concentrations; the 

higher TKN values reflect the fact that there are many more 

TKN than TN values in the highway and urban-runoff databases (table 17). The high urban-runoff concentrations may 

represent the effects of fertilizer application in urban areas that 

were not typical of conditions within the highway right-ofway. The simulations performed using the lumped data and 

simulated statistics shown in table 18 indicated that the two 

largest measured values (19 and 29 mg/L) from the current 

study may be outliers with actual exceedance probabilities 

that were smaller than would be assumed by the sample size. 

In comparison, the robust skew statistic for data from the 

monitoring station on State Route 20 was higher than the 

skew calculated by using traditional methods, which resulted 

in a high outlier for the robust alternative simulation for the 

bridge on State Route 20 (fig. 26B). In this case, the maximum 

simulated value for bridge-deck runoff from State Route 20 

was 253 mg/L. With the exception of the two outliers (130 and 

245 mg/L) in the State Route 20 monitoring station dataset, 

the robust alternatives produced simulated populations that 

were representative of the data and that were more consistent 

with highway-runoff maxima from other studies in the HRDB 

than the simulations done with the classic statistics. Unlike 

urban areas, which may be fertilized, bridge decks may have 

a limited source of nitrogen controlling the upper bounds of 

possible values. However, as data from urban runoff, human 

wastewater, agricultural wastewater, precipitation, and receiving waters indicated, the simulated values were well within 

concentrations expected from dissolved and suspended matter 

in flowing water (table 17). 

The effects of selected statistics (table 18) on simulated 

TP concentrations are shown in figure 27. TP skew values 

were small and, with the exception of State Route 2A monitoring station dataset and the lumped dataset, were not significantly different from zero. Therefore, unlike TN, neither the 

data nor the simulation populations were strongly concave up 

on figure 27 and extreme values were not generated. Maximum values simulated with traditional and robust statistics 

were well below the maximum for urban runoff (table 17), 

which probably included runoff from fertilized areas. Maximum values simulated with traditional statistics, however, 

were between 10 and 20 mg/L and so were comparable to 

typical values for strong human wastewater. Concentrations 

simulated by using the robust alternative statistics were well 

within the existing highway maximum in table 17. The simulations performed using the lumped data and simulated statistics 

shown in table 18 indicated that the two largest measured values measured at the State Route 2A and State Route 20 monitoring stations may have been outliers with actual exceedance 

probabilities that were smaller than would be assumed by the 

sample size. As with TN, comparison with data from urban 

runoff, human wastewater, agricultural wastewater, and receiving waters indicated the simulated TP values were well within 

concentrations expected from dissolved and suspended matter 

in flowing water (table 17). 

The effects of selected statistics (table 18) on simulated 

SS concentrations are shown in figure 28. In this case, the 

maximum simulated values of SS were for the State Route 20 

dataset (more than 1,000,000 mg/L). With the exception of the 

State Route 2A data, the skews of the SS samples were very 

small and none of the skew values were significantly different 

from zero. In this case, the relatively large SD for the State 

Route 20 dataset (table 18) was the statistic that was driving 

these extreme values. The SDs for the traditional and robust 

alternative statistics were large. A few high outliers did not 

cause these relatively large SDs. The large range and the relatively high percentage of SS concentrations below 300 mg/L at 

State Route 20 caused the large SDs calculated by using both 

methods and therefore the large simulated values. Although 

concentrations approaching or exceeding 1,000,000 mg/L may 

seem unrealistic, receiving-water monitoring data indicated 

that such hyperconcentrated flows did occur in environments 

with high-energy flows and a large supply of erodible sediments (table 17). Although the bridge deck may become sediment limited in a long or large storm, figures 18 and 20 and 

tables 5 and 6 indicate that a large amount of sediment may be 

available for wash off. The available sediment could produce 

large concentration in a small but intense storm. The statistics 

from this dataset indicated that the maximum measured value 

of 140,000 mg/L had a smaller percent exceedance than would 

be calculated from the sample size (fig. 28), but statistics 

indicated that this large value may have an exceedance risk 

representing a 1-in-10- or a 1-in-20-year event. 

Because the traditional and robust alternative statistics 

produced extreme concentrations in these simulations, additional simulations were made by using L-moment statistics, 

which are not as influenced by extreme values in the data 

as other calculation methods (Hosking, 1990; Stedinger and 

others, 1993). The average, SD, and skew of the common 

(base 10) logarithms of event-mean concentrations in composite samples of bridge-deck runoff calculated by using 

L-moment statistics are documented in table 19. The medians 

of each L-moment statistic and the L-moment statistics for the 

lumped data are also shown in table 19. The L-moment averages were equal to the averages calculated by using traditional 

methods (table 18) because they were calculated with the same 

equations. The L-moment SDs and skew statistics in table 19, 

however, were substantially lower than the analogous statistical values shown in table 18 because L-moments are linear 

combinations of probability-weighted moments. Examination 

of simulation results, however, indicated that the L-moment 

statistics are not suitable for simulating runoff concentrations 

with the frequency-factor method (fig. 29). No extreme values 

were generated, but the simulated values did not represent 

measured values because the SDs and skews did not result in 

simulated values that were comparable to measured values. 

Analysis of the calculated statistics and simulated values 

indicated that if the median of statistics from the monitored sites were used, neither the traditional nor the robust 
Example Bridge-Deck Runoff Simulations 53

0.10.03 99.97 0.01

0.1

1

10

100

B

Total phosphorus, in milligrams per liter

Percentage of storm events in which the event-mean

concentration was equaled or exceeded

0.01

0.1

1

10

100 A

Total phosphorus, in milligrams per liter

99.999.599989590807060504030201051 20.5

EXPLANATION

 State Route 2A (422108071052501) data

 Interstate 90 (422025071154501) data

 State Route 20 (421247071470201) data

 Lumped data

 Simulation of State Route 2A data 

 Simulation of Interstate 90 data 

 Simulation of State Route 20 data 

 Simulation of runoff quality with the median of traditional statistics

 Simulation of lumped data

Figure 27. Measured total phosphorus concentrations and concentrations simulated by using A, traditional statistics and B, statistics 

calculated by using robust alternative methods. The selected statistics are shown in table 18.
54 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

10

100

1,000

10,000

100,000

1,000,000

1

5,000,000

A

Suspended sediment concentration, in milligrams per liter

Percentage of storm events in which the event-mean

concentration was equaled or exceeded

10

100

1,000

10,000

100,000

1,000,000

5,000,000

B

Suspended sediment concentration, in milligrams per liter

0.10.03 99.9799.999.599989590807060504030201051 20.5

EXPLANATION

 State Route 2A (422108071052501) data

 Interstate 90 (422025071154501) data

 State Route 20 (421247071470201) data

 Lumped data

 Simulation of State Route 2A data 

 Simulation of Interstate 90 data 

 Simulation of State Route 20 data 

 Simulation of runoff quality with the median

 Simulation of lumped data

1

Figure 28. Measured suspended sediment concentrations and concentrations simulated by using A, traditional 

statistics and B, statistics calculated by using robust alternative methods. The selected statistics are shown in table 18.
Example Bridge-Deck Runoff Simulations 55

Table 19. Average, standard deviation, and skew of the 

common logarithms of event-mean concentrations in composite 

samples of bridge-deck runoff collected from State Route 

2A in Boston (422108071052501), Interstate 90 near Weston 

(422025071154501), and State Route 20 near Quinsigamond 

Village (421247071470201) in eastern Massachusetts, 2014–16, as 

calculated by using L-moment methods.

[Locations of stations are shown in figure 1. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. 

Numbers have been rounded to three significant figures. L-moments are 

linear combinations of probability-weighted moments used for estimating 

sample statistics (Hosking, 1990; Stedinger and others, 1993). N, nitrogen; 

P, phosphorus]

Bridgemonitoring site Count Average Standard 

deviation Skew

Total nitrogen as N (p00600) (milligrams per liter)

State Route 2A 52 0.236 0.198 0.167

Interstate 90 46 0.317 0.172 0.134

State Route 20 51 0.474 0.195 0.014

Median of sites 51 0.317 0.195 0.134

Lumped data 149 0.342 0.198 0.107

Total phosphorus as P (p00665) (milligrams per liter)

State Route 2A 53 -0.123 0.192 0.140

Interstate 90 46 -0.280 0.153 0.079

State Route 20 51 -0.196 0.219 0.035

Median of sites 51 -0.196 0.192 0.079

Lumped data 150 -0.196 0.191 0.091

Suspended sediment concentration (p80154) (milligrams per liter)

State Route 2A 54 3.38 0.311 0.097

Interstate 90 54 3.35 0.320 0.042

State Route 20 55 3.19 0.466 0.022

Median of sites 54 3.35 0.32 0.042

Lumped data 163 3.30 0.372 0.009

alternative statistics produced extreme simulated values (table 

18). Analysis by robust alternatives provides information 

about the effect of outliers on sample statistics and the probable exceedance risks of such outliers. In this study, the robust 

alternative statistics were within the 95-percent confidence 

intervals of the traditional statistics. If the medians of site 

statistics are used to simulate runoff quality for risk analyses 

or to estimate annual loads, then the traditional statistics will 

be suitable for such analyses.

Runoff-Quality Risk Analysis

In Massachusetts, the National Pollutant Discharge 

Elimination System permits for stormwater runoff currently 

(2017) specify required management measures rather than 

numeric discharge limits (Massachusetts Department of 

Environmental Protection, 2013). This approach reflects the 

fact that relatively small isolated BMPs cannot meet discharge 

standards developed for actively managed municipal wastewater treatment systems; however, stringent numerical dischargequality standards have been proposed in other States (Granato 

and Jones, 2015). SELDM can be used to assess the risk of 

exceeding any proposed discharge-concentration standard with 

and without use of BMPs. In this example, numeric effluent 

criteria of 8 mg/L for TN and 1 mg/L for TP were selected as 

examples because these are published standards for discharges 

from wastewater treatment plants (WWTPs) using the best 

available technology (Massachusetts Department of Environmental Protection, 2013). 

It should be noted that bridge-deck flows also are orders 

of magnitude smaller than WWTP flows. Many of the WWTPs 

subject to numerical standards commonly discharge more than 

1 million gallons per day (Mgal/d). In this example bridgerunoff simulation, the average-annual discharge per acre of 

bridge deck was 115,497 cubic feet per year, which is equivalent to 0.0024 million gallons per day per acre during an entire 

year or about 0.037 million gallons per day per acre during 

the periods of runoff that only occur about 6.5 percent of the 

year on the basis of the input statistics used for this study. In 

comparison, about 2.6 acres of the State Route 2A bridge, the 

largest bridge in this study, is over water.

The risks for exceeding the example TN discharge 

criteria of 8 mg/L were substantial for the measured data and 

simulated long-term concentrations. The percentage of measured storm-event samples that exceeded 8 mg/L ranged from 

about 6.7 (Interstate 90 bridge) to 11 percent (State Route 20 

bridge) with an average exceedance of about 8.4 percent of 

events (fig. 30A). Bridge-deck runoff event-mean concentrations simulated by using the median of the robust statistics 

exceeded the criteria in about 5 percent of events. The differences in exceedances for the data in comparison to the values 

simulated by using the robust median statistics may indicate 

that the simulated values were conservative or that the extreme 

values occurred in the sampled events. Event-mean concentrations in BMP discharges simulated by using the generic 

(median) BMP statistics (table 10) exceeded the criteria in 

about 1.6 percent of events. Therefore, neither the simulated 

runoff nor the simulated BMP discharge quality would meet a 

water-quality criterion of 8 mg/L if the commonly used recurrence interval of one event in 3 years is applied because the 

exceedance risk for one event in 3 years was about 0.58 percent in these simulations.

The risks for exceeding the example TP discharge criteria 

of 1 mg/L were even more substantial for the measured data 

and simulated long-term concentrations than for the TN 

values. The percentage of measured storm-event samples that 

exceeded 1 mg/L ranged from about 14 (Interstate 90 bridge) 

to 30 percent (State Route 20 bridge) with an average exceedance of about 25 percent of events (fig. 30B). Bridge-deck 

runoff event-mean concentrations simulated by using the 

median of traditional statistics exceeded the 1-mg/L criterion 

in about 28 percent of events. Event-mean concentrations 
56 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

0.1

1

10

100

1,000 A

Total nitrogen, in milligrams per liter

0.01

0.1

1

10

100

B

Total phosphorus, in milligrams per liter

10

100

1,000

10,000

100,000

1,000,000

1

2,000,000 C

Suspended sediment concentration, in milligrams per liter

Percentage of storm events in which the event-mean

concentration was equaled or exceeded

0.10.03 99.9799.999.599989590807060504030201051 20.5

EXPLANATION

 State Route 2A (422108071052501) data

 Interstate 90 (422025071154501) data

 State Route 20 (421247071470201) data

 Lumped data

 L-moment simulation with State Route 2A data 

 L-moment simulation with Interstate 90 data 

 L-moment simulation with State Route 20 data 

 L-moment simulation with runoff quality with the median

 L-moment simulation with lumped data

Figure 29. Concentrations simulated by using L-moments and measured A, total nitrogen; B, total phosphorus; and 

C, suspended sediment concentrations. The selected statistics are shown in table 19.
Example Bridge-Deck Runoff Simulations 57

0.1

1

10

100

A

Example Massachusetts total nitrogen wastewater discharge

effluent limit (8 milligrams per liter)

Total nitrogen, in milligrams per liter

Percentage of storm-events in which the event mean

concentration was equaled or exceeded

EXPLANATION

Interstate 90 (422025071154501) data

State Route 20 (421247071470201) data

State Route 2A (422108071052501) data

Example Massachusetts total phosphorus wastewater

discharge effluent limit (1 milligram per liter)

Highway runoff simulated by using the median of

traditional statistics

Simulated stormwater best-management practice discharge

0.01

0.1

1

10

100

Total phosphorus, in milligrams per liter

B

0.10.03 99.9799.999.599989590807060504030201051 20.5

Figure 30. Probability plots of A, total nitrogen and B, total phosphorus, including measured concentrations, concentrations 

simulated with the median of robust statistics (table 18), and concentrations simulated by modifying the simulated highway 

runoff by using the generic best-management practice statistics (table 10).

in BMP discharges simulated by using the generic (median) 

BMP statistics (table 10) exceeded the 1-mg/L criterion in 

about 13 percent of events. These exceedances, therefore, 

did not meet the exceedance risk for one event in 3 years 

(about 0.58 percent). 

In these simulations, the BMP reduced the percentage of 

concentration exceedances of both TN and TP by more than 

one-half. Better reductions in the concentration exceedances 

may be achieved if a specific BMP, designed for nutrient 

reduction, is used instead of the median of category medians 

as used in this example. However, this simulation indicates 

that the numeric WWTP standards for these nutrients may be 

unattainable, with or without a BMP, unless greater criterion 

concentrations or a larger exceedance risk is acceptable.

Runoff-Quality Annual Yield Analyses

Eleven simulations of 29–30 years were performed with 

the same hydrologic and concentration statistics and different 
58 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

random seeds to estimate yields of TN, TP, and SS. In these 

simulations, SELDM calculated an annual yield in pound per 

acre per year of pavement for each annual-load accounting 

year. Yields were simulated for runoff from 1 acre of pavement by using precipitation statistics for ecoregion 59 (the 

Northeastern Coastal Zone) and standard highway-runoff coefficient statistics (Granato, 2013). The medians of traditional 

statistics from the three sites (table 18) were used to model 

long-term constituent yields. Doing multiple simulations with 

the same hydrologic statistics accounts for stochastic variability in the combinations of concentrations, flows, and loads that 

could occur during a long period (Granato, 2013; Granato and 

Jones, 2014, 2017).

The populations of annual yields of TN, TP, and SS from 

the 11 simulations and the average and median of the values 

for each plotting position are shown in figure 31. To construct 

this graph, plotting-position results for the 30-year simulations 

were adjusted by using linear interpolation with the normal 

variate of each plotting position percentile to calculate yields 

that were equivalent to the 29-year simulation results. The 

results of individual simulations show a tight cluster around 

the median and mean except for the highest and lowest yields 

(fig. 31). The values of large concentration outliers do not 

matter for discharge exceedance analysis once they are already 

over the criterion concentration, but an extreme concentration 

outlier may have a substantial effect on the maximum annual 

EXPLANATION

 Individual simulations

 Average

 Median

 Individual best-management practice discharge simulations

 Average best-management practice discharge simulation

Percentage of years in which the annual yield was equaled or exceeded

2

3

4

5

6

7

8

9

20

30

40

50

60

10

1

70

A

Total nitrogen yield, in pounds per acre per year

2

3

4

5

6

7

8

9

10

1

20

B

Total phosphorus yield, in pounds per acre per year

1 2 99

10,000

100,000

1,000

300,000 C

Suspended sediment yield, in pounds per acre per year

1 2 98959080706050403020105 99 1 2 98959080706050403020105 99 98959080706050403020105

Figure 31. Yields of A, total nitrogen; B, total phosphorus; and C, suspended sediment in pounds per acre per year for each 

simulated year by percentile. The graph shows the individual, average, and median yields from 11 individual simulations.
Example Bridge-Deck Runoff Simulations 59

yield. An extreme flow event (such as the rainfall volume that 

may be associated with an intense storm) also may contribute 

to a high annual yield.

The results of the analyses are shown as long-term 

average-annual runoff yields for each of the simulations 

(table 20). The long-term average-annual yield is the sum 

of the loads, per unit area, for the entire simulation period 

divided by the number of years in the simulation. The medians of the average-annual yields in the different simulations 

are presented in table 20 as the best estimator to account for 

stochastic variability from simulation to simulation. Results 

of simulations indicated that long-term average yields of TN, 

TP, and SS may be about 21.4, 6.44, and 40,600 pounds per 

acre per year, respectively. Granato and Jones (2017) did a 

similar annual yield analysis for different road classes by 

using concentration statistics from sites in Massachusetts from 

version 1.0.0a of the HRDB (Smith and Granato, 2010). They 

used the average of precipitation statistics from 11 National 

Weather Service stations in eastern Massachusetts and Rhode 

Island. The percent differences between the runoff-event precipitation statistics used by Granato and Jones (2017) and the 

precipitation statistics used in this study were about 0.85 for 

event volume, 5.2 for event duration, and 1.3 for the number 

of hours between event midpoints. The comparable yields of 

TN, TP, and SS in this current bridge-runoff study (table 20) 

were about 1.3, 3.4, and 16 times the ultra-urban highway 

yields simulated by Granato and Jones (2017), respectively. 

This comparison indicates that bridge-deck yields are not 

representative of yields from the entire road network in Massachusetts; however, bridge-deck yields may be a substantial portion of annual yields from highways in a watershed 

because the bridge-deck yields were much higher than yields 

for all the road classes simulated by Granato and Jones (2017). 

Furthermore, many bridges are over and discharge to receiving waters, whereas State roadways typically discharge to the 

local land surface. 

Runoff Treatment Analyses

Runoff treatment was analyzed to examine the potential 

effects of flow reductions and concentration reductions by 

stormwater BMPs on annual yields from bridge-deck runoff. The SELDM BMP-treatment module has provisions for 

stochastic modeling of three stormwater treatments: volume 

reduction, hydrograph extension, and water-quality treatment 

(Granato, 2013, 2014). Hydrograph extension is an important variable for dilution analyses, but the duration of BMP 

discharges does not substantially affect the total-annual yields. 

For this analysis, a generic BMP was simulated by using the 

median of treatment statistics for flow reductions, concentration reductions, and MICs from seven BMP categories with 

data from the 2012 International BMP database (Geosyntec 

Table 20. Simulated long-term average-annual bridge-deck runoff and stormwater control measure best-management practice 

discharge yields, in pounds per acre per year for total nitrogen, total phosphorus, and suspended sediment.

[The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. run, stochastic empirical loading and dilution model run 

number and median of values from the eleven runs; seed, Monte Carlo random seed; years, annual-load accounting years; N, nitrogen; lb/ac/yr, pound per acre 

per year; runoff, bridge-deck runoff; BMP discharge, stormwater control measure best-management practice discharge; P, phosphorus; NA, not applicable]

Run Seed Years

Total nitrogen as N (p00600),

in lb/ac/yr

Total phosphorus (p00665) as P,

in lb/ac/yr

Suspended sediment (p80154),

in lb/ac/yr

Runoff BMP discharge Runoff BMP discharge Runoff BMP discharge

1 11,093 30 21.4 10.6 6.44 2.88 36,900 3,980

2 856 29 22.4 10.9 6.29 2.77 41,400 4,470

3 2,519 29 21.5 10.3 6.46 2.89 38,000 4,270

4 3,366 29 20.4 9.72 6.30 2.70 40,700 5,100

5 3,418 29 23.7 11.1 6.77 2.76 40,600 4,790

6 6,996 30 21.7 10.7 6.04 2.63 41,100 4,360

7 10,482 30 20.5 9.47 6.60 2.80 35,400 4,120

8 8,418 29 20.7 9.63 6.56 2.77 41,000 4,260

9 4,370 29 20.8 9.84 6.51 2.84 34,700 3,860

10 5,240 29 20.3 10.0 6.23 2.56 45,400 4,890

11 7,022 29 21.4 10.1 6.12 2.62 38,000 4,070

Median NA NA 21.4 10.1 6.44 2.77 40,600 4,270
60 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

Consultants and Wright Water Engineers, 2016) (table 10); 

BMP yields for TN, TP, and SS from bridge-deck runoff are 

shown in table 20.

The results of the yield analyses of TN, TP, and SS for 

bridge-deck runoff are shown in figure 31. The reduction in 

annual yields at each percentile represents the effects of flow 

and concentration reduction. Although average-annual yields 

with and without the BMP treatments are shown, these values 

represent results of stochastic simulations for each runoff 

event, not application of average performance statistics for the 

entire simulation period (Granato, 2013, 2014). Individually, 

flow reduction and concentration reduction have a substantial 

effect on the annual yields of TP in these simulations; however, the BMPs can produce excess flows and concentrations 

in some storms, which can reduce or eliminate the combined 

effectiveness for those storms. For example, if concentrations 

are reduced in a given storm but the discharge flow is larger 

than the runoff inflow, then the total BMP discharge load may 

be greater than or equal to the runoff load for a particular 

storm event. Increases in flow can be caused by carryover 

from previous storms and (or) groundwater discharge to the 

BMP (especially for wet BMPs). Resuspension of previously 

deposited constituents can cause increases in discharge concentrations. Results for this runoff-treatment analysis indicated 

that use of a BMP to treat bridge-deck runoff may reduce TN, 

TP, and SS discharge yields to about 10, 2.8, and 4,300 pounds 

per acre per year, respectively. In these simulations, the 

median BMP with performance statistics shown in table 10 

attenuates long-term average-annual bridge-deck runoff yields 

by about 52 percent for TN, 57 percent for TP, and 89 percent 

for SS (table 20). 

Summary

The U.S. Geological Survey (USGS) conducted a field 

study between 2014 through 2016 to document the quality 

of bridge-deck runoff from three bridges maintained by the 

Massachusetts Department of Transportation (MassDOT) in 

eastern Massachusetts. Bridge sites monitored in this study 

include State Route 2A (Massachusetts Avenue Bridge) in the 

city of Boston, Interstate 90 in the town of Weston, and State 

Route 20 near Quinsigamond Village in the city of Worcester. 

The annual average daily traffic volumes for these bridges 

ranged from 21,200 to 124,000 vehicles per day. The land use 

surrounding each bridge was primarily developed land (43 to 

78.8 percent) with accompanying high impervious area (25 to 

67 percent).

At each bridge, a monitoring system was installed to 

collect continuous measurements of water level and rainfall (rainfall measurements were not available at the State 

Route 2A bridge location, but estimated from data collected at 

a nearby USGS streamgage) and to collect composite samples 

of bridge-deck runoff. Bridge-deck runoff was diverted from 

a scupper outlet at each bridge to a shelter containing an 

H flume where flow was measured. Composite samples of 

bridge-deck runoff were collected on a flow-proportional basis 

by an automatic sampler for more than 50 runoff events at 

each bridge-deck-monitoring station and analyzed for concentrations of suspended sediment (SS), SS particle size, total 

phosphorus (TP), dissolved nitrogen (DN), particulate nitrogen 

(PN), loss on ignition of suspended solids (LOI), and particulate carbon (PC). Samples of runoff were collected year round 

and during events that were characteristic of the range of antecedent dry periods and event rain totals that existed throughout 

the study period (August 2014 through August 2016). Samples 

of bridge-deck sediment also were collected three times during 

the study period to characterize the distribution of sediment 

across each bridge deck. A composite sample of sediment 

from each bridge was analyzed for concentrations of TP and 

10 total-recoverable metals in three particle-size ranges.

Quality-assurance data were collected at each monitoring station to ensure the accuracy of the flow and constituent 

data. Redundant measurements of water level were collected 

at each monitoring station, runoff coefficients were calculated 

to help identify sensor error and stormflow alterations, and the 

theoretical level-flow relation of each flume was tested at each 

bridge. Field blank and replicate-split samples were collected 

to identify potential bias in processing methods and contamination resulting from the sampling equipment and the sample-collection, processing, and analysis process. During the 

study period, 10 field blanks and 33 concurrent replicate-split 

samples of bridge-deck composite samples were collected and 

submitted for chemical and sediment analysis. Concentrations 

of SS in field blanks were slightly greater than the laboratory 

reporting limit in 7 of 10 field blank samples. Concentrations 

of LOI, PC, particulate organic carbon (POC), and DN were 

detected less often in field blanks than concentrations of SS, 

but in general, contamination bias was low for all constituents. 

The range of concentrations for each constituent in concurrent 

replicate-split samples was similar to the range of constituent 

concentrations measured in composite samples collected during the study. The median relative percent difference (RPD) 

for concentrations of SS and particle-associated constituents 

ranged from 10 to 25 percent, except for particulate inorganic 

carbon (PIC), which was 65 percent, and the median RPD 

for DN was less than 2 percent. The RPD for replicate-split 

samples of bridge-deck sediment for TP and total-recoverable 

metals generally was less than 12 percent in the fine-sediment 

fraction and ranged from 0 to 64 percent for most metals in the 

coarser sediment fractions. These differences in the replicatesplit samples represent a measure of the variability associated 

with sample processing and analytical methods. The RPDs 

associated with the composite samples of runoff are relatively 

high but not unusual for water samples containing high concentrations of sand-size particles.

Concentrations of SS, SS particle size, nutrients, 

LOI, and PC were measured in more than 160 flowweighted composite samples of runoff collected from the 

three bridge-deck-monitoring stations. Concentrations of 

SS in composite samples of bridge-deck runoff from the 
Summary 61

three bridge-deck-monitoring stations ranged from 44 to 

142,000 milligrams per liter (mg/L); however, median concentrations of SS per site ranged from 1,490 to 2,020 mg/L. 

Concentrations of LOI and PC in composite samples of runoff 

ranged from 15 to 1,740 mg/L and 6.68 to 1,360 mg/L, respectively, and generally represented less than 10 and 3 percent 

of the median mass of SS, respectively. Concentrations of PC 

were primarily represented by the particulate organic carbon 

(POC) fraction. Concentrations of TP and total nitrogen (TN) 

(sum of DN and PN) in composite samples of runoff ranged 

from 0.09 to 7.02 mg/L and 0.36 to 29.0 mg/L, respectively. 

Median concentrations of TP ranged from 0.505 to 0.690 mg/L 

and were highest on the bridge on State Route 2A in Boston. 

Median DN concentrations ranged from 0.64 to 0.90 mg/L and 

generally represented about 40 percent of the TN concentration at each bridge. Median concentrations of DN were similar 

to annual precipitation-weighted mean concentrations of nitrogen (ammonia plus nitrate) reported in Massachusetts. 

Results for one-way analysis of variance and post hoc 

Tukey pairwise comparison tests performed on the ranktransformed data for each bridge indicated that concentrations 

of SS, LOI, PC, and TP were not significantly different from 

bridge to bridge. The mean of the rank-transformed data for 

TN collected on State Route 20 was significantly higher than 

the means of the rank-transformed data for the other bridges. 

Test results for the fractional portion of TN for bridge pairs 

State Route 2A and Interstate 90, and Interstate 90 and State 

Route 20 were not statistically different; however, test results 

for the bridge pair State Route 2A and State Route 20 were 

significant and indicated that the fractional portions of TN 

were both significantly higher at the State Route 20 bridge 

compared to State Route 2A. Median concentrations of TN 

were about 93 percent lower at State Route 2A in Boston compared to the median concentrations of TN at State Route 20 

near Quinsigamond Village. 

Samples of sediment were collected from five fixed locations (equally spaced) on three occasions during dry weather 

on each bridge road surface between April 2015 and September 2016 to assess the distribution of sediment yields on the 

bridge surface in respect to the scupper inlet location where 

samples of runoff were collected. Samples of bridge-deck 

sediment were collected with portable vacuums in the travel 

lane of the instrument scupper on each bridge, the sediment 

was dried to a constant weight, and yields were estimated on 

the basis of the sediment mass and curb distance sampled. 

Yields of bridge-deck sediment for each of the five sampling 

locations ranged from 26 to 25,000 pounds per curb-mile (lbs/

curb-mi) and were similar to yields reported elsewhere in 

Massachusetts and the United States. 

Yields of bridge-deck sediment, normalized to the 

yield measured proximate to the monitoring station at each 

bridge, indicated that sediment on each bridge surface was 

not uniform. On State Route 2A, the mean yield of bridgedeck sediment measured proximate to the monitoring station 

(680 lbs/curb-mi) on the east end of the bridge near Boston 

was about 55 percent lower than the mean yield of sediment 

for the entire eastward bridge-deck span, which might be 

explained by depositional decay of vehicle tracking through 

the fixed sampling locations as the yield distribution decreased 

with flow of traffic. Normalized bridge-deck sediment yields 

for Interstate 90 near Weston indicated that the sediment mass 

increased from east to west. The mean yield of bridge-deck 

sediment for Interstate 90 measured proximate to the monitoring station (240 lbs/curb-mi) on the west end of the bridge was 

similar to the mean yield of sediment for the entire westward 

bridge-deck span. The distribution of the sediment mass across 

this bridge was likely related to the contribution of solids 

from the deterioration of the bridge-deck surface, median 

barrier, and shoulder area; these solids migrate westward 

with the bridge-deck slope and flow of traffic. Normalized 

bridge-deck sediment yields for Route 20 near Quinsigamond 

Village indicated that the sediment mass decreased from east 

to west. The mean yield of bridge-deck sediment proximate to 

the monitoring station on State Route 20 (7,200 lbs/curb-mi) 

was about 26 percent higher than the mean yield of sediment 

for the entire westward bridge-deck. The distribution of the 

sediment mass across this relatively new bridge likely resulted 

from sediments that washed onto the bridge from the eastern 

roadway uphill of the bridge. Although these data only represent a semiquantitative assessment of the sediment distribution, visual observations throughout the study period support 

the findings.

A composite sample of bridge-deck sediment from each 

bridge was sieved into fine, intermediate, and coarse size 

fractions (less than 0.0625 millimeter, greater than or equal 

to 0.0625 to 0.25 millimeter, and greater than or equal to 

0.25 millimeter in diameter, respectively), and analyzed for 

concentrations of TP and 10 total-recoverable metals. These 

analyses indicated that bridge-deck sediment contained high 

concentrations of phosphorus and various metals. In particular, 

the two fine sediment fractions typically had higher concentrations than the coarse sediment fraction. Concentrations of 

TP in the fine fraction were about 6 times greater than in the 

coarse fraction. Total-recoverable concentrations of aluminum 

(Al), manganese (Mn), and nickel (Ni) were about two times 

greater in the fine fraction; total-recoverable concentrations 

for arsenic (As), barium (Ba), cadmium (Cd), chromium (Cr), 

and zinc (Zn) were about 2 to 17 times greater in the fine 

sediment fraction; and total-recoverable concentrations of 

lead (Pb) were about 8 to 65 times greater in the fine sediment 

fraction compared to the concentration in the coarse sediment 

fraction. The only total-recoverable metal not detected in the 

coarse fraction of any of the bridge-deck sediment samples 

was copper (Cu), but concentrations in the fine fraction were 

13 to 47 times more concentrated than concentrations in the 

intermediate sediment fraction.

The distribution of the sediment in the three size fractions and the relation of TP and total-recoverable metals to 

each sediment-size fraction indicated that for a given mass of 

sediment, about 54 percent of the estimated TP is associated 

with the intermediate and coarse sediment mass. The cumulative proportion of the mass of As, Ba, Cd, Cu, Pb, and Zn in 
62 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16

the intermediate and coarse sediment fraction was less than 

56 percent for a given mass of sediment. Only about 5 percent of the sediment-associated Cu was associated with the 

intermediate sediment fraction. In contrast, as much as 66 to 

84 percent of Al, Cr, Mn, and Ni was associated with the intermediate and coarse fractions of bridge-deck sediment.

Results for Mann-Whitney tests indicated that concentrations of SS in composite samples of bridge-deck runoff for this 

study were significantly larger (p-values less than 0.001) than 

in composite samples of highway runoff collected on State 

Route 2, Interstate 190, Interstate 495, Interstate 95, and Interstate 93 in eastern Massachusetts during a 2005–7 study using 

similar sampling methods. Highway-runoff samples were 

collected at the outlet of catch basins, whereby some pretreatment by the catch basin results in a reduction of SS concentrations; therefore, the difference between the two datasets, in 

part, is likely explained by the lack of any SS reduction by the 

bridge scuppers. Other factors such as the high bridge walls, 

concrete barriers in the bridge median, deterioration of bridge 

components, and bridge wash-on also may have contributed to 

higher concentrations of SS in samples of bridge-deck runoff. 

Concentrations of TP and TN in samples of bridge-deck runoff 

in this study were significantly higher than in samples collected from the selected highway-monitoring stations in eastern Massachusetts during 2005–7. Both of these nutrients were 

determined to be closely associated with bridge-deck sediment 

and SS; therefore, the higher concentrations of TP and TN 

in bridge-deck runoff may be explained by the significantly 

higher concentrations of SS measured in composite samples of 

bridge-deck runoff.

Comparisons between bridge-deck sediment quality in 

this study and highway sediment quality collected in 2005–7 

indicated that bridge-deck sediment was less enriched in phosphorus. Concentrations of phosphorus in highway sediment 

were 3 to 5 times greater in the fine fraction and 6 to 40 times 

greater in the intermediate and coarse fractions compared to 

concentrations in the same fractions of bridge-deck sediment. 

Except for concentrations of Cr and Cu, concentrations of 

total-recoverable metals in the fine fraction of bridge-deck 

sediment were similar (within 50 percent) to the concentration 

in the same fraction of highway sediment. Concentrations of 

Cr were 5 to 17 times greater in the intermediate and coarse 

fraction of highway sediment and Cu was as much as 49 times 

greater in the intermediate fraction of highway sediment in 

comparison to concentrations in the same respective size fractions in bridge sediment samples.

The particle-size distribution and association of each 

constituent to each sediment-size fraction affects the overall 

constituent concentration associated with the mass of SS. 

Bridge-deck sediment as a whole was less enriched chemically 

compared to highway sediment because it was dominated by 

the chemically dilute coarse sediment fraction. 

Version 1.0.2 of the Stochastic Empirical Loading and 

Dilution Model (SELDM) was used to simulate concentrations 

and annual yields of SS, TN, and TP in bridge-deck runoff 

and in discharges from a hypothetical stormwater treatment 

best-management practice (BMP) structure. Simulations were 

performed for a 1-acre portion of a bridge deck by using precipitation statistics for ecoregion 59 (the Northeastern Coastal 

Zone) and by using the standard SELDM runoff-coefficient 

statistics. The flow and concentration performance of the 

BMP was simulated by using the median of statistics from 

nine BMP categories in the 2012 version of the International 

BMP database.

High variability in measured concentrations during this 

study resulted in extreme simulated concentrations when the 

available data were used to simulate a long-term (29–30-

year) record; therefore, three methods were used to calculate 

statistics for stochastic simulations. The first method was to 

use the average, standard deviation (SD), and skew calculated 

by using standard equations. The second method was to use 

the average and SD calculated by using robust regression on 

order statistics with the Kendall-Theil Robust Line. The robust 

skew was calculated by using Pearson’s second skew with the 

robust average and SD values. The third method was to use 

the average, SD, and skew calculated by using the L-moments 

methods. These methods were used to calculate statistics 

for each of the three bridges and for a lumped dataset. The 

medians of these statistics also were calculated. These median 

statistics were selected for the interpretive simulations so that 

the simulations could be used to estimate concentrations and 

yields from other, unmonitored bridges in Massachusetts. 

Comparisons of the standard and robust statistics indicated 

that simulation results with either method would be similar, 

which indicated that the large variability in simulated results 

was not caused by a few outliers. Comparison to simulations performed using statistics calculated by the L-moments 

methods indicated that L-moments do not produce extreme 

concentrations, but they do not produce results that represent 

the bulk of concentration data.

Runoff-quality risk was analyzed to show how SELDM 

can be used to evaluate runoff-quality management alternatives. These simulations were done with concentration-criteria 

discharge standards commonly used for large, advanced 

wastewater treatment plants. Effluent criteria of 8 mg/L for 

TN and 1 mg/L for TP were selected as hypothetical criteria 

but not as suggested targets for small stormwater discharges. 

The risk analysis indicated that TN in bridge-deck runoff 

may exceed the selected TN criterion in about 5 percent of 

events. This simulated risk was similar to the exceedance risk 

estimated from measured data at the bridge sites. In comparison, simulated BMP discharge concentrations exceeded the 

selected TN criteria in about 1.6 percent of events. The risk 

analysis indicated that TP in bridge-deck runoff may exceed 

the selected TP criterion in about 28 percent of events, which 

was similar to the exceedance risk estimated from measured 

data at the bridge sites. In comparison, simulated BMP discharge concentrations exceeded the selected TN criteria in 

about 13 percent of events.

Eleven simulations of 29–30 years were performed with 

the same hydrologic and concentration statistics and different 

random seeds to estimate yields of TN, TP, and SS. Results of 
References Cited 63

simulations indicated that long-term average yields of TN, TP, 

and SS may be about 21.4, 6.44, and 40,600 pounds per acre 

per year, respectively. The long-term average-annual yield is 

the sum of the loads, per unit area, for the entire simulation 

period divided by the number of years in the simulation. The 

TN, TP, and SS yields were about 1.3, 3.4, and 16 times the 

simulated ultra-urban highway yields in Massachusetts. This 

comparison indicated that bridge-deck yields were not representative of yields from the entire road network in Massachusetts; however, bridge-deck yields may be a substantial portion 

of annual loads from highways in a watershed. Furthermore, 

many bridges are over and discharge to receiving waters, 

whereas a high proportion of State roadway miles discharge to 

the local land surface.

Runoff treatment was analyzed to examine the potential 

effects of flow reductions and concentration reductions by 

stormwater BMPs on annual yields from bridge-deck runoff. 

For this analysis, a generic BMP was simulated by using the 

median of treatment statistics for flow reductions, concentration reductions, and minimum irreducible concentration from 

seven BMP categories with data from the 2012 International 

BMP database. This analysis indicated that use of a BMP to 

treat bridge-deck runoff may reduce TN, TP, and SS discharge 

yields to about 10, 2.8, and 4,300 pounds per acre per year, 

respectively. These changes represent long-term average 

reductions of about 52 percent for TN, 57 percent for TP, and 

89 percent for SS. These results are based on the stochastic 

performance of a generic BMP, and better results may be possible with a better-performing BMP design.

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68 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 7. Quality-control samples collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16. [Locations of stations are shown in figure 1. Time is hours and minutes in military time format with leading zeroes omitted. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. USGS, U.S. Geological Survey; yyyy, year; mm, month; dd, day; Composite, composite runoff sample; Replicate, concurrent replicate-split sample; <, less than laboratory reporting limit; NA, not available; RPD, absolute relative percent difference between environmental and replicate sample values] USGS station number Begin date (yyyym- mdd) Begin time End date (yyyym- mdd) End time Sample type Concentration (milligrams per liter) Loss on ignition of suspend- ed solids (p00535) Par- ticulate carbon [inor- ganic plus organic] (p00694) Particulate inorganic carbon (p00688) Particulate organic carbon (p00689) Total phos- phorus (p00665) Particulate nitrogen (p49570) Total dissolved nitrogen (p62854) Suspended sediment concentra- tion (p80154) 421247071470201 20140829 1400 NA NA Field blank 1 0.76 NA NA <0.01 <0.030 <0.05 1 422108071052501 20140903 1200 NA NA Field blank <0.5 <0.05 NA NA <0.01 <0.030 0.07 1 422108071052501 20150317 1200 NA NA Field blank <0.5 <0.05 <0.03 <0.05 <0.01 <0.030 <0.05 <0.5 421247071470201 20150327 1200 NA NA Field blank 1 0.11 <0.03 0.11 <0.01 <0.030 <0.05 1 421247071470201 20150611 1400 NA NA Field blank 1 0.06 <0.03 0.06 <0.01 <0.030 <0.05 1 421247071470201 20150903 1100 NA NA Field blank NA <0.05 <0.03 <0.05 <0.01 <0.030 <0.05 1 421247071470201 20151209 1330 NA NA Field blank <0.5 <0.05 <0.03 <0.05 <0.01 <0.030 <0.05 1 422108071052501 20160303 1330 NA NA Field blank NA <0.05 <0.03 <0.05 <0.01 <0.030 <0.05 <0.5 422025071154501 20160614 1430 NA NA Field blank NA 0.1 <0.03 <0.10 <0.01 <0.030 <0.05 <0.5 422025071154501 20160817 1430 NA NA Field blank 2 <0.05 <0.03 <0.05 <0.01 <0.030 <0.05 2

421247071470201 20141016 43 20141016 1400 Composite 99 58.7 0.37 58.4 0.55 2.9 0.94 977

421247071470201 20141016 42 20141016 1400 Replicate 144 41 0.55 40.4 0.56 1.89 0.94 1,040

RPD (percent) 37 35.5 39.1 36.4 1.8 42 0.00 6.2421247071470201 20141101 1133 20141101 2126 Composite 25 17.5 0.25 17.3 0.18 0.448 0.52 308

421247071470201 20141101 1132 20141101 2126 Replicate 29 9.15 0.41 8.75 0.17 0.212 0.53 342

RPD (percent) 15 63 48 66 5.7 72 1.9 10
 Table 7 69 Table 7. Quality-control samples collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.—Continued [Locations of stations are shown in figure 1. Time is hours and minutes in military time format with leading zeroes omitted. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. USGS, U.S. Geological Survey; yyyy, year; mm, month; dd, day; Composite, composite runoff sample; Replicate, concurrent replicate-split sample; <, less than laboratory reporting limit; NA, not available; RPD, absolute relative percent difference between environmental and replicate sample values] USGS station number Begin date (yyyym- mdd) Begin time End date (yyyym- mdd) End time Sample type Concentration (milligrams per liter) Loss on ignition of suspend- ed solids (p00535) Par- ticulate carbon [inor- ganic plus organic] (p00694) Particulate inorganic carbon (p00688) Particulate organic carbon (p00689) Total phos- phorus (p00665) Particulate nitrogen (p49570) Total dissolved nitrogen (p62854) Suspended sediment concentra- tion (p80154) 421247071470201 20141126 951 20141128 1854 Composite 147 69.2 0.94 68.2 0.52 2.35 0.73 3,480 421247071470201 20141126 950 20141128 1854 Replicate 144 57.9 0.27 57.6 0.58 2.14 0.74 2,610 RPD (percent) 2.1 18 111 17 10.9 9.4 1.4 29 421247071470201 20150403 2356 20150404 629 Composite 470 297 20.3 277 1.26 5.25 1.83 35,700 421247071470201 20150403 2355 20150404 629 Replicate 745 287 174 113 2.43 5.15 1.85 60,000 RPD (percent) 45 3.4 158 84 63 1.9 1.1 51 421247071470201 20150408 1601 20150409 624 Composite 34 30 0.1 29.9 0.19 0.956 0.86 122 421247071470201 20150408 1600 20150409 624 Replicate 31 16.1 <0.03 16.1 0.19 0.407 0.85 122 RPD (percent) 9.2 60 NA 60 0.00 81 1.2 0.00 421247071470201 20150609 1745 20150610 117 Composite 161 63.6 0.91 62.7 1.43 2.11 1.08 6,390 421247071470201 20150609 1744 20150610 117 Replicate 205 54.9 6.18 48.8 1.47 1.55 1.1 7,750 RPD (percent) 24 15 149 25 2.8 31 1.8 19 421247071470201 20150615 504 20150615 1638 Composite 163 64.7 1.79 63 1.73 2.18 0.74 6,720 421247071470201 20150615 503 20150615 1638 Replicate 170 53.7 3.21 50.5 1.41 1.92 0.74 6,510 RPD (percent) 4.2 19 57 22 20 13 0.00 3.2 421247071470201 20150621 10 20150621 1233 Composite 76 46.8 2.22 44.6 0.56 1.85 0.56 1,140 421247071470201 20150621 9 20150621 1233 Replicate 72 46.7 0.36 46.4 0.54 1.45 0.57 1,070 RPD (percent) 5.4 0.21 144 4.00 3.6 24 1.8 6.3 421247071470201 20150910 1540 20150911 626 Composite 409 209 14.2 194 1.7 4.41 0.73 10,700 421247071470201 20150910 1539 20150911 626 Replicate 376 237 27.3 209 2.87 12.1 0.74 10,900 RPD (percent) 8.4 13 63 7.4 51 93 1.4 1.9
70 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 7. Quality-control samples collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.—Continued [Locations of stations are shown in figure 1. Time is hours and minutes in military time format with leading zeroes omitted. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. USGS, U.S. Geological Survey; yyyy, year; mm, month; dd, day; Composite, composite runoff sample; Replicate, concurrent replicate-split sample; <, less than laboratory reporting limit; NA, not available; RPD, absolute relative percent difference between environmental and replicate sample values] USGS station number Begin date (yyyym- mdd) Begin time End date (yyyym- mdd) End time Sample type Concentration (milligrams per liter) Loss on ignition of suspend- ed solids (p00535) Par- ticulate carbon [inor- ganic plus organic] (p00694) Particulate inorganic carbon (p00688) Particulate organic carbon (p00689) Total phos- phorus (p00665) Particulate nitrogen (p49570) Total dissolved nitrogen (p62854) Suspended sediment concentra- tion (p80154) 422025071154501 20141126 953 20141126 2237 Composite 93 54.4 0.26 54.1 0.46 1.33 0.61 1,240 422025071154501 20141126 952 20141126 2237 Replicate 99 36 0.33 35.7 0.41 0.747 0.58 1,320 RPD (percent) 6.3 41 24 41 11 56 5.0 6.3 422025071154501 20150103 2123 20150104 2220 Composite 59 16.5 1.06 15.5 0.38 0.451 0.71 1,300 422025071154501 20150103 2122 20150104 2220 Replicate 67 44.9 0.07 44.9 0.34 0.522 0.69 1,220 RPD (percent) 13 93 175 97 11 15 2.9 6.3 422025071154501 20150404 536 20150404 623 Composite 190 80.5 37.3 43.2 3 1.6 0.79 5,240 422025071154501 20150404 535 20150404 623 Replicate 196 112 4.66 107 2.64 2.14 0.78 4,730 RPD (percent) 3.10 33 156 85 13 29 1.3 10 422025071154501 20150420 1245 20150421 523 Composite 175 54.2 1.94 52.3 1.55 1.16 0.52 6,660 422025071154501 20150420 1244 20150421 523 Replicate 170 80.5 5.47 75 0.92 3.65 0.52 6,020 RPD (percent) 2.9 39 95 36 51 104 0.00 10 422025071154501 20150531 407 20150602 809 Composite 113 50.1 2.43 47.7 0.27 1.21 0.72 4,020 422025071154501 20150531 406 20150602 809 Replicate 78 57.6 4.66 53 0.26 1.39 0.73 3,810 RPD (percent) 37 14 63 11 3.8 14 1.40 5.4 422025071154501 20150609 1911 20150609 1935 Composite 86 39.3 0.38 39 0.57 1.15 1.32 1,110

422025071154501 20150609 1910 20150609 1935 Replicate 107 42.1 12 30.1 0.61 1.19 1.34 1,340

RPD (percent) 22 6.9 188 26 6.8 3.4 1.5 19
 Table 7 71 Table 7. Quality-control samples collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.—Continued [Locations of stations are shown in figure 1. Time is hours and minutes in military time format with leading zeroes omitted. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. USGS, U.S. Geological Survey; yyyy, year; mm, month; dd, day; Composite, composite runoff sample; Replicate, concurrent replicate-split sample; <, less than laboratory reporting limit; NA, not available; RPD, absolute relative percent difference between environmental and replicate sample values] USGS station number Begin date (yyyym- mdd) Begin time End date (yyyym- mdd) End time Sample type Concentration (milligrams per liter) Loss on ignition of suspend- ed solids (p00535) Par- ticulate carbon [inor- ganic plus organic] (p00694) Particulate inorganic carbon (p00688) Particulate organic carbon (p00689) Total phos- phorus (p00665) Particulate nitrogen (p49570) Total dissolved nitrogen (p62854) Suspended sediment concentra- tion (p80154) 422025071154501 20150621 51 20150621 1327 Composite 68 7.74 0.03 7.71 0.45 0.289 0.47 1,680 422025071154501 20150621 50 20150621 1327 Replicate 68 9.68 0.86 8.83 0.52 0.417 0.44 1,840 RPD (percent) 0.00 22 187 14 14 36 6.6 9.1 422025071154501 20150710 6 20150710 300 Composite 25 9.55 0.06 9.49 0.24 0.315 0.29 481 422025071154501 20150710 5 20150710 300 Replicate 37 10.1 <0.03 10.1 0.19 0.241 0.28 601 RPD (percent) 39 5.6 NA 6.2 23 27 3.5 22 422025071154501 20150804 1601 20150804 1623 Composite 65 25 0.22 24.8 0.4 0.761 0.82 615 422025071154501 20150804 1600 20150804 1623 Replicate 75 35.1 0.24 34.8 0.53 0.921 0.79 684 RPD (percent) 14 34 8.7 34 28 19 3.7 11 422025071154501 20150811 1013 20150811 1257 Composite 241 140 14.8 125 2.1 3.85 0.54 5,970 422025071154501 20150811 1012 20150811 1257 Replicate 334 119 9.43 110 1.34 2.42 0.56 7,870 RPD (percent) 32 16 44 13 44 46 3.6 27 422025071154501 20150930 200 20150930 1509 Composite 34 30.2 0.24 29.9 0.32 1.26 0.32 588 422025071154501 20150930 201 20150930 1509 Replicate 38 16 0.33 15.6 0.28 0.907 0.32 613 RPD (percent) 11 61 32 63 13 33 0.00 4.2 422108071052501 20141106 1117 20141106 2017 Composite 107 54.3 0.66 53.6 1.02 1.31 1.1 4,050 422108071052501 20141106 1116 20141106 2017 Replicate 131 44.3 0.98 43.3 2.96 1.05 1.11 4,230 RPD (percent) 20 20 39 21 97 22 0.90 4.3
72 Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16 Table 7. Quality-control samples collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.—Continued [Locations of stations are shown in figure 1. Time is hours and minutes in military time format with leading zeroes omitted. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. USGS, U.S. Geological Survey; yyyy, year; mm, month; dd, day; Composite, composite runoff sample; Replicate, concurrent replicate-split sample; <, less than laboratory reporting limit; NA, not available; RPD, absolute relative percent difference between environmental and replicate sample values] USGS station number Begin date (yyyym- mdd) Begin time End date (yyyym- mdd) End time Sample type Concentration (milligrams per liter) Loss on ignition of suspend- ed solids (p00535) Par- ticulate carbon [inor- ganic plus organic] (p00694) Particulate inorganic carbon (p00688) Particulate organic carbon (p00689) Total phos- phorus (p00665) Particulate nitrogen (p49570) Total dissolved nitrogen (p62854) Suspended sediment concentra- tion (p80154) 422108071052501 20141117 409 20141117 1841 Composite 76 50.8 0.13 50.6 0.53 1.15 0.4 2,310 422108071052501 20141117 408 20141117 1841 Replicate 75 45.2 0.13 45.1 0.8 0.979 0.37 1,910 RPD (percent) 1.3 12 0.00 11 41 16 7.8 19 422108071052501 20141126 1051 20141126 1529 Composite 58 19.6 0.33 19.3 0.36 0.392 0.39 1,570 422108071052501 20141126 1050 20141126 1529 Replicate 54 13.4 0.14 13.2 0.37 0.337 0.41 1,470 RPD (percent) 7.1 38 81 38 2.7 15 5.0 6.6 422108071052501 20141224 1436 20141224 1838 Composite 172 66.1 1.49 64.7 0.62 1.39 0.5 1,650 422108071052501 20141224 1435 20141224 1838 Replicate 158 74.6 0.63 74 0.69 1.57 0.47 1,230 RPD (percent) 8.5 12 81 13 11 12 6.2 29 422108071052501 20150103 2356 20150104 327 Composite 86 34.8 4.33 30.5 1.67 0.68 0.35 6,790 422108071052501 20150103 2355 20150104 327 Replicate 93 28.3 1.26 27 1.12 0.826 0.35 7,750 RPD (percent) 7.8 21 110 12 39 19 0.00 13 422108071052501 20150326 1448 20150327 712 Composite 119 93 0.49 92.5 1.02 1.81 NA 1,010 422108071052501 20150326 1443 20150327 712 Replicate 141 71.4 0.86 70.5 1.01 1.64 NA 1,850 RPD (percent) 17 26 55 27 0.99 9.9 NA 59 422108071052501 20150408 1726 20150409 120 Composite 306 61.9 5.24 56.7 5.31 1.38 0.46 28,300

422108071052501 20150408 1725 20150409 120 Replicate 267 63.7 3.03 60.6 6.28 1.23 0.48 25,600

RPD (percent) 14 2.9 53 6.6 17 11 4.3 10
 Table 7 73 Table 7. Quality-control samples collected at U.S. Geological Survey bridge-deck-monitoring stations on State Route 2A in Boston (422108071052501), Interstate 90 near Weston (422025071154501), and State Route 20 near Quinsigamond Village (421247071470201) in eastern Massachusetts, 2014–16.—Continued [Locations of stations are shown in figure 1. Time is hours and minutes in military time format with leading zeroes omitted. The alpha-numeric identifiers starting with “p” are the U.S. Geological Survey parameter codes. USGS, U.S. Geological Survey; yyyy, year; mm, month; dd, day; Composite, composite runoff sample; Replicate, concurrent replicate-split sample; <, less than laboratory reporting limit; NA, not available; RPD, absolute relative percent difference between environmental and replicate sample values] USGS station number Begin date (yyyym- mdd) Begin time End date (yyyym- mdd) End time Sample type Concentration (milligrams per liter) Loss on ignition of suspend- ed solids (p00535) Par- ticulate carbon [inor- ganic plus organic] (p00694) Particulate inorganic carbon (p00688) Particulate organic carbon (p00689) Total phos- phorus (p00665) Particulate nitrogen (p49570) Total dissolved nitrogen (p62854) Suspended sediment concentra- tion (p80154) 422108071052501 20150531 1544 20150602 729 Composite 40 30.7 0.3 30.4 0.56 0.809 0.36 1,570 422108071052501 20150531 1543 20150602 729 Replicate 49 23 0.15 22.8 0.57 0.646 0.37 1,830 RPD (percent) 20 29 67 29 1.8 22 2.7 15 422108071052501 20150615 619 20150615 1542 Composite 1,110 1,490 87.6 1,410 0.21 39.2 2.23 89,500 422108071052501 20150615 618 20150615 1542 Replicate 1,740 1,360 255 1,100 0.62 26.7 2.19 142,000 RPD (percent) 44 9.1 98 25 99 38 1.8 45 422108071052501 20150621 107 20150621 1304 Composite 33 11.7 0.1 11.6 0.42 0.495 0.41 1,290 422108071052501 20150621 106 20150621 1304 Replicate 34 18 <0.03 18 0.48 0.266 0.4 1,260 RPD (percent) 3.0 42 NA 43 13 60 2.5 2.4 422108071052501 20150710 19 20150710 258 Composite 31 15.7 <0.03 15.7 0.3 0.375 0.34 967 422108071052501 20150710 18 20150710 258 Replicate 26 10.3 0.37 9.9 0.51 0.211 0.35 924 RPD (percent) 18 42 NA 45 52 56 2.9 4.5 422108071052501 20150811 1022 20150811 2010 Composite 330 102 5.53 96.6 2.99 2.09 0.96 17,200 422108071052501 20150811 1021 20150811 2010 Replicate 280 101 4.4 96.7 1.8 1.97 0.95 15,900 RPD (percent) 16 0.99 23 0.10 50 5.9 1.0 7.9 422108071052501 20160116 434 20160116 932 Composite 26 13.3 0.05 13.2 0.24 0.333 0.31 302 422108071052501 20160116 433 20160116 932 Replicate 26 9.74 <0.03 <9.74 0.2 0.251 0.31 283 RPD (percent) 0.00 31 NA NA 18 28 0.00 6.5

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Smith and others—Characterization of Stormwater Runoff From Bridge Decks in Eastern Massachusetts, 2014–16—Scientific Investigations Report 2018–5033

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ISSN 2328-031X (print)

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20185033