U.S. Department of the Interior 

U.S. Geological Survey 

Potential Effects of Structural 

Controls and Street Sweeping on 

Stormwater Loads to the Lower 

Charles River, Massachusetts 

Water-Resources Investigations Report 02-4220 

In cooperation with the 

U.S. ENVIRONMENTAL PROTECTION AGENCY, the


MASSACHUSETTS DEPARTMENT OF ENVIRONMENTAL PROTECTION, and the


MASSACHUSETTS WATER RESOURCES AUTHORITY

Cover photo: View of the lower Charles River

looking toward the Boston city skyline

Courtesy of: Philip Greenspun http://philip.greenspun.com
U.S. Department of the Interior

U.S. Geological Survey

Potential Effects of Structural 

Controls and Street Sweeping on 

Stormwater Loads to the Lower 

Charles River, Massachusetts

By PHILLIP J. ZARRIELLO, ROBERT F. BREAULT, and PETER K. WEISKEL

Water-Resources Investigations Report 02-4220

In cooperation with the 

U.S. ENVIRONMENTAL PROTECTION AGENCY, the 

MASSACHUSETTS DEPARTMENT OF ENVIRONMENTAL PROTECTION, and the 

MASSACHUSETTS WATER RESOURCES AUTHORITY 

Northborough, Massachusetts

2002
U.S. DEPARTMENT OF THE INTERIOR

GALE A. NORTON, Secretary 

U.S. GEOLOGICAL SURVEY

Charles G. Groat, Director

The use of trade or product names in this report is for identification 

purposes only and does not constitute endorsement by the 

U.S. Government.

For additional information write to: Copies of this report can be purchased from:

Chief, Massachusetts-Rhode Island District U.S. Geological Survey

U.S. Geological Survey Branch of Information Services

10 Bearfoot Road Box 25286

Northborough, MA 01532 Denver, CO 80225-0286

or visit our Web site at

http://ma.water.usgs.gov
Contents III

CONTENTS

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

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

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

Acknowledgments....................................................................................................................................................... 4

Structural Controls ................................................................................................................................................................ 4

Removal Efficiencies................................................................................................................................................... 4

Estimated Contaminant-Load Removal in the Village Brook Subbasin ..................................................................... 7

Street Sweeping..................................................................................................................................................................... 10

Contaminants on Streets.............................................................................................................................................. 10

Street Sweeping as a Water-Quality-Management Practice........................................................................................ 12

Sweeper Types and Efficiencies........................................................................................................................ 12

Factors that Affect Sweeper Performance......................................................................................................... 13

Model Simulation ....................................................................................................................................................... 14

Calibration......................................................................................................................................................... 15

Sweeping Efficiencies and Frequencies Evaluated ........................................................................................... 19

Model Limitations............................................................................................................................................. 20

Sensitivity Analysis........................................................................................................................................... 22

Simulated Contaminant Removal by Street Sweeping................................................................................................ 23

Calibrated-Model Load Removals ................................................................................................................... 23

Alternative-Model Load Removals ................................................................................................................... 24

Potential Effects of Structural Controls and Street Sweeping on Loads to the Lower Charles River .................................. 26

Water Year 2000 .......................................................................................................................................................... 27

Design Year ................................................................................................................................................................. 32

Design Storms ............................................................................................................................................................. 35

Summary and Conclusions.................................................................................................................................................... 39

References ............................................................................................................................................................................. 40

FIGURES

1. Map showing principal geographic features, precipitation stations, and subbasins of the lower Charles 

River Watershed, Massachusetts............................................................................................................................ 3

2. Box plot showing distribution of constituent removal efficiencies for structural controls, summarized by 

control type ............................................................................................................................................................ 6

3. Bar graphs comparing (A) land use and (B) road types in the lower Charles River Watershed and the 

Village Brook Subbasin ......................................................................................................................................... 8

4. Scatter plot showing relation of simulated constituent loads to measured loads for eight sampled storms 

between January 10 and July 27, 2000, at the single-family land-use subbasin, lower Charles

River Watershed..................................................................................................................................................... 17

5–9. Line graphs showing:

5. Suspended-solids buildup on streets simulated by the Michaelis-Menton method with and without 

street sweeping at two sweeper efficiencies and 2-day sweeping intervals by the (A) Stormwater 

Management Model and (B) an adjusted buildup rate corrected to the load that remains after sweeping.... 20

6. Schematic representation of the percent load removed by street sweeping at 2-day intervals when 

calculated by the Stormwater Management Model and by a buildup rate adjusted for the load 

remaining after sweeping .............................................................................................................................. 21

7. Sensitivity of (A) annual suspended-solids loads and (B) percent load removed by street sweeping 

to buildup and washoff variable values in the Stormwater Management Model .......................................... 22
IV Contents

8. Simulated constituent-load removal for various sweeper efficiencies at selected sweeping 

intervals in the single-family land-use subbasin, lower Charles River Watershed, 2000 water year............ 24

9. Estimated percent load reduction for the 2000 water year by street sweeping at various efficiencies 

and frequencies for the (A) lower Charles River (excludes the loads above Watertown Dam) and 

(B) the entire Charles River Watershed (estimated by the street density ratio)............................................. 29

10. Bar graph showing constituent loads from non-combined-sewer-overflow sources from major subbasins, 

and estimated load reductions by hypothetical structural controls and street sweeping, lower

Charles River Watershed, 2000 water year............................................................................................................ 33

11. Box plots and line graphs showing distribution of rainfall and antecedent storm characteristics for the 

2000 water year, design year, and 1970–95 period, lower Charles River Watershed............................................ 34

12. Bar graphs showing estimated 3-month and 1-year design-storm loads from non-combined-seweroverflow sources from each of the major subbasins and estimated load reductions by hypothetical 

structural controls and street sweeping for (A) suspended solids and total lead loads and (B) fecal 

coliform and total phosphorus ............................................................................................................................... 37

TABLES

1. Categories of structural-control types, their characteristics, and the major physical or chemical 

processes that affect water quality .......................................................................................................................... 5

2. Inventory of structural controls identified by the Center for Watershed Protection for the Village Brook 

Subbasin, lower Charles River Watershed .............................................................................................................. 9

3. Estimated removal efficiencies in stormwater loads and total loads by hypothetical structural controls 

in the Village Brook Subbasin, lower Charles River Watershed............................................................................. 10

4. Reported and simulated constituent loads for the single-family land-use subbasin, 2000 water year, 

lower Charles River Watershed............................................................................................................................... 15

5. Calibrated Stormwater Management Model variable values for constituent buildup and washoff for the 

single-family land-use subbasin, lower Charles River Watershed.......................................................................... 16

6. Measured and simulated storm runoff and constituent loads for sampled storms in the single-family 

land-use subbasin, lower Charles River Watershed ................................................................................................ 18

7. Efficiencies of street sweepers simulated for removing selected contaminants ..................................................... 19

8. Measured and simulated loads of suspended solids for sampled storms, single-family land-use subbasin, 

2000 water year, lower Charles River Watershed ................................................................................................... 25

9. Percent removal of constituent loads by street sweeping simulated with the highest efficiency sweeper 

once every two days with the calibrated and an alternative Stormwater Management Model in the 

single-family land-use subbasin, lower Charles River Watershed.......................................................................... 25

10. Subbasin street length and density, lower Charles River Watershed ...................................................................... 26

11. Street and subbasin contaminant loads used to calculate the weighting factor for estimating potential 

contaminant removal by street sweeping, 2000 water year, lower Charles River Watershed................................. 27

12. Estimated range in the percent decrease in annual loads by hypothetical structural controls, lower 

Charles River Watershed......................................................................................................................................... 28

13. Simulated percent annual-load reductions to the lower Charles River, Massachusetts, by street 

sweeping with the highest efficiency sweepers at various sweeping intervals and the (A) calibrated 

model and (B) an alternative model ........................................................................................................................ 31

14. Percent annual-load reductions by combined hypothetical structural controls and street-sweeping 

practices, lower Charles River Watershed............................................................................................................... 32

15. Simulated runoff volume for the 2000 water year and the design year, lower Charles River Watershed............... 35

16. Estimated design-year constituent loads from non-combined-sewer-overflow sources for each of the 

major subbasins to the lower Charles River............................................................................................................ 36
Contents V

CONVERSION FACTORS AND ABBREVIATIONS

CONVERSION FACTORS

ABBREVIATIONS

AE Average error

AVSWP Model availability factor

BMP best management practice

CFU colony forming units

CSO combined sewer overflow

CWP Center for Watershed Protection

EMC event mean concentration

FHWA Federal Highway Administration

MADEP Massachusetts Department of Environmental Protection

MWRA Massachusetts Water Resources Authority

RMSE Root mean square error

SWMM StormWater Management Model

TCFU trillion colony forming units

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

μg micrograms 

Multiply By To obtain

acre 0.405 hectare

cubic foot (ft3) 0.02832 cubic meter

foot (ft) 0.3048 meter

gram (g) 0.002205 pound

inch (in.) 2.54 centimeter

kilogram (kg) 2.205 pound

metric ton 1.1025 ton

microns (μm) 0.00003937 inch

mile (mi) 1.609 kilometer

Abstract 1

Potential Effects of Structural Controls

and Street Sweeping on Stormwater Loads

to the Lower Charles River, Massachusetts

By Phillip J. Zarriello, Robert F. Breault, and Peter K. Weiskel

Abstract

The water quality of the lower Charles River 

is periodically impaired by combined sewer overflows (CSOs) and non-CSO stormwater runoff. 

This study examined the potential non-CSO load 

reductions of suspended solids, fecal coliform bacteria, total phosphorus, and total lead that could 

reasonably be achieved by implementation of 

stormwater best management practices, including 

both structural controls and systematic street 

sweeping. Structural controls were grouped by 

major physical or chemical process; these included 

infiltration-filtration (physical separation),

biofiltration-bioretention (biological mechanisms), 

or detention-retention (physical settling). For each 

of these categories, upper and lower quartiles, 

median, and average removal efficiencies were 

compiled from three national databases of structural control performance. Removal efficiencies 

obtained indicated a wide range of performance. 

Removal was generally greatest for infiltrationfiltration controls and suspended solids, and least 

for biofiltration-bioretention controls and fecal 

coliform bacteria. 

Street sweeping has received renewed interest as a water-quality control practice because of 

reported improvements in sweeper technology 

and the recognition that opportunities for implementing structural controls are limited in highly 

urbanized areas. The Stormwater Management 

Model that was developed by the U.S. Geological 

Survey for the lower Charles River Watershed 

was modified to simulate the effects of street 

sweeping in a single-family land-use basin. Constituent buildup and washoff variable values were 

calibrated to observed annual and storm-event 

loads. Once calibrated, the street sweeping model 

was applied to various permutations of four 

sweeper efficiencies and six sweeping frequencies 

that ranged from every day to once every 30 days. 

Reduction of constituent loads to the lower 

Charles River by the combined hypothetical practices of structural controls and street sweeping was 

estimated for a range of removal efficiencies 

because of their inherent variability and uncertainty. This range of efficiencies, with upper and 

lower estimates, provides reasonable bounds on 

the load that could be removed by the practices 

examined. The upper estimated load reduction 

from combined street sweeping and structural controls, as a percentage of the total non-CSO load 

entering the lower Charles River downstream of 

Watertown Dam, was 44 percent for suspended 

solids, 34 percent for total lead, 14 percent for 

total phosphorus, and 17 percent for fecal coliform 

bacteria. The lower estimated load reduction from 

combined street sweeping and structural controls 

from non-CSO sources downstream of Watertown 

Dam, was 14 percent for suspended solids, 11 percent for total lead, 4.9 percent for total phosphorus, and 7.5 percent for fecal coliform bacteria. 

Load reductions by these combined management 

practices can be a small as 1.4 percent for total 

phosphorus to about 4 percent for the other constituents if the total load above Watertown Dam is 

added to the load from below the dam. Although 

the reductions in stormwater loads to the lower 

Charles River from the control practices examined 

appear to be minor, these practices would likely 

provide water-quality benefits to portions of the 
2 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

river during those times that they are most 

impaired—during and immediately after storms. It 

should also be recognized that only direct measurements of changes in stormwater loads before 

and after implementation of control practices can 

provide definitive evidence of the beneficial 

effects of these practices on water-quality conditions in the lower Charles River.

INTRODUCTION

The Charles River downstream of the Watertown 

Dam (herein referred to as the lower Charles River, 

fig. 1), is a major recreational and ecological resource 

for the Boston metropolitan region, including the 

municipalities of Boston, Cambridge, Newton, 

Brookline, and Watertown. The U.S. Environmental 

Protection Agency (USEPA) has described the Charles 

River as one of the premier urban rivers in America and 

a crown jewel of the Boston area. Although seriously 

impaired for decades, the water quality of the lower 

Charles River has improved substantially in recent 

years as major sources of contamination, including 

combined sewer overflows (CSOs), illicit discharges, 

and upstream sources are controlled or eliminated. At 

the turn of the 19th century, Pritchett and others (1903) 

described parts of the lower Charles River as “putrid”; 

and as recently as 1996, the USEPA State of the New 

England Environment (U.S. Environmental Protection 

Agency, 1996) described the river as “becoming notable not for its recreation potential, but for the stench of 

sewage.” In 1996, the USEPA graded the water quality 

of the lower Charles River at C-; but in more recent 

years, the USEPA has upgraded the river to a B (U.S. 

Environmental Protection Agency, 2002). This higher 

grade reflects the progress made in removing CSO discharges and illicit connections to the storm-sewer 

system. Further improvements in water quality are now 

recognized to be more difficult because pollution 

sources are smaller and more diffuse. 

The USEPA has set a goal of achieving “fishable 

and swimmable” water-quality standards for the lower 

Charles River by the year 2005. Following storms,

the water quality of the river often fails to meet these 

standards and can even fall below standards for contact 

recreation, such as boating. The degraded water quality 

impairs the river’s recreational uses and poses potential 

health risks. A water-quality assessment of the Boston 

Harbor drainage area by the Massachusetts Water 

Resources Authority (1994) indicated that stormwater 

contaminant loads to the lower Charles River were 

comparable to loads from sewage-associated sources 

(CSOs and illicit connections). The Massachusetts 

Water Resources Authority (MWRA) assessment 

prompted a closer examination of stormwater loads of 

nutrients, fecal bacteria, and metals to the lower 

Charles River (Breault and others, 2002). That study 

indicated that non-CSO stormwater is an appreciable 

source of contaminants to the river and underscored the 

need to address urban stormwater runoff if waterquality standards are to be met. Structural and 

nonstructural management practices can help meet the 

water-quality objectives for the river, but a better 

understanding of their potential benefits is needed. In 

2001, the U.S. Geological Survey (USGS), in cooperation with the USEPA, MWRA, and Massachusetts 

Department of Environmental Protection (MADEP), 

undertook a study to provide information for waterquality managers on the potential reductions of 

stormwater-contaminant loads to be expected from 

various structural and nonstructural management 

practices.

Purpose and Scope

This report presents an analysis of potential 

contaminant-load reductions that could be achieved by 

implementing selected stormwater controls, or Best 

Management Practices (BMPs), in the lower Charles 

River Watershed. The BMPs evaluated include three 

categories of structural controls (filtration-infiltration 

structures, detention-retention basins, biofiltration systems) and street sweeping. Potential load reductions by 

structural controls were estimated from contaminantremoval efficiencies reported in three national BMP 

databases. The potential contaminant removal by street 

sweeping is presented from simulations made by 

calibrating a previously developed rainfall-runoff 

model of the single-family land-use subbasin to 

buildup and washoff of loads of suspended solids, fecal 

coliform bacteria, total phosphorus, and total lead. 

Removal rates by street sweeping were simulated for 

various sweeper efficiencies and sweeping frequencies. 

Finally, this report provides estimates of the combined 

potential load reductions that could be achieved by 

structural controls and street sweeping for the 2000 

water year (October 1, 1999, through September 30, 

2000), a “design year” or “typical year”, and 3-month 

and1-year design storms used by the MWRA for 

facilities planning. 
ATLANTIC

OCEAN

LOGAN 

AIRPORT

EASTERN 

MASSACHUSETTS

LOWER CHARLES

RIVER WATERSHED

UPPER 

CHARLES

RIVER 

WATERSHED

UNGAGED MODEL AREA UNGAGED MODEL AREA

WATERTOWN WATERTOWN ATERTOWN

CAMBRIDGE

NEWTON

BOSTON

BROOKLINE

MUSEUM

OF SCIENCE

MUDDY

RIVER 

SUBBASIN

STONY BROOK SUBBASIN

FANEUIL

BROOK 

SUBBASIN

LAUNDRY

BROOK 

SUBBASIN

NEW CHARLES

RIVER DAM

WATERTOWN 

DAM

UNGAGED MODEL AREA

Charles River

Village

Brook

Subbasin

Single-family

land-use subbasin

012 MILES

012 3 KILOMETERS

SURFACE WATER

MUNICIPAL BOUNDARY

SUBBASIN BOUNDARY

NESTED SUBBASINS

PRECIPITATION STATION

EXPLANATION

Introduction 3

Figure 1. Principal geographic features, precipitation stations, and subbasins of the lower Charles River Watershed, 

Massachusetts. 
4 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

Acknowledgments

The authors are grateful to Mark Voorhees of 

USEPA, Lise Marx of MWRA, and Kevin Brander of 

MADEP, for reviewing the project approach and the 

report manuscript. Jason Sorenson of the USGS did 

an initial literature review of contaminant-removal 

efficiencies associated with structural controls. The 

national data and methods synthesis on highway runoff 

and water-quality management controls by the USGS 

and Federal Highway Administration (FHWA) provided many useful references cited in this study 

(Granato, 2001).

STRUCTURAL CONTROLS

The feasibility of implementing structural 

controls in selected subbasins in the lower Charles 

River Watershed was investigated by the Center 

for Watershed Protection (1999). The Center for 

Watershed Protection (CWP) selected subbasins that 

represent a cross-section of land uses in the lower 

Charles River Watershed. For each of these subbasins 

they identified potential sites and structural controls 

that could be implemented given the land use and other 

constraints. The CWP presented a variety of conceptual 

designs and costs associated with the construction and 

maintenance of structural controls in their 1999 report 

to the USEPA; however, it should be recognized that 

many other types of structural controls, which were not 

considered in this analysis, particularly proprietary 

manufactured devices, could provide additional waterquality benefits. The CWP did not specify the removal 

efficiency or the potential contaminant-load reductions 

to the lower Charles River that could be realized by the 

management practices recommended. Without this 

information, appropriate management decisions may 

not be made regarding the applicability (planning, 

design, construction, and maintenance) of structural 

controls to reduce contaminant loading to the lower 

Charles River Watershed. 

Removal Efficiencies

Removal efficiencies for three types of structural 

controls identified for the lower Charles River 

Watershed were determined by compiling data from 

three national databases—the National Pollutant 

Removal Performance Database for Stormwater Best 

Management Practices (Brown and Schueler, 1997); 

Stormwater Best Management Practices in an UltraUrban Setting: Selection and Monitoring (Shoemaker 

and others, 2000); and the National Stormwater Best 

Management Practices Database (American Society of 

Civil Engineers, 2002). Studies cited in these databases 

that were relevant to this report are cross-referenced by 

researcher, study location, and date, and include a variety of researchers, municipalities, and state and gov

ernment agencies. Ancillary information needed to 

standardize removal-efficiency data to a common basis, 

however, was limited. For example, some of the studies 

reported more than one method to calculate removal 

efficiency, resulting in different reported efficiency values. Removal efficiencies are typically calculated by 

comparing contaminant concentrations or loads entering and exiting a structural control. Contaminant concentrations and loads can be instantaneous, eventbased, or yearly, and can be measured or modeled, or 

both. Furthermore, different values are reported for the 

same study in different databases; these discrepancies 

likely stem from different statistical methods used to 

calculate central tendency in each study. In these cases, 

the removal efficiency was averaged from the values 

given in different databases. 

Removal efficiencies of structural controls 

depend on many factors including (1) the type and 

design; (2) the intrinsic properties of the constituent 

being removed; (3) the interaction of constituents in 

stormwater (ions, trace elements, plant material, 

debris); (4) the fraction of the constituent that is suspended; (5) site characteristics such as soil type, catchment size, land use, percent impervious area, traffic 

volume, antecedent conditions (Schueler, 1987); 

(6) storm size and intensity, short circuiting or bypass 

issues, and retention time; (7) the geochemical environment [those chemical and physical variables that 

dictate how a contaminant might interact with its environment, including acidity (pH), ionic strength, temperature, and oxidation-reduction potential (Wilde, 

1994; Brown and Schueler, 1997; Bricker, 1999)]; 

(8) monitoring limitations, including study design, field 

methods, laboratory-analysis techniques, and the 

number of storms sampled (Breault and Granato, 2000; 

Tasker and Granato, 2000); and (9) climate and seasonality. In addition to the above factors, the age and maintenance history of a structural control can affect its 

performance. Most removal-efficiency studies have 

been conducted in the southeastern and western United 

States (Shoemaker and others, 2000). The removal 
Structural Controls 5

efficiency of structural controls in the lower Charles 

River Watershed could be appreciably different than 

that reported for studies in areas that do not have a distinct non-growing season.

Removal efficiencies of structural controls 

were grouped by similar major physical or chemical 

process (table 1). Grouping of similar control types 

provided a sufficient number of studies for a reasonable 

evaluation of the central tendency of removal efficiency, given that some controls have been little 

studied. Controls were grouped by the following categories—infiltration-filtration (physical separation), 

biofiltration-bioretention (biological mechanisms), or 

detention-retention (physical settling). Changes in 

stormwater loads by structural controls identified by 

CWP were estimated from the upper and lower quartiles, median and average removal efficiencies compiled for each control category (fig. 2). Existing ponds 

identified by CWP as detention-retention type controls 

were not considered in this study because their effects 

are already incorporated into load esti-mates to the 

lower Charles River. The removal efficiencies obtained 

are summarized below for total suspended solids, fecal 

coliform bacteria, total phosphorus, and total lead. A 

negative efficiency indicates an increase in the contaminant load at the outflow relative to the inflow to the 

control by remobilization of previously settled contaminants, or measurement error in the load values used to 

calculate removal efficiency. 

Suspended Solids: Removal efficiencies for suspended solids were reported in 93 percent of the 224 

studies examined in the national databases and ranged 

from -170 to nearly 100 percent among control categories. Infiltration-filtration controls generally were most 

efficient at removing solids (averaged 78 percent) and 

biofiltration-bioretention controls were generally least 

efficient (averaged 45 percent). Detention-retention 

controls removed 62 percent of the suspended solids, 

on average. Calculated removal efficiencies can be 

affected by the analytical method used to determine 

solids loads. Gray and others (2000) found that the 

sand-sized fraction can be appreciably underreported 

for suspended solids in samples analyzed from pipetted 

aliquots (historically referred to as total suspended 

solids—TSS) relative to samples analyzed from filtered 

samples (historically referred to as suspended solids 

concentration—SSC) when the sand fraction represents 

more than about 25 percent of the total solids present. 

As a result, studies in which samples were analyzed for 

SSC can underestimate the removal efficiency of the 

BMP because the coarse material, which would likely 

be removed by the BMP, was underreported at the 

inflow (Bent and others, 2001). 

Fecal Coliform Bacteria: Removal efficiencies 

for fecal coliform bacteria, reported in 28 percent of the 

224 studies examined in the databases, ranged from 

-600 to 99 percent among control categories. The average removal efficiencies for infiltration-filtration and 

detention-retention type controls were about 32 and 43

Table 1. Categories of structural-control types, their characteristics, and the major physical or chemical processes that affect 

water quality 

[Modified from Shoemaker and others, 2000]

Category Types of controls Comments Major physical or

chemical process

InfiltrationFiltrationInfiltration trenches, infiltration 

basins, underground filters, surface 

filters, organic-media filters, porous 

pavement

Soil chemical and physical characteristics 

affect efficiency; effective suspended 

solids removal; regular maintenance 

essential to prevent clogging

Infiltration, adsorption, straining, 

chemical transformation, 

microbial decomposition

BiofiltrationBioretentionBioretention, dry and wet swales, 

vegetated filter strips

Soil chemical and physical characteristics 

affect efficiency; low cost; easy to install

Biodegradation, precipitation, 

infiltration, filtration, 

adsorption, precipitation 

processes

DetentionRetentionDetention ponds, wetlands/shallow 

marsh systems, detention tanks and 

vaults, oil-grit separators, catchbasin inserts, manufactured systems

Adequate hydrologic detention time 

required; mainly pretreatment

Particulate settling and biological 

filtering (wetlands)
REMOVAL EFFICIENCY, IN PERCENT

InfiltrationFiltrationBiofiltrationBioretentionDetentionRetention

InfiltrationFiltrationBiofiltrationBioretentionDetentionRetention

STRUCTURAL CONTROL TYPE

FECAL COLIFORM BACTERIA

-100

-80

-60

-40

-20

0

20

40

60

80

100

TOTAL PHOSPHORUS TOTAL LEAD

-100

-80

-60

-40

-20

0

20

40

60

80

100

SUSPENDED SOLIDS

(57) (32) (21) (36) (8) (21)

(68) (39) (102) (68) (41) (99)

(39)

EXPLANATION

Median

25th Percentile

75th Percentile

10th Percentile

90th Percentile

Average

Number of 

observations

6 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

percent, respectively, whereas biofiltration-bioretention 

controls averaged a -3 percent efficiency. Average 

removal efficiencies for fecal coliform bacteria were 

lowest compared to the removal efficiencies for the 

other constituents examined for each control type.

Total Phosphorus: Removal efficiencies for total 

phosphorus were reported in 93 percent of the 224 

studies examined in the national databases and ranged 

from -162 to 100 percent among control types. In 

general, removal was most efficient for infiltrationfiltration controls (averaged 56 percent) and least 

efficient for biofiltration-bioretention controls (averaged 32 percent); detention-retention controls averaged 

46 percent removal. The average removal efficiency for 

total phosphorus was greater than the average for fecal 

coliform bacteria, but less than the average removal 

efficiency for suspended solids and total lead for each 

type of control.

Total Lead: Removal efficiencies for total lead 

were reported in 70 percent of the 224 studies examined in the national databases; removal efficiencies 

were similar to those reported for suspended solids 

Figure 2. Distribution of constituent removal efficiencies for structural controls, summarized by control type.
Structural Controls 7

among control types and ranged from -162 to 100 percent. In general, removal was most efficient for infiltration-filtration controls (averaged 66 percent) and least 

efficient for biofiltration-bioretention controls (averaged 34 percent); detention-retention controls averaged 

54 percent removal. The similarity between the total 

lead and suspended solids removal is consistent with 

the known affinity between these constituents (Breault 

and Granato, 2000).

Average removal efficiencies for each of the 

constituents examined were generally greatest for 

infiltration-filtration controls and least for biofiltrationbioretention controls. For each control type, average 

removal efficiencies were greatest for suspended solids 

and total lead and least for fecal coliform bacteria and 

total phosphorus. In general, the average removal efficiency was less than the median efficiency; this discrepancy indicates that the average efficiency is skewed 

by small or negative removals. 

Estimated ContaminantLoad Removal in the 

Village Brook Subbasin

Contaminant-load reductions by hypothetical 

structural controls were determined for the Village 

Brook Subbasin in Brookline. The Village Brook Subbasin was selected as a “template” for potential load 

reductions across the lower Charles River Watershed 

for the following reasons:

• Potential construction and implementation of 

structural controls in the Village Brook Subbasin 

were more thoroughly evaluated by the CWP than 

controls in other subbasins in the lower Charles 

River Watershed (Center for Watershed 

Protection, 1999);

• Land use in the Village Brook Subbasin is generally 

representative of land use in the lower Charles 

River Watershed (fig. 3A);

• Road types and densities in the Village Brook 

Subbasin are generally representative of the roads 

in the lower Charles River Watershed (fig. 3B);

• The Village Brook Subbasin is relatively large, 

composing about 10 percent of the lower Charles 

River Watershed area;

• The types of BMP designs and the drainage areas to 

the controls selected for the Village Brook 

Subbasin are typical of structural BMPs identified 

by the CWP.

The CWP identified 19 potential structural controls in the Village Brook Subbasin that are mostly of 

the infiltration-filtration and biofiltration-bioretention 

types. Control types identified by CWP included bioretention structures, sand filters, dry swales, ponds, and 

forebays (table 2). Loads removed from existing ponds 

were not considered in this study, because the load 

reductions by these features were already incorporated 

into the lower Charles River loading estimates by 

Breault and others (2002); inclusion of these features 

would erroneously double their calculated effects on 

potential load reductions to the river. Drainage areas to 

the proposed structural controls range from less than an 

acre to 145 acres, but generally are only a few acres or 

less (median of 3.4 acres). Collectively, the contributing drainage area to the controls (471 acres) is about 21 

percent of the Village Brook Subbasin. When the contributing drainage areas were normalized to 1.0 in. of 

treated runoff (drainage area multiplied by the inches 

of runoff treated by the control), the effective contributing drainage area to the controls was less than 10 percent of the Village Brook drainage area, because most 

of the controls treat only the first 0.5 in. of runoff. For 

example, the drainage area to VB-5 (65.0 acres) was 

multiplied by the inches of runoff reportedly treated by 

the control (0.5 in.) to yield the effective treated area 

for 1.0 in of rainfall (32.5 acre-inch).

The potential contaminant load reduction by

the proposed structural controls in the Village Brook 

Subbasin first required estimates of inflow loads to 

each control. Inflow loads (Lin(i, j) in equation 1) to 

each control were estimated from the yields (mass 

per unit area per unit time) reported by Breault and 

others (2002) from the single-family land-use subbasin 

multiplied by the drainage area to each control. The 

single-family land-use subbasin yields were chosen 

because the Village Brook Subbasin is predominantly 

single family (54 percent) and because the singlefamily land-use subbasin yields were believed to be 

the least affected by non-stormwater contamination. 
Forest Transportation

Single 

family

>1/2 

acre

Single

 family

1/4-1/2 

acre

Single

Family

 <1/4 

acre

MultifamilyOpen

Space

Institutional 

Space

Commercial

Industrial

Water Agriculture

0

5

15

20

25

30

35

10

PERCENT

LAND-USE CATEGORY

0

2

4

6

8

10

12

14

16

Interstate Other 

Urban Principal 

Arterial

Minor 

Arterial 

ROAD DENSITY, 

IN MILES PER SQUARE MILE

FUNCTIONAL CLASS OF URBAN ROADS

LOWER CHARLES 

RIVER WATERSHED

VILLAGE BROOK SUBBASIN

EXPLANATION

A. LAND USE

B. ROAD TYPES

8 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

Figure 3. Comparison of (A) land use and (B) road types in the lower Charles River Watershed and Village Brook 

Subbasin. 
Structural Controls 9

The removal efficiency (Eff(i, j) in equation 1) for each 

constituent (i) was multiplied by Lin(i, j) to determine 

the mass retained by each control (j). The mass retained 

by each control was calculated for the median, upper 

quartile, lower quartile, and average removal 

efficiencies (table 3) to provide a range of load 

reductions that can be expected on the basis of 

performance data reported in the literature. The load 

exiting from each control (Lout(i, j)) was calculated by: 

Lout( ) i j , Lin( ) i j , Lin( ) i j , Eff( ) i j , = – ( ) × , (1)

where

Lin(i, j) is the load of constituent i entering control j, in 

mass per unit time;

Eff(i, j) is the removal efficiency (percent) of 

constituent i for control j; and,

Lout(i, j) is the load of constituent i exiting control j, in 

mass per unit time.

The potential percentage of contaminant load 

that could be removed in the Village Brook Subbasin 

by structural controls was:

PLRi

Lin( ) i j , Lout( ) i j , ( ) –

j = 1

n

∑

Li

---------------------------------------------------------

   

 

= , (2)

Table 2. Inventory of structural controls identified by the Center for Watershed Protection for the Village Brook Subbasin, lower 

Charles River Watershed, Massachusetts 

[Modified from Brown and Schueler, 1997. CWP, Center for Watershed Protection; --, means not applicable because the load removal by these existing ponds 

was already incorporated into the lower Charles River loading estimates]

CWP

No. Category

Contributing

drainage area

(acres)

Runoff 

treated

(inches)

Site location

VB-1 Biofiltration-Bioretention 1.47 1.0 Boston College Alumni Field Parking

VB-2 Biofiltration-Bioretention 28.0 1.0 Cleveland Circle

VB-3 Infiltration-Filtration 3.01 .5 Chestnut Hill near Cleveland Circle

VB-4 Infiltration-Filtration 60.0 .5 Waldstein Playground

VB-5 Detention-Retention 65.0 .5 Reservoir Road and Crafts Road

VB-6 Infiltration-Filtration, 

Biofiltration-Bioretention

3.81 .5 Boylston Street near Reservoir Road

VB-7 Biofiltration-Bioretention 1.94 1.0 Fairway Road near Reservoir Road

VB-8 Biofiltration-Bioretention 4.03 1.0 Runkle Elementary School

VB-9 Biofiltration-Bioretention 3.01 .5 Newbury College

VB-10 Biofiltration-Bioretention 1.47 .75 Newbury College

VB-11 Infiltration-Filtration 1.02 1.0 Lincoln Primary School

VB-12 Infiltration-Filtration 50.0 .5 Cypress Playground

VB-13 Infiltration-Filtration 100 .33 Robinson Playground

VB-14 Biofiltration-Bioretention .32 1.0 Brookline Public Housing near Chestnut Street and Pond Avenue

VB-15 Infiltration-Filtration 145 .25 Adjacent to Willow Pond

VB-16 Detention-Retention -- -- Willow Pond

VB-17 Detention-Retention -- -- Leverett Pond

VB-18 Biofiltration-Bioretention 3.01 1.0 Park near North End of Leverett Pond

VB-19 Detention-Retention -- -- Village Brook Outfall to Leverett Pond

where

PLRi is the percent load reduction of constituent i;

Lin(i, j) is the load of constituent i entering control j, in 

mass per unit time;

Lout(i, j) is the load of constituent i exiting control j, in 

mass per unit time;

n is the number of controls in the subbasin; and

Li is the total uncontrolled subbasin load for 

constituent i, in mass per time.
Table 3. Estimated removal efficiencies in stormwater loads 

and total loads by hypothetical structural controls in the 

Village Brook Subbasin, lower Charles River Watershed, 

Massachusetts

Measure

Percent removal

Suspended

solids

Fecal

coliform 

Total

phosphorus

Total

lead

Stormwater load

Lower quartile 15 7.8 7.5 9.3

Median 18 13 12 15

Upper quartile 19 16 17 18

Average 15 6.4 11 13

Total load

Lower quartile 14 7.5 6.2 8.9

Median 17 13 10 15

Upper quartile 18 16 14 17

Average 15 6.2 9.2 12

10 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

The stormwater load PLRi for the Village Brook 

Subbasin determined from the average removal efficiency (fig. 2) was 15 percent for suspended solids, 6.4 

percent for fecal coliform bacteria, 11 percent for total 

phosphorus, and 13 percent for total lead (table 3). 

The PLRi decreased slightly for the annual loads 

because the dry-weather load (for example, contaminants conveyed by base flow from sources such as 

illicit connections, wash waters, and contaminated 

ground water) would not be affected by structural 

stormwater controls. The annual load decrease was 

the most pronounced for total phosphorus because a 

larger percentage of its total annual load was associated 

with dry weather. 

STREET SWEEPING

Street sweeping as a water-quality management 

practice has received renewed attention by stormwater 

managers in recent years. This renewed interest stems 

from reported improvements in sweeper technology 

and the recognition that opportunities for implementing 

structural controls are limited in highly urbanized 

areas. Street sweeping affects only areas that are 

sweepable; hence, understanding the potential effectiveness of this management practice as a water-quality 

control requires knowledge of the relative importance 

of and the buildup process for contaminants originating 

from streets. The characteristics of street contaminants, 

reviewed from the literature, provided a framework

for modifying the StormWater Management Model 

(SWMM) (Huber and Dickinson, 1992). The SWMM 

application developed by the USGS to simulate runoff 

in the single-family land-use basin of the lower 

Charles River Watershed (Zarriello and Barlow, 2002) 

was modified to simulate contaminant buildup and 

washoff, and the effects of street sweeping, on selected 

constituents in this basin.

Contaminants on Streets

Reported loading from street surfaces varies 

widely among source area studies and from storm to 

storm within the same study. Pitt and Bozeman (1980), 

Bannerman and others (1993), Pitt and others (1995), 

Steuer and others (1997), and Smith (2002) attribute 

this variability to many factors, including differences in 

basin characteristics, study methods, and the inherent 

variability in the source loading factors. Consequently, 

it would be unreasonable to assume that constituent 

buildup and loadings from streets can be represented 

with great certainty. Nonetheless, the literature was 

reviewed to establish the relative importance of streets 

as sources of water-quality contaminants and to guide 

the SWMM water-quality buildup and washoff calibration. Emphasis was given to recent publications 

because these studies represent advances in sampling 

and analytical methods compared to the main body of 

literature, which was completed in the late 1970s and 

early 1980s under the National Urban Runoff Program 

(NURP).

In summarizing the studies, Sartor and Gaboury 

(1984) report that on average, one kilogram of street 

dirt contains 3 million colony forming units (CFU) of 

fecal coliform bacteria. Burnhart (undated) examined 

sources of bacteria in stormwater at commercial, industrial, and residential-institutional land-use sites in 

Wisconsin. Runoff samples were collected from streets, 

parking lots, roofs, lawns, sidewalks, and driveways. 

These samples indicated that nearly 92 percent of the 

bacteria originated from streets in the residential-institutional land-use site, whereas only about 33 and 19 

percent of the bacteria originated from streets in the 

industrial and commercial land-use sites, respectively. 

Burnhart suggests that bacteria incubate in puddles on 

street surfaces between storms. He also concluded that 

dog feces accounted for only about 12 percent of the 

total bacteria at the storm-sewer outfall. 
Street Sweeping 11

Steuer and others (1997) examined source loads 

from a predominantly residential basin in Marquette, 

Michigan. Their findings indicate that, for a given constituent, areas producing the highest concentrations can 

change from storm to storm, which they suggest is 

caused by varying buildup mechanisms. The dominant 

source of suspended solids was from streets with low 

traffic volume (27 percent) and the dominant source of 

total phosphorus (26 percent) was from lawns. For each 

type of source area, Steuer and others (1997) compared 

the ratio of constituent load from each source area to 

the total basin load, to the ratio of the runoff volume 

from that type of source area to the total basin runoff 

volume. This comparison indicated that suspended 

solids were proportional to runoff volume from low 

traffic streets, but lawns contributed a total phosphorus 

load that was 4.5 times their contributing runoff 

volume. 

Waschbusch and others (1999) collected runoff 

samples from two predominantly residential land-use 

subbasins. Sample analysis indicated that streets were 

the source of most suspended solids (73 and 81 percent 

of the total load for the two study sites), which is about 

twice the percentage of the runoff volume from streets 

(37 and 38 percent of the total basin runoff). Total 

phosphorus loads were proportional to the percentage 

of runoff at one site (37 percent of the total) and about 

half of the percentage of runoff at the other site (14

percent). 

Bannerman and others (1993) reported that 78 

percent of the fecal coliform bacteria load for one of 

the same residential land-use study subbasins studied 

by Waschbusch and others (1999) originated from 

streets. Bannerman and others (1993) report that for 

most constituents, 75 percent or more of the total residential basin loads originate from street surfaces; total 

phosphorus was one of the exceptions, which originated mostly from driveways and lawns. This study 

also concluded that streets and parking lots are a critical source area for many contaminants in the commercial and industrial land-use areas. It was further concluded that best management practices that target 

streets and parking lots would provide the most costeffective way for controlling contaminant loads.

Baldys and others (1998) investigated urban 

stormwater quality from 26 basins in Dallas–Fort 

Worth, Texas, and found that residential land-use 

basins produced higher concentrations of bacteria and 

nutrients than commercial and industrial land-use 

basins. Industrial land-use basins, however, produced 

higher concentrations of suspended solids and trace 

metals than did the residential and commercial 

land-use basins.

Pitt (1985) characterized buildup of street dirt 

from two residential areas in Bellevue, Washington, 

and summarized findings by others. Initial loadings of 

solids (load on streets after the first few days following 

a cleansing storm) ranged from 59 to 1,130 kg/curb-mi; 

the initial median load was 320 kg/curb-mi and the 

mean was about 370 kg/curb-mi. Accumulation rate of 

solids on streets several days after a cleansing storm 

ranged from 2.6 to 119 kg/curb-mi/d; median and mean 

accumulation rates were 2.2 to 33 kg/curb-mi/d, 

respectively. Initial loading of solids on rough streets 

was an average of seven times greater than the initial 

loading on smooth streets and as much as 14 times 

greater than the accumulation rate on smooth streets 

several days after a storm. Bender and Terstriep 

(1984) reported an average solids buildup rate of 

1.2 kg/curb-mi/d on a residential basin in Champaign, 

Illinois.

Pitt and others (1995) examined the toxicity of 

runoff from urban areas and found that most runoff 

originating from streets (67 percent of the samples 

taken) was moderately toxic. Buckler and Granato 

(1999) reviewed existing highway-runoff studies and 

determined that road runoff was not always acutely 

toxic, but was associated with adverse ecological 

effects, including increased contaminant concentrations in aquatic organisms and receiving waters, and 

decreased aquatic-organism diversity. Findings from 

these studies underscore the importance of streets as 

a source of contaminants to receiving waters and 

the need to control contaminated runoff from street 

surfaces.

Fine-grained particles provide the main bonding 

sites for contaminant adsorption. The amount of phosphorus adsorbed to clay-size minerals depends on the 

phosphorus concentrations and the number of available 

ion-exchange sites (White, 1981). Sartor and others 

(1972) found that particles less that 104 μm had 

adsorbed 86 percent of the street phosphates, but made 

up only 16 percent of the suspended solids by dry 

weight. Sartor and Gaboury (1984) summarized pollutant concentrations as a function of particle size from 

the NURP studies; these studies indicated that 
12 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

fine-grained size fractions (smaller than 250 μm) 

adsorbed 62 percent of the total lead but made up only 

42 percent of the mass by dry weight. Sartor and Boyd 

(1975) reported that 56 percent of the phosphates were 

associated with particles smaller than 43 μm, but made 

up only 6 percent of the suspended solids by dry weight 

in street runoff from a low-density road area. The studies by Sartor and Boyd (1972) and Sansalone and 

others (1995) found that heavy metals were more 

equally distributed among size fractions, but still 

tended to have more of their mass associated with finegrained particles. Sartor and Boyd (1972) reported that 

50 percent of metals were sorbed to particles smaller 

than 43 μm. Street sweeping was found to have little 

effect on water quality by the Washington State Dept. 

of Transportation (1995), because the sweepers did not 

effectively remove fine particles. Thus, it can be 

expected that sweepers that are ineffective in removing 

fine-grained particles will be less effective in removing 

constituents associated with those particles. 

Street Sweeping as a 

Water-Quality-Management 

Practice 

Efficiencies of street sweepers in removing dirt 

and associated contaminants differ widely. Few studies 

report the sweeper efficiencies for removing phosphorus and metals, and information about the removal of 

bacteria by street sweeping is virtually nonexistent. 

Most street sweeper studies have examined the effects 

of sweeping by analysis of end-of-pipe runoff from 

relatively large drainage areas. Hence, the efficiencies 

reported could reflect factors other than sweeper 

performance.

Sweeper Types and 

Efficiencies

Three types of street sweepers are commonly in 

use: mechanical, vacuum-assisted, and regenerative air. 

Performance of vacuum and regenerative-air sweepers 

is poorly documented in the peer-reviewed literature. 

Most literature on these types of sweepers is published 

in trade journals or other non-scientific publications. 

Mechanical sweepers lift dirt off the street by a 

rotating broom and feed it to a hopper by a conveyor 

system. A water spray is often used to control dust. 

This is the most common type of sweeper, but typically 

removes only coarse particles (larger than 400 μm) and 

is ineffective at removing fine particles (Horner and 

others, 1994). Most National Urban Runoff Program 

(NURP) studies conducted in the late 1970s and early 

1980s examined the effectiveness of mechanical 

sweepers; those studies concluded that street sweeping 

was not a viable water-quality management practice 

(Smith and Lord, 1990; Sartor and Gaboury, 1984; 

Athayde and others, 1983; Sartor and Boyd, 1975). 

Under favorable conditions, Sartor and Gaboury (1984) 

state that end-of-pipe loads of solids can be decreased 

by 30 percent by street sweeping. One key criterion for 

favorable performance is a distinct wet and dry season, 

a climatic condition that is not typical of the Boston 

area.

Bender and Terstriep (1984) examined the effectiveness of brush-style sweepers in a residential and 

commercial land-use basin in Champaign, Illinois; the 

effectiveness of solids removal by sweeping averaged 

42 percent (with a range of 14 to 55 percent for one 

sweeping per week and 23 to 62 percent for two sweepings per week). They found that particles smaller than 

250 μm were poorly captured by sweeping, as evidenced by the mean size of particulates remaining on 

the street after sweeping. Their findings also indicate 

that log-normal distributions of event mean concentrations (EMCs) before and after street sweeping were not 

significantly different for the residential basin at the 

90-percent confidence interval for most constituents, 

including the constituents examined in this study. 

Shoemaker and others (2000) reported that 

mechanical sweepers removed 55 percent of suspended 

solids, but the efficiency of mechanical sweepers in 

capturing particles smaller than 10 μm ranged from 

-6.7 to 8.6 percent. A negative efficiency indicates that 

more material was added than removed, possibly 

because the abrasive action of the mechanical sweeper 

broke large particles into small ones.

Pitt (1985) concluded that sweeper performance 

was a function of the initial constituent load; the effectiveness of street sweeping was poor to negligible until 

a minimal initial load existed at which point the effectiveness would slowly approach 30 percent for suspended solids. Pitt also concluded that intensive street 

sweeping may remove only a tenth of the constituent 

load associated with particulate matter.

The Northern Virginia Planning District 

Commission (1992) reported that a mechanical street 

sweeper making one pass per week in a mediumdensity residential area removed 9 to 11 percent of 
Street Sweeping 13

the total phosphorus load and 13 to 15 percent of the 

total lead load. Shoemaker and others (2000) report 

mechanical sweep efficiencies of 40 percent for total 

phosphorus and 35 percent for total lead.

Vacuum-assisted sweepers combine a mechanical sweeper (such as a rotating broom) with a highpower vacuum. Some vacuum-assisted sweepers

use a water spray to control dust and others operate 

completely dry with a continuous filtration system. 

Shoemaker and others (2000) report removal efficiencies of 93 percent for suspended solids, 74 percent 

for total phosphorus, and 76 percent for total lead 

for dry vacuum-assisted sweeper. Shoemaker and 

others (2000) also report that two different models of 

wet vacuum-assisted sweepers removed 40 and 82

percent of particles finer than 10 μm and a dry vacuumassisted sweeper removed nearly 100 percent of 

particles finer than 10 μm. 

The Northern Virginia Planning District 

Commission (1992) reported that one pass per week 

with a vacuum-assisted sweeper in a medium-density 

residential area removed 14 to 18 percent of the total 

phosphorus load and 28 to 33 percent of the total lead 

load. The type of vacuum-assisted sweeper was not 

identified in this report, but nevertheless, the removal 

rates reported were about twice those reported for 

mechanical sweepers. The Northern Virginia Planning 

District Commission, in conjunction with the Lake 

Barcroft Watershed Improvement District (Finely, 

2000), found that a dry-vacuum-assisted sweeper 

removed 141 percent more material than a modern 

mechanical sweeper it swept directly behind and 44 

percent more material than a regenerative air sweeper it 

swept directly behind.

The Terrene Institute (1998) reported that dryvacuum-assisted sweepers reduced solid loads by 35 to 

80 percent and nutrients loads by 15 to 40 percent. 

Bannerman (1999) stated that dry vacuum-assisted 

sweepers removed as much as 98 percent of the street 

dirt, including the fine-grained size fraction. The 

removal rate reported for nutrients by the Terrene 

Institute appears to be inconsistent with the solids 

removal rate reported by Bannerman; comparable 

removal rates would be expected for nutrients if finegrained particles were effectively removed. Sutherland 

and Jelen (1997) reported simulated removal rates for 

solids by dry vacuum-assisted sweepers of 87 percent 

when swept twice per week to 51 percent when swept 

once a month.

Regenerative-air sweepers combine a mechanical sweeper to loosen dirt with forced air to dislodge 

the remaining dirt. A high-power vacuum with a continuous filtration system captures the dirt and recycles 

the air. Shoemaker and others (2000) reported a regenerative-air sweeper removed 31 percent of particles 

smaller than 10 μm. Sutherland and Jelen (1996) 

reported simulated removal rates for solids by regenerative-air sweepers of 71 percent when swept twice per 

week to 43 percent when swept once a month. 

Factors that Affect 

Sweeper Performance

Accumulation of dirt and associated pollutants 

depends in large part on whether the street is curbed 

(Smith and Lord, 1990). The Northern Virginia 

Planning District Commission (1992) found that 90 

percent of the roadside dirt accumulates within the first 

foot of the curb. The Federal Highway Administration 

(Young and others, 1996) suggested that eliminating 

curbs allows scattering of particulates onto vegetative 

areas and can reduce buildup of street dirt and pollutants. The simulated buildup rates described later in this 

report are typical of curbed streets, which are characteristic of most streets in the study area. Buildup rates 

in this analysis may overestimate buildup of dirt on 

non-curbed streets.

One of the major impediments to removing 

debris from streets is vehicular parking or other 

obstructions that prevent effective sweeping (Camp, 

Dresser, and McKee, 1993; Washington State Department of Ecology, 1999; Northern Virginia Planning 

District Commission, 1992; Washington State Department of Transportation, 1995). Thus, street parking 

would need to be regulated to allow effective sweeping. 

Street condition and material also can affect the 

performance of street sweepers (Pitt, 1985; Shoemaker 

and others, 2000). Streets in poor condition (cracked 

and broken pavement) decrease the effectiveness of 

street sweeping because particles lodge in the irregularities of the street surface, but these particles could wash 

off during storms. Street condition likely affects 

mechanical sweepers more than vacuum or regenerative air sweepers; however, no information was found 

concerning sweeper performance under different street 

conditions. The effectiveness of mechanical and 

vacuum sweepers decreases if the street surface is wet 

(Shoemaker and others, 2000). 
14 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

The effectiveness of street sweeping can increase 

if multiple passes are made; the Washington State 

Department of Ecology (1999) found that 50 percent of 

the solids were removed by one pass and 75 percent 

with two passes of a mechanical sweeper. Operation 

speed also affects the effectiveness of street sweepers. 

Sweepers are typically designed to operate at a maximum efficiency of 6 to 8 mi/hr (Shoemaker and others, 

2000); sweepers operated at higher speeds have 

decreased effectiveness. 

Model Simulation

The rainfall-runoff model, SWMM, previously 

developed by Zarriello and Barlow (2002) to simulate 

runoff in the single-family residential land-use 

subbasin, was modified to include simulation of constituent buildup, washoff, and the effects of street 

sweeping on suspended solids, fecal coliform bacteria, 

total phosphorus, and total lead in stormwater runoff. 

This subbasin was selected to simulate the effects of 

street sweeping because it was previously calibrated to 

simulate storm runoff (Zarriello and Barlow, 2002) and 

because constituent loads were measured during multiple storms (Breault and others, 2002). This subbasin 

also represents the predominant land-use type in the 

lower Charles River Watershed.

The single-family residential land-use subbasin 

model was modified from one subcatchment into two 

to allow simulation of two land-use types—street and 

non-street. Separate simulation of street and non-street 

types allows sweeping to be applied only to the street 

subcatchment. While the model availability factor 

(AVSWP) could have been used to scale street sweeping to the area that is sweepable in the single subcatchment model, the AVSWP variable represents a direct 

fraction of the sweepable area in the two-subcatchment 

model. Therefore, the value of AVSWP can be easily 

modified to test alternative availability factors without 

having to scale the value by the proportion of street and 

non-street areas. The separate land-use model also 

facilitates extrapolation of street-sweeping effects to 

other areas based on road density.

The street subcatchment area (39.6 acres) 

was set equal to the effective impervious area of the 

single subcatchment model (17.4 percent of 227.7 

acres) previously obtained from model calibration 

and examination of rainfall-runoff coefficients for 

small summer storms (Zarriello and Barlow, 2002) 

because the street subcatchment area is about equal 

to the total subbasin road length (9.8 mi) times a width 

of 33 ft. It was assumed that streets primarily represent 

the effective impervious area (areas that drain directly 

to the storm-sewer system) in the subbasin and that 

other impervious surfaces such as driveways and

rooftops are primarily non-effective (because they 

drain onto pervious areas). Simulated runoff by the 

two-subcatchment model is the same as the runoff 

simulated by the single-subcatchment model.

The two-subcatchment model also enabled constituent buildup to be treated differently for each landuse type. Constituent buildup was simulated on streets 

as a load per unit curb length by the Michaelis-Menton 

method (Huber and Dickinson, 1992). This method 

computes constituent buildup as a rapid initial rate that 

asymptotically approaches a defined limit by: 

Q QFACT( ) 1 × t

QFACT( ) 3 + t = ------------------------------------ , (3)

where

Q is the built-up load (mass),

QFACT(1) is the buildup limit (mass per 100 ft of road 

curb),

QFACT(3) is time for the buildup load to reach half the 

buildup limit (day) and

t is the elapsed time (day).

In a summary of several studies across the United 

States, Sartor and Boyd (1972) indicated that solids 

buildup on residential streets follows a pattern best 

described by the Michaelis-Menton method. Ball and 

others (1996), however, concluded that the buildup of 

solids and metals on a suburban street in Sydney, 

Australia, approximates a power function, but 

attributed this difference to climate differences and 

changes in source loadings rates since the early 1970s. 

These findings underscore that constituent buildup 

processes are poorly understood. Variable values used 

to simulate buildup are generally obtained from model 

calibration and are not measured directly. 
Street Sweeping 15

Non-street constituent buildup was simulated as 

a load per unit area by a power equation. Constituent 

buildup is less rapid than with the Michaelis-Menton 

equation initially, but both methods limit constituent 

buildup by a user-defined value. 

Washoff from impervious and pervious surfaces 

is simulated by the same method in SWMM. The 

washoff of constituent load built up over the catchment 

is estimated by the following equation:

POFF RCOEF 3600 ( ) ⁄ r

WASHPO = × × PSHED , (4)

where:

POFF is the load washed off at each time step 

(mass per sec),

RCOEF is a washoff coefficient (in/hr-WASHPO/s),

r is the runoff rate (in/hr),

WASHPO is an exponent of the runoff rate (unitless), 

and

PSHED is the quantity of the constituent built up at 

each time step (mass).

The constituent washoff load (POFF) is subtracted 

from the quantity of the constituent built up over the 

catchment (load available for washoff) at each time 

step to calculate the new load remaining on the 

subcatchment. Examination of the washoff equation 

indicates that increasing the value of WASHPO results 

in lower values of POFF because runoff (r), in inches 

per hour, is typically less than one. Land-use types 

were assigned different WASHPO and RCOEF values 

for each constituent. Neither buildup or washoff 

variable values are allowed to change seasonally in 

SWMM.

The dry-weather load was considered unavailable to street sweeping because this load is transported 

by ground water and is not considered part of the builtup load that is sweepable. To simulate dry-weather 

loads described by Breault and others (2002), a constant constituent concentration was specified for 

ground water to preclude this portion of the subbasin 

load from being swept. Loads that result from erosion 

or precipitation contributions, and load removals by 

catch basins (structures that trap solids) were not 

included in the simulations. Erosion was considered 

negligible because this subbasin is an older, undisturbed residential area that channels runoff in streets 

and storm drains that are not prone to erosion. Erosion 

could be a factor in disturbed areas and in areas that are 

not storm sewered, which would diminish the relative 

benefits of street sweeping. The quality of precipitation 

could affect the source loadings for total phosphorus 

simulations (Colman and others, 2001), but is likely 

not a factor for the other constituents simulated. Huber 

and Dickinson (1992) state that catch basins usually 

have a negligible effect on simulation results, and 

efforts to simulate these features are seldom justified. 

In addition, the estimated constituent loads by Breault 

and others (2002) reflect the effects of existing catch 

basins in the watershed, which in turn have been incorporated into the variable values for the calibrated 

model.

Calibration

Model-simulated constituent loads for the singlefamily land-use subbasin were calibrated to loads 

reported by Breault and others (2002) for the 2000 

water year. Annual dry-weather, wet-weather, and 

total loads were calibrated to the average-mean and 

weighted-mean loads reported (table 4). Although 

the simulated and reported annual loads match closely, 

the model representation of source areas, buildup 

processes and washoff rates could be non-unique. 

Therefore, this calibration does not ensure an accurate, 

predictive water-quality model. Other combinations of 

process-variable values could produce similar results.

Table 4. Reported and simulated constituent loads for the 

single-family land-use subbasin, 2000 water year, lower 

Charles River Watershed, Massachusetts

[Reported values are the average of the average-mean and weighted-mean 

loads reported by Breault and others, 2002. TCFU, trillion colony forming 

units] 

Annual 2000 

water year 

Suspended

solids

(kilograms)

Fecal 

coliform

(TCFU)

Total

phosphorus

(kilograms)

Total

lead

(grams)

Dry

Reported 522 9.24 23.8 313

Simulated 522 9.25 23.8 317

Wet

Reported 13,200 48.3 55.1 5,850

Simulated 13,200 53.4 55.1 5,840

Total 

Reported 13,700 57.6 78.9 6,160

Simulated 13,700 57.5 78.9 6,160
16 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

Alternative model variable values can also affect 

load reductions obtained by street sweeping as 

described later in the report.

After the model was calibrated to annual 

loads, the simulated loads for eight sampled storms 

were compared to the measured loads reported by 

Breault and others (2002). The sampled storms represent the best available individual storm-load data 

for evaluating and adjusting the water-quality simulations. The initial load for suspended solids was 

oversimulated for largest storm (June 6, 2000) and 

all but the smallest storms were undersimulated. The 

buildup and washoff variables were adjusted to 

better match individual storm loads while the annual 

load calibration was maintained. The final calibrated 

variable values (table 5) provided a close match 

between simulated and measured storm loads 

(fig. 4). The average error (AE) and the root mean 

square error (RMSE) of the percent difference 

between measured and simulated values were computed (table 6). These values provide a measure of 

the model accuracy within the range of storm conditions sampled and the accuracy of the estimated 

loads. AE and RMSE are calculated by

AE ∑r

n

------     = × 100 , (5)

RMSE ∑r

2

n

--------       = × 100 , (6)

where

r

simulated value observed – value

observed value = ----------------------------------------------------------------------------------------- ,

and

n number = of storms.

The AE and RMSE were least for total lead 

and greatest for fecal coliform bacteria. The AE for 

suspended solids and total lead loads indicated that 

the model undersimulated these loads when the 

runoff was undersimulated and oversimulated loads 

when the runoff was oversimulated for about half 

the storms. Fecal coliform bacteria colonies were 

oversimulated when runoff was oversimulated and 

undersimulated when runoff was undersimulated for 

five of the eight sampled storms. Total phosphorus 

loads were generally undersimulated when the

Table 5. Calibrated Stormwater Management Model variable 

values for constituent buildup and washoff for the single-family 

land-use subbasin, lower Charles River Watershed, 

Massachusetts 

[Street buildup is represented by the Michaelis-Menton equation, which does 

not use variable QFACT(2); non-street buildup is represented by a linear 

equation. --, no value]

Land use QFACT(1) QFACT(2) QFACT(3) WASHPO RCOEF

Suspended solids

Street 2. 6 -- 2.5 1.85 10.7

Non-street 10.2 8.6 12.1 .30 15.4

Fecal coliform bacteria

Street .005 -- 3.5 2.87 9.1

Non-street .82 2.5 6.0 .86 15.0

Total phosphorus

Street .008 -- 2.1 1.44 9.77

Non-street .860 1.0 8.0 .76 14.5

Total lead

Street .002 -- 2.2 1.56 2.70

Non-street .005 1.0 6.0 .61 12.7

runoff was oversimulated and oversimulated when runoff 

was undersimulated. This indicates that for suspended solids, total lead, and fecal coliform bacteria, the model error 

in simulating these constituents can be at least partly 

attributed to error in simulating the storm runoff volume, 

whereas the model error in simulating total phosphorus is 

attributed to error in the representation of the buildupwashoff process.

Of the eight sampled storms, simulated constituent 

loads from pervious areas were generated only during the 

June 6, 2000, storm. This was also the only storm 

simulated during the 2000 water year that generated 

appreciable pervious-area runoff. Total runoff from the 

June 6 storm accounted for about 10 percent of the total 

annual runoff; pervious-area runoff from this storm was 

less than 1 percent of the total annual runoff. The June 6 

storm accounted for about 8 percent of the total annual 

load for suspended solids, 12 percent for fecal coliform 

bacteria, 8 percent for total phosphorus, and 5 percent 

for total lead. The pervious-area load from this storm 

(which represents most of the pervious area runoff 

during the water year) was a small percentage of the total 

annual load—about 5 percent for suspended solids, 9 percent for fecal coliform bacteria, 6 for total phosphorus, 

and 1 for total lead. 
TOTAL LEAD

0

100

200

300

400

500

0 100 200 300 400 500

MEASURED LOAD, IN GRAMS

SIMULATED LOAD, IN GRAMS

TOTAL PHOSPHORUS

0

2

4

6

8

02 4 6 8

SIMULATED LOAD, IN KILOGRAMS

MEASURED LOAD, IN KILOGRAMS

FECAL COLIFORM BACTERIA 

0

2

4

6

8

024 68

SUSPENDED SOLIDS

SIMULATED LOAD, IN KILOGRAMS

MEASURED STORM LOAD,

 IN MILLIONS OF COLONY FORMING UNITS

SIMULATED STORM LOAD,

 IN MILLIONS OF COLONY FORMING UNITS

0

500

1,000

1,500

0 500 1,000 1,500

MEASURED LOAD, IN KILOGRAMS

Street Sweeping 17

Seemingly, appreciable percentages of contaminants should be available for collection by street 

sweeping in this subbasin since they were simulated as 

originating from streets. Street runoff accounted for 95 

percent of the total annual surface-runoff load for suspended solids, 91 percent for fecal coliform bacteria, 

94 percent for total phosphorus, and 99 percent for 

total lead. Dry-weather loads, which were unavailable 

for sweeping, accounted for 4 percent of the total 

annual load for suspended solids, 16 percent for fecal 

coliform bacteria, 30 percent for total phosphorus, 

and 5 percent for total lead. Therefore, the portion 

of the total constituent load available for sweeping 

ranged from about 92 percent for suspended solids, 

76 percent for fecal coliform bacteria, 66 percent for 

total phosphorus, and 94 percent for total lead.

Figure 4. Relation of simulated constituent loads to measured loads for eight sampled storms between January 10 and 

July 27, 2000, at the single-family land-use subbasin, lower Charles River Watershed, Massachusetts. 
Table 6. Measured and simulated storm runoff and constituent loads for sampled storms in the single-family land-use subbasin, 

lower Charles River Watershed, Massachusetts 

[AE, average error; RMSE, root mean square error]

Storm

date Measured Simulated Percentdifference

Storm

date Measured Simulated Percentdifference

Runoff, in cubic feet Suspended solids load, in kilograms

1-10-2000 154,000 127,000 -18 1-10-2000 375 348 -7.2

4-09-2000 102,000 98,800 -3.1 4-09-2000 307 262 -15

5-18-2000 66,300 83,300 26 5-18-2000 96 116 22

6-02-2000 69,200 85,500 24 6-02-2000 497 398 -20

6-06-2000 713,000 520,000 -27 6-06-2000 1,210 1,120 -7.6

7-09-2000 75,900 61,800 -19 7-09-2000 170 297 74

7-16-2000 38,300 32,200 -16 7-16-2000 28.4 88.7 210

7-26-2000 244,000 237,000 -2.9 7-26-2000 311 396 27

AE -4.5 AE 36

RMSE 19 RMSE 81

Fecal coliform, in TCFU Total phosphorus load, in kilograms

1-10-2000 700 1,070 53 1-10-2000 0.82 1.4 28

4-09-2000 100 265 160 4-09-2000 .54 1.23 99

5-18-2000 500 69.9 -86 5-18-2000 .53 1.03 -9.3

6-02-2000 300 1,770 490 6-02-2000 1.70 1.46 -22

6-06-2000 6,800 6,600 -3 6-06-2000 5.90 6.35 4.6

7-09-2000 1,900 510 -73 7-09-2000 1.10 1.33 6.7

7-16-2000 200 85.8 -57 7-16-2000 .40 .615 13

7-26-2000 2,100 1,580 -25 7-26-2000 .84 1.400 43

AE 58 AE 20

RMSE 190 RMSE 41

Total lead load, in grams

1-10-2000 240 183 -24

4-09-2000 160 127 -21

5-18-2000 56 62.8 12

6-02-2000 250 188 -25

6-06-2000 450 402 -11

7-09-2000 110 122 11

7-16-2000 24 39.6 65

7-26-2000 230 254 10

AE 2.3

RMSE 28

18 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

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.......................................................... ..........................................................

.......................................................... ..........................................................

.......................................................... ..........................................................

..........................................................

..........................................................
Street Sweeping 19

Sweeping Efficiencies and 

Frequencies Evaluated

Street-sweeping efficiencies evaluated for the 

purposes of this study (table 7) were assigned to be 

representative of mechanical sweepers (low-end 

efficiencies), wet assisted-vacuum sweepers and 

regenerative-air sweepers (mid-range efficiencies), and 

dry assisted-vacuum sweepers (high-end efficiencies). 

Removal efficiencies were also simulated for the “best 

available technology” sweepers, which were assigned 

from the highest removal efficiencies reported in the 

literature; however, these sweeper efficiencies are not 

well documented. For each class of sweeper simulated, 

removal efficiencies were highest for suspended solids, 

lowest for fecal coliform bacteria and total phosphorus, 

and intermediate for the removal of lead. Lead was 

assigned a higher sweeper efficiency than phosphorus 

and fecal coliform bacteria because the literature indicates that (1) its bonding sites are not as strongly associated with fine particles as phosphorus, and (2) lead 

originates from streets to a greater extent than phosphorus and bacteria. 

All simulations assumed an availability factor 

(SWMM variable AVSWP) of 80 percent (not reflected 

in sweeper efficiencies in table 7). This factor accounts 

for an incomplete sweeping of streets that can result 

from parked vehicles or other obstructions, operator 

inefficiencies, poor pavement conditions, or combinations of these and other factors that likely affect 

sweeper performance.

Street-sweeping frequency is an important variable in the effectiveness of removing constituent loads 

that build up on streets between storms. If streets are 

swept less frequently than it rains, then the effectiveness is decreased because contaminants are potentially 

washed from the streets prior to removal by sweeping. 

An optimal street sweeping frequency would provide at 

least one cleaning between storms. The average dry 

period between measurable storms, separated by a minimum dry period of 12 hrs, recorded at Logan Airport 

from 1970 through 1995, was 85 hrs; the average dry 

period between storms increased slightly, to 89 hours, 

for the months May through October. The average time

Table 7. Efficiencies of street sweepers simulated for 

removing selected contaminants 

[Values are in percent. Best available technology efficiencies are based on 

highest reported literature values]

Type Suspended 

solids

Fecal 

coliform

bacteria

Total

phosphorus

Total

lead

Mechanical 25 5 5 10

Wet vacuum and 

regenerative air

45 20 20 30

Dry vacuum 80 50 50 70

Best available 

technology

95 90 90 95

since the last 0.10 in. of precipitation was about 125 hrs 

for all storms and the average time since the last 

0.20 in. of precipitation was about 160 hrs for all 

storms. Storms with total precipitation volumes greater 

than 0.25 in., or greater than 0.50 in., had similar antecedent conditions as for all storms. This indicates that, 

on average, weekly street sweeping may provide contaminant removal prior to storms that are of sufficient 

size to “wash” the street surfaces. 

Six sweeping frequencies (SWMM variable 

CLFREQ) were simulated in this study—once every 

30, 15, 7, 3.5 days and once daily. Monthly and 

biweekly cleaning frequencies were evaluated because 

sweeping at more frequent intervals may not be 

possible or justified. Cleaning frequencies of less than 

one week were simulated to evaluate removal rates that 

are less than the average dry period between storms. 

The sweeping frequency specified by the model variable CLFREQ is not necessarily the time between 

sweeping because only dry time steps (runoff volume 

less than 0.0005 in/hr) are counted in the cumulative 

time since the last sweeping. Thus, the actual time 

between sweepings can be appreciably longer than that 

specified by CLFREQ if appreciable runoff is simulated. The number of days since the last cleaning 

(SWMM variable DSLCL), which only affects the 

beginning of the model simulation, was set to one half 

the sweeping frequency.
20 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

Model Limitations

In the implementation of street 

sweeping in SWMM, the variable 

TCLEAN (the time since the last cleaning) is updated at each time step until 

TCLEAN = CLFREQ (the street cleaning 

frequency); then TCLEAN is reset to zero. 

Again, it should be emphasized that 

CLFREQ is not necessarily the sweeping 

interval specified by the user because only 

dry time steps are counted in the cumulative time since the last sweeping. SWMM 

reinitializes time after sweeping, therefore, constituent buildup immediately following sweeping is characterized by the 

buildup rate between time zero and 

CLFREQ, regardless of the load remaining on the street after cleaning. As a result, 

the simulated contaminant buildup can be 

faster with street sweeping than without. 

For example, the buildup of solids in 

the street land-use subcatchment with 

no sweeping and with sweeping every 

2 days (CLFREQ =2) is illustrated in 

figure 5A. Given an initial buildup rate 

of QFACT(3), the Michaelis-Menton 

equation then calculates a time-varying 

buildup rate that asymptotically 

approaches zero as the built-up load 

approaches the buildup limit, QFACT(1), 

as shown for the buildup pattern with 

no sweeping. This buildup pattern is 

assumed to represent equilibrium between 

constituent buildup and dispersion by 

wind and traffic. When sweeping is simulated, the load is decreased every 2 days by 

the effective efficiency (street-sweeper 

efficiency, REFF, times the availability 

factor, AVSWP), but the buildup following sweeping is the same as the rate from 0 

to 2 days regardless of the load remaining on the street. A stair-step pattern 

results, as shown for the low-efficiency 

case (fig. 5A), which causes the simulated 

built-up load with sweeping to exceed the 

built-up load without sweeping. 

A buildup pattern following regular 

sweeping that does not exceed the suspended solids buildup with no sweeping is

Suspended solids buildup without sweeping

Suspended solids buildup without sweeping

0 2 4 6 10 12 14

TIME, IN DAYS

LOAD, IN KILOGRAMS

8

0

100

200

300

400

500

600

700

A. BUILDUP SIMULATED BY THE STORMWATER

 MANAGEMENT MODEL

B. BUILDUP RATE CORRECTED TO THE LOAD THAT 

 REMAINS AFTER SWEEPING

Time correction for the 

first sweeping at an 

effective sweeper 

efficiency of 20 percent 

 Buildup limit (QFACT(1))

SIMULATED LOAD, IN KILOGRAMS

0

100

200

300

400

500

600

700

Median dry period between storms 

during the 2000 water year (2.7 days)

Low (20 percent) 

High (64 percent)

800

900

1,000

1,100

1,200

Suspended solids buildup 

after sweeping at an effective 

sweeper efficiency of:

Figure 5. Suspended-solids buildup on streets simulated by the MichaelisMenton method with and without street sweeping at two sweeper efficiencies and 

2-day sweeping intervals by the (A) Stormwater Management Model and (B) an 

adjusted buildup rate corrected to the load that remains after sweeping.
Street Sweeping 21

shown in figure 5B. This pattern is based on the 

assumption that the rate of buildup after sweeping is 

dependent on the load remaining after sweeping, 

whereby the time in the Michaelis-Menton equation 

immediately after sweeping is set to the time at the 

equivalent buildup load without sweeping. This 

adjusted time prevents the buildup load with sweeping 

from exceeding the buildup load without sweeping. 

After 14 days with no storms, the simulated street 

solids load with no sweeping was about 550 kg in the 

street subcatchment simulated in this study. With street 

sweeping every 2 days and with no storms, the SWMM 

simulated load just prior to sweeping on day 14 was 

about 1,160 and 550 kg when swept at an effective efficiency of 20 and 64 percent, respectively; the adjusted 

buildup load (fig. 5B) was about 430 and 330 kg when 

swept at an effective efficiency of 20 and 64 percent, 

respectively. A similar stair-step pattern would result 

from sweeping with other buildup functions available 

in SWMM because the upper load limit increases each 

time TCLEAN is reset to zero.

As illustrated, resetting time to zero after sweeping can appreciably affect the simulated load, particularly when there is a long dry period between storms, 

and the sweeping frequency is short. The effect of the 

model-simulated buildup rate upon the percentage of 

the load removed by sweeping is minor, however, 

compared to the percentage that would be obtained 

from an adjusted buildup rate, for two reasons. First, 

the percent load removed by sweeping is a relative 

measure, which is calculated by the swept load divided 

by the built-up load. Since the swept load is a product 

of the built-up load and the effective sweeping efficiency, the swept load increases as the built-up load 

increases. A small difference in the percent load 

removed results from the model simulated buildup rate 

relative to the adjusted buildup rate. The SWMM 

buildup rate is greater than the adjusted buildup rate 

following sweeping; thus, as time increases since the 

last sweeping, the difference in the built-up load 

between the two methods increases, which causes the 

model to undersimulate the load removed by sweeping 

compared to what would be calculated by an adjusted 

buildup rate (shown schematically in fig. 6).

The second factor that minimizes the difference 

in the percent load removed associated with the two 

buildup rates is the frequency with which storms punctuate the buildup-sweeping cycle in the Boston area. 

During the 2000 water year, and during the 1970–2000 

period, the median dry period between storms was 

about the same (2.7 days). Because storms in the 

Boston area generally are frequent, constituent buildup 

rarely approaches the asymptote of the buildup curve. 

Thus, the constituent buildup is typically characterized

0 2 4 6 10 12 14 8

TIME, IN DAYS

0

Percent load removed as calculated 

by the SWMM buildup model 

Percent load removed as calculated 

by the buildup load that is adjusted 

to the load remaining after sweeping

PERCENT LOAD REMOVED

Maximum potential load 

removal determined by the 

effective sweeping efficiency

100

20

40

60

80

Figure 6. Schematic representation of the percent load removed by street sweeping at 2-day 

intervals when calculated by the Stormwater Management Model and by a buildup rate 

adjusted for the load remaining after sweeping. 
22 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

by rapid rates that follow storms (similar to the buildup 

rate that follows sweeping), rather than the slower 

rates, which characterize later times in the buildup process. The frequent storms result in a minimal difference 

(2.6 percent) between the surface buildup loads simulated with sweeping (once every 2 days at 20 percent 

effective sweeping efficiency) and the loads that would 

be buildup with no sweeping, whereas the expected 

buildup load with sweeping at this frequency and effectiveness would have been about twice the load with no 

sweeping after 14 days with no storms (fig. 5A).

The buildup following storms, unlike the buildup 

following sweeping, is calculated by the model on the 

basis of the load remaining (much like the adjusted 

buildup described above). This can result in considerable differences in the built-up load relative to the 

swept load, depending largely on the values used for 

the washoff variables. Washoff-variable values that 

cause less built-up load to be removed by storms make 

more of the load available to sweeping. Conversely, 

washoff-variable values that cause more built-up load 

to be removed by storms make less load available to 

sweeping. The effects of variable values on the removal 

rates calculated by sweeping are examined in the 

sensitivity analysis. 

Sensitivity Analysis

The sensitivity of the simulated suspended solids 

loads in runoff and the load removed by street sweeping were examined as a function of model buildup and 

washoff variable values to facilitate interpretation of 

the model results. One variable value was changed at a 

time, incrementally, to evaluate its effect on the runoff 

load and removal by street sweeping. Street sweeping 

was only evaluated for an effective efficiency of 76 percent (representing the highest sweeper efficiency and 

an availability factor of 80 percent) at a two-day 

sweeping frequency. The variables tested included 

QFACT(1) and QFACT(3), which express rates of constituent buildup, and WASHPO and RCOEF, which 

express rates of constituent washoff. The values for 

these variables were increased by 20, 50, 100, and 500 

percent and then decreased by 30, 50, and 80 percent 

from the calibrated model values.

Results of the sensitivity analysis (fig. 7) indicate 

that changes in the annual solids load are about linearly 

proportional to changes in QFACT(1). The simulated 

annual solids load was relatively insensitive but 

inversely related to the value of QFACT(3). The annual 

solids load increased disproportionately to decreased 

values of WASHPO; the load initially decreased in

0

10

20

30

40

50

60

70

PERCENT CHANGE IN VARIABLE VALUE

SUSPENDED SOLIDS LOAD, IN METRIC TONS

QFACT(1)

QFACT(3)

WASHPO

RCOEF

Buildup variables

Washoff variables

-100 0 100 200 300 400 500

0

10

20

30

40

50

60

70

80

90

PERCENT LOAD REMOVED BY SWEEPING

-100 0 100 200 300 400 500

Street sweeping simulated 

once every 2 days at a net 

efficiency of 76 percent

A. ANNUAL SUSPENDED-SOLIDS LOADS

B. PERCENT LOAD REMOVED BY 

 STREET SWEEPING

Figure 7. Sensitivity of (A) annual suspended-solids loads and (B) percent load removed by street sweeping to buildup and 

washoff variable values in the Stormwater Management Model.
Street Sweeping 23

rough proportion to increased values of WASHPO, 

but these decreases taper off at higher values of 

WASHPO. The annual solids load decreased in rough 

proportion to values of RCOEF values, but was 

relatively insensitive to increased values of RCOEF. 

This analysis indicates that the suspended solids 

load and swept load are both highly sensitive to the 

value of WASHPO. Increasing values of WASHPO 

cause large increases in the simulated load removal by 

sweeping. As WASHPO increases, less of the built-up 

load is removed by storms, particularly small storms; 

hence, more of the built-up load is available for sweeping, which increases the percent load removed by 

sweeping. The percent load removed by sweeping 

also increases as values of QFACT(3) and RCOEF 

decrease. Decreasing values of RCOEF decreases 

washoff by storms; this change increases the load available for sweeping, but less drastically than do increases 

in values of WASHPO. Decreasing QFACT(3)—the 

initial buildup rate—decreases the built-up load that is 

divided into the swept load. This diminishes the difference in the built-up load calculated by SWMM compared to that calculated by the adjusted buildup rate 

following sweeping (fig. 6). The load removed by 

sweeping is relatively insensitive to changes in 

QFACT(1). 

Simulated Contaminant 

Removal by Street Sweeping

SWMM simulations provide a tool to assess various sweeper efficiencies and sweeping frequencies 

under the dynamics of actual storm and antecedent 

conditions. Results of simulations of various sweeper 

efficiencies and sweeping frequencies on the singlefamily land-use subbasin are presented below. 

Calibrated-Model Load 

Removals

Simulation results for the 24 permutations of 

sweeper efficiencies and sweeping frequency intervals 

(fig. 8) generally indicate that less than 10 percent of 

the total built-up solids and lead loads was removed 

and less than 5 percent of the built-up fecal coliform 

bacteria and phosphorus loads was removed at sweeping frequencies of 7 days or longer. The dry-weather 

load is not included in these removal rates because 

dry-weather loads were simulated as a constant load 

originating from ground water and not considered part 

of the built-up load that is sweepable. The percent load 

removal by sweeping is calculated by the model as the 

swept load divided by the total surface buildup. Therefore, to obtain the percent of the total subbasin load 

removed by sweeping, these values must be multiplied 

by the fraction of the surface load to the total load. For 

suspended solids and total lead, the percentage of the 

total load removed by sweeping is nearly the same 

because only a small fraction of the total load (4 and 5 

percent, respectively) is attributed to dry-weather 

sources. For fecal coliform bacteria and total phosphorus, the percentage of the total load removed by sweeping is less because dry-weather sources contribute 16 

and 30 percent, respectively, to the total load; thus, 

assuming a 5 percent swept load, the percentage of the 

total load removed by sweeping is about 4 to 3 percent, 

respectively.

The percent removal increased markedly at 

sweeping frequencies less than 7 days for suspended 

solids and total lead; the percent removal of fecal 

coliform bacteria and total phosphorus also increases at 

sweeping frequencies less than 3.5 days, but not as 

dramatically as that for solids and lead. This result 

underscores the importance of sweeping frequency relative to the average inter-storm dry periods—if the 

sweeping interval is greater than the average interstorm dry period, there is little opportunity to remove 

contaminants regardless of the sweeping efficiency. 

Simulation results suggest that street sweeping, 

even at the highest efficiency (effective sweeper efficiencies of 76 percent for suspended solids and lead 

and 72 percent for fecal coliform bacteria and total 

phosphorus), removes only a small fraction of the total 

load unless intensive sweeping programs are implemented. Simulations indicate less than a 5 percent 

load reduction of fecal coliform bacteria and less 

than an 8 percent load reduction for total phosphorus 

for sweeping with the highest efficiency sweeper once 

per day. The lower removal rates for fecal coliform 

bacteria and total phosphorus relative to the removal 

rates for suspended solids and total lead are attributed 

to the lower sweeping efficiencies and higher perviousarea loadings for fecal coliform bacteria and total 

phosphorus.

These simulation results are consistent with the 

findings of Pitt (1978, 1985), who found load reductions of solids in the range of 5 to 10 percent for 

NURP-era sweepers. Sutherland and Jelen (1997) 

report considerably higher simulated removal rates, 
24 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

which they calculated using the SIMPTM model they 

developed. Their simulations indicate that sweeping 

twice per month and twice per week yielded solids load 

removals, respectively, of 20 to 30 percent for new 

mechanical sweepers, 50 to 70 percent for regenerative 

air sweepers, and 60 to nearly 90 percent for dryvacuum sweepers. Sutherland and Jelen (1997) did not 

provide information on the efficiencies of the sweepers 

simulated, the street availability factor, frequency of 

storms, or the model calibration; thus, their findings 

cannot be related to the conditions simulated in this 

study. Undoubtedly, sweeper performance has 

improved markedly since completion of the NURP 

studies, but little independent information is available 

to assess whether these advanced sweepers can help 

achieve the desired water-quality objectives. 

Alternative-Model Load 

Removals

CLEANING INTERVAL, IN DAYS

2 6 10 14 18 26 22 30

0

5

10

15

20

25

30

0

2

4

6

8

10

 LOAD REMOVAL, IN PERCENT

2 6 10 14 18 26 22 30

SUSPENDED SOLIDS

FECAL COLIFORM BACTERIA TOTAL PHOSPHORUS

72 percent

Net efficiency

40 percent

4 percent

16 percent

72 percent

Net efficiency

40 percent

4 percent

16 percent

76 percent

Net efficiency

64 percent

20 percent

36 percent

76 percent

Net efficiency

56 percent

8 percent

24 percent

TOTAL LEAD

Given the sensitivity of the effectiveness of 

sweeping to the models’ variable values, especially 

WASHPO, an alternative model of the single-family 

land-use subbasin was constructed for simulating 

Figure 8. Simulated constituent-load removal for various sweeper efficiencies at selected sweeping intervals in the singlefamily land-use subbasin, lower Charles River Watershed, Massachusetts, 2000 water year.
Street Sweeping 25

suspended solids buildup and washoff. The 

alternative model allowed less washoff of builtup loads during small storms. The variable 

WASHPO was increased from 1.85 to 3.0

(about a 60 percent increase) and the variable 

RCOEF was decreased from 10.7 to 2.0 (about a 

80 percent decrease). This model was calibrated 

by adjusting the buildup variable values 

QFACT(1) and QFACT(3) until the simulated 

annual load matched the estimated annual load. 

These model changes increased the percentage of suspended solids load removed by 

street sweeping about four-fold; annual suspended solids removal increased from 15 percent to 66 percent by sweeping every 2 days at 

an effective efficiency of 76 percent. A similar 

improvement in the load removal could be 

expected over the previous model results for the 

other constituents, sweeping frequencies, and 

sweeper efficiencies.

Although the alternative model was 

calibrated to the total annual load, the model fit 

for the eight sampled storms was not as good as 

that for the previous model. The percent difference between measured and simulated suspended solids loads increased for five of the 

sampled storms, but decreased for three of the 

storms (table 8), all of which occurred in July. 

This indicates that the effects of sweeping may 

not be as small as first simulated, because 

buildup and washoff processes may not be 

described by a single set of model values 

(SWMM does not allow variable values to 

change seasonally). The alternative model values, which markedly increased the simulated 

constituent load removal by sweeping (table 9), 

appear to better represent these processes during 

the mid-summer. The potential reduction in 

stormwater contaminant loads achieved by 

street sweeping in the summer is of considerable importance, because recreational use of the 

lower Charles River is greatest at this time. 

This analysis indicates that street sweeping should not be discounted as a stormwaterquality management practice. Only through 

direct field investigation of the relevant processes can the effectiveness of street sweeping

Table 8. Measured and simulated loads of suspended solids for 

sampled storms, single-family land-use subbasin, 2000 water year, 

lower Charles River Watershed, Massachusetts

[AE, average error; RMS, root mean square error; kg, kilograms]

Storms

Load (kg)

Percent

difference

Alternative 

model load 

(kg)

Percent

difference Measured Calibrated

model

1-10-2000 375 348 -7.0 146 -61

4-09-2000 307 262 -15 33 -89

5-18-2000 96 116 22 8 -92

6-02-2000 497 398 -20 300 -40

6-06-2000 1,210 1,120 -8.0 1,860 54

7-09-2000 170 297 74 62 -64

7-16-2000 28.4 88.7 210 10 -66

7-26-2000 311 396 27 343 10

AE 36 -43

RMS 81 63

Table 9. Percent removal of constituent loads by street sweeping 

simulated with the highest efficiency sweeper once every 2 days with 

the calibrated and an alternative Stormwater Management Model in 

the single-family land-use subbasin, lower Charles River Watershed, 

Massachusetts

Sweeping 

frequency

(days)

Percent removal

Suspended

solids

Fecal

coliform

Total

phosphorus

Total

lead

Calibrated model

30 5.4 1.4 2.0 7.5

15 7.6 1.9 2.7 10

7 9.3 2.3 3.3 13

3.5 12 2.9 4.3 17

2 15 3.5 5.5 21

1 20 4.3 7.2 28

Alternative model

30 17 1.7 2.8 17

15 24 2.3 3.9 23

7 29 2.8 4.7 29

3.5 39 3.5 6.2 38

2 49 4.3 7.8 49

1 64 5.3 10.3 64

......................................

......................................
26 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

as a water-quality control be established. The effectiveness of this type of control could be evaluated by independently measuring built-up loads, swept loads, and 

stormwater loads under a range of conditions (including pavement conditions), sweeper technologies and 

operator practices, and a range of storms that represent 

different seasons, sizes and intensities. Concurrent 

modeling efforts would improve understanding of 

buildup, washoff, and sweeping processes in the watershed. This information is needed to determine, with 

reasonable certainty, if street sweeping can provide 

theload reductions necessary to meet water-quality 

objectives in the lower Charles River. 

POTENTIAL EFFECTS OF 

STRUCTURAL CONTROLS AND 

STREET SWEEPING ON 

LOADS TO THE LOWER 

CHARLES RIVER 

The potential contaminant load reductions 

obtainable by implementing structural controls in the 

lower Charles River Watershed were estimated by 

assuming that a similar matrix of control types could 

be put into practice at the density and with proportionally similar contributing areas to the Village Brook 

Subbasin. Constituent removal rates obtained in the 

Village Brook Subbasin (table 3) were then applied

to each of the major lower Charles River subbasins 

(fig. 1)—Laundry Brook, Faneuil Brook, Muddy River, 

Stony Brook, and the ungaged area defined in studies 

by Zarriello and Barlow (2002) and Breault and others 

(2002). 

Implementing structural controls in the Village 

Brook Subbasin in accordance with the CWP recommendations (table 2) would, on average, have the greatest effect on loads of suspended solids and the least 

effect on loads of fecal coliform bacteria. Overall, 

structural controls are estimated to reduce total loads 

by about 15 percent for suspended solids loads and 

about 6 percent for fecal coliform bacteria at the outlet 

of the Village Brook Subbasin. Total phosphorus load 

reductions are affected by dry-weather loads, which are 

proportionally greater than the dry-weather loads for 

the other constituents considered; dry-weather loads 

are assumed to be unaffected by structural controls. 

This is believed to be a reasonable assumption because 

the structural controls recommended by the CWP are 

designed to treat surface runoff and the dry-weather 

loads are believed to originate from sources such as 

illicit connections that are unaffected by the structural 

controls considered.

Removal efficiencies of contaminants by street 

sweeping from model simulations of the single-family 

residential land-use subbasin were extrapolated to 

other areas of the lower Charles River Watershed by 

two methods that weighted the simulated removal

rates differently. The first method was based on the 

assumption that the load available for sweeping in 

other subbasins of the lower Charles River Watershed 

is proportional to the ratio of road density in the 

subbasin to that in the single-family land-use subbasin. 

This ratio constitutes the “road-density-weighting 

factor” shown in table 10; the ratio is multiplied by the 

removals simulated for various sweeping frequencies 

and efficiencies to estimate contaminant removal in 

each of the major subbasins.

The second method used to weight the simulated 

contaminant removal by sweeping was based on the 

assumption that the contaminant load from streets is 

similar to that in the single-family land-use subbasin 

(load per unit street length) and that the subbasin load 

available for sweeping is a function of the street load 

relative to the total subbasin load. Street loads in each 

of the other subbasins were estimated from their total 

road miles (table 10) and the annual street load per

mile obtained from the calibrated model. Computed 

contaminant yields in the single-family land-use 

subbasin were 3.61 kg/d/mi for suspended solids, 

13.95 million CFU/d/mi for fecal coliform bacteria, 

14 g/d/mi for total phosphorus, and 2.05 g/d/mi for 

total lead. 

Table 10. Subbasin street length and density, lower Charles 

River Watershed, Massachusetts

[mi/mi2, miles per square mile]

Subbasin

Street Roaddensity

weighting

factor

Length

(miles)

Density

(mi/mi2)

Single family 9.8 28 1.00

Laundry Brook 97 20 .71

Faneuil Brook 30 21 .75

Muddy River 115 21 .75

Stony Brook 252 21 .75

Ungaged area 290 23 .82

Lower Charles River 

Watershed

784 21 .75
Potential Effects of Structural Controls and Street Sweeping on Loads to the Lower Charles River 27

The street-weighting factor was calculated from 

the estimated street contaminant load (table 11) divided 

by the subbasin load averaged from the different estimation techniques reported by Breault and others 

(2002). In some cases, the street load divided by the 

subbasin load is greater than one because of the inaccuracies of this extrapolation method; in these instances, 

the weighting factor was assigned a value of one. This 

factor was used as a multiplier to adjust the simulated 

load removal by sweeping at various frequencies and 

efficiencies. 

Water Year 2000

Annual contaminant loads to the lower Charles 

River from non-CSO sources below Watertown Dam 

reported by Breault and others (2002) decreased by 

hypothetical CWP-recommended structural controls. 

Estimated load reductions based on the median 

structural-control removal efficiencies decreased by 17 

percent for suspended solids, 15 percent for total lead, 

13 percent for fecal coliform bacteria, and 9.7 percent 

for total phosphorus (table 12). The percent load reductions from implementing structural controls are somewhat less when upstream loads (Charles River 

Watershed above Watertown Dam) are considered 

(table 12). Loads from the watershed above Watertown 

Dam are assumed to remain constant, however.

Annual load reductions to the lower Charles 

River that could be expected from street sweeping in 

the lower watershed at various efficiencies and frequencies are shown in figure 9. These load reductions 

are based on results of the calibrated model simulations 

of the single-family land-use subbasin extrapolated to 

the entire lower watershed by the street-density-ratio 

method. Load reductions (as a percentage of the total 

annual load excluding load from the upper watershed) 

ranged from about 3.5 to 15 percent for suspended solids, 0.5 to 3.3 percent for fecal coliform bacteria, 0.7 to 

5.5 percent for total phosphorus, and 3.8 to 21 percent 

for total lead, estimated by the street-density-ratio 

method. Load reductions (excluding loads from the 

upper watershed) estimated by the ratio of the street 

load to the subbasin load were somewhat less—2.7 to 

12 percent for suspended solids, 0.3 to 2.0 percent for 

fecal coliform bacteria, 0.5 to 3.8 percent for total 

phosphorus, and 1.6 to 8.8 percent for total lead. 

Only a small fraction of the total annual load to 

the lower Charles River is removed by street sweeping 

if the load contribution from the upper watershed is 

included. At best, the total annual contaminant load 

removed by intensive street sweeping (once per day), 

with the best available technology, is only 4 percent for 

suspended solids, 2 percent for fecal coliform bacteria, 

about 1 percent for total phosphorus, and about 8 percent for total lead. These load reductions are based on 

results of the calibrated-model load reductions from

Table 11. Street and subbasin contaminant loads used to calculate the weighting factor for estimating potential contaminant 

removal by street sweeping in 2000 water year, lower Charles River Watershed, Massachusetts

[Street: Street load calculated from the product of the simulated annual street load per mile in the single-family land-use subbasin and the street length in 

miles for each subbasin. Subbasin: Subbasin load reported load by Breault and others (2002) averaged from storm event mean concentrations (EMC), flow 

weighted EMC, and the stepwise regression methods. Weighting factor: Weighting factor is the street load divided by the subbasin load; weighting factors 

greater than 1.0 were assigned a value of 1.0. kg, kilograms; g, grams; TCFU, trillion colony forming units]

Subbasin

Suspended solids 

(metric tons/yr)

Fecal coliform 

(TCFU/yr)

Total phosphorus 

(kg/yr)

Total lead 

(g/yr)

Street Subbasin

Weighting 

factor Street Subbasin

Weighting 

factor Street Subbasin 

Weighting 

factor Street Subbasin 

Weighting 

factor

Laundry Brook 127 64 1.00 494 279 1.00 507 317 1.00 73 44 1.00

Faneuil Brook 39 50 .78 153 389 .39 157 123 1.00 22 24 .96

Muddy River 151 335 .45 586 1,570 .37 601 1,570 .38 86 287 .30

Stony Brook 332 673 .49 1,280 2,740 .47 1,310 2,130 .62 189 813 .23

Ungaged area 382 242 1.00 1,470 1,080 1.00 1,510 1,200 1.00 217 182 1.00

Lower Charles 

River Watershed

1,031 1,364 .76 3,983 6,058 .66 4,085 5,340 .77 587 1,350 .44
Table 12. Estimated range in the percent decrease in annual 

loads by hypothetical structural controls, lower Charles River 

Watershed, Massachusetts 

[The load from the upper watershed is assumed to remain constant]

Constituent

Structural control removal efficiency

Lower

quartile Median Upperquartile Average

Lower Charles River Watershed Load

(excludes load above Watertown Dam)

Suspended solids 14 17 18 15

Fecal coliform 7.5 13 16 6.2

Total phosphorus 5.8 9.7 13 8.6

Total lead 8.9 15 17 12

Entire Charles River Watershed

(includes load above Watertown Dam)

Suspended solids 3.7 4.5 4.9 3.9

Fecal coliform 4.6 7.8 9.7 3.8

Total phosphorus 1.0 1.6 2.2 1.5

Total lead 3.4 5.6 6.7 4.8

28 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

street sweeping in the single-family land-use subbasin 

and the street-density-ratio extrapolation method 

(table 13). 

In contrast, the alternative model developed for 

the single-family land-use subbasin shows an increased 

effectiveness for the highest efficiency sweeper of 

about threefold for suspended solids, 20 percent for 

fecal coliform bacteria, 40 percent for total phosphorus, and twofold for total lead. The alternative model 

indicated a slightly better fit than the original model for 

measured storms in the summer months; hence, the 

higher removal rates could be realized if the alternative 

model buildup and washoff patterns are representative 

of actual conditions. 

The constituent-load reductions to the lower 

Charles River by combined hypothetical structural 

controls and street sweeping were estimated for a range 

of BMP performances (table 14). Load reductions 

are given as percentages of the wet weather load and 

total load from non-CSO sources below Watertown 

Dam and as percentages of the entire watershed load 

(includes the load from above Watertown Dam). A 

range that provides reasonable bounds on the constituent load that could be removed by the practices is

presented because of the inherent variability and uncertainty of removal efficiencies. The estimated lower load 

reductions are based on the lower quartile of the 

structural-control removal rates combined with the

load removed by street sweeping once every 30 days, 

as simulated by the calibrated model with lowefficiency sweepers and extrapolated by the street-loadto-subbasin-load method. The estimated upper load 

reductions are based on the upper quartile of the 

structural-control removal rates combined with the load 

removed by street sweeping once per week, as simulated by the alternative model with the best available 

technology sweepers and extrapolated by the streetdensity-ratio method. Weekly sweeping was chosen 

as an upper constituent removal estimate for street 

cleaning because water-quality managers considered 

this the most frequent interval that could be reasonably 

expected by communities (Mark Voorhees, U.S. 

Environmental Protection Agency and Kevin Brander, 

Massachusetts Department of Environmental 

Protection, oral commun., 2001). The average estimated load reductions are based on the medium 

structural-control removal rates combined with the 

load removed by street sweeping with high-efficiency 

sweepers once every 2 weeks; the average swept-load 

reductions reflect the average removal rates simulated 

by the calibrated model and alternative model at this 

sweeper efficiency and frequency and the average 

extrapolated load estimate. This range of removal efficiencies was applied to the stormwater portion of the 

total annual load because these practices are assumed 

not to affect the dry-weather load. 

The upper estimated load reductions by structural controls and street sweeping indicate that these 

practices can achieve 44 and 34 percent reductions of 

suspended solids and total lead, respectively, as a percentage of the total load from non-CSO sources below 

Watertown Dam. Phosphorus and fecal coliform bacteria loads decreased, at best, by 14 and 17 percent, 

respectively, as a percentage of the total load from nonCSO sources below Watertown Dam. The estimated 

load reductions were slightly higher for all constituents 

and all BMP performances as a percentage of wetweather load because the dry-weather load, which 

generally constituted a small percentage of the total 

load, was considered to be unaffected by the BMP 

practices considered. Of the constituents examined,

the percent load reduction by BMPs for total phosphorus for wet-weather loads increased the most relative to 

the total loads, because of its relatively high percent of 

dry-weather load to total load. 

The estimated lower load reductions, and load 

reductions as a percentage of the entire watershed load 

(includes the Charles River load above Watertown 
0

5

10

15

20

25

30

 LOAD REMOVAL, IN PERCENT

CLEANING INTERVAL, IN DAYS

0

2

4

6

8

10

Suspended solids

A. LOWER CHARLES 

 RIVER WATERSHED

B. ENTIRE CHARLES RIVER 

 WATERSHED

Fecal coliform Fecal coliform

Suspended solids

0 5 10 15 25 20 30 0 5 10 15 25 20 30

Low

Medium

High

Best available technology

Potential Effects of Structural Controls and Street Sweeping on Loads to the Lower Charles River 29

Figure 9. Estimated percent load reduction for the 2000 water year by street sweeping at various efficiencies 

and frequencies for the (A) lower Charles River (excludes the loads above Watertown Dam) and (B) the entire 

Charles River Watershed, Massachusetts (estimated by the street density ratio).

Dam) are appreciably less than previously stated as 

percentages of the load reduction from non-CSO 

sources below Watertown Dam. For example, the lower 

estimated reduction of total phosphorus was only 1.0 

percent of total load. The relative contributions of contaminant loads from various sources indicate that, for 

suspended solids and total phosphorus, most of the

load is from the upper watershed, and only a small 

fraction of the load would be removed by implementation of BMP practices in the watershed below 

Watertown Dam. This is particularly evident for the 

total phosphorus load (fig. 10), which is dominated by 

upstream, dry-weather sources. Although most of the 

non-CSO fecal coliform bacteria contamination to the 

Charles River is from sources below Watertown Dam, 

the BMP practices examined would likely have only a 

modest effect on these sources, when considered on an 

annual basis. Nevertheless, BMP implementation could 

produce substantial water-quality benefits in the lower 

Charles River near the stormwater outfalls, during and 

immediately after storm events. These water-quality 

benefits would be most easily quantified by incorporating the simulated load reductions from BMP implementation into a receiving-water model of the lower 

Charles River.
CLEANING INTERVAL, IN DAYS

0

2

4

6

8

10

0

5

10

15

20

25

30

0 5 10 15 25 20 30 0 5 10 15 25 20 30

Low

Medium

High

Best available technology

Total phosphorus

Total lead Total lead

Total phosphorus

 LOAD REMOVAL, IN PERCENT

A. LOWER CHARLES 

 RIVER WATERSHED—Continued

B. ENTIRE CHARLES RIVER 

 WATERSHED—Continued

30 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

Figure 9. Estimated percent load reduction for the 2000 water year by street sweeping at various efficiencies and 

frequencies for the (A) lower Charles River (excludes the loads above Watertown Dam) and (B) the entire Charles 

River Watershed, Massachusetts (estimated by the street density ratio)—Continued.
Table 13. Simulated percent annual-load reductions to the lower Charles River, Massachusetts, by street sweeping with the 

highest efficiency sweepers at various sweeping intervals and the (A) calibrated model and (B) an alternative model

Sweeping

frequency

(days)

Percent load reduction from the

lower Charles River Watershed

Percent load reduction from the

entire Charles River Watershed

Suspended

solids

Fecal

coliform

bacteria

Total

phosphorus Total lead Suspendedsolids

Fecal

coliform

bacteria

Total

phosphorus Total lead

A. CALIBRATED MODEL 

Estimated constituent removal by the street-density ratio method

30 4.1 1.1 1.5 5.7 1.1 0.6 0.3 2.2

15 5.8 1.4 2.1 8.0 1.5 .9 .4 3.1

7 7.1 1.7 2.5 9.8 1.9 1.0 .4 3.8

3.5 9.2 2.2 3.3 13 2.4 1.3 .6 5.0

2 12 2.7 4.2 16 3.1 1.6 .7 6.3

1 15 3.3 5.5 21 4.0 2.0 .9 8.2

Estimated constituent removal by the street load to subbasin-load ratio method

30 3.3 .6 1.0 2.3 .9 .4 .2 .9

15 4.6 .9 1.4 3.3 1.2 .5 .2 1.3

7 5.6 1.0 1.7 4.0 1.5 .6 .3 1.6

3.5 7.3 1.3 2.2 5.3 1.9 .8 .4 2.0

2 9.1 1.6 2.8 6.7 2.4 1.0 .5 2.6

1 12 2.0 3.8 8.8 3.1 1.2 .6 3.4

B. ALTERNATIVE MODEL1

Estimated constituent removal by the street-density ratio method

30 13 1.3 2.2 13 3.4 0.8 0.4 5

15 18 1.8 3.0 18 4.8 1.1 .5 7

7 22 2.1 3.6 22 6.0 1.3 .6 8

3.5 30 2.7 4.7 29 7.9 1.6 .8 11

2 37 3.2 6.0 37 9.9 2.0 1.0 14

1 48 4.0 7.8 49 13 2.4 1.3 19

Estimated constituent removal by the street load to subbasin-load ratio method

30 10 .8 1.5 5.2 2.7 .5 .2 2.0

15 14 1.1 2.0 7.3 3.8 .6 .3 2.8

7 18 1.3 2.5 9.1 4.7 .8 .4 3.5

3.5 23 1.6 3.2 12 6.2 1.0 .5 4.6

2 29 1.9 4.0 15 7.8 1.2 .7 5.9

1 38 2.4 5.3 20 10 1.5 .9 7.8

Potential Effects of Structural Controls and Street Sweeping on Loads to the Lower Charles River 31

1Entire watershed includes loads from above Watertown Dam. 
Table 14. Percent annual-load reductions by combined 

hypothetical structural controls and street-sweeping 

practices, lower Charles River Watershed, Massachusetts 

[Lower: Lower estimate based on lower quartile of structural control 

removal rate and sweeping once every 30 days with low-efficiency 

sweepers. Average: Average estimate based on medium structural control 

removal rate and sweeping once every two weeks with high-efficiency 

sweepers. Upper: Upper estimate based on upper quartile of structural control removal rate and sweeping once a week with best available technology 

sweepers. CSO, combined sewer overflow]

Constituent

Percent load reduction

Lower Average Upper

Percentage of non-CSO wet-weather load

below Watertown Dam

Suspended solids 15 25 46

Fecal coliform 7.9 14 18

Total phosphorus 6.7 12 19

Total lead 11 22 35

Percentage of non-CSO total load

below Watertown Dam

Suspended solids 14 24 44

Fecal coliform 7.5 13 17

Total phosphorus 4.9 8.7 14

Total lead 11 21 34

Percentage of non-CSO total load from

the entire watershed

Suspended solids 3.7 6.1 11

Fecal coliform 4.4 7.9 10

Total phosphorus 1.0 1.8 2.8

Total lead 4.1 8.1 13

32 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

Design Year

Previous analyses of stormwater discharge to the 

lower Charles River and CSO facilities planning by the 

MWRA have relied upon a design year (“typical year”) 

and design storms with 3-month and 1-year recurrence 

intervals. Zarriello and Barlow (2002) reported the 

non-CSO design-storm discharges and Breault and 

others (2002) reported the constituent loads associated 

with these storms. The MWRA will further examine 

impacts of stormwater quality on the lower Charles 

River under these design conditions through simulations with a receiving-water model. For this reason, 

discharge and load estimates are also given for the 

design year, because they were not provided in the 

reports described above. 

The design-year loads were calculated by 

simulating runoff with SWMM models previously 

developed for the lower Charles River Watershed

(Zarriello and Barlow, 2002) and stormwater 

constituent concentrations as estimated by stepwise 

regression methods, average stormwater event mean 

concentrations, and flow-weighted event mean concentrations (Breault and others, 2002). Sharon Ho-Tsay 

(Metcalf and Eddy, Inc., written commun., 2002) provided the precipitation hyetograph for the design year, 

which was modified from actual 1992 precipitation 

data to include a range of storm sizes. These precipitation data were used in the SWMM models to simulate 

runoff and in the regression equations to estimate 

constituent concentrations. 

Precipitation and other storm characteristics for 

the design year were found to be generally similar to 

those observed in the 2000 water year (fig. 11). The 

design-year and 2000 water-year storm characteristics 

tended to fall between the median and upper quartile of 

the long-term (1970–95) characteristics observed at 

Logan Airport. One exception to this pattern was the 

storm antecedent dry period. During the design year, 

the antecedent dry period was near the upper quartile of 

the long-term data set for the April to August period, 

while the 2000 water year values were closer to the 

median of the long-term data set. Longer antecedent 

dry periods for the design year increase the stormwater 

constituent concentration calculated by the multivariate 

regression equations (Breault and others, 2002) relative 

to concentrations obtained for the long-term median 

and the 2000 water year. Long antecedent dry periods 

allow constituent loads to buildup, which allows for 

greater removal by street sweeping. Hence, the loads 

removed during the design year by sweeping may be 

better represented by the upper estimates of the 

simulated load removed.

Runoff for the design year was simulated for 

the watershed below Watertown Dam using SWMM 

(Zarriello and Barlow, 2002) and estimated for the 

Charles River at Watertown Dam from the ratio of 

the upper and lower watershed runoff values for the 

2000 water year (table 15). A ratio was used to estimate 

runoff from the upper watershed because the design 

year relies upon a synthesized hyetograph that precludes use of measured data from nearby Charles River 

gaging stations. The estimated runoff from the upper 

watershed for the design year is 15,500 million ft3 or 

about 92 percent of the total runoff to the lower Charles 

River. Runoff from the lower watershed during the 

design year was simulated to be about 1 percent greater 

than the runoff during the 2000 water year. 
Average

Lower

Dry-weather

load

Upper

Total 

stormwater 

load

EXPLANATION

Subbasins downstream of Watertown Dam Subbasins downstream of Watertown Dam LOAD, IN KILOGRAMS

0

200

400

600

800

1,000

1,200

0

1,000

2,000

3,000

4,000

5,000

8,000

TRILLIONS OF COLONY FORMING UNITS7,000

6,000

TOTAL LEAD

SUSPENDED SOLIDS

LOAD, IN METRIC TONS 1,500

2,000

2,500

3,000

TOTAL PHOSPHORUS

0

5

10

15

20

25

LOAD, IN METRIC TONS

0

500

Laundry

Brook

Faneuil

Brook

Muddy

River

Stony

Book

Ungaged

Areas

Laundry Total

Brook

Faneuil

Brook

Muddy

River

Stony

Book

Ungaged

Areas

Total

1,000

FECAL COLIFORM

Upstream of 

Watertown

Dam

(management

practices

 not applied)

Upstream of 

Watertown

Dam

(management

practices

 not applied)

CONSTITUENT LOAD—Estimated lower, average, and upper load removed 

 by combined management practices

 Street sweeping

 Dry-weather load

 Structural controls

 Loads remaining after management practices applied

TOTAL—Is the sum of loads from subbasins below Watertown Dam with 

 Stony Brook before combined sewer separation

Figure 10. Constituent loads from non-combined-sewer-overflow sources from major subbasins, and estimated load 

reductions by hypothetical structural controls and street sweeping, lower Charles River Watershed, Massachusetts, 2000 

water year (unadjusted loads from Breault and others, 2002).

33 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts
ONDJFMAMJJAS

ONDJFMAMJJAS

0

55

110

165

220

275

A. Antecedent dry period

TIME, IN HOURS

0

8

16

24

32

40

B. Storm duration

0

0.04

0.08

0.12

0.16

Minimum 0.01 inches

C. Average rainfall intensity

 INTENSITY, IN INCHES PER HOUR

0

0.1

0.2

0.3

0.4

0.5

D. Maximum rainfall intensity

0

0.4

0.8

1.2

1.6

2.0

E. Total rainfall

RAINFALL, IN INCHES

EXPLANATION

Median

75 Percentile

10 Percentile

BOX PLOT OF 

MONTHLY AVERAGED 

VALUES FROM LOGAN 

AIRPORT, 1970-95

90 Percentile

25 Percentile

MONTHLY AVERAGED 

VALUES FROM SIX GAGES IN 

THE LOWER CHARLES RIVER 

WATERSHED, 2000 WATER 

MONTHLY AVERAGED VALUES 

FOR DESIGN YEAR 

Figure 11. Distribution of rainfall and antecedent storm characteristics for the 2000 water year, design 

year, and 1970–95 period, lower Charles River Watershed, Massachusetts. 

34 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts
Table 15. Simulated runoff volume for the 2000 water 

year and the design year, lower Charles River Watershed, 

Massachusetts 

Site

Runoff volume

(millions of cubic feet)

2000

water

year

Design

year

Percent

difference

Laundry Brook 82.3 80.3 -2.4

Faneuil Brook 49.1 44.5 -9.4

Muddy River1 340 337 1.0

Stony Brook 489 523 7.0

Ungaged areas 284 275 -3.2

Upper watershed 15,300 215,500 1.2 1Muddy River includes conduit discharge for the water year and 

design year (183 and 201 million cubic feet, respectively). 2Upper watershed volume was estimated from the ratio of lower to 

upper watershed runoff during the 2000 water year. 

Potential Effects of Structural Controls and Street Sweeping on Loads to the Lower Charles River 35

The largest source of runoff to the lower Charles River 

(excluding the upper watershed) was Stony Brook, 

which contributed 42 percent of the inflow. The Muddy 

River and the ungaged areas contributed about equal 

proportions of runoff (about 27 and 22 percent, 

respectively). Laundry Brook contributed about 6 

percent and Faneuil Brook contributed about 3 percent 

of the total runoff during the design year, excluding 

inflow from the upper watershed.

Design-year wet- and dry-weather runoff volumes from the upper watershed were estimated from 

the ratio of wet- to dry-weather runoff volumes 

observed in the 2000 water year. The volumes obtained 

were then multiplied by the appropriate EMC and a 

constant to obtain constituent loads. Stony Brook 

loads, which reflect conditions during the 2000 water 

year, include CSO loads. Constituent loads estimated 

for the design year (table 16) are generally similar to 

the loads estimated for the 2000 water year reported by 

Breault and others (2002). The design-year loads were 

generally slightly larger than the 2000 water-year loads 

at Laundry Brook and Stony Brook and slightly smaller 

at Faneuil Brook and Muddy River. Differences 

between the design-year loads and the 2000 water-year 

loads were generally largest for loads estimated by the 

regression method. Percent load reductions that could 

be expected by implementing structural controls and 

street sweeping are assumed to be the same for the 

design year as for the 2000 water-year reductions 

shown in table 14.

Design Storms

Design storms with approximately 3-month and 

1-year recurrence intervals were selected by MWRA to 

evaluate the spatial and temporal effects of CSO and 

non-CSO contaminant loads on the lower Charles 

River by simulations with a receiving-water model. 

Predicted changes in loads resulting from implementation of structural controls and street sweeping will provide alternative boundary conditions for the receivingwater model currently under development by the 

MWRA. The load reductions by structural controls and 

street sweeping were assumed to be the same as that 

reported in table 14. These practices were estimated to 

achieve non-CSO contaminant-load reductions, as a 

percentage of the load below Watertown Dam, of up to 

46 and 35 percent for suspended solids and total lead, 

respectively, and about 20 percent for total phosphorus 

and fecal coliform bacteria. The estimated lower contaminant-load reductions, as a percentage of the nonCSO sources load below Watertown Dam, ranged from 

7 to 15 percent. 

The relative contributions of contaminant loads 

from various sources indicated that loads from nonCSO sources below Watertown Dam are larger than 

loads from the upper watershed for all constituents and 

design storms, except for total phosphorus for the 

3-month storm (fig. 12). Fecal coliform bacteria loads 

were estimated to be about 2.5 times larger from nonCSO sources below Watertown Dam than from sources 

upstream of the dam, under present conditions. The 

combined load reductions from structural controls and 

street sweeping indicated that these practices could 

achieve load reductions for suspended solids and total 

lead that would result in comparable loads for these 

constituents from the upper and lower watersheds.

The planned sewer separation project in the 

Stony Brook Subbasin is anticipated to cause a large 

decrease in the annual volume of CSO discharge and 

a slight increase in the non-CSO stormwater discharge 

(Donald Walker, Metcalf & Eddy, Inc., written 

commun., November 13, 2001). These changes are 

expected to cause slight increases in the loads of

suspended solids and total lead, due to the increased 

volume of storm runoff after sewer separation and 

the comparable concentration of these constituents in 

storm runoff and CSO discharge. Annual fecal 

coliform bacteria loads from Stony Brook are expected 

to decrease by about 33 percent after sewer separation, 

on the basis of data from the 2000 water year (Breault 
36 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

and others, 2002). Decreases in fecal coliform bacteria 

loads from the 3-month and 1-year design storms after 

separation are expected to be somewhat less (22 and 

23 percent, respectively). Because Stony Brook contributes about 61 percent of the annual stormwater 

load of fecal coliform bacteria to the lower Charles 

River (excluding upstream sources), sewer separation 

is expected to produce an overall decline of 20 percent 

in the stormwater load of fecal coliform bacteria 

entering the lower Charles River. 

Table 16. Estimated design-year constituent loads from non-combined-sewer-overflow sources for each of the major subbasins 

to the lower Charles River, Massachusetts 

[TCFU, trillion colony-forming units; --, means regression model was not a good predictor of constituent concentration]

Site Suspended solids 

(metric tons)

Fecal coliform

(TCFU)

Total phosphorus

(kilograms)

Total lead

(kilograms)

Stormwater loads calculated from stepwise regression concentrations 

Laundry Brook 84.1 189 383 --

Faneuil Brook -- 368 165 --

Muddy River -- 881 -- 310

Stony Brook1 756 3,070 2,660 691

Ungaged area 291 771 1,440 247

Stormwater loads calculated from average event mean concentration

Charles River at Watertown 1,460 5,690 13,800 696

Laundry Brook 73.5 354 363 52.6

Faneuil Brook 83.5 586 206 38.1

Muddy River 270 1,210 1,480 204

Stony Brook1 835 5,060 3,380 750

Ungaged area 267 1,250 1,390 196

Stormwater loads calculated from flow weighted event mean concentration

Laundry Brook 54.4 366 262 39.1

Faneuil Brook 60.2 621 161 28.4

Muddy River 314 1,380 1,260 204

Stony Brook1 490 2,670 2,030 430

Ungaged area 250 1,350 1,090 170

Stormwater loads after sewer separation

Stony Brook2 885 3,050 2,820 815

Dry-weather loads calculated from average dry-weather mean concentration

Charles River at Watertown 1,280 1,730 23,300 899

Laundry Brook 1.71 11.5 54.4 1.38

Faneuil Brook 8.89 262 74.7 2.48

Muddy River 17.3 14.5 307 11.3

Stony Brook1 17.0 3.30 1,280 16.2

Ungaged area 6.56 16.7 143 4.58

1Stony Brook loads are prior to sewer separation.

2Loads for Stony Brook after sewer separation reflect decreases in CSO discharge volume and increases in stormwater-discharge volume; runoff 

volumes after sewer separation were provided by Metcalf and Eddy, Inc. (written commun., Nov. 13, 2001).
LOAD, IN METRIC TONS LOAD, IN KILOGRAMS

100

80

60

40

20

0

SUSPENDED SOLIDS

TOTAL LEAD

70

60

50

40

30

20

10

0

Laundry

Brook

Faneuil

Brook

Muddy

River

Ungaged 

areas

Total Upstream of 

Watertown

Dam

Laundry

Brook

Faneuil

Brook

Muddy

River

Stony Brook Stony Brook Ungaged 

areas

Total

Subbasins downstream of Watertown Dam Subbasins downstream of Watertown Dam

3-Month Design Storm 1-Year Design Storm

(1) (2) (1) (2)

(management

practices

 not applied)

Upstream of 

Watertown

Dam

(management

practices

 not applied)

A.

Lower

Average

Upper

EXPLANATION

Total 

stormwater 

load

CONSTITUENT LOAD—Estimated lower, 

 average, and upper load removed by 

 combined management practices

 Street sweeping

 Structural controls

 Loads remaining after management

 practices applied

TOTAL—Is the sum of loads from subbasins below 

 Watertown Dam with Stony Brook before 

 combined sewer separation

STONY BROOK

 (1) Load before combined sewer separation

 (2) Load after combined sewer separation

Figure 12. Estimated 3-month and 1-year design-storm loads from non-combined-sewer-overflow sources from each of the 

major subbasins and estimated load reductions by hypothetical structural controls and street sweeping for (A) suspended 

solids and total lead loads and (B) fecal coliform and total phosphorus.

37 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts
Lower

Average

Upper

EXPLANATION

Laundry

Brook

Faneuil

Brook

Muddy

River

Ungaged 

areas

Total Upstream of 

Watertown

Dam

Laundry

Brook

Faneuil

Brook

Muddy

River

Stony Brook Stony Brook Ungaged 

areas

Total

Subbasins downstream of Watertown Dam Subbasins downstream of Watertown Dam

(1) (2) (1) (2)

(management

practices

 not applied)

Upstream of 

Watertown

Dam

(management

practices

 not applied)

Total 

stormwater 

load

CONSTITUENT LOAD—Estimated lower, 

 average, and upper load removed by 

 combined management practices

 Street sweeping

 Structural controls

 Loads remaining after management 

 practices applied

TOTAL—Is the sum of loads from subbasins

 below Watertown Dam with Stony Brook before 

 combined sewer separation

STONY BROOK

 (1) Load before combined sewer separation

 (2) Load after combined sewer separation

TRILLION OF COLONY FORMING UNITS LOAD, IN KILOGRAMS

3-Month Design Storm 1-Year Design Storm

500

400

300

200

100

0

1,000

800

600

400

200

0

FECAL COLIFORM

TOTAL PHOSPHORUS

B.

Figure 12. Estimated 3-month and 1-year design-storm loads from non-combined-sewer-overflow sources from each of the 

major subbasins and estimated load reductions by hypothetical structural controls and street sweeping for (A) suspended 

solids and total lead loads and (B) fecal coliform and total phosphorus—Continued.

38 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts
Summary and Conclusions 39

SUMMARY AND 

CONCLUSIONS

The lower Charles River is an important 

recreational asset to Boston and its surrounding communities. At times its use is impaired because of 

contaminant loads from stormwater runoff. The goal 

of making the river fishable and swimmable will 

likely require a variety of management actions, 

including control of non-CSO stormwater loads. This 

study examined the potential reductions in non-CSO 

stormwater loads that could be achieved by implementing best management practices. Practices examined

as part of this study included structural controls identified by the Center for Watershed Protection as suitable 

for the lower Charles River Watershed and street 

sweeping. The constituents considered in this study 

(suspended solids, fecal coliform bacteria, total 

phosphorus, and total lead) are representative of classes 

of contaminants that pose a concern for urban water 

and sediment quality.

Structural controls were grouped by similar 

major physical or chemical process; these included 

infiltration-filtration (physical separation), biofiltration-bioretention (biological mechanisms), or detention-retention (physical settling). For each of these categories removal efficiencies were compiled from three 

national databases of structural-control performance. 

Of the constituents examined, suspended solids were 

the most consistently and efficiently removed; efficiencies ranged from -170 to nearly 100 percent and averaged 45 to 78 percent, among control categories. 

Removal efficiencies for fecal coliform bacteria were 

the least reported, most variable, and the least effectively removed of the constituents examined; efficiencies ranged from -600 to 99 percent among control

categories. Average removal efficiencies for fecal 

coliform bacteria for infiltration-filtration and 

detention-retention controls were about 32 and 43 percent, respectively, whereas biofiltration-bioretention 

controls had an average removal efficiency of -3 percent. Removal efficiencies for total phosphorus ranged 

from -162 to 100 percent and averaged from 32 to 56 

percent among control types. Removal efficiencies for 

total lead were similar to those reported for suspended 

solids; efficiencies ranged from -162 to 100 percent 

and averaged 34 to 66 percent among control types. 

Average removal efficiencies were generally greatest 

for infiltration-filtration-type controls and least for

biofiltration-bioretention-type controls. 

Street sweeping as a water-quality management 

practice has received renewed interest because of 

reported improvements in sweeper technology and the 

recognition that opportunities for implementing structural controls are limited in highly urbanized areas. 

Streets are known to be a major source of stormwater 

contaminants; hence, street sweeping merits close 

examination as a stormwater best management practice. The SWMM model that was developed by the 

U.S. Geological Survey for the single-family land-use 

basin of the lower Charles River Watershed was modified to simulate constituent buildup, washoff, and the 

effects of street sweeping. Constituent buildup and 

washoff variables were calibrated to annual-load 

estimates and measured storm loads. Once calibrated, 

street sweeping was applied for the various permutations of four sweeper efficiencies and six sweeping frequencies. The sweeper efficiencies reflect the types of 

sweepers currently in use. Sweeping frequencies 

ranged from once every day to once every 30 days. 

Simulations generally indicate that less than 10 

percent of the built-up suspended solids and total lead 

and less than 5 percent of the built-up fecal coliform 

bacteria and total phosphorus were removed by street 

sweeping at frequencies of seven days or longer. The 

percentage of the built-up load removed by sweeping 

increases markedly at sweeping frequencies shorter 

than 7 days, particularly for suspended solids and total 

lead. Simulation results suggest that sweeping, even 

with the best available technology (effective sweeper 

efficiencies of 76 percent for suspended solids and 

total lead and 72 percent for fecal coliform bacteria and 

total phosphorus), removes only a small fraction of the 

total load unless intensive sweeping programs are 

implemented.

Simulated load reductions by street sweeping are 

highly sensitive to the washoff-variable value, which 

largely controls the removal of built-up contaminants 

during storms. Higher values of this variable removed 

less of the built-up load during small storms, which 

resulted in more contaminant load available for sweeping. An alternative model with lower washoff-variable 

values increased loads removed by street sweeping by 

about threefold for suspended solids, twofold for total 

lead, 40 percent for total phosphorus, and 20 percent 

for fecal coliform bacteria. The alternative-model 

storm loads had a slightly better fit with measured 

storm loads during the later summer months; thus, the 

buildup and washoff represented by the alternative 
40 Potential Effects of Structural Controls and Street Sweeping on Stormwater Loads to the Lower Charles River, Massachusetts

model could better represent these processes during 

this period, which results in improved simulated 

removal rates by street sweeping. 

Reduction of constituent loads to the lower 

Charles River by the combination of hypothetical 

structural controls and street sweeping was estimated 

for a range of performance estimates. Ranges of 

constituent-load reductions were computed rather than 

a single value because of the inherent variability and 

uncertainty of removal efficiencies, and because a 

range provides reasonable bounds on the load that 

could be removed by the practices under consideration. 

Upper estimates of combined-load reductions (sweeping once weekly with the best available technology and 

upper quartile of load removal by structural controls) as 

a percentage of the non-CSO sources load below 

Watertown Dam were 44 percent for suspended solids, 

34 percent for total lead, 14 percent for total phosphorus, and 17 percent for fecal coliform bacteria. The 

lowest estimates of combined load reduction (sweeping 

once monthly with low efficiency sweepers and the 

lower quartile of load removal by structural controls) as 

a percentage of the total non-CSO load entering the 

lower Charles River downstream of Watertown Dam 

were 14 percent for suspended solids, 11 percent for 

total lead, 4.9 percent for total phosphorus, and 7.5 percent for fecal coliform bacteria. Load reductions by 

these combined management practices, however, can 

be as small as 1.4 percent for total phosphorus to about 

4 percent for the other constituents examined when the 

entire watershed load is considered (includes the load 

above Watertown Dam).

Although the reductions in stormwater loads to 

the lower Charles River by the management practices 

examined appear to be minor when considered on

an annual basis over the entire lower Charles River 

Watershed, the potential water-quality benefits to be 

gained from these practices cannot be discounted. 

During and shortly following storms, these practices 

could provide water-quality benefits in the immediate 

vicinity of stormwater outfalls. Furthermore, the load 

reductions by structural controls and street sweeping 

are highly variable and depend, in part, upon processes 

that are not well understood. With respect to street 

sweeping in particular, only direct investigations of 

contaminant buildup, washoff, and sweeping processes, and controlled studies of sweeping effects on 

stormwater loads, will provide definitive evidence of 

the water-quality benefits that these practices can 

provide to the lower Charles River and other urban 

waterways. 

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