United States 

Department of 

Agriculture 

Forest Service 

Northeastern Forest 

Experiment Station 

General Technical 

Report N E- 1 86 

Chicago's Urban Forest Ecosystem: 

Results of the Chicago 

Urban Forest Climate Project 

E. Gregory McPherson 

David J. Nowak 

Rowan A. Rowntree 
McPherson, E. Gregory; Nowak, David J.; Rowntree, Rowan A. eds. 1994. Chicago's urban 

forest ecosystem: results of the Chicago Urban Forest Climate Project. Gen. Tech. Rep. 

NE-186. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest 

Experiment Station: 201 p. 

Results of the 3-year Chicago Urban Forest Climate Project indicate that there are an estimated 

50.8 million trees in the Chicago area of Cook and DuPage Counties; 66 percent of these trees 

rated in good or excellent condition. During 1991, trees in the Chicago area removed an estimated 

6,145 tons of air pollutants, providing air cleansing valued at $9.2 million dollars, These trees also 

sequester approximately 155,000 tons of carbon per year, and provide residential heating and 

cooling energy savings that, in turn, reduce carbon emissions from power plants by about 12,600 

tons annually. Shade, lower summer air temperatures, and a reduction in windspeed associated 

with increasing tree cover by 10 percent can lower total heating and cooling energy use by 5 to 10 

percent annually ($50 to $90 per dwelling unit). The projected net present value of investment in 

planting and care of 95,000 trees in Chicago is $38 million ($402 per planted tree), indicating that 

the long-term benefits of trees are more than twice their costs. Policy and program opportunities to 

strengthen the connection between city residents and city trees are presented. 

Refrevial Terms: urban climate, air pollution, urban forestry, energy conservation, carbon dioxide, 

urban ecosystem 

Northeastern Forest Experiment Station 

5 Radnor Corporate Center 

100 Matsonford Road, Suite 200 

P.O. BOX 6775 

Radnor, Pennsylvania 19087-4585 

June 1994 
Chicago's Urban Forest Ecosystem: 

Results of the Chicago 

Urban Forest Climate Project 

E. Gregory McPherson 

David J. Nowak 

Rowan A. Rowntree 

Contents 

Executive Summary ..................................................................................................................... iii 

Chapter 1 

The Role of Vegetation in Urban Ecosystems ............................................................................... 1 

Rowan A. Rowntree, E. Gregory McPherson, David J. Nowak 

Chapter 2 

Urban Forest Structure: The State of Chicago's Urban Forest ..................................................... 3 

David J. No wak 

Chapter 3 

Investigation of the Influence of Chicago's Urban Forests on Wind and Air Temperature 

Within Residential Neighborhoods ................................................................................ 19 

Gordon M. Heisler, Sue Grimmond, Richard H. Grant, Catherine Souch 

Chapter 4 

Local Scale Energy and Water Exchanges in a Chicago Neighborhood .................................... 41 

Sue Grimmond, Catherine Souch, Richard Grant, Gordon Heisler 

Chapter 5 

Air Pollution Removal by Chicago's Urban Forest .................................................................... 63 

David J. Nowak 

Chapter 6 

Atmospheric Carbon Dioxide Reduction by Chicago's Urban Forest ......................................... 83 

David J. Nowak 

Chapter 7 

Energy-Saving Potential of Trees in Chicago .............................................................................. 95 

E. Gregory McPherson 

Chapter 8 

Benefits and Costs of Tree Planting and Care in Chicago ........................................................ 115 

E. Gregory McPherson 

Chapter 9 

Sustaining Chicago's Urban Forest: Policy Opportunities and Continuing Research .............. 135 

E. Gregory McPherson, David J. Nowak, Rowan A. Rowntree 

Appendices ........ ............................................................................................................ ............ 139 

USDA Forest Service Gem Tech. Rep. NE-186. 1994. i 

Executive Summary 

Chicago's Urban Forest Ecosystem: 

Results of the Chicago Urban Forest Climate Project 

David J. Nowak, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Chicago, IL 

E-Gregory McPherson, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Davis, CA 

Rowan A. Rowntree, Program Leader, USDA Forest Service, Northeastern Forest Experiment Station, Albany, CA 

The Chicago Urban Forest Climate Project (CUFCP) was a 

3-year study to quantify the effects of urban vegetation on 

the local environment and help city planning and management organizations increase the net environmental benefits 

derived from Chicago's urban forest. The CUFCP study area 

consists of three sectors: Chicago, Cook County (exclusive 

of Chicago), and DuPage County (Figure 1). This report 

presents study results as well as information on continuing 

urban-forest research in the Chicago area. Numerous 

interrelated studies in the Chicago region were completed as 

part of the CUFCP, ranging from region-wide analyses of 

urban-forest ecosystems to investigations of individual trees 

and leaves. Research results can be summarized in the 

following five research topics. 

I. Chicago's Urban Forest Ecosystem and its 

Effect on Air Quality and Atmospheric 

Carbon Dioxide 

lnformation on the structure of Chicago's urban forest (e.g., 

species composition, tree leaf-surface area) provides the 

basis for understanding the functions of the urban forest that 

affect the city and its inhabitants. There are currently 4.1 

million trees in the City of Chicago, with an estimated 50.8 

million trees across the Chicago area of Cook and DuPage 

Counties. Most of these trees are small and on institutional, 

residential, and vacant lands. Relatively short-lived pioneer 

species contribute significantly to the Chicago area's urban 

forest, are most prevalent on land uses with minimal or 

naturalistic management (e-g., forest stand conditions), and 

may constitute an even more important component of the 

Chicago area's urban forest structure in the future. The most 

common trees in the Chicago area are buckthorn, green1 

white ash, Prunus spp., boxelder, and American elm. 

Field sampling of leaves of urban trees was used to develop 

equations to estimate leaf-surface area, the plant surface 

where atmospheric gases are actively exchanged. The most 

dominant species in leaf area in the Chicago area are silver 

maple, greenlwhite ash, white oak, American elm, and 

boxelder. These species likely have the greatest effect on 

the environment in the Chicago area. 

Street trees are a significant part of Chicago's landscape, 

accounting for 10 percent of the city's trees and 24 percent 

of the total leaf-surface area. Street trees are less significant 

in more suburban or rural areas. The most common ground 

surfaces in the study area are maintained grass, tar, herbaWISCONSIN Q 

AcHenry Co. Lake Co. 

Lake Michigan 

Cook Co. 

Kendall Co. Will Co. ! 

! 

! INDIANA 

Figure 1. -The Chicago Urban Forest Climate Project study 

area includes the City of Chicago, and Cook and DuPage 

Counties. 

ceous cover (e.g., crops), and buildings. lnformation on the 

structure of the Chicago ~irban forest ecosystem was used 

to help quantify the ecosystem functions of air pollution 

removal and carbon dioxide sequestration by urban trees. 

Removal of Air Pollution 

Air pollution is a multibillion dollar problem nationally that 

affects most major U.S. cities. Air pollution affects human 

health, damages vegetation and various anthropogenic 

materials, and reduces visibility. Trees can remove air pollution by intercepting particulates and absorbing gaseous 

pollutants (Figure 2). In 1991, trees in Chicago removed an 

estimated 15 metric tons (t) (17 tons) of carbon monoxide 

(CO), 84 t (93 tons) of sulfur dioxide (SO2), 89 t (98 tons) of 

nitrogen dioxide (N02), 191 t (210 tons) of ozone (Oz), and 

212 t (234 tons) of particulate matter less than 10 microns 

(PMIO). Across the Chicago area, trees (in-leaf season) removed an average of 1.2 Vday (1.3 tonslday) of CO, 3.7 ffday 

(4.0 tonslday) of SO2, 4.2 Vday (4.6 tonslday) of NO2, 8.9 tfday 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Executive Summary 
Figure 2. -Monthly estimates of pollution removal by trees 

in study area in 1991. Ozone removal estimates are for MayOctober only. PM10 estimates assume 50 percent 

resuspension of particles. 

(9.8 tonslday) of PMlO and 10.8 t/day (1 1.9 tonslday) of 03. 

The estimated value of pollution removal in 1991 was $1 

million for trees in Chicago and $9.2 million for trees across 

the Chicago area. Average hourly improvement (in-leaf season) in air quality due to all trees in the Chicago area 

ranged from 0.002 percent for CO to 0.4 percent for PM10. 

Maximum hourly improvement was estimated at 1.3 percent 

for S02, though localized improvements in air quality can 

reach 5 to 10 percent or greater in areas with relatively high 

tree cover, particularly under stable atmospheric conditions 

during the daytime of the in-leaf season. Large, healthy trees 

remove an estimated 60 to 70 times more pollution than 

small trees. 

Sequestration of Carbon Dioxide 

Increasing levels of atmospheric carbon dioxide (C02) and 

other "greenhouse" gases are thought by many to be leading 

to increased atmospheric temperatures through the trapping 

of certain wavelengths of heat in the atmosphere. In terms of 

reducing atmospheric C02, trees in urban areas offer the 

double benefit of direct carbon storage and the avoidance of 

C02 production by fossil-fuel power plants through energy 

conservation from properly located trees. Trees in Chicago 

store an estimated 855,000 t of carbon (942,000 tons), and 

trees throughout the Chicago area store approximately 5.6 

million t (6.1 million tons). Carbon storage by shrubs is 

approximately 4 percent of the amount stored by trees. Total 

carbon storage and annual sequestration are greatest on 1-3 

family residential lands, institutional lands dominated by 

vegetation (e.g., parks, forest preserves) and vacant lands. 

The estimated net sequestration of carbon in the Chicago 

area is 140,600 t (1 55,000 tons). Carbon storage by urban 

forests nationally likely is between 400 and 900 million t (440 

and 990 millions tons). 

Carbon storage by individual trees is up to 1,000 times 

greater in large than in small trees, with sequestration rates 

up to 90 times greater for healthy large than healthy small 

trees. Estimated carbon emissions avoided annually due to 

energy conservation from existing trees throughout the 

Chicago area is 11,400 t (1 2,600 tons). Total carbon stored 

by trees in the Chicago area, which took years to store, is 

equivalent to the amount of carbon emitted from the residential sector in the Chicago area during a 5-month period. Net 

annual sequestration equals the amount of carbon emitted 

from transportation use in the Chicago area in 1 week. The 

amount of carbon sequestered annually by one tree less 

than 8 cm (3 inches) in trunk diameter (d.b.h.) equals the 

amount emitted by one car driven 16 km (10 miles). Reasonable additional tree planting in conjunction with efforts to 

sustain existing tree cover could increase carbon storage in 

the Chicago area by another 1.2 million t (1.3 million tons), or 

the amount of carbon emitted by transportation use in the 

Chicago area in less than 2 months. 

11. Effect of Urban Trees on Wind and Air 

Temperature 

By transpiring water, blocking winds, shading surfaces, and 

modifying the storage and exchanges of heat among urban 

surfaces, trees affect local climate and consequently energy 

use in buildings, human thermal comfort, and air quality. 

Models that accurately estimate the effect of urban trees on 

local windspeed and air temperature at the height of people 

and residential buildings are lacking, partly because of the 

complexity of the multiple surfaces in urban areas. 

To develop models for estimating the effect of trees on urban 

microclimates, measurements of windspeed, air temperature, and humidity were taken at 39 sites in and near 

residential neighborhoods in Chicago over an 11 -month 

period (July 1992 to June 1993). Equations to predict the 

influence of trees on local climate are being developed by 

analyzing the interrelationships among climatic variables and 

local urban morphology (e.g., tree and building attributes). 

Preliminary analyses for a 1-week summer period indicate 

that residential morphology (buildings and trees combined) 

reduced windspeeds by an average of 46 to 85 percent 

(relative to an open field site at O'Hare International Airport) 

depending on the specific neighborhood morphology. The 

reductions in wind speed were significantly related to indicators of urban morphology. Residential air temperatures 

generally were warmer than the open-field site due to the 

predominance of building surfaces which tend to warm the 

local environment. Continuing work is quantifying the 

specific effect of urban trees on local windspeed, air 

temperature, and humidity. 

Ill. Local-Scale Energy and Water Exchanges 

The complex mix of anthropogenic surfaces (e.g., buildings, 

roads) and natural surfaces (e.g., trees, grass) in urban 

areas affects how energy and water are partitioned and 

cycled through the urban system (Figure 3). The replacement 

of natural surfaces with anthropogenic surfaces alters the 

thermal and moisture properties of the area, thereby 

modifying the local atmosphere and generating an "urban 

climate" that is commonly characterized by increased air 

iv Executive Summary USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
surface layer / 

constant tlux layer 

Figure 3.-Schematic representation of spatial scales and 

atmospheric processes in urban areas (adapted from Oke 

1984; Oke et al. 1989). 

temperatures and poorer air quality. Extensive climatic measurements across the north-side of Chicago and intensive 

measurements of a predominantly residential area in and 

around Chicago were conducted to quantify how urban 

morphologies affect local energy and water exchanges. Intensive observations consisted of direct measurements of 

sensible and latent heat flux, and net all-wave radiation. 

Convective fluxes were quantified using eddy-correlation 

techniques which seek to measure the flux directly by sensing properties of eddies as they pass through a measurement 

level on an instantaneous basis. 

Calculation of the Bowen ratio for a period during July 1992 

indicates that more energy (available from the sun and earth) 

was going to drying surfaces (latent heat flux) than to warming the air (sensible heat flux). This result is different from 

that observed in the summer in Tucson, Arizona, and in 

Sacramen.to and Los Angeles, California. However, the results 

for Chicago are realistic considering the meteorological 

conditions of July 1992 (i.e., relatively high frequency of 

rainfall). Of the net available energy from solar and earth 

radiation during the daytime, 32 percent went to heating the 

air, 38 percent to evaporating water, and 30 percent to 

heating urban surfaces. Work is in progress to correlate the 

latent and sensible heat fluxes with tree cover. This correlation will reveal the effect of trees on flux partitioning and help 

determine to what degree trees cool the local environment. 

Numerical models are being developed to predict the effect 

of different tree-planting scenarios on local-scale energy and 

water exchanges. 

IV. Potential Building Energy Savings from 

Urban Trees 

Trees can reduce building energy use by lowering summertime temperatures, shading buildings during the summer, 

and blocking winter winds. However, trees also can increase 

building energy use by having their branches shade buildings during the winter, and can increase or decrease building 

energy use by blocking summertime breezes. Computer 

simulations of microclimates and building energy performance 

were used to investigate the potential of shade trees to 

reduce the use of residential heating and cooling energy in 

Chicago. Increasing tree cover by 10 percent (or about three 

trees located in optimal energy-conserving locations per 

building) could reduce total heating and cooling energy use 

by 5 to 10 percent ($50 to $90). On a per-tree basis of this 

mass planting, annual heating energy use can be reduced by 

about 1.3 percent ($10, 2 MBtu), cooling energy use by about 

7 percent ($15, 125 kwh), and peak cooling demand by 

about 6 percent (0.3 kW). Benefit-cost ratios of 1.40 for trees 

planted around typical two-story buildings and 1.96 for trees 

near energy-efficient wood frame buildings indicate that a 

utility-sponsored shade tree program could be cost-effective 

for both existing and new construction in Chicago. 

Street trees are a major source of building shade in Chicago. 

Shade from a large street tree located to the west of a typical 

brick residence can reduce the annual use of air-conditioning 

energy by 2 to 7 percent ($17 to $25, 138 to 205 kwh) and 

peak cooling demand by 2 to 6 percent (0.16 to 0.6 kW). 

Street trees that shade the east side of buildings can produce 

similar cooling savings, have a negligible effect on peak 

cooling demand, and can slightly increase heating costs. 

Shade from large street trees to the south increase heating 

costs more than they decrease cooling costs. Planting "solar 

friendly" trees to the south and east can minimize the energy 

penalty associated with blocking irradiance during the heating season. Design guidelines and recommended tree 

species for energy-efficient landscapes are presented. 

V. Benefits and Costs of Urban Tree Planting 

and Care 

Benefit-cost analysis was used to estimate the net present 

value, benefit-cost ratio, and discounted payback periods of 

proposed tree plantings in Chicago. A "typical" tree species, 

green ash, was located in "typical" park, residential yard, 

street, highway, and public housing sites. The 30-year stream 

of annual costs and benefits associated with the planting of 

95,000 trees was estimated. Assuming a 7-percent discount 

rate, a net present value of $38 million, or $402 per planted 

tree, was projected. Projected benefit-cost ratios were largest for trees planted in residential yards and public housing 

sites (33, and least for parks (2.1) and highways (2.3). 

Discounted payback periods ranged from 9 to 15 years 

(Figure 4). Expenditures for planting alone accounted for 

over 80 percent of projected costs except at public housing 

sites, while the largest benefits were attributed to "other" 

benefits (e.g., scenic, social, economic values) and energy 

savings. Findings indicate that despite the expense of planting and caring for trees in Chicago, with time the benefits 

that healthy trees produce can exceed their costs. 

Several policies and programs could expand the current role 

of residents, businesses, utilities, and governments in the 

planning and management of Chicago's future urban forest. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Executive Summary 
Acknowledgments 

- Park - Yard -1- Street 

-* Highway -- Public Housing 

Figure 4. -Discounted payback periods depict the number 

of years before the benefit-cost ratio exceeds 1.0. This 

analysis assumes a 30-year planning period and -/-percent 

discount rate. 

Potential new policies and programs include developing a 

comprehensive set of urban-forest planning principles which 

address such issues as job training opportunities, conservation education, neighborhood revitalization, mitigation of heat 

islands, and energy conservation; partnerships to enhance 

tree planting and care in public and low-income housing 

areas; an urban-forest stewardship program to provide financial assistance for professional care of existing trees; a 

yard-tree planting program to reduce building energy use 

that is sponsored by local utility companies; and a public 

education program that informs residents about the benefits 

of healthy and productive urban forests in ways to strengthen 

the connection between city residents and city trees. 

There are many individuals and organizations without whose 

help this project may never have been completed. We sincerely thank The City of Chicago, particularly Mayor Richard 

M. Daley; Chicago Department of Environment, particularly 

Henry Henderson, Suzanne Malec Hoerr, Edith Makra, and 

Dave Inman; Chicago Bureau of Forestry, particularly Steve 

Bylina Jr., Jerry Dalton, Bill Brown, Timothy Vaughan, and 

Ray Toren Jr.; Illinois Department of Mental Health, particularly Marva Arnold, Jess McDonald, and the Chicago Read 

Mental Health Center; Chicago Park District, particularly Geri 

Weinstein, Robert Megquier, Ron Nemchausky and Al 

Neiman; USDA Forest Service North Central Forest Experiment Station, particularly John Dwyer; Illinois Environmental 

Protection Agency, particularly Robert Swinford; Village 

of Oak Park, particularly Mike Stankovich; Village of Glen 

Ellyn, particularly Peggy Young; Village of Bloomingdale, 

particularly Larry Slavicek; Hendricksen The Care of Trees, 

particularly ~ar@ Hall; Chicago Gateway Green Committee, 

particularly Vince Pagone; Illinois Department of Transportation, particularly Duane Carlson and Rick Wanner; Commonwealth Edison, particularly Claire Saddler and Tom 

Hemminger; Peoples Gas, particularly Gene Waas and Bob 

Pendlebury; Center for Neighborhood Technology, particularly Ray Lau and John Katrakis; Illinois State Police, 

particularly Ed Bean and Mike Cima; Chicago O'Hare 

International Airport, particularly Edward McCall; the many 

residents of the Chicago area who permitted us to survey 

their vegetation and make climatic measurements; Indiana 

University undergraduate and graduate student helpers; USDA 

Forest Service, Northeastern Forest Experiment Station, 

particularly Robert Lewis, Rich Guldin, Gerald Walton, Lee 

Philbin, Paul Sacamano, Scott Prichard, Jack Stevens, Una 

Arnold, Wayne Zipperer, Sue Sisinni, Mary Buchanan, and 

Marty Jones; USDA Forest Service, Pacific Southwest 

Research Station, particularly Barbara Weber, Enoch Bell, 

Esther Kerkmann, Maureen Davis, and Kathy Stewart. 

vi Executive Summary USDA Forest Service Gen. Tech.Rep. NE-186. 1994. 
Chapter I 

The Role of Vegetation in Urban Ecosystems 

Rowan A . Rowntree, Program Leader, USDA Forest Service, Northeastern Forest Experiment Station, Berkeley, CA 

E. Gregory McPherson, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Davis, CA 

David J. Nowak, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Chicago, IL 

Abstract 

The Chicago Urban Forest Climate Project (CUFCP) evaluates the role of trees and other vegetation in the regional 

urban forest ecosystem. Ecosystem analysis provides an 

effective approach to planning and controlling the distribution of benefits and costs associated with ecological effects. 

The flow of energy, water, carbon, and pollutants through the 

ecosystem can be changed by changing the amount and 

spatial distribution of trees. Continuing research in Chicago 

and collaborating cities will refine the information needs for 

urban ecosystem management. 

Purpose of this Study 

The goal of this research is to add to our knowledge of how 

vegetation in and near cities affects the human environment. 

This report summarizes the 3-year Chicago Urban Forest 

Climate Project which examined how trees and plants of the 

Chicago area affect selected components of the regional 

urban ecosystem. 

Vegetation is part of the region's infrastructure, woven into a 

complex network of power lines, roads, aqueducts, and sewers that together help to sustain human health and quality of 

life. Yet, little is known about how this green infrastructure 

creates benefits and costs for people. In fact, most of the 

world's cities have scant information about the composition 

and geography of their urban forest. 

Urban forest is now a common term that means all of the 

vegetation and soils of an urban region. For this study, we 

occasionally substitute the term "urban forest ecosystem" to 

emphasize the ecological approach the scientific team has 

taken in conducting the research. This approach proceeds 

from the assumption that the Chicago region operates as a 

result of multiple interactions among vegetation, soils, water, 

insects, wildlife, dimate, anthropogenic surfaces, and people. 

The goal is to manage that operation so that benefits far 

exceed costs. 

The initial report of this research project, "Chicago's Evolving Urban Forest," describes the history of vegetation and 

Changes in the urban forest in the Chicago region since the 

beginning of urbanization (McPherson et al. 1993) Because 

research is continuing into 1995, a book will be published in 

the next several years updating our knowledge about 

Chicago's urban forest ecosystem. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

Manipulating Vegetation to Guide 

Ecosystem Operation 

Some elements of the urban ecosystem can be readily 

manipulated and others cannot. Vegetation is one element 

of the ecosystem that can be manipulated in a planned and 

cost-effective way. Vegetation is renewable and has the 

potential to yield a wide range of important benefits. The 

body of knowledge about the role of vegetation in the urban 

ecosystem and for enhancing human well being is inadequate for managers to make informed decisions about how 

much to invest, when and where, and for what outcomes. 

This weak technical foundation has plagued decisionmakers 

over the last decades in the face of increasing public interest 

in urban afforestation and urban forestry. 

Planners and managers must know what vegetation does, 

because it affects nearly every other component of the 

regional urban ecosystem. Herbs, shrubs, and trees change 

the temperature and humidity of the air. They intercept 

rainfall and capture air pollutants. Vegetation mediates chemical exchanges between the soil and the atmosphere. The 

urban forest provides habitat for local and migratory birds. 

Therefore, to effectively manage the ecological processes in 

an urban region, we must manage the vegetation. To do that, 

we must understand its structure and function. 

The ecosystem concept has been used for many years to 

understand how oortions of natural landscaoes function. The 

standard approa'ch is first to describe the hain components 

of the system. The second task is to understand how energy, 

water, and matter (e.g., nutrients) move through the ecosystem. In this study of the Chicago region, we follow this same 

sequence. First we quantified the structure of the vegetation. 

Then the research team examined how vegetation affected 

the flux, or flow, of energy, water, and air pollution through 

the ecosystem in ways that produce benefits or costs. 

Managing an Urban Region Using the 

Ecosystem Approach 

Today, federal and state land-management agencies are 

using ecosystem management to bring a science-based approach to caring for complex landscapes. This study is one 

of the first to approach the analysis of an urban landscape 

with an eye toward employing ecosystem management 

in the future. The research takes the first steps towards 

building a model that can support ecosystem management 

of an urban region by stewarding vegetation. 

Chapter 1 
Given the complexity of ecological and socioeconomic 

processes in an urban region, ecosystem management is 

the most effective approach for the following reasons: 

(1) Ecosystem management requires documentation of all 

components and potential relationships. No factor is left off 

the list. The level of documentation and understanding will 

vary among the components. For example, as a result of this 

research we know much more about Chicago's urban forest, 

but our understanding of how the forest cools summer air 

masses is relatively weak. A survey of how much we know 

about each component and each potential relationship 

provides managers with a map of their technical strengths 

and weaknesses. They can make decisions accordingly and 

request more technical information where it is needed. 

(2) Ecosystem management views processes that generate 

benefits and costs at different but related scales of time and 

space. Management decisions can be assessed in the context 

of long-term processes such as changes in tree cover over 

time. For example, in this report we offer a method for 

spreading the distribution of benefits and costs of tree planting over future years. This method allows the decisionmaker 

to see what has been invested and what benefits have been 

generated at any point in time. Small-scale (in both time and 

space) processes, such as neighborhood tree planting events, 

can be assessed in the framework of long-term afforestation 

programs that will have a spectrum of associated benefits 

and costs. Thus, a resident planting a tree is seen not as an 

isolated event but as influencing larger-scale (in both time 

and space) meteorological, energy, and air-pollution processes. 

Simply, ecosystem management gives the planner, 

policymaker, and manager an accounting system and map 

that aggregates small events into larger processes, and disaggregates large, complex processes into simpler elements. 

(3) Ecosystem management is responsible for inter-regional 

and inter-generational effects. Because of the expanded 

time and space scale cited, this approach makes the management of each ecosystem responsible for how it affects 

adjacent and distant but related ecosystems. And, ecosystem 

management is responsible for how future generations of 

people will be affected. While this may seem to place a 

greater burden on those who manage an ecosystem, this 

approach-if applied uniformly across all ecosystems-will 

result in lower costs and greater benefits for all of society. 

(4) Ecosystem management brings private and public land 

owners and managers together for a common purpose. Once 

it is understood how the ecosystem operates, landowners 

can see how their actions influence processes that generate 

benefits and costs. Most ecosystems are made up of private 

and public land managed for a range of purposes, from parks 

to supermarkets. When individual land owners and agency 

officials understand the systemwide effects of their actions, 

they will be able to better manage their land. 

In summary, the information requirements for managing urban 

ecosystems are high, but the short-, medium-, and long-term 

benefits far exceed the investment. This is recognized in 

many cities and urban areas, and citizens and organizations 

are seeking ways of taking the next step toward ecosystem 

management in their area. 

Transferring the Chicago Ecosystem 

Model to Other Cities 

The Chicago study was conducted with federal funds by a 

team of USDA Forest Service researchers, in cooperation 

with several university colleagues, to provide knowledge' for 

future stewardship of the Chicago region, but also to act as a 

model for other cities in the United States and around the 

world. Already, several cities are making preparations to 

conduct similar studies of their ecosystems to determine 

precisely the role of vegetation. It is the research team's 

hope that the concepts, methods, and procedures developed 

in Chicago will be tested and streamlined in the next few 

years so that cities can do this work themselves with 

scientists serving only as technical advisors. 

Acknowledgments 

This research was requested in 1990 by Mayor Richard M. 

Daley and funded by a special appropriation from the U.S. 

Congress through the USDA Forest Service, Northeastern 

Forest Experiment Station. Many people and organizations 

in the Chicago area assisted the research team in their work. 

We are grateful for this cooperation. We especially thank Dr. 

John Dwyer, Project Leader with the USDA Forest Service's 

North Central Forest Experiment Station in Chicago, for 

providing the research team with scientific input, office space, 

and a collegial scientific environment. 

Literature Cited 

McPherson, E. G.; Nowak, D. J.; Sacamano, P. L.; Prichard, S. E.; 

Makra, E. M. 1993. Chicago's evolving urban forest: initial 

report of the Chicago Urban Forest Climate Project. Gen. 

Tech. Rep. NE-169. Radnor, PA: U.S. Department of Agriculture, 

Forest Service, Northeastern Forest Experiment Station. 55 p. 

2 Chapter 1 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

.Illl*IC .. 
Chapter 2 

Urban Forest Structure: 

The State of Chicago's Urban Forest 

David J. Nowak, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Chicago, IL 

Abstract 

lnformation on urban forest structure (species composition, 

tree size and location, etc.) provides the basis for understanding the urban forest functions that affect urban 

inhabitants and for improving management to maximize the 

environmental and social benefits of urban forests. There 

are an estimated 4.1 million trees in the City of Chicago, with 

an estimated 50.8 million trees across the study area of 

Cook and DuPage Counties. Most of these trees are small 

and on institutional, residential, and vacant lands. 

Relatively short-lived pioneer species contribute significantly 

to the Chicago area urban forest. The invasive buckthorn is 

the most common tree, accounting for 12.7 percent of the 

total tree population but only 2.9 percent of total leaf-surface 

area. Other common trees are greedwhite ash, Prunus spp., 

boxelder, and American elm. The most dominant species in 

leaf area are silver maple, greedwhite ash, white oak, American elm, and boxelder. Native pioneer tree species (e.g., 

boxelder, green ash, willow, cottonwood) and buckthorn are 

most prevalent on land uses with minimal or naturalistic 

management (e.g., forest stand conditions) and may constitute 

an even more important component of the Chicago area's 

urban forest structure in the future. 

Streets trees are a significant part of Chicago's landscape, 

accounting for 10 percent of the city's trees and 24 percent 

of the total leaf-surface area. Street trees are less significant 

in more suburban or rural areas. Common ground surfaces 

in the study area are maintained grass, tar, herbaceous 

cover (e.g., crops) and buildings. This paper presents formulas for estimating the leaf-surface area of urban trees and 

discusses the importance of urban forest structure, particularly leaf-surface area, and how managers and planners can 

direct urban forest structure to a desired outcome. 

Introduction 

Urban forest structure is the three-dimensional spatial 

arrangement of vegetation in urban areas (species 

composition, tree size and health, number and location of 

trees, etc.). lnformation on this structure provides the basis 

for understanding the urban forest functions that affect urban 

inhabitants (air temperature modifications, human stress 

reduction, air pollution mitigation, improved sense of community, etc.) and for improving management to maximize the 

environmental and social benefits of urban forests. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

Urban forest structure is determined by three broad factors: 

urban morphology, which creates the spaces available 

for vegetation; natural factors, which influence the amounts 

and types of biomass likely to be found within cities; and 

human management systems, which account for intraurban 

variations in biomass configurations according to land use 

distributions (Sanders 1984). There are significant variations 

in urban forest structure both within and among cities. Aerial 

photographic analyses of urban tree canopy cover reveal 

that tree cover varies between 5 and 60 percent among 

land-use types within four eastern US. cities, while overall 

urban tree cover ranged from 24 to 37 percent among the 

cities (Rowntree 1984). 

There has been little ground-based research evaluating the 

urban forest structure of an entire city. Many researchers 

have evaluated the street-tree component of the urban forest 

(Impens and Delcarte 1979; Richards and Stevens 1979; 

Dawson and Khawaja 1985; Talarchek 1985; Jim 1986; 

Stevens and Richards 1986; McPherson and Rowntree 1989) 

or limited portions of non-street tree urban forests (e.g., 

Derrenbacher 1969; Schmid 1975; Whitney and Adams 1980; 

Airola and Buchholz 1982; Boyd 1983; Buhyoff et al. 1984; 

Dorney et al. 1984; McBride and Froehlich 1984; Miller and 

Winer 1984; Richards et al. 1984; Schroeder and Green 

1985; Schroeder and Cannon 1987; Profous et al. 1988, 

Profous and Rowntree 1993), but ground-based urban forest 

structural analyses of an entire urban area have been conducted only for the Los Angeles Basin (Horie et al. 1991) and 

Oakland, California (Nowak 1991). The Los Angeles study 

focused on leaf biomass and volatile organic emissions from 

vegetation. The Oakland study focused on variations in 

urban forest structure and its overall effect on forest compensatory value, atmospheric carbon storage and volatile 

organic emissions from vegetation (Nowak 1993a,b). 

Since many environmental functions are related to leafsurface area (e.g., reductions in air temperature, air pollution 

removal, volatile organic emissions, carbon dioxide sequestration), understanding the leaf-area contribution of various 

tree species is important to urban-forest researchers, managers and planners. The measure of tree-species dominance 

reflects the relative contribution of a species to the overall 

leaf-surface area of the forest. Species with the greatest 

proportion of leaf-surface area are the most dominant and 

likely have the greatest influence on the local environment. 

Many social benefits of trees also may be related to leafsurface area. For example, large trees contribute more scenic 

beauty than smaller ones (Buhyoff et al. 1984; Schroeder 

and Cannon 1987). 

Chapter 2 
Leaf-area indices (LAI) are another common means of comparing the relative contribution of leaf area among different 

areas or tree species on an equal-area basis. LA1 is the total 

leaf area (one surface only) divided by the ground area 

occupied by the plant. A LA1 of 4 means that for every square 

meter of ground below the tree canopy, 4 m2 of leaves lie 

above it. Net primary productivity (individual plant growth) of 

forests is greatest at a LA1 of approximately 4. However, the 

yield (growth) per unit of ground area is low in such open 

stands (LA1 < 4). Maximum gross productivity usually occurs 

at LA1 values of 8 to 10 (Kramer and Kozlowski 1979); 

LA1 varies with plant size, age, spacing, species, and site 

characteristics. 

Typical LAl's are 10 to 11 for tropical rain forests, 5 to 8 for 

deciduous forests, and 9 to 11 for boreal coniferous forests 

(Barbour et al. 1980). The LA1 of some Piedmont hardwood 

forests range from 4.5 to 7.4 (Hedman and Binkley 1988), 

and LAl's of a subalpine Sierra Nevada forest range from 3.6 

to 11.7 (Peterson et al. 1988). Little research has been 

conducted on the LA1 of urban trees. Data from individual 

urban trees and shrubs in Warsaw, Poland, show LAl's for 

individual trees ranging from 1 to 15 with an average LA1 of 

individual trees for various areas in Warsaw of 3.5 to 4.8 

(Gacka-Grzesikiewicz 1980). 

Because information is scarce on the variation in forest 

structure within urban areas, on how urban forest structure 

combines to create an urban forest ecosystem, and on leafsurface area of urban trees, the objectives of this study were 

to: 1) quantify urban forest structure and its variation by 

land-use type in the Chicago area; and 2) measure the 

leaf-surface area of individual open-grown urban trees and 

develop predictive equations of leaf-surface area to estimate 

tree species dominance in the Chicago area. This information will be used to reveal key urban forest characteristics 

and aid in quantifying various environmental functions (see 

Nowak 1994a,b: Chapters 5 and 6, this report). 

Met hods 

Study Area 

The study area encompasses Cook and DuPage Counties 

(3,350 km2; 1,292 mi2) and contains nearly six million people. 

To reveal regional variation within the Chicago area, the 

study area was subdivided into the City of Chicago, Cook 

County exclusive of Chicago (hereafter referred to as suburban Cook County), and DuPage County (Figure 1). Chicago 

is the most densely populated sector, accounting for 18 

percent of the entire study area and 47 percent of the total 

population. Suburban Cook County contains 56 percent of 

the study area and 40 percent of the total population, and 

many of the older suburban communities in the Chicago 

region. DuPage County is the least densely populated, mist 

agricultural, and most rapidly urbanizing sector within the 

study area. It contains 13 percent of the population and 

occupies 26 percent of the study area. Tree crowns cover an 

average of 11 percent of the land area in Chicago, 23 percent in suburban Cook County, and 19 percent in DuPage 

County (McPherson et al. 1993). Crown cover also varies by 

individual land-use types within each sector (Table 1). 

Cook Co. 

I 

i 

! INDIANA 

Figure 1. - Study area includes City of Chicago, suburban 

Cook County, and DuPage County. 

Ground Sampling of Vegetation 

Urban vegetation and other surface data were collected on 

652 randomly located plots established as a sample of grid 

points (213 plots in Chicago, 222 in suburban Cook County 

and 21 7 in DuPage County). Because the focus of this study 

is on urban trees, the number of sample plots allocated to 

each land-use type was proportional to the estimated tree 

cover in the land use.1 

Plot structure varied by land-use type2 Residential plots 

were subdivided into smaller ground units, whose area was 

measured to aid in estimating ground-surface cover (to the 

nearest 5 percent). Building size on each residential plot was 

measured and building-surface characteristics were noted. 

The amount of ground area occupied by various materials 

(tar, cement, buildings, small structures, other impervious 

material, maintained or unmaintained grass, shrubs, soil, 

herbaceous, rock, duff, water, wood) was measured or 

estimated on each plot. 

' Overall, 249 plots were located on 1-3 family residential lands, 

26 plots on multifamily residential lands (apartments with four or more 

units), 194 plots on institutional lands dominated by vegetation (e.g., 

parks, cemeteries, golf courses, forest preserves), 22 plots on institutional lands dominated by buildings (e.g., schools, churches), 52 plots 

on commercial/industriaI lands, 45 plok on vacant lands, 39 plots on 

transportational lands (e.g., airports, freeways), and 25 plots on 

agricultural lands. 

On 1-3 family residential lands, the entire residential lot (midroad to mid-alley) was measured. For other land use types, 0.04- 

hectare (ha) (0.1-acre) plots were measured. 

4 Chapter 2 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 1. -Mean percent tree cover and standard'error by land-use type in Chicago, suburban Cook County, DuPage County, 

and entire study area 

chicas0 Cook Co. DuPage Co. Study area 

Land use Mean SE Mean SE Mean SE Mean SE 

Transportation (freeway) 3.8 0.8 0.5 0.5 0.0 0.0 1.1 0.3 

Transportation (other) 1.8 0.3 2.1 1 .O 2.4 2.0 2.1 0.7 

Large commerciaVindustriaJ 2.9 0.3 2.4 0.5 1.4 0.6 2.3 0.3 

Small commercial/industriala 1.8 0.3 3.5 1.2 1.4 1.3 2.6 0.6 

Agriculture 0.0 0.0 4.1 0.6 2.4 0.5 2.9 0.4 

Institutional (buildinglb 7.1 0.7 6.4 1.2 9.9 1.9 7.3 0.8 

MultiresidentialC 6.6 0.5 8.9 1.7 10.2 2.7 8.1 0.8 

Commercial (~andscaped)~ 12.1 7.7 15.6 6.8 6.3 6.1 11.5 4.5 

Institutional (~egetation)~ 26.4 1 .O 16.7 1.6 20.4 2.2 19.7 1.1 

I3esidentialf 15.0 0.4 24.4 0.7 25.3 1 .O 22.8 0.5 

Vacant 19.6 1.5 39.2 1.9 31.7 2.3 33.7 1.2 

Forest preserve 53.8 3.2 66.6 1.4 75.2 2.7 70.0 1.2 

Total 11 .O 0.2 22.5 0.4 18.6 0.5 19.4 0.3 -- -- 

a Small street-front commercial stores, etc. 

Dominated by buildings (e.g., schools, churches). 

Apartments with four or more units. 

Hereafter inmrporated in the commerciayindustrial hnd-use dass in subsequent tables and analyses. 

Dominated by vegetation (e.g., parks, cemeteries, golf courses). This Wuse class indudes forest preserves in subsequent tables and analyses. 

1-3 family residential units. 

SE - denotes the standard error of the corresponding estimate. 

The size and species of individual shrub masses were recorded (length, width, height). On every 10th plot measured, 

stem diameters of individual shrubs at 15 cm (6 inches) 

above groundline were measured. Data were collected on 

8,996 trees and shrubs that were growing in tree form (i.e., 

relatively large open-grown individuals). The data included 

species, trunk diameter at breast height (d.b.h. - diameter at 

1.37 m or 4.5 ft), total tree height, height to base of crown, 

crown width, crown shape, percent of crown occupied by 

leaves, tree location (street-tree locations between sidewalk 

and road, or on median, were noted), and condition. Estimates 

of tree condition were based on foliage characteristics. Trees 

were rated as excellent if less than 5 percent of the crown 

showed dieback or leaf discoloration. Other ratings were 

good (5 to 25 percent dieback or discoloration), moderate 

(26 to 50 percent), poor (51 to 75 percent), dying (76 to 99 

percent), and dead (no leaves). 

Plot information was combined to produce aggregate estimates on vegetation and other urban-forest attributes by 

land-use type in each sector of the study area (Gerald Walton, 

USDA Forest Service, 1992, pers. commun.). 

Leaf Area of Urban Trees 

To estimate leaf-surface area of urban trees, data were 

collected from 54 healthy, open-grown park trees in Chicago 

that were selected specifically for their excellent condition 

(1 0 American elm, 10 green ash, 10 hackberry, 10 honeylocust, 

and 14 Norway maple). The crown height (base of crown to 

crown top) of sampled trees ranged from 3.4 to 9.1 m (1 1.2 

to 29.9 ft); crown width ranged from 4.1 to 12.0 m (13.5 to 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

39.4 ft) and individual LAl's ranged from 0.7 to 12.5. The 

volume of each tree crown was mapped (including areas 

devoid of leaves) using a telescoping pole3 Crown height 

and distance from the tree base were measured at crown 

boundary points every 1.5 m (5 ft) vertically and at every 45" 

angle radially (i.e., eight points around the tree at every 1.5 

m vertically). Ten 0.4 ms (14.1 ft3) samples of foliage were 

collected from random points within the tree crown using a 

high-lift truck.4The number of leaves per sample were counted 

and approximately 30 leaves were randomly subsampled for 

analysis of leaf area. For samples with 50 leaves or less, all 

leaves were analyzed for leaf area. Individual leaf areas 

were measured with a leaf-area meter (CID Inc., Conveyor 

Area Meter C1251). Average sample leaf area (one-surface 

only) per unit crown volume (mUm3) was extrapolated using 

the total crown volume (m3) to estimate total leaf area for 

each tree. Following leaf-area analyses, all leaves were 

dried at 65°C (1 49°F) for 24 hours and then weighed. 

Total leaf-surface area for smaller urban trees was obtained 

from Gacka-Grzesikiewicz (1 980). Data from 34 trees (1 2 

species) that ranged in crown height (H) from 0.7 to 12.8 m 

(2.3 to 42.0 ft) and in crown width (D) from 0.5 to 4.6 m (1.6 

to 15.1 ft) were combined with field data on leaf-surface area 

SA sliding pole that displays the height at the top of the pole. 

4A computer program was written to map the measured treecrown dimensions and calculate crown volume. Random distances 

along x, y, and z coordinates from the tree base were selected to 

determine sampling locations within each tree crown. Sample locations in the tree crown were approached with the high-lift truck bucket 

so as not to disturb the sample prior to leaf collection. 

Chapter 2 5 
of individual trees to produce equations for estimating total 

leaf-surface area of individual urban trees based on crown 

parameters. Other variables included in the predictive equations were a factor for leaf-surface area based on the outer 

surface of the tree crown (S= nD(H + D)/2) (GackaGrzesikiewicz 1980) and average shading coefficients for 

individual species (percent sunlight intercepted by foliated 

tree crowns) (McPherson 1984). 

Least-squares linear regression was used to produce two 

regression equations for estimating total leaf area of individual urban trees. One equation included shading coefficients, 

the other excluded shading coefficients to aid in estimating 

leaf area of species for which shading coefficients are unknown (40 percent of the total population). Because logarithmic 

equations slightly underestimate leaf area (Crow 1988) a 

correction factor of one-half of the estimated variance of the 

estimate was added to the untransformed value (y = ex + 

var(x) 12) for each equation (G. Walton, 1993, pers. commun.). 

The regression formula estimated for log-leaf area of trees 

with measured shading coefficients was: 

where Y = total leaf area (ma), H = crown height (m), D = 

crown diameter (m), Sh = shading coefficient (Appendix A, 

Table I), and S=wD(H + D)/2. The correction factor (0.1 159), 

added to the untransformed estimate, resulted in the following estimate for leaf area: 

For trees for which shading coefficients are unknown, the 

estimated log-leaf area relationship was: 

The correction factor added to the untransformed estimated 

value was 0.1 824. 

Total leaf area, derived from trees in excellent condition, was 

adjusted according to the condition class of the tree. Estimates 

of total leaf area were multiplied by 1 for trees in excellent 

condition, by 0.85 for trees in good condition, by 0.625 for 

moderate trees, by 0.375 for poor trees, by 0.125 for dying 

trees, and by 0 for dead trees. 

For trees with characteristics outside the range of conditions 

under which the regression equations were derived (H > 12 

m, D > 12 m, HID > 3, S z 500 or S < 1; n = 759,8.4 percent 

of the sample), leaf area was estimated using a volumetric 

approach. The volume of individual crowns occupied by 

leaves (foliated-crown volume) was estimated based on 

measured crown height, width, shape, and percent of crown 

occupied by leaves. Average leaf dry weight (gfms) was 

calculated based on measured data and information from 

the literature on individual tree species (Winer et al. 1983; 

Nowak 1991). Factors for average leaf dry weight were 

applied to the foliated-crown volume to estimate total leaf dry 

weight of the tree. This estimate was converted to leaf area 

using conversion factors (m2fg) calculated from measured 

data and from the literature (McLaughlin and Madgwick 1968; 

Monk et al. 1970; Gacka-Grzesikiewicz 1980; Box 1981; 

6 Chapter 2 

Shelton and Switzer 1984; Bacon and Zedaker 1986; Vose 

and Allen 1988; Reich et al. 1991 ; Cregg 1992). If no conversion data were found for an individual species, the genera 

average was substituted; if no genera data were found, the 

average conversion value for the hardwood or conifer group 

was used. 

Relative dominance of a tree species was calculated as the 

total leaf-surface area of all trees of one species as a percentage of the total leaf-surface area of trees of all species. 

Reliable estimates of error of leaf area estimates could not 

be made because it was not possible to determine the amount 

of error regarding factors associated with estimates of leaf 

area, for example, regression formula transformations, conversions used in the volumetric approach, and adjustments 

for crown condition. Thus, standard errors are not reported 

for estimates of species dominance. 

Average LAl's for individual trees were calculated by dividing 

the sum of leaf-surface areas by the sum of crown projections (individual ground area = nDU4). The total LA1 for the 

study area was calculated by dividing the estimate of the 

total leaf-surface area in the study area by the total area 

occupied by trees (from aerial photograph interpretation) 

(McPherson et al. 1993). Ground projections based on aerial 

photographs account for the multiple layering effect of trees 

(combined effect of overstory and understory trees). 

Results 

There are approximately 50.8 million trees in the study area, 

with 4.1 million trees in Chicago, 31.8 million in suburban 

Cook County, and 14.9 million in DuPage County (Table 2). 

The largest proportion of trees (49 percent) is on institutional 

lands dominated by vegetation (e.g., parks, forest preserves, 

cemeteries, golf courses), followed by 1-3 family residential 

land (25 percent), and vacant land (21 percent) (Table 2). 

These land uses also have the highest tree densities with 

institutional lands dominated by vegetation having 563 treesf 

ha (228 treesfacre). Vacant lands have 488 treesfha (197 

treesfacre) and 1-3 family residential lands have 93 treesfha 

(38 treesfacre) (Table 3). Overall tree density is highest in 

DuPage County at 173 treesfha (70 treesfacre), followed by 

suburban Cook County with 169 treesfha (68 treesfacre) and 

Chicago with 68 treesfha (28 treesfacre) (Table 3). Most of 

the estimated leaf-surface area (87.5 percent) is on 1-3 

family residential lands and institutional lands dominated by 

vegetation (Table 4). 

Cottonwood and greenfwhite ash are the most common 

species in Chicago. Buckthorn and greenlwhite ash are most 

common in suburban Cook County, and willow and boxelder 

are the most common species in DuPage County (Table 5; 

Appendix A, Tables 2-6). Species that dominate in leaf area 

are cottonwood and greenlwhite ash in Chicago, silver maple 

and American elm in suburban Cook County, and white oak 

and silver maple in DuPage County (Table 5; Appendix A, 

Tables 2-6). Composition and leaf-area dominance of tree 

species by land-use type for each sector of the study area 

are given in Appendix A, Tables 7-1 4. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 2. -Estimated number of trees (in thousands) by land-use type in Chicago, suburban Cook County, DuPage County, and 

entire study area 

i Land use 

I lnstitutional (bldg.) i Transportation 

Agriculture 

Multiresidential 

Commercial/indust. 

Vacant 

Residential 

lnstitutional (veg.) 

Total 

Chicago Cook County DuPage County Study area 

Total SE Total SE Total SE Total SE 

73 55 0 0 57 27 130 61 

225 175 0 0 28 28 253 178 

0 0 0 0 442 342 442 342 

1 99 134 232 89 153 31 584 164 

33 33 1,021 873 81 30 1,136 874 

494 248 3.863 1,455 6,443 2,406 10,799 2,822 

1,258 180 6.712 586 4,529 647 12,500 892 

1,845 505 19,978 3,300 3,163 706 24,985 3,412 

4.128 634 31.806 3.758 14,897 2.612 50.830 4.620 

Table 3.-Tree density (no. treedha) by land-use type in Chicago, suburban Cook County, DuPage County, and entire study 

area (divide by 2.471 to convert sterndha to stemdacre) 

chii Cook County DuPage County Study area 

Land use Totat SE Total SE Total SE Total SE I Institutional (bldg.) 25 19 0 0 20 9 9 4 

Agriculture 0 0 0 0 26 20 12 10 

Transportation 40 31 0 0 13 13 15 10 

Commercial/indust. 2 2 32 27 10 3 21 16 

Multiresidential 34 23 56 21 70 14 48 13 

Residential 52 7 91 8 124 18 93 7 

Vacant 256 128 315 119 81 o 303 488 127 

Institutional (veg.) 332 91 674 111 345 77 563 77 

Overall 88 10 1 69 20 173 30 1 52 14 

Table 4. -Percentage of land area, total number of trees (tree population), and total 

leaf area within the study area, by land-use type 

Land use Land area Tree populakm Leaf area 

Institutional (bldg.) 4.1 0.3 0.6 

Transportation 5.2 0.5 1 .O 

Agriculture 10.6 0.9 0.4 

Multiresidential 3.7 1.1 1.3 

Commercial/indust. 16.3 2.2 0.8 

Vacant 6.6 21.2 8.4 

Residential 40.2 24.6 49.7 

Institutional (veg.) 13.3 49.2 37.8 

Total 100.0 100.0 100.0 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 2 
Table 5. -Tree-species composition in Chicago, suburban Cook County, DuPage County, and entire study area; includes top 20 

species in number and percentage of trees and species dominance based on percentage of total leaf-surface area in each sector 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

CHICAGO 

Cottonwood 

Greedwhite ash 

American elm 

Prunus spp. 

Hawthorn 

Buckthorn 

Honeylocust 

Boxelder 

Mulberry 

Silver maple 

Norway maple 

Yew 

Ash (other) 

Ailanthus 

Crabapple 

Elm (other) 

Hackberry 

Chinese elm 

Blue spruce 

White oak 

Swamp white oak 

Redhlack oak 

Basswood 

Linden 

SUBURBAN COOK COUNIY 

Buckthorn . 

Greedwhite ash 

Prunus spp. 

American elm 

Boxelder 

Hawthorn 

Alder 

Silver maple 

Redblack oak 

Poplar (other) 

Black locust 

Slippery elm 

Cottonwood 

Sugar maple 

White oak 

Crabapple 

Honeylocust 

Mulberry 

Bur oak 

Norway maple 

Willow 

Swamp white oak 

8 Chapter 2 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Table 5. --continued 

Tree population Species dominance 

S~ecies -. Number SE Percent Rank Percent Rank 

DUPAGE COUNTY 

Willow 

Boxelder 

Buckthorn 

Pmnus spp. 

Greenlwhite ash 

Cottonwood 

Hawthorn 

Shagbark hickory 

American elm 

Mulberry 

Redtblack oak 

Blue spruce 

Silver maple 

Bur oak 

Basswood 

Black locust 

Jack pine 

White oak 

Crabapple 

Walnut 

Norway maple 

Pin oak 

Honeysuckle 

SNDY AREA 

Buckthorn 

Greedwhite ash 

Prunus spp. 

Boxelder 

American elm 

Hawthorn 

Willow 

Cotton wood 

Silver maple 

Redblack oak 

Alder 

Btack locust 

Poplar (other) 

Mulberry 

Shagbark hickory 

Slippery elm 

White oak 

Crabapple 

Honeylocust 

Norway maple 

Bur oak 

Siberian elm 

Norway spruce 

Walnut 

Swarn~ white oak 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 2 
Common andlor dominant species that contribute the most 

leaf area on a per-tree basis are white oak, swamp white 

oak, Norway spruce, silver maple, and Norway maple (Table 

6). Species that contribute the most large-diameter trees to 

the study area are silver maple, white oak, American elm, 

bur oak, and cottonwood (Table 7). Common small-diameter 

tree species are buckthorn, Prunus spp., greenlwhite ash, 

boxelder, and willow (Table 8). 

Fifty-six percent of the trees in the study area are less than 7 

cm (3 inches) in diameter and 76.9 percent are less than 15 

cm (6 inches) d.b.h. (Table 9). Chicago has the highest 

proportion of large trees greater than 46 cm (18 inches) 

d.b.h. (7.5 percent). Land uses with the highest proportion of 

large trees are institutional land dominated by buildings (29 

percent) and 1-3 family residential land (1 0 percent) (Appendix A, Table 15). 

About 55 percent of the trees in the study area were rated 

in good condition and 10.5 percent were rated as dead or 

dying (Table 10). Land uses with the highest proportion of 

dead and dying trees are institutional land dominated by 

vegetation (16 percent), followed by institutional lands dominated by buildings (1 1 percent), and vacant land (9.5 percent) 

(Appendix A, Table 16). 

Table 6. -Average leaf-surface area (m2) per tree for top 20 

species (in number and species dominance) in entire study 

area (index value is averaae s~ecies leaf area Der tree 

divided by average leaf are: pe; tree for entire pdpu~ation 

(81 m2)) 

Species 

White oak 

Swamp white oak 

Norway spruce 

Silver maple 

Norway maple 

Walnut 

Siberian elm 

Bur oak 

Red oak 

American elm 

Cottonwood 

Crabapple 

Honeylocust 

Mulberry 

Greedwhite ash 

Willow 

Shagbark hickory 

Boxelder 

Poplar (other) 

Slippery elm 

Hawthom 

Prunus spp. 

Black locust 

Buckthorn 

Alder 

Leaf area per tree Index value 

436 5.4 

422 5.2 

292 3.6 

253 3.1 

253 3.1 

21 9 2.7 

171 2.1 

162 2.0 

117 1.4 

109 1.3. 

100 1.2 

94 1.2 

91 1.1 

79 1 .o 

77 1 .o 

70 0.9 

60 0.7 

55 0.7 

48 0.6 

43 0.5 

42 0.5 

38 0.5 

20 0.2 

19 0.2 

10 0.1 

The average LA1 of individual trees is 4.3 in Chicago, 4.2 in 

suburban Cook County, 4.5 in DuPage County and 4.3 in the 

study area. The maximum LA1 calculated using the regression equations for an individual tree was 18.1 with only 0.05 

percent of the estimated LAl's for individual trees greater 

than 15. The estimated LA1 for the entire study area, which 

accounts for the multiple layering of trees, is 6.3. The overall 

LA! may be slightly overestimated because of a likely conservative estimate of tree cover in Chicago. The large amount 

and size of buildings in Chicago tend to obscure small trees. 

This obstruction likely results in an underestimation of tree 

Table 7. -Most common large trees given as percentage of 

total number of trees larger than 46 cm (18 inches) d.b.h. 

Species Percent 

Silver maple 14.2 

White oak 12.3 

American elm 8.0 

Bur oak 6.8 

Cottonwood 6.7 

Willow 5.5 

Siberian elm 4.6 

Greedwhite ash 4.6 

Red oak 4.6 

Honeylocust 4.6 

Norway maple 2.5 

Mulberry 2.2 

Prunus spp. 1.5 

Boxelder 1.5 

Hawthorn 1.5 

Table 8. -Most common small trees given as percentage of 

total number of trees less than 7 cm (3 inches) d.b.h. 

Species Percent 

Buckthorn 18.7 

Prunus spp. 

Greedwhite ash 

Boxelder 

Willow 

American elm 

Hawthorn 

Alder 

Cottonwood 

Black locust 

Shagbark hickory 

Red oak 

Slippery elm 

Sugar maple 

Silver maple 

Mulberrv 

10 Chapter 2 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 9. -Distribution of tree diameters in Chicago, suburban Cook County, DuPage County, and entire study area 

Chicago Cook County DuPage County Study area 

D.b.h. class (cm) PercenP SE PercenP SE PercenP SE PercenP SE 

0-7 41.3 4.6 58.5 2.2 54.5 5.2 56.0 2.1 

8-15 22.2 1.8 20.2 1.2 22.2 3.0 20.9 1.2 

16-30 19.9 2.1 12.7 1.2 15.0 2.3 13.9 1 .O 

31 46 9.1 1.1 5.1 0.6 4.3 0.5 5.2 0.4 

47-61 3.5 0.7 2.2 0.3 2.4 0.4 2.3 0.2 

62-76 1.9 0.4 0.7 0.2 1.3 0.2 1.0 0.1 

77+ 2.1 0.8 0.6 0.2 0.4 0.1 0.7 0.1 

All classes 100.0 100.0 100.0 100.0 

Table 10. --Distribution of trees by condition in Chicago, suburban Cook County, DuPage County, and the entire study area 

chicas0 Cook County DuPage County Study area 

Condition class PercenP SE PercenP SE PercenP SE PercenP SE 

Excellent 9.4 1.2 9.4 1.1 14.6 1.8 10.9 0.9 

Good 50.5 3.5 56.0 2.4 53.1 4.4 54.7 2.0 

Moderate 25.9 2.4 17.8 1.3 15.3 2.4 17.7 1.1 

Poor 7.9 1.3 5.2 0.7 8.0 1.7 6.2 0.7 

Dying 1.4 0.2 2.2 0.5 2.4 0.6 2.2 0.3 

Dead 5.0 1 .O 9.4 1.2 6.6 1.3 8.3 0.8 

All dasses 100.0 100.0 100.0 100.0 

cover and consequently a slight overestimation of the overall 

LAI. Thus, an overall LA1 of 6.0 is probably more likely for the 

Chicago area. Conifers account for 6 percent of the leafsurface area in the study area. 

Populations of Street Trees 

There are an estimated 1,463,700 street trees in the study 

area (SE = 151,900), with 416,000 in Chicago (SE = 48,500), 

854,300 in suburban Cook County (SE = 139,400), and 193,400 

in DuPage County (SE = 35,700). Norway maple and 

honeylocust are the most common street trees in Chicago, 

silver maple and greenlwhite ash in suburban Cook County, 

and greedwhite ash and Norway maple in DuPage County 

(Table 11). Street trees in the study area tend to be larger 

than trees in general- 51.5 percent of all street trees are 16 

to 46 cm (6 to 18 inches) d.b.h. (Table 12). Chicago has the 

highest proportion of large street trees with 28.7 percent 

larger than 46 cm d.b.h. (Table 12). 

Most street trees in the study area were rated as good (46 

percent) or excellent (34 percent) (Table 13). Only 0.5 percent 

were rated as dead or dying. No dead or dying street trees 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

were found in Chicago or suburban Cook County. Street 

trees account for only 2.9 percent of the total tree population 

but 9.5 percent of the total leaf-surface area (Table 14). 

Street trees are most significant in Chicago where they 

account for 10.1 percent of the total population and 24 

percent of total leaf-surface area. Dominance of street trees 

varies by land-use type with the greatest proportion occurring 

on residential lands in Chicago where street trees account 

for 27.9 percent of the trees and 43.7 percent of leaf-surface 

area (Table 14). 

Urban Ground Cover 

The most common ground surfaces in the study area are 

maintained grass, tar, and herbaceous plants; common surfaces in Chicago are tar, maintained grass, and buildings (Table 

15). Ground cover varied by land-use type with maintained 

grass the most common ground cover type on institutional 

and 1-3 family residential lands, tar most common on commercial/industrial and transportational lands, herbaceous cover 

most abundant on agricultural and vacant lands, and building cover most common on multifamily residential lands 

(Appendix A, Table 17). 

Chapter 2 
Table 11. -Top 25 street tree species in study area by sector 

Chii Cook County DuParre - County Studv area 

Species percenta SE Rank PercenP SE Rank PercenP SE Rank Percenta SE Rank 

Silver maple 13.1 

~reenlwhite ash 

Norway maple 

Honeylocust 

Prunus spp. 

Sugar maple 

Linden 

American elm 

Chinese elm 

S RedMack oak 

a Siberian elm 

Hackberry 

Pear 

Maple (other) 

Catalpa 

Ailanthus 

Norway spruce 

Golden-rain tree 

Basswood 

- Hawthorn 

Pin oak b 

, Redmaple 

Horsechestnut 

White birch 

. Oak (other) 0.0 

K 

P AH species 100.0 100.0 100.0 100.0 

? 

-I 8 a petcatage of populah = 
I Table 12. --Diameter distribution of street trees in Chicago, suburban Cook County, DuPage County, and entire study area 

I Chicago Cook County DuPage County Study area 

I D.b.h. class (cm) PenxnP SE PercenP SE Percenta SE Percents SE 

I 0-7 15.7 5.6 7.1 3.4 29.5 11.6 12.5 3.0 

8-1 5 4.0 2.2 24.0 12.5 13.9 5.8 17.0 7.4 

16-30 30.3 6.6 26.8 6.8 20.5 6.5 27.0 4.5 I 31-46 21.4 4.7 27.2 6.5 19.0 9.7 24.5 4.2 

47-6 1 12.8 3.8 10.6 3.5 7.0 4.9 10.7 2.4 1 62-76 7.6 3.0 4.3 2.6 5.2 3.1 5.4 1.8 

77+ 8.3 5.3 0.0 0.0 4.9 2.9 3.0 1.5 

All dasses 100.0 100.0 100.0 100.0 

I a Percentage of population 

Table 13. -Distribution of street trees by condition in Chicago, suburban Cook County. DuPage County, and entire study area 

chi- Cook County DuPage County Study area 

Condition class Ped SE Pem& SE PenxnP SE PercenP SE 

Excellent 18.8 4.8 41.7 13.8 30.3 12.2 33.7 8.3 

Good 52.5 9.3 41.2 9.3 55.0 11.0 46.2 6.2 

Moderate 26.0 6.9 14.7 4.9 8.2 3.7 17.0 3.5 

Poor 2.7 1.6 2.4 1.7 3.0 2.1 2.6 1.1 

Dying 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Dead 0.0 0.0 0.0 0.0 3.6 2.7 0.5 0.4 

All classes 100.0 100.0 100.0 100.0 

a Pembage of population 

Table 14. --Street trees as a percentage of total tree population (OAPOP) and percentage of total leaf-surface area (%LSA) in 

Chicago, suburban Cook County, DuPage County, and entire study area 

Chiio Cook County DuPage County Study area 

Land use %POP %LSA %POP %LSA %POP %LSA %POP %LSA 

Agriculture NA NA NA NA 0.0 0.0 0.0 0.0 

Institutional (bldg.) 0.0 0.0 NA NA 0.0 0.0 0.0 0.0 

Vacant 1.0 0.9 0.0 0.0 0.0 0.0 0.0 0.1 

Institutional (veg.) 0.7 6.3 0.0 0.0 0.0 0.0 0.1 0.6 

Multiresidential 10.3 8.5 11.1 10.1 3.2 1.1 8.8 7.6 

Residential 27.9 43.7 10.2 19.7 3.8 5.9 9.7 18.0 

Transportation 11.5 5.5 NA NA 0.0 0.0 10.3 3.8 

Commercial/indust. 0.0 0.0 14.2 18.5 20.0 41.0 14.2 25.8 

Total 10.1 24.0 2.7 9.5 1.3 3.6 2.9 9.5 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

- -- -- 

Chapter 2 
Table 15. -Distribution of ground-surface materials in Chicago, suburban Cook County, DuPage County, and entire study area 

chicas0 Cook County -- DuPage County Study area 

Surface type -. Pemnta SE PercenP SE Ped SE Ped SE 

Grass (maintained) 20.4 1.4 30.7 2.0 32.6 1.8 29.3 1.2 

Tar 21.3 2.6 13.3 1.8 11.5 1.2 14.3 1.1 

Herbaceous 3.4 0.7 12.6 1.5 20.1 2.0 12.9 1 .O 

Building 16.5 2.1 9.1 1.3 8.0 1.2 10.1 0.9 

Cement 12.2 1.2 5.8 0.7 3.7 0.5 6.4 0.5 

Soil 4.5 0.6 7.5 1.4 4.1 1.2 6.1 0.8 

Shrub 2.4 0.5 6.2 0.7 6.4 0.7 5.5 0.5 

Grass (unmaintained) 2.5 0.8 3.4 0.7 7.7 1.8 4.3 0.6 

Other structure 4.2 0.4 3.5 0.9 1.9 0.2 3.2 0.5 

Rock 4.9 1.4 2.8 0.7 1.3 0.2 2.8 0.5 

Other impervious 5.8 2.0 1.4' 1.0 0.3 3.0 1.9 1.0 

Duff 1.2 0.4 1.9 0.4 1.4 0.3 1.6 0.3 

Water 0.3 0.2 1.8 1.0 1.0 0.3 1.3 0.6 

Wood 0.3 0.2 0.1 0.0 0.1 0.0 0.1 0.0 

All surfaces 100.0 100.0 100.0 100.0 - - 

a Percentage of population 

Discussion 

Urban Forest Structure in the Chicago Area 

The Chicago area's urban forest is composed mostly of small 

trees less than 15 cm d.b.h. (76.9 percent). Small trees also 

account for the majority of trees in other cities. In Shorewood, 

Wisconsin, and Oakland, California, 67 percent and 60.9 

percent of the trees are less than 15 cm d.b.h., respectively 

(Dorney et al. 1984; Nowak 1993a). However, the distribution 

of tree sizes varies among and within land-use types depending on the duration and intensity of vegetation management. 

Less-managed (e.g., vacant) or naturalistically managed lands 

(e.g., forest preserves) had the highest proportion of small 

trees. Highly managed areas, particularly those managed for 

a relatively long period (e.g., street trees, residential areas), 

tend to have a higher proportion of large trees. However, 

there are some large old remnant trees throughout the 

Chicago area, particularly in forest preserves. 

Most of the trees in the study area were classified as being 

in good condition. Ratings on tree condition are affected by 

urban-environmental stresses (e.g., salt, soil compaction, 

vandalism, injury), plant competition (related to tree density) 

and natural aging processes (tree size), all of which tend to 

increase crown discoloration and dieback (e.g., Nowak and 

McBride 1991). Consequently, relatively few trees were rated 

as excellent. Most -of the dead and dying trees are in areas 

with minimal maintenance, naturalistic management, or 

in areas with more large trees that are not intensively 

managed (institutional land dominated by buildings). Dead 

and dying trees tend to be removed in the more intensively 

managed areas. 

Species Composition 

The most common species is the exotic and highly invasive 

buckthorn, accounting for 12.7 percent of the tree population. 

Seven of the 10 most common trees are native; three are 

genera of both native and exotic species. Four of the eight 

most common species are native pioneer species: green 

ash, boxelder, willow, cottonwood. These species have a 

propensity to colonize sites but have a shorter lifespan than 

more shade-tolerant species (Spurr and Barnes 1980; Burns 

and Honkala 1990). These species are common on all land 

uses but most common on vacant lands where they account 

for 47 percent of the population.. Buckthorn is common on 

the three land uses that contain 95 percent of the trees 

(institutional lands dominated by vegetation, 1-3 family residential, and vacant lands). These land uses include many 

areas with relatively low maintenance (e.g., tree stands), 

which facilitates invasion by buckthorn. The most common 

ornamental species, exclusive of major pioneer species, 

planted on residential lands are silver maple, Prunus spp., 

blue spruce, crabapple, mulberry, Norway maple, arborvitae, 

honeylocust, American elm, and junipers. 

The most common trees in Chicago are cottonwood and 

greedwhite ash, which make up 25 percent of the city's tree 

population. Greedwhite ash, both a pioneer and common 

ornamental tree, is common on most land uses in Chicago 

and accounts for 12 percent of all trees in the city. Cottonwood, 

which generally is not planted as an ornamental species, is 

the most common tree on vacant lands and institutional 

lands dominated by vegetation in Chicago. These land uses 

contain many low maintenance sites which facilitate invasion 

by cottonwood. 

Species and Individual Tree Dominance 

The most dominant species in total leaf area are silver 

maple, greedwhite ash, white oak, and American elm. These 

four species most likely have the greatest impact on the 

surrounding environment and constitute 34.8 percent of total 

leaf-surface area. Institutional lands dominated by vegetation 

14 Chapter 2 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
are dominated by American elm, white oak, greenlwhite ash, 

and redlblack oak (39.8 percent of total leaf-surface area); 

1-3 family residential areas are dominated by silver maple, 

greeniwhite ash and white oak (31.7 percent); and vacant 

lands are dominated by the pioneer species of cottonwood, 

boxelder, willow, and poplar (other) (50.7 percent). Although 

buckthorn is the most common tree in the study area, it 

accounts for only 2.9 percent of total leaf-surface area due to 

its relatively small size. 

The greatest average leaf-surface area on a per-tree basis 

occurs on white oak, swamp white oak, Norway spruce, 

silver maple, and Norway maple. Management activities should 

be directed toward preserving dominant individuals in a healthy 

condition so that their large environmental and social 

benefits, relative to smaller trees, are sustained (e.g., 

Schroeder and Cannon 1987; Nowak 1994a,b). 

Diameter-growth rates of individual open-grown urban trees 

are relatively high (Nowak 199413) and these growth rates 

are explained partially by the average LA1 of individual trees 

in the study area (4.3), which is near the index level of 

maximum net growth. The overall urban tree LA1 of 6.0 is at 

the low end of the normal range of LAl's exhibited for deciduous forests (Barbour et al. 1980). This relatively low index 

level is understanda.ble considering the relative lack of lower 

level canopy (understory trees) in some urban areas that are 

common in deciduous forests. The urban forest understory 

of more intensively managed land uses often is occupied by 

grass or impervious surfaces. 

Street Trees 

Street trees in Chicago constitute 1 of every 10 trees overall 

and 1 of every 4 trees in 1-3 family residential areas. Chicago's 

street trees contribute 24 percent of the total city leaf-surface 

area, and 44 percent of total leaf area on 1-3 family residential lands. Street trees play a less important role in less 

urbanized areas, but can still contribute significantly to the 

street-corridor environment (Schroeder and Cannon 1987). 

In suburban Cook County, street trees constitute 1 of every 

37 trees (9.5 percent of total leaf-surface area) and 1 of 

every 10 trees on residential land. In the least urbanized 

sector, DuPage County, street trees account for 1 of every 

77 trees (3.6 percent of total leaf-surface area) and 1 of 

every 26 trees on residential land. Thus, street trees become 

a more important component of the urban forest in more 

urbanized areas as artificial surfaces and land-use activities 

compete for tree space. 

A high percentage of street trees in the Chicago area are 

greater than 46 cm d.b.h. (Chicago: 28.7 percent; suburban 

Cook County: 14.9 percent; DuPage County: 17.1 percent). 

There is a 4 to 6 times higher percentage of large street trees 

than non-street trees. Large trees are important to the urban 

environment, contributing significantly more air quality and 

carbon dioxide sequestration benefits than small trees (see 

Nowak 1994a,b: Chapters 5 and 6, this report). 

Urban Ground Surfaces 

Besides trees, a wide range of other urban surfaces interact 

with the surrounding environment and affect local gas and 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 

energy exchanges, visual quality, human stress, etc. The 

most abundant urban ground surfaces in the study area are 

maintained grass, tar, herbaceous plants (e.g., agriculture 

crops) and buildings. Impervious surfaces cover 60 percent of 

Chicago, 33 percent of suburban Cook County, and 25 

percent of DuPage County. Tar generally is the most common 

ground-surface cover of commercial/industrial and transportation lands. Maintained grass often is the most abundant 

surface on residential and institutional lands. Converting nonessential impervious surfaces (e.g., abandoned parking lots) 

to more pervious surfaces (e.g., soil) could facilitate the 

formation of vegetation and reduce surface runoff. Understanding how various urban surfaces interact to affect the local 

environment and city inhabitants remains to be investigated. 

Factors Influencing Current Vegetation Patterns 

Vegetation within urban and urbanizing areas changes through 

time and space. Land use is one of the most significant 

factors affecting local vegetation patterns and distribution. In 

conjunction with its associated patterns of buildings and 

other artificial surfaces, land use influences the space available for trees and to some extent whether those spaces will 

be filled with trees and how they will be managed. Most of 

the nearly 51 million trees in Cook and DuPage Counties are 

on instituiional lands dominated by vegetation, 1-3 family 

residences, and vacant land. This distribution pattern is similar to that for trees in Oakland, California (Nowak 1993a). 

These land uses generally are the most amenable to tree 

growth in urban areas and are likely where most of the trees 

exist in U.S. cities. Management plans should consider 

differences in tree distribution among land-use types to optimize tree configurations across the entire urban area. By 

understanding tree variations among land-use types, managers could focus planting efforts in areas typically lacking 

trees and direct species composition in more heavily-treed 

areas to meet specific management objectives and enhance 

the local environment. 

In regions such as the Chicago area where trees are readily 

established through natural seeding, available planting space 

that is not filled with trees often has been actively managed 

to prohibit trees (e.g., mowing, use of herbicides, planting of 

herbs, selective tree removal). Such activities are necessary 

for land uses such as agriculture, airports, prairies, and 

sporting fields, but uses such as residential, commercial, 

and some transportation corridors could be used to increase 

tree cover if desired. 

Tree cover can be increased through education and other 

promotional efforts that support tree planting and maintenance and/or encourage reducing management activities that 

prohibit trees and thereby allow trees to become established 

on the site naturally. Natural tree establishment can facilitate 

the development of invasive species so management activities should be directed toward altering species composition if 

certain invasive species are deemed undesirable. 

The intensity of urban development also influences the amount 

of trees in a city, with tree density generally decreasing with 

urbanization. Average tree density in the Chicago area ranged 

from 68 treeslha (28 treesiacre) in Chicago to 173 treesiha 

Chapter 2 15 
(70 treeslacre) in DuPage County. There are two primary 

reasons for the decrease in tree density with increased 

urbanization. First, in more heavily urbanized areas, more of 

the land is occupied by uses that preclude tree establishment 

(e.g., commercial/industriaI, transportation).s Second, tree 

space tends to be more limited in highly urban areas (i.e., 

residential lots tend to be smaller; impervious surfaces 

occupy a higher proportion of the ground area). 

Tree density on residential and commercial land in Chicago 

is comparable to those in Shorewood, Wisconsin, for the 

same land uses (Dorney et al. 1984). Tree density from other 

urban areas are 120 treeslha (49 treeslacre) in Oakland, 

California (Nowak 1993a) and 373 and 40 treeslha (151 and 

16 treeslacre) for portions of South Lake Tahoe and Menlo 

Park, California, respectively (McBride and Jacobs 1986). 

By contrast, the average live tree density on timberland 

in Illinois is 1,186 trees/ha (480 treeslacre) (Raile and 

Leatherberry 1988). 

Besides affecting management and various environmental 

functions, tree density affects visual quality of a landscape. 

Optimal foreground density for aesthetic quality in municipal 

parks has been estimated at approximately 125 treeslha (51 

treeslacre) (Schroeder and Green 1985). High tree densities 

and large trees are also preferred along streets (Schroeder 

and Cannon 1987). 

Most of the differences in vegetation patterns within the 

study area are due to differences in land-use distribution, 

intensity of urbanization, and age of development. Chicago 

is the oldest, most urbanized area while DuPage County is 

the most suburban to rural area with newer residential developments and the highest proportion of agricultural areas. 

Directing Future Urban Forest Structure in the 

Chicago Area 

The future urban forest in the Chicago area, as indicated by 

the distribution of tree species less than 7 cm d.b.h., is likely 

to be dominated by greedwhite ash, boxelder, willow, cottonwood, black locust, and shagbark hickory. Other common 

species (buckthorn, Prunus spp., hawthorns, alders) in this 

smallest d.b.h. class generally do not reach a dominating 

size. American elm also is a common small tree, but sanitation 

programs andlor the planting of cultivars that resist Dutch 

elm disease must be continued or utilized if American elms 

are to maintain a dominant position in the Chicago area's 

urban forest. 

This probable future forest will mean a shift from silver maple 

and white oak that codominate today toward more invasive 

pioneer species. While silver maple, white oak, and bur oak 

account for one-third of the trees greater than 46 cm d.b.h., 

they make up only 3.3 percent of the trees less than 7 cm 

d.b.h. However, planners and managers can alter or direct 

future species composition and structure (Nowak 1993~). 

Rural areas also can have land uses where low tree densities 

are typical (e.g., agriculture, vacant land in desert areas). 

Education and management can influence the amount, type, 

and location of urban vegetation (e.g., tree planting in backyards and parking lots) and thereby direct future urban forest 

structure to a desirable outcome. Trees are not appropriate 

in all locations or land uses. However, where trees are 

desirable, planning and management can facilitate proper 

urban forest structure. The more space available for tree 

planting that is not inhibited by the existing land use, 

the more the natural environment and local planning and 

management can influence vegetation structure (e.g., vacant lands, parks). 

Management plans should consider directing current urban 

forest structure toward a future structure that enhances 

healthy, functional leaf-surface area and optimizes species 

composition to maximize both social and environmental 

benefits of trees. Management plans should be developed to 

meet specific local needs, for example, enhancing the scenic 

beauty of a park or reducing air pollution in a certain area. 

Managing for one need or to maximize one benefit may 

reduce some other benefits derived from urban trees, so 

local and regional management priorities and plans must be 

developed. Besides preserving large trees, multilayer forest 

structures (stand conditions) should be sustained where 

appropriate, and healthy canopies should be maintained to 

maximize many tree benefits. Also, ample water should be 

supplied to trees to optimize benefits that are linked with 

transpiration (e.g., removal of gaseous pollutants and 

reduced air temperatures). 

Implications for Research 

The equations developed to predict the leaf-surface area of 

individual urban trees appear to yield reasonable estimates 

when applied within the bounds in which the regression 

equations were developed. However, more work is needed on 

developing shading coefficients and leaf-area predictions for 

individual species, particularly for large trees and coniferous 

species. Also needed is additional research on urban-forest 

structure and its link to various functions for other U.S. cities 

to help clarify and determine existing urban-forest patterns 

and processes. Finally, researchers need to investigate 

changes in urban forest structure and functions through time 

to better predict and understand the dynamics of these ecosystems, and to determine how urban surfaces interact in 

affecting the local environment and inhabitants. 

Conclusion 

Urban forest planning and management can direct urban 

forest structure toward a desired outcome. One of the first 

steps in properly directing urban forest structure is to understand if, and what, changes are necessary by analyzing 

the existing urban forest structure. By understanding forest 

structure and determining the relationships between structure and forest functions, various social and environmental 

benefits can also be quantified. The Chicago area urban 

forest contains 50.8 million trees, approximately 9 trees per 

resident. Most of the trees are small and predominantly 

found on institutional, residential and vacant lands. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
The current pattern of urban vegetation has been formed 

through both present and past human and environmental 

factors. Education of both the public and private sectors can 

facilitate directing future urban forest structure toward 

desired results as dictated by urban forest management 

plans. However, the urban environment (e.g., land uses) 

presents many constraints on urban forest structure that 

managers and planners must consider. 

Relatively short-lived pioneer species contribute significantly 

to the Chicago area urban forest and are most prevalent on 

land uses with minimal or naturalistic management (e.g., 

forest stand conditions). Street trees are also important 

elements of the urban forest, particularly in the City of Chicago. Trees are just one of many surfaces that interact to 

influence the urban environment; other prominent ground 

surfaces include tar and grass. 

Acknowledgments 

I sincerely thank Al Neiman, Ron Nemchausky, and The 

Chicago Park District for use of a high-lift truck and assistance in data collection; Henry Henderson, Edith Makra, 

Chicago Department of Environment, Steve Bylina Jr., Jerry 

Dalton, Bill Brown, and the Chicago Bureau of Forestry for 

local technical assistance; Gerald Walton for statistical 

advice and assistance; John Dwyer, Marty Jones, Rowan 

Rowntree, and Gerald Walton for reviewing this article; and 

Scott Prichard, Rachel Mendoza, Steve Wensman, Merle 

Turner, Joanna Mignano, Wendy Vear-Hanson, Brad Bonner, 

Marcia Henning, Paul Sacamano, Hyan-kil Jo, Jennifer Lixey, 

Lisa Blakeslee, and Jennifer Lee for assistance in field data 

collection and entry. 

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USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 2 
Nowak, D. J. 1994b. Atmospheric carbon dioxide reduction by 

Chicago's urban forest. In: McPherson, E. G., Nowak, D.J. and 

Rowntree, R. A. eds. Chicago's urban forest ecosystem: results 

of the Chicago Urban Forest Climate Project. Gen. Tech. Rep. 

NE-186. Radnor, PA: U.S. Department of Agriculture, Forest 

Service, Northeastern Forest Experiment Station. 

Nowak, D. J.; McBride, J. R. 1991. Comparison of Monterey pine 

stress in urban and natural forests. Journal of Environmental 

Management. 32: 383-395. 

Peterson, D. L.; Arbaugh, M. J.; Lardner, M. A. 1988. Leaf area of 

lodgepole pine and whitebark pine in a subalpine Sierra 

Nevada forest. Canadian Journal of Forest Research. 19: 401- 

403. 

Profous, G. V.; Rowntree, R. A. 1993. The structure and management of the urban forest in Prague, Czechoslovakia. I. Growing space in metropolitan Prague. Arboricultural Journal. 17: 

1-13. 

Profous, G. V.; Rowntree, R. A,; Loeb, R. E. 1988. The urban 

forest landscape of Athens, Greece: aspects of structure, 

planning and management. Arboriculture Journal. 12: 83-1 07. 

Raile, G. K.; Leatherberry, E. C. 1988. Illinois forest resource. 

Resour. Bull. NC-105. St, Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station. 

113p. 

Reich, P. 6.; Walters, M. B.; Ellsworth, D. S. 1991. Leaf age and 

season influence the relationships between leaf nitrogen, 

leaf mass per area and photosynthesis in maple and oak 

trees. Plant, Cell and Environment. 14: 251 -259. 

Richards, N. A.; Mallette, J. R.; Simpson, R. J.; Macie, E. A. 1984. 

Residential greenspace and vegetation in a mature city: 

Syracuse, New York. Urban Ecology. 8: 99-1 25. 

Richards, N. A,; Stevens, J. C. 1979. Streetside space and street 

trees in Syracuse -1978. Syracuse, NY: State University of 

New York, College of Environmental Science and Forestry. 66 p. 

Rowntree, R. A. 1984. Forest canopy cover and land use in four 

eastern United States cities. Urban Ecology. 8: 55-67. 

Sanders, R. A. 1984. Some determinants of urban forest structure. Urban Ecology. 8: 13-27. 

Schmid, J. A. 1975. Urban vegetation: a review and Chicago 

case study. Res. Pap. (vol. 161). Chicago, IL: University of 

Chicago, Department of Geography. 266 p. 

Schroeder, H. W.; Cannon, W. N. 1987. Visual quality of residential streets: both street and yard trees make a difference. 

Journal of Arboriculture. 13(10): 236-239. 

Schroeder, H. W.; Green, T. L. 1985. Public preference for tree 

density in municipal parks. Journal of Arboriculture. 1 l(9): 

272-277. 

Shelton, M. G.; Switzer, G. L. 1984. Variation in the surface area 

relationships of loblolly pine fascicles. Forest Science. 30(2): 

355-363. 

Spurr, S. H.; Barnes, B. V. 1980. Forest ecology. New York: John 

Wiley and Sons. 687 p. 

Stevens, J. C.; Richards, N. A. 1986. Village and city street tree 

resources: a comparison of structure. Arboricultural Journal. 

10: 45-52. 

Talarchek, G. M. 1985. The New Orleans urban forest: structure 

and management. New Orleans, LA: Xavier University of Louisiana, University Research Center. 104 p. 

Vose, J. M.; Allen, H. L. 1988. Leaf area, stemwood growth, and 

nutrition relationships in loblolly pine. Forest Science. 34(3): 

547-563. 

Whitney, G. G.; Adams, S. D. 1980. Man as a maker of new plant 

communities. Journal of Applied Ecology. 17: 431 -448. 

Winer, A. M.; Fitz, D. R.; Miller, P. R.; Atkinson, R.; Brown, D. E.; 

Carter, W. P.; Dodd, M. C.; Johnson, C. W.; Myers, M. A.; 

Neisess, K. R.; Poe, M. P. Stephens, E. R. 1983. Investigation 

of the role of natural hydrocarbons in photochemical smog 

formation in California. Riverside, CA: Statewide Air Pollution 

Research Center. 234 p. 

18 Chapter 2 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

P 
Chapter 3 

Investigation of the lnf luence of Chicago's Urban Forests 

on Wind and Air Temperature Within 

Residential Neighborhoods 

Gordon M. Heisler, Meteorologist, USDA Forest Service, Northeastern Forest Experiment Station, Syracuse, NY 

Sue Grimrnond, Assistant Professor, Climate & Meteorology Program, Department of Geography, 

lndiana University, Bloomington, IN 

Richard H. Grant, Associate Professor, Department of Agronomy, Purdue University, West Lafayette, IN 

Catherine Souch, Assistant Professor, Department of Geography, lndiana University, Indianapolis, IN 

ity and then to develop equations to relate differences in these 

climate variables to measures of urban structure. By urban 

Abstract structure or morphology, we mean here the three-dimensional 

pattern of buildings, trees, and ground-surface characteristics 

Ongoing research is examining the degree to which climate (paved, grass, water, bare soil, etc.). The degree of success 

that surrounds people and houses in residential neighbor- that we have in developing the relational equations will largely 

hoods in the City of Chicago and adjacent communities is determine our ability to evaluate the effects of trees on climate 

influenced by trees. The general research approach is to use within the urban area. The equations or models must be able 

windspeed, air temperature, and humidity at the nearest to separate tree effects from building effects. Average 

airport as reference conditions to compare differences in windspeed and air temperature are the climate variables 

these climate variables between points in residential neighborhoods. Regression analysis is used to develop models to 

relate climate differences to measures of urban structure. 

The climate variables were measured for about 11 months at 

O'Hare International Airport, at two other reference locations, and in residential neighborhoods. The measurements 

in neighborhoods were made with four portable meteorological 

systems that were moved to sample 39 locations during the 

study period. Preliminary analyses indicate that it is possible 

to derive equations to predict the effect of buildings on 

windspeed separately from the effects of trees. The practical 

application of this is that, upon completion of the analysis, 

equations will be available to indicate the effect on wind 

within a neighborhood if the numbers or sizes of trees are 

changed. A goal of the study is to derive similar equations for 

tree effects on air temperature. Over three summertime days, 

temperatures in residential neighborhoods were higher on 

average than at the airport, though they were sometimes 

lower and sometimes higher than at the airport, depending 

largely of the net radiation balance. In the middle of a day 

with clear skies and bright sun, temperatures were slightly 

higher in a narrow space between two buildings than in a 

front yard near street trees. The relationships between climate and urban structure will apply best in the Chicago area, 

but extrapolation to other areas with a similar general climate 

and urban structure should be possible. These relationships 

are necessary for predicting effects of trees on energy use in 

buildings, human thermal comfort, and air quality. 

Introduction 

In this paper we describe ongoing research that is examining 

the degree to which climate at the height of people and houses 

in the Chicago area is influenced by trees. The general approach is to measure windspeed, air temperature, and humidfor primary consideration, though possible influences of tree 

distribution on humidity will also be examined. 

Trees can have a major impact on the human environment 

in residential neighborhoods (Heisler 1986a; Oke 1989). 

For example, tree influences on wind (Heisler 1990a), air 

temperature and humidity (Grant 1991), and solar and 

long-wave radiation influence energy use in buildings (Heisler 

1986a, 1990b; McPherson 1994; McPherson et al. 1988), 

human thermal comfort, air quality (Nowak 1994a), growth of 

smaller vegetation, and insect distribution (Heisler and Dix 

1991). The influence of trees on solar radiation is directly 

related to geometrical factors that, although complex, have 

been studied sufficiently to provide at least approximate 

quantification of tree influences (e.g., Heisler l986b, l99l). 

However, considering either a point in a residential neighborhood or the neighborhood generally, few tree effects on 

below-canopy air-its motion, temperature, humidity, and 

polluting constituents-can be estimated with sufficient accuracy for planning purposes. Below-canopy refers to the space 

below the general level of the tallest trees or buildings. 

There have been few measurements of wind within residential 

neighborhoods (Heisler 1990a), and most available study 

reports, though containing valuable information, are for one 

season of the year or for a small number of sampling points 

(e.g., McGinn 1983). The general pattern of analysis in this 

study follows that used in a previous study in central Pennsylvania that showed a strong relationship between tree 

cover in the upwind direction and reductions in average 

windspeed in several neighborhoods that were typical of 

suburban developments (Heisler 1990a). Earlier studies with 

measurements in Dayton, Ohio, initially demonstrated the 

feasibility of developing prediction equations by statistical 

methods to relate windspeed at street level to building 

dimensions in the central business district (Grant et al. 1985; 

Heisler and Grant 1987). 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 3 19 
Many studies have investigated the influence of urbanization 

on air temperature in both the above- and below-canopy 

space. Air temperatures have been related to land use, and 

clear distinctions in spatial and temporal patterns of air 

temperature have been observed between, for example, 

parks with many trees and surrounding building areas. The 

parks generally are cooler. However, such studies do not 

indicate the separate effects of buildings and trees. For 

example, given park land with 30-percent tree cover, it does 

not follow that a nearby neighborhood with streets and houses 

will have a similar temperature pattern if tree cover there 

also is 30 percent. 

In discussions of tree effects on energy use, the potential of 

trees to save air conditioning costs through reductions in air 

temperature by evapotranspiration is often mentioned and 

incorporated in models (e.g., Huang et al. 1987). However, 

trees influence air temperature through other important 

aerodynamic and thermodynamic effects. For example, the 

trees throughout a neighborhood influence wind flow, which 

in turn influences exchange of the air below the general level 

of tree crowns with the air above. Some measurements 

(McGinn 1983) suggest that with moderate tree cover in a 

residential neighborhood, air temperatures may tend to be 

higher than with either more or less tree cover. This could be 

the result of the trees in the moderate-cover neighborhood 

reducing the air exchange while allowing most of the solar 

radiation to penetrate to ground level. In a forest with a 

complete canopy, there is little exchange of air between 

above- and below- canopy layers, but little solar radiation 

penetrates to heat the ground and below-canopy air. A 

complete forest may be approximated by the trees in a 

neighborhood with high tree cover, whereas with moderate 

tree cover, the trees cause significant reductions in below- to 

above-canopy air exchange but relatively small reductions in 

penetration of solar radiation to below-canopy species. Though 

solar radiation penetration may be greater in neighborhoods 

with low than with moderate tree cover, air exchange may be 

sufficient in the low tree density neighborhoods to keep them 

cooler at the height of people and buildings than in the 

neighborhoods with moderate cover. 

Analogies can be made between the effects of the aggregate 

of trees in residential neighborhoods and traditional tree row 

windbreaks (Heisler and DeWalle 1988, McNaughton 1989). In 

the protected zone close behind windbreaks, air temperatures 

tend to be higher during the day, than upwind or farther 

downwind. At night, air temperatures in the near lee behind 

windbreaks may be relatively low because there are large 

losses of heat from the ground by long-wave radiation 

and relatively little mixing between the sheltered air and air 

flowing above the windbreak. Of course, in residential neighborhoods the situation is more complex because of interactive effects of trees and buildings on wind flow, heat storage, 

and radiation exchanges. 

This study was carried out in conjunction with two other 

meteorological studies in the Chicago Urban Forest Climate 

Project. One study includes a description of the relationship 

between general weather patterns and air-flow fields over the 

city of Chicago (Grant 1993). That work is essential for interpreting meteorological observations in this study. The general 

area for meteorological data collection (Figure 1) was identical 

20 Chapter 3 

to that described in the study of local-scale energy and water 

exchange (Grimmond et al. 1994: Chapter 4, this report); data 

from the fixed meteorological measurement points at O'Hare 

Airport, the tall tower (ISPT3), and the Belmont Harbor light 

tower provide the reference conditions for this study. The landuse database described in Chapter 4 provides information for 

quantifying the urban structure in this study. 

A general assumption is that climate variables at the airport 

site, which is in the middle of a large open area, are uninfluenced by trees and buildings. For purposes of developing 

the predictive models in this study, the differences that we 

are seeking to model generally are those between the hourly 

averages of windspeed and air temperature at points in 

residential neighborhoods and the reference point at O'Hare 

Airport. These differences form the dependent variables in 

the analysis. Descriptors of the structure of trees and buildings 

around the climate sample points in the residential neighborhoods form the independent variables. Some of the descriptors 

are derived from plat maps and aerial photographs and 

analyzed via a geographical information system (GIs); others 

are derived from analysis of hemispherical photographs taken 

from the climate sample points. An important objective of this 

study is to evaluate the efficiency with which descriptors can 

be developed by the different methods. 

If the predictive model building is successful, the models will 

provide research tools to answer such questions as: What 

happens to wind and air temperature at- specified kinds of 

sites or generally in a neighborhood configuration if we add a 

given number of trees of given sizes? The models will apply 

most directly to Chicago residential neighborhoods that have 

building and tree cover densities within the range of those 

included in this study. With this same constraint on range of 

cover densities, the models could be extrapolated to other 

cities with similar climates. The minimum input required to 

use the models would be some quantification of existing 

building and tree structure and general weather data for the 

period of interest. Weather data could be in the form of 

averages for each hour of a typical year. These data sets are 

available for over 200 cities in the United States (National 

Climate Center 1981). 

Windspeed, wind direction, air temperature, and humidity 

were measured with 10 sets of sensors that operated almost 

continuously for nearly 11 months. The sensors were distributed among the three reference points and 39 below-canopy 

locations in residential neighborhoods (Figure 2). In this 

paper we describe the methods of data collection and the 

methods being used in the analysis of the entire data set. 

That analysis is not yet complete, but a partial analysis for a 

sample of the total meteorological and urban structural data 

is presented here to illustrate the methods. 

METHODS 

Meteorological Instrumentation 

The meteorological sensors measured averages of windspeed, 

wind direction, air temperature, and humidity along with 

associated maximum and minimum values and standard 

deviations from July 16, 1992, to June 14, 1993. The wind, 

temperature, and humidity sensors were mounted permaUSDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
PARK 

RIDCG. 

RESEARCH AREA . -F~ni'nS 

AND VICINITY 

Figure I .-Research area and meteorological reference points in and near Chicago. 

The tall tower is ISPT3 in Grimmond et al. (1994: Chapter 4,this report). The large 

portion of the shaded study area is bordered by Touhy Avenue on the north, Pulaski 

Road on the east, Chicago Avenue on the south, and Mannheim Road on the west. 

nently at three reference locations: 1) within 8 feet (2.4 m) of 

the ground about 50 feet (15 m) from the National Weather 

Service instrument tower at O'Hare Airport; 2) at the 81-, 

141-, and 228-foot (25-, 43-, and 69-m) levels on a radio 

tower about 6 miles (9.7 km) east of the airport location; and 

3) on the shore of Lake Michigan at Belmont Harbor, about 

15 miles (24 km) east of the airport (Figure 1). Specific 

instruments at the three reference sites are listed by brand 

name in Table 3, Chapter 4. 

Below-canopy meteorological data were measured at the 39 

sites (Figure 2) with five portable instrument packages mounted 

on TV antenna tripods (Figure 3) that were at a particular site 

for varying time periods. These measurements included air 

temperature and relative humidity at the 5-foot (1.5-m) height, 

and windspeed and direction at 7.8 feet (2.35 m). 

Meteorological data were recorded on compact portable data 

loggers of a type that is widely used in environmental 

measurements. The loggers were programmed to provide 

instantaneous measurements every 5 seconds and, with one 

exception, average these over 15 minutes. For final analysis, 

the 15-minute averages will be combined into 1 -hr averages 

of the meteorological data. There usually is a natural period 

in meteorological data near the surface of the earth such that 

averages over 30 minutes to 1 hour tend to represent the 

general trend of conditions, whereas averages over periods 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

much shorter than 30 minutes include considerable random 

scatter associated with large-scale turbulent eddies (Panofsky 

and Dutton 1984). Because we had to substitute a data 

logger with a smaller memory for one that failed at O'Hare 

Airport, the averages there are over 1 -hr periods for about 6 

of the 11 months of data collection. 

To acquire accurate temperature data, it is important to place 

the temperature sensor in a well-shielded and ventilated 

location to prevent errors from the influence of solar radiation 

on the temperature measurement. Although commercially 

produced shields are available, our experience is that none 

provides adequate shielding for the conditions we faced - 

some measurements in deep shade, some in full sun. With 

some temperature-measurement systems, errors frequently 

exceed 2°F (1 "C). The requirement for battery operation for 

the portable units made design of the shield particularly 

crucial; the shields we used were designed specifically for 

this study (Grant and Heisler 1994). Each radiation shield 

held a small-bead thermistor inside a I -inch-diameter inner 

tube and a combination temperature and humidity sensor 

that was protected only by a larger outer tube. A fan pulled 

air over both types of sensors. Tests of shielding efficiency 

suggest that the maximum radiation error for the small thermistor was about 0.18"F (O.lO°C), whereas the maximum 

radiation error for the temperature sensor in the humidity unit 

was about 0.90°F (0.5"C). 

Chapter 3 
Figure 2.-Location of below-canopy meteorology sampling points and the pattern of land-use polygons in the 

land-use data base. Square symbols mark ten points used in ellipse spatial analysis. 

22 Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Figure 3.-Schematic of portable tripod and instruments for 

below-canopy measurements. 

Each week, all sites in the network of meteorological instruments were visited for maintenance, to collect the data, and to 

move portable units scheduled for rotation. The below-canopy 

units generally performed well until mid-December 1992, when 

an ice storm apparently damaged some of the small-bead 

thermistors and caused some of the fans to fail. Fans on the 

below-canopy units and at the airport were changed, generally within several days of detected malfunctions. 

Observation Site Selection 

One of the five below-canopy units was maintained for the 

entire time in an area of tall grass near the lSPT3 tall tower 

(Figure 2). The other four units were rotated between sites in 

back yards, in front yards, in vacant lots, in narrow spaces 

between houses, and in an extensive woodlot, all between 3 

and 9 miles (6 and 15 km) easterly from the airport, for 1 to 

11 weeks (Table I). All except for the woodlot site (which is 

just off the east side of the GIs map) were in areas with 5 to 

50 percent of the area covered by trees (Figure 4) and at 

least I 0-percent coverage with trees, grass, and/or shrubs 

(Figure 5). A large proportion of points are located in Oak 

Park (Figures 1 and 2) partly because that community is 

developing a very complete tree inventory and GIs database 

of building structural features that will be made available for 

our analyses. 

The sampling pattern and schedule had to be fairly flexible to 

accommodate homeowners' wishes. Location of the points 

depended partly on finding lawn space that was not heavily 

used for some purpose such as playing ball and where there 

was some degree of security. A goal was to sample each 

point in both summer and winter; however because of changes 

in ownership or homeowners' wishes, some points were 

sampled in only one season (Table I). Ideally, the rotation of 

instruments would have been done more frequently and 

each point would have been sampled several times during 

each season; however this was precluded by the limited 

availability of field personnel. More frequent rotation would 

have resulted in smaller differences between the sites in 

general weather conditions sampled. At some sites where 

the instruments provided particularly minimal inconvenience 

for the homeowner and also included morphologies that 

were in short supply elsewhere, we sampled for longer 

periods than at other sites. 

If building and tree effects are to be separated in statistical 

models, it is necessary to sample over a wide range of both 

building and tree morphologies (particularly for areas 

covered by trees and buildings). Further, there must not be 

a high degree of correlation between the tree and building 

morphology. The number of points required to sample a 

sufficient range of building and tree morphologies depends 

in part on the variability of morphologies within the neighborhoods where measurements are made. To accommodate 

these requirements in so far as possible, we used aerial 

photographs and satellite images to visually explore the 

study area. We had some difficulty in finding a wide range of 

tree and building morphologies in the study area. Almost the 

entire area has older homes with relatively high building 

density and moderate tree cover. Tree cover tends to be 

inversely proportional to building density, and neighborhoods 

with either very low or very high cover are rare. We located 

the sample point in the woodlot to provide a sample of 

conditions at the upper limit of tree density. To the west of 

O'Hare International Airport there are many typical suburban 

neighborhoods with a wide range of building density and tree 

cover, but travel time and the lack of a tall tower reference 

prevented our sampling there. 

Fortunately, the method of analysis, with the airport for a 

reference, greatly reduced the importance of uniform general 

weather conditions at each climate sampling point. Also, 

the range of structural conditions sampled varied substantially even at individual points, as the vegetation or buildings 

with greatest influence changed with wind direction. The 

Results Section has further discussion of the degree to 

which we succeeded in sampling in neighborhoods with 

differing morphologies. 

For many of the points, a special effort was made to find 

lawn spaces between houses that were at least as wide as 

most of the houses so that meteorological conditions near 

the middle of the lawn would be representative of a possible 

house location. However, other points sampled a range of 

distances to nearest buildings, to dense conifer trees, to 

tall-crowned deciduous trees, and to hedges. Some points 

sampled narrow spaces between houses. In the prototype 

study by Heisler (1989 and 1990a), anemometers were 

located to sample the effects of the general aggregate of 

vegetation throughout the neighborhoods; dense tree rows 

and hedgerows were avoided. In this study we included the 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 3 23 
Table 1. -Location of below-canopy meteorological instruments. Unit indicates which of the five below-canopy 

systems was used; and the "loc" column is the order of site placement, alphabetically, for that unit. 

22 17 Nov 92 

24 Nov 92 

8 Dec 92 

22 Dec 92 

16 Feb 93 

16 Feb 93 

19 May 93 

16 Feb 93 

9 Mar 93 

19 May 93 

9 Mar 93 

19 May 93 

9Mar93 

23 Mar 93 

23 Mar 93 

23 Mar 93 

25 Mar 93 

30 Mar 93 

725 S. Clinton 

175 N. Lombard, Oak Park 60302 

,175 N. Lombard, Oak Park 60302 

* I=in leaf, F=fall transition(0ct. 13- Nov.17, Days 287-322), O=out of leaf, 

S=spring transition(Apr. 13 to May 25, Days 73-1 15). 

=[ Finished 17 

~id1 Leaves* 

1345 24 Ju192 

1000 23 Mar 93 1245 

1220 11 2 Feb 93 1 1446 11 0 iq-GqEqE 

1100 29 Dec 92 0930 

qm;E 

1445 26 Jan 93 iqpq3Gq~ 

1400 29 Dec 92 1345 

24 Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
'"Z I arr 

TREE COVERAGE OF CHICAGO 

URBAN FOREST STUDY AREA 0% to 5% 

5% to 1wo 

10% to 5Wo 

> Wo 

0 1 2 3h 

I I I 

0 1 2 3 mile I I 

. BELOW-CANOPY 

DATA POINT 

Figure 4.-Tree cover within study area and below-canopy points. 

~ 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 3 
BELOW-CANOPY 

DATA POINT 

Figure 5.-Cover of all vegetation within study area and below-canopy points. 

26 Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
local effects of dense tree rows by locating some sampling 

points within one tree height of dense rows. 

Reference Conditions 

Although we make the assumption that the airport site is 

relatively uninfluenced by buildings and trees, we cannot 

assume that the general air flow over the airport site always 

is identical to the flow over the neighborhood sites, which are 

3 to 9 miles (6 to 15 km) closer to the lake. Airport reference 

conditions will have to be adjusted to account for differences 

in wind, air temperature, and humidity between the boundarylayer air at the airport and over the below-canopy sites. The 

adjustments essentially will be an extrapolation from the 

airport conditions by first extrapolating vertically upward 

from the airport site, then across horizontally to above the 

residential neighborhoods, and then back down to the level 

of the below-canopy instruments at approximately 8 feet (2 

m). The extrapolation must account for mesoscale variations, 

primarily the lake effect which prevails during part of the year 

(Grant 1993). The extrapolation will be derived for five classes 

of general (synoptic) weather conditions, as described in 

Grant (1993), so that for any hour of our observations, the 

lake effects can be estimated by knowing the general synoptic pattern. Vertical profiles of wind and air temperature 

derived from the three levels of measurement on the tall 

tower (ISPTB) along with the Belmont Harbor observations 

will facilitate the extrapolation. Indices of atmospheric 

thermal stability, which causes variations in the vertical 

profiles of wind and temperature, will aid in the extrapolations. The indices will be derived from our observations 

of net all-wave radiation (Grimmond and Cleugh 1994), which 

was measured at both the airport and ISPT3, and from 

the standard deviation of wind direction by a method of 

Slade (1 968). 

In the complete analysis, dependent variables will be formed 

as the differences between the values of windspeed and air 

temperature at the below-canopy sites and the extrapolated 

reference conditions. In the results presented here for tree 

and building effects on windspeed, the differences between 

the airport and below-canopy sites form the dependent variables, without extrapolation. This is a reasonable approach 

because results here are for essentially the same time period, 

and the below-canopy points are relatively close together. 

Characterizing Urban Structure 

Many characteristics of urban structure can be related to the 

meteorological differences that we measured. Looking from 

above in plan view, some possible characteristics are the 

areal coverage as a percentage or decimal fraction of 

buildings, trees, and impervious surfaces. Combined with 

these attributes, the average height of buildings and trees 

within land-use units adds the third dimension. These characteristics can be averaged over differently shaped and 

sized areas in the upwind direction in search of correlations 

with observed meteorological differences. Looking horizontally 

from below-canopy points, the heights of buildings and trees 

and the density of tree crowns in upwind directions, and to 

a smaller extent in downwind directions, also are related to 

microclimate, particularly windspeed. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

In this study we are developing a set of independent variables to describe tree and building morphology, generally in 

the upwind direction from each below-canopy climate data 

point, to be entered into a data set with separate observations for each instrument-hour for each below-canopy point. 

The variables for describing the more distant morphology 

generally will be derived by GIS spatial analysis. 

One source of data will be the surface database for the 8- by 

8-mile (13- by 13-km) area used for hydroclimate analysis as 

described in Grimmond et al 1994: Chapter 4, this report. For 

each of the more than 2500 polygons shown in Figure 2, a 

set of attributes is assigned to indicate the percentage of 

area covered by buildings, trees, other vegetation or other 

surface characteristics (Table 6, Chapter 4 ). Because this 

database was developed for classes of land-use polygons, 

and some of the polygons have considerable variation in 

attributes within them, this database has limitations for 

developing descriptors of morphology for the near vicinity of 

particular points. The accuracy with which some of the 

attributes could be determined also was limited by the blackand-white aerial photos, which were available only for the 

leaf-off season for trees. 

To provide land-use coverage for some of the sites near the 

edge or just off the original square area (Figure 2), we will 

digitize some additional areas on the northwest and northeast 

corners and around Oak Park. The sites included in the initial 

analysis reported here are near the center of the study area. 

In our initial spatial-analysis to develop descriptors of morphology we used ARC/INFO GIs software, to average the 

attributes on an area-weighted basis across elliptically shaped 

areas in the upwind direction from each point. The ellipse 

shapes were cut from the coverage (cookie cutting) to determine the area of each land-use polygon within each ellipse 

as a proportion of ellipse area. The weighted average of an 

attribute within an ellipse was the sum over all land-uses in 

the ellipse of the attribute value for each polygon times 

proportional area. The attributes that have been used to date 

are: building cover; average building height; tree cover; total 

vegetation cover; and impervious, bare, and water-surface 

areas. The product of building cover times average building 

height forms an estimate of building volume (with dimensions feet3 of building per foot2 of land area), the building 

attribute that we expect to be most closely related to reductions in windspeed. 

The spatial-analysis program averaged the attributes for 

ellipses centered on each 15 degrees for each of the belowcanopy points. Thus, for each shape and attribute, there 

were 24 average values for each point. The average attributes were merged with the wind data by rounding wind 

direction over the residential area to the nearest 15-degree 

azimuth for which morphology averages were obtained in 

the spatial analysis. Wind direction at the ISPT3 tower is 

assumed to represent direction across the study area. The 

elliptical sample areas had lengths of 328, 984, 1640, and 

3280 feet (100, 300, 500, and 1000 m), with widths equal to 

half the lengths, and with the downwind vertex over each 

below-canopy point. The spatial analysis for the ellipses has 

been completed for 10 of the 39 points. After the spatial 

Chapter 3 27 
analysis using ellipses was completed, average tree and 

shrub height was added as an attribute for each polygon, 

and this attribute will be used in any further analyses. The 

product of average tree and shrub height times tree and 

shrub cover fraction will provide an index of the volume of 

tree and shrub crowns. 

Unlike the state of the technology related to above canopy 

source areas for vertical transfer of heat and vapor (Grimmond 

et al. 1994), there are few guidelines from previous experimentation that would aid in assigning appropriate shapes for 

averaging land-use structure that would relate to belowcanopy microclimate. The elliptical averaging shapes were 

chosen for initial analysis partly because of their mathematical 

simplicity. Other shapes may better represent the land-use 

areas that influence wind and air temperature in the belowcanopy space. The next step in analysis of the land-use 

database is to average attributes over sections of 

concentric circular bands at different distances from the 

below-canopy points. The band sections will be centered on 

mean wind direction and weighting will be applied according 

to angular distance from mean direction based on the standard 

deviation of wind direction on the tall tower during the 

sampling period. The band sections will be plus and minus 2 

standard deviations, and weighting along the band, perpendicular to wind direction, will be based on area under a 

normal curve. Standard deviations on the tower are usually 

between 8 and 20 degrees. Hence, the band sections will 

range from about 30" to 80" wide as viewed from the belowcanopy points. Five bands will be used: 0 to 100, 100 to 205, 

205 to 41 0, 410 to 820, and 820 to 1640 feet from the point. 

To provide more accurate descriptors of building morphology for areas near below-canopy points, another spatial GIs 

database of building footprints within 600 feet (180 m) of 

each below-canopy point (Figure 6) is being developed. The 

information sources are plat maps which are available for all 

Chicago locations and aerial photographs for other communities. A field survey and estimation from black-and-white 

stereo photos is providing approximate heights for each 

building. The building footprint database will provide average 

building density, height, and volume for differently shaped 

upwind areas, by a spatial analysis process similar to that 

applied to the larger land-use database. Ideally, color infrared 

aerial photographs for the trees-in-leaf season would have 

been available for development of a tree-cover database on 

the scale of the building footprint data, but no such current 

photos could be located. 

The descriptors for building and tree morphology visible from 

the below-canopy points are being acquired from 180-degree 

hemispherical slide photos. These were taken at each point 

from a height of 3 feet (1 m) with the camera lens pointing 

directly overhead and with the top of the camera oriented 

toward north. The slides are projected onto polar grids from 

which technicians record, by 15-degree sector, average tree 

crown density and the maximum and minimum vertical angles 

from the horizon of the photo to the tops of visible buildings 

and trees. Tree crown density is estimated for upper and 

lower halves of the space between the horizon and the 

tallest tree within each sector. Separate photo sets were 

taken for the points where meteorological data were collected 

in both summer and winter. Changes in leaf phenology in the 

28 Chapter 3 

fall and spring transition periods (Table 1) were tracked with 

photos at a subset of the sample points. 

Regression Analysis 

Multiple regression models are being used to develop prediction equations to describe the influence of the vegetation 

and building morphology on the differences in airport 

to below-canopy wind and air temperature. Some of the 

morphological indicators are combined in physically meaningful ways prior to insertion in the model. For example, from 

the hemispherical photo data, distance to upwind buildings 

or trees relative to the building or tree height can be derived 

from the vertical angle from horizon to the top of the object. 

The product of normalized distances to upwind and downwind objects provides a descriptor that, if small, indicates 

that the point is between closely spaced obstacles and that 

wind tends not to penetrate downward into the canopy, but 

occurs mainly as skimming flow above the canopy (Oke 

1987), resulting in large wind reductions below canopy. 

The regression models are the usual general linear models 

with polynomial terms (Neter et al. 1985) or nonlinear models 

(Wilkinson 1990). The linear models are of the form 

Y = Bo + BiX, + B2X2 + BizXiXz + Bi IXIXT + BzzX2X2 +. . .+E 

[ll 

with E as the normally distributed error term with constant 

variance across all Y and X. In studying effects on windspeed, 

the dependent variable Y is, for example, a fractional reduction 

in windspeeds in the neighborhoods compared to the airport 

reference, and the Xi's are descriptors of either morphology 

or atmospheric conditions. In discussing wind reductions by 

trees, buildings, or other obstacles it is common practice to 

use a nondimensional normalized form rather than absolute 

windspeed (e.g., Heisler and DeWalle 1988, McNaughton 

1989). Indices of atmospheric thermal stability calculated 

from vertical wind and temperature gradients, from net radiation (Grimmond and Cleugh 1994), or from windspeed and 

cloud cover (Turner's index, Panofsky and Dutton 1984) can 

be used to form descriptors of atmospheric conditions. The 

Bl's are regression coefficients. This is mathematically an 

additive effects model; each independent variable adds an 

effect, such as a fractional reduction in windspeed. The 

intercept Bo will be near 0 if the X variables together account 

for most of the reductions in windspeed. 

For studying effects of urban morphology on air temperature, 

the Xl's can include some of the same morphological characteristics as for windspeed in addition to others that 

are related to radiation exchanges, heat storage, moisture 

availability, and deficit of moisture in the air. Radiation 

exchange can be indexed by percent of unobscured sky 

above the below-canopy meteorological measurement point. 

In addition to building volume, heat storage may be significantly related to percentage of impervious cover from the 

land-use analysis. Impervious cover may also be related to 

moisture availability. Another index of moisture availability 

may be derived from the amount of precipitation over various 

lengths of time preceding the observation time. Moisture 

deficit is calculated as the difference between actual vapor 

pressure and vapor pressure if the air were saturated at the 

same temperature. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
.... i? 

i:.. 

.... i 

I 

: ,.

:- 

I 

i 

BUILDING FOOTPrnS 

AND 

LOCATION OF SITES 1- 4 

0 

BELOW-CANOW 

DATA SITE 

Figure 6.-Example of building footprints in GIs and location of four below-canopy points used in this analysis. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Chapter 3 
We might expect that the influence of morphology on microclimatic variables would be nonlinear. Nonlinear models can 

take various forms, such as 

Here the Y would be, for example, a relative windspeed, that 

is, wind in the neighborhood divided by wind at the 

reference. Such models can be fit with standard nonlinear 

methods [e.g. SYSTAT (Wilkinson 1990)] depending on how 

many variables are included (interpretation of results 

becomes more difficult with each parameter that is added). 

Equation 2 is a multiplicative or exponential model, in that 

each independent variable has a multiplicative effect. 

Results and Discussion 

Land-Use Attributes 

The study area has a complex pattern of land uses (Figure 

4a, Chapter 4), including large areas in forest that are part of 

the Forest Preserve (areas with greater than 50 percent tree 

cover in Figure 4). Although overall tree cover is not high 

within Chicago (Nowak 1994a: Chapter 2, this report), the 

study area contains land-use categories with a wide range 

of tree cover (Figure 4). All vegetation combined typically 

covers 20 to 50 percent of the area in residential neighborhoods in which our below-canopy measurements were-made 

(Figure 5). 

One concern in interpreting the regression results is that 

some morphological descriptors that serve as independent 

variables are naturally correlated. Specifically, when building 

density is very high as in much of Chicago residential areas, 

tree cover generally also cannot be high. The relationship 

between building cover and tree cover is illustrated in the left 

side of Figure 7, which is derived from the land-use analysis 

with elliptic averaging shapes of different lengths and areas. 

The data for each scatter diagram are for 10 below-canopy 

points. Building cover ranged up to nearly 0.7 in some of the 

328-foot (1 00-m) ellipses, and tree cover ranged up to about 

0.4. The scatter of points shows a high degree of correlation 

between tree and building cover, particularly for the 328-foot 

ellipses. A small part of the reason for the close relation is 

an artifact of these data, because in development of the 

land-use database, only one type of coverage was allowed 

for any given sample point. Hence, where trees overhung 

buildings, the coverage category was trees rather than trees 

and buildings. 

Steps can be taken to account for relationships between 

some independent variables in the regressions. The product 

of building-area coverage times height forms a building 

volume, which seems to be less well-correlated with tree 

cover (Figure 7, right column). Groups of below-canopy 

meteorological sites that have a wide range of morphological 

characteristics can be selected. 

Initial Model Building 

To illustrate the analysis that is being done to evaluate 

the effects of urban trees on wind, preliminary regression 

analyses were done for four sites, using a selection of the 

meteorological data collected within a 13-day period, July 21 

(day 203) to August 2, 1992 (day 215). (The day of the year 

system is used because of ease of referring to dates in graphs.) 

The sites 

The locations of the sites, numbered 1 to 4, are plotted on a 

section of the GIs map of land-use in Figure 8. These four 

sites were all within 1000 feet (300 m) of each other and 

within about the same distance of the tall tower. Hence, 

these results serve to illustrate the range of microclimate 

within a short distance. 

The hemispherical camera views (Figure 9) show the tree 

and building structure visible from each point. Site 1 was in a 

relatively open location in a large grassy field, but a natural 

stand of 25-foot (7.5-m) deciduous trees edges the north 

side of the field, about 75 feet (25 m) from the meteorological 

unit. Site 2 was in a vacant lot on the north edge of a 

residential development just 230 feet (70 m) south of site 1. 

Sites 3 and 4 were farther south within the development. Site 

3 was in a small front yard along a street with many large 

street trees with crowns almost overhead; site 4 was in a 

narrow space between two houses. 

General conditions 

Windspeeds at O'Hare Airport ranged up to about 12 mph 

(5.5 m/s) between July 21 and July 24, days 203 through 206 

(Figure 10). (Data for sites 2, 3, and 4 are available for these 

days only; site 1 also has data for days 21 2-21 5.) Windspeeds 

followed a diurnal pattern that is typical of locations within 

the atmospheric boundary layer-low speeds at night when 

the air becomes thermally stable because of radiational 

cooling near the ground. Figure 11 shows that day 203 had a 

smooth trace for both solar and net all-wave radiation, 

indicating a clear sky, resulting in high positive net radiation 

during the day and strong negative radiation at night 

compared to cloudy conditions on following nights). About 

0.25 inch (3.8 mm) of rain fell on days 204 and 205 (Figure 8, 

Chapter 4). 

Air temperatures 

Air temperatures at below-canopy sites remained within 3.6OF 

(2°C) of the temperature at the same height at the airport 

(Figure 12a). Sites 2,3, and 4, all in the residential neighborhood, were 0.5" to 0.7'F (0.2a0 to 0.39%) warmer, on average, 

than the airport site. The general diurnal pattern, with temperatures in neighborhoods being warmer than the airport at 

night and cooler during the day is probably caused largely by 

different rates of heating and cooling in the neighborhoods 

compared to the airport. This pattern is fairly typical of the 

so-called urban heat island phenomenon (Oke 1987, 1989). 

For example, on day 203, which was cloud free, net radiation 

at night was strongly negative and open sites such as the 

airport cooled more quickly than the neighborhoods. This is 

more clearly seen in Figure 12b which shows that periods 

when sites 2, 3, and 4 were decidedly warmer than the 

airport (by up to 3.3OF or 1.8"C) are associated with negative 

net radiation. Neighborhood sites also tend to be warmer 

under periods of high positive net radiation resulting from 

high solar radiation. The fact that site 3 was close to trees 

and site 4 on the adjacent property was in a narrow space 

between two houses (Figure 9) appears to have resulted 

30 Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Tree cover Tree cover 

Tree cover 

Tree cover 

0.7 1 I 

Tree cover 

Tree cover 

Figure 7.-Average building cover (fraction of area covered) and average building 

volume (cubic units of volume per squared units of area) versus tree cover (fraction 

of area covered) in elliptic sampling areas cut from the GIs database around ten of 

the below-canopy points. Different symbols show values for different points. 

in site 3 being about 0.5OF (0.3"C) cooler at high values of net with a large diurnal swing accompanying the period of 

radiation (Figure 12b), even though the difference in overall clear skies. 

average temperatures at the two sites was within 

the limits of instrumental error (0.18°F). Site 1 was cooler Effects of morphologyon windspeed 

on average than the other below-canopy sites and had nearly Figure 10 shows that except for a few 15-minute observation 

the same mean temperature as the airport. The pattern periods with low windspeed at the airport, windspeeds at the 

of actual temperatures during days 203 through 206 (Figure below-canopy sites were lower than at the airport. However, 

13) generally reflects the influence of the radiation balance, there is considerable scatter in the 15-minute averages. A 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Chapter 3 
BELOW- CANOPY DATA 

POINTS 1- 4 IN CHICAGO 

URBAN FOREST 

STUDY AREA 

A TALL TOWER 

0 BELOW- CANOPY 

DATA POINTS 

Figure 8.-Land-uses around the four below-canopy meteorology sampling points. See Table 5 in Chapter 4 for 

land- use categories. Near the points the classes are: VT, trees and shrubs; VGR, grass; A4, high density housing, 

yards small, mainly grass, few trees; CB, large commercial buildings, fewer than 6 stories; CS, small commercial 

buildings; AR3, apartments, highly mixed. (The tall tower is lSPT3 in Grimmond et al., Chapter 4, this report.). 

32 Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Site 1 Site 2 

Site 3 Site 4 N 

E 

Figure 9.-Hemispherical photo views from horizon to zenith, from height of 3 feet at four sites. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 3 
Figure 10.-Windspeed at O'Hare airport (upper curve) and wind direction 

at the airport (lower solid line) and on the lower level of the tall tower 

(dotted) on July 21 to July 24, 1992. The dates are shown at midnight 

starting the day. 

Figure 11 .-Shortwave solar (dashed) and net all-wave radiation (solid) at the fixed tower. 

34 Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Day 

Site 3 

Site 1 

6, 1 : 

-2 1 I 

-200 0 200 400 600 800 

Net radiation. Wrf2 

-200 0 200 400 600 800 

Net radiation. WmF2 

Site 3 

6, 1 Site 4 I 

. . -2 1 I 

-200 0 200 400 600 800 

Net radiation. Wm" Net radiation. Wm-2 

Figure 12.-Pattern of air-temperature differences (airport minus below-canopy) between 

O'Hare Airport and four below-canopy sites; a) time series, b) versus net radiation. 

USDA Forest Service Gen. Tech. Rep. NE-186.1994. Chapter 3 
better sense of the pattern of windspeed differences is shown 

by plots of a normalized reduction in windspeed: 

In Figure 14a, normalized reductions in windspeed are 

plotted for each site in a time series. The anemometers that 

we used had a threshold windspeed of 0.45 mph (0.2 m/s). 

Though the cups did not rotate until windspeed reached the 

threshold, the data loggers were programmed to indicate 

0.45 mph (0.2 mls) as a minimum speed, so that as wind 

reached the threshold speed and the cups began to rotate, 

the speed indicated was correct. However, the minimum 

recorded speed places a significant bias on the apparent 

reductions when wind is slow and anemometers at the 

below-canopy sites are stopped while the control at the 

airport is measuring a speed that is just slightly higher than 

the threshold. For airport speeds of 6.7 mph (3 m/s) or 

greater, the below-canopy anemometers generally indicated 

speeds above the threshold, and bias was negligible. Hence, 

data for airport speeds less than 6.7 mph were omitted from 

Figure 14a. From this point the discussion will pertain to the 

higher speed wind conditions. 

With the higher reference windspeeds, the apparent effects 

of trees and buildings on windspeed vary less than at low 

relative windspeeds, and derivation of models to predict the 

effects of these obstacles is thus relatively more precise for 

the higher speeds. Also, influences of trees at higher 

windspeeds generally are of greatest importance for concerns 

such as energy use. 

In Figure 14a we see a pattern of differences in windspeed 

reductions from site to site that is to some extent related 

to the amount of sky blockage in the hemispherical views 

(Figure 9). However, there is considerable within-site scatter, particularly at sites 1 and 2. Much of this scatter is 

explained by looking at wind reduction versus above-canopy 

wind direction (Figure 14b). For example, site 1 has large 

Figure 13.-Air temperatures at 5-foot height at O'Hare International Airport. 

36 Chapter 3 

wind reductions when wind is from the north, apparently 

because wind is blocked by the tree row in that direction 

(Figure 9). The east is relatively free of obstacles and wind 

reductions are low in that direction (90 degree azimuth). At 

site 2, reductions are small at 45 degrees, evidently because 

wind comes relatively unabated through the opening between 

north and northeast. The very close buildings and street tree 

crowns account for large reductions at sites 3 and 4. 

The descriptors obtained from the hemispherical photos and 

a nonlinear regression model provided an initial means of 

quantifying the relationship between morphology and reductions in windspeed. The photos were first analyzed in 15" 

sectors (see Methods). In the results reported here, we 

combined three sectors to describe average morphology in 

45" sectors in the upwind and downwind directions (based 

on airport wind direction) for each 15-minute windspeed 

average for each below-canopy site. The most successful 

model included four independent variables. For buildings, 

we averaged the highest and lowest angles to the tops of 

- buildings in the ugwind direction (UBA) and in the downwind 

direction (DBA). For trees, similar descriptors were formed 

(UTA and DTA), but average angles were multiplied by fractional tree-crown density (0 to 1.00) estimated from the 

hemispherical photos. Thus a solid tree stand, with a visual 

density of nearly 1 .OO as seen to the north of site 1 (Figure 9) 

would yield UTA and DTA values nearly equal to angular 

height. The street trees near site 3 have an overall visual 

density of less than 1.00, primarily because of the open 

space at the bottom and would yield UTA or DTA values of 

less than their angular height. Hence, trees often were 

weighted less than buildings of the same angular height. 

The relationship between wind reductions and the morphology descriptors was explored by plots of wind reduction 

versus the descriptors or various combinations of descriptors. 

A combination of building and tree descriptors in the upwind 

and downwind directions that showed one of the closest 

relationships with wind reduction was BTUD; where 

BTUD = max(UBA, UTA) + (max(DBA,DTA))/3, [4] 

"max" yields the larger of the two values in the following 

parentheses, and the divisor 3 is based on the trial assumption that downwind trees and buildings reduce windspeeds 

one-third as much as upwind buildings and trees. The scatter 

diagram of observations (Figure 15) suggested an exponential relationship with the general form of equation 3. The 

regression model 

where a and b are parameters to be estimated, produced a 

good fit to the data (Figure 15) with a corrected correlation 

coefficient, R2, of 0.78, indicating that about 78 percent of 

the wind reduction is explained by model [5]. Adding net 

radiation as an additional variable helped to explain additional 

variation and reduced residuals by about 0.1 at high positive 

values of net radiation. 

With the four components of BTUD in the model separately, as 

where a, b, c, and d were coefficients to be estimated, 

R2 increased to 0.80. The estimated coefficients were all 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Site 3 

i 

Site 4 - 

Wind direction azimuth, degree Wind direction azimuth, degree 

Site 3 

, 45 135 225 315 405 

Wind direction azimuth. degree Wind direction azimuth. degree 

Figure 14.-Normalized reductions in windspeed at four below-canopy sites compared to 

the airport, with airport windspeeds greater than 6.7 mph (3 ms-1); a) as time series, b) 

versus wind direction. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 3 
significantly different from 0. (Because all variables were 

correlated over time, and because of the nature of nonlinear 

estimation, the test based on R2 values is approximate.) 

With the estimated coefficients, equation 6 becomes 

Equations of this type can be used to predict tree and building effects on windspeed, though care must be taken in 

interpretation. In the case of equation 7, the estimated coefficient d for downwind buildings DBA is positive, indicating 

smaller reductions with downwind buildings nearby. However, 

in this particular data, upwind and downwind building angles 

are positively correlated, and it is likely that one buildingangle term tends to overestimate the building effect, while 

the other compensates for the overestimation. Inclusion of 

data from other sites combined with analysis of residuals 

(observed values minus estimates from the regression) will 

help in interpreting regression results. 

Some of the residuals from the regressions are inflated 

partly by trees and structures obscured from view in Figure 

9, partly by random turbulent eddies, partly perhaps because 

the assumption of no obstacle effect on wind at the airport is 

not completely met, and possibly in part by differences in 

thermal stratification in the atmosphere. The probability of 

this last effect being significant was reduced by our selection 

of higher speed winds for analysis. Future regressions will 

be based on hourly averaged data, which will reduce the 

effect of the random fluctuations. Descriptors of building and 

tree morphology from the GIs analysis will be included as 

independent variables to account for buildings and trees not 

visible in the hemispherical photos. 

Conclusions and Application 

Preliminary analysis of tree and building effects on windspeed 

and air temperature at points in one Chicago residential 

neighborhood over approximately one July week showed 

that windspeed was reduced by 83 to 85 percent on average 

compared to a location in the middle of O'Hare Airport, 6 

miles to the west. Buildings occupied about 40 percent and 

tree crowns covered about 10 percent of the area within the 

neighborhood. In a long narrow open field adjacent to the 

residential area, windspeed was reduced an average of 46 

percent, but reductions varied with distance to obstructions. 

When wind came to the field site from the direction of a 25- to 

30-foot deciduous forest stand about 75 feet to the north, 

windspeeds were similar to those in the residential area. 

Average air temperatures in the open field were essentially 

the same as the airport, but at times open field temperatures 

were from 25°F (1.4"C) greater to 2.3"F (1.3"C) less than at 

the airport in a pattern that reflected differences between the 

sites in rates of cooling and heating responses to the net 

radiation balance. Within the residential neighborhood, a 

0 20 40 60 80 100 

Upwind & Downwind maximum obstacle function 

Figure 15.-Normalized wind reductions for all four sites versus a descriptor 

of upwind and downwind trees and buildings (BTUD) defined in the text. The 

curve is fit to the points by a nonlinear regression technique. 

Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
similar range and pattern of temperature differences from 

the airport were observed, but average temperatures were 

0.5" to 0.7"F (0.28' to 0.3g°C) higher in the neighborhood 

than in the open field. 

One approach to developing information for planning tree 

management to save energy for heating and cooling is 

to simulate the effects of particular tree arrangements on 

energy use (Heisler 1991, McPherson 1994). This can be 

done by comprehensive, commercially available energyanalysis programs that include an hour-by-hour analysis of 

energy use in a building for an entire year. Input for these 

programs includes averaged or representative hourly weather 

data prepared specifically for energy analysis. However, the 

energy analysis programs do not include built-in procedures 

to estimate tree effects. 

One method for including tree effects on wind, air 

temperature, and humidity in energy-use predictions, is to 

preprocess the representative weather data by algorithms 

that predict tree effects on these microclimatic variables. A 

primary goal of this study is to provide the algorithms to 

preprocess weather data. Although considerable analysis 

remains, the initial results reported here show considerable 

promise of success in predicting wind climate in residential 

neighborhoods. Most important, there is a strong likelihood 

that tree and building effects on windspeed can be reasonably well separated. The data from our airport reference site 

adjacent to a standard weather observing system, from which 

long-term weather data is archived, will enhance development 

of equations for preprocessing weather data for energy 

calculations. In further analysis, emphasis will be given to 

developing and using predictor variables that could be gathered without undue difficulty in extrapolating the methodology 

to other locations. 

Different approaches to analysis of tree effects in temperature 

are possible using the 11 months of data. There are periods 

of 1 to 3 weeks in which the below-canopy sampling pattern 

remained stationary and when the sites were about the same 

distance from the lake. With data from such periods, temperature differences can be related to differences in tree and 

building cover directly, without extrapolation to the airport, 

thus reducing extrapolation errors. One reason for not using 

this method exclusively is that the range of morphological 

conditions sampled within each period generally will be smaller 

than when longer time periods and more sites are included. 

This method is similar to that used in an ongoing study in two 

neighborhoods in the Los Angeles area in which Simpson et 

al. (1 994) used the below-canopy average temperature as a 

reference for comparing the neighborhoods. 

The analysis has not yet proceeded to prediction equations 

for air temperature, and here the probability of success is 

less certain, at least in terms of separating tree and building 

effects. The differences in temperature will be relatively subtle 

and the physical causes of temperature difference between 

sites are far more complex than for wind. The comparisons 

of temperatures between neighborhoods as presented in the 

results indicate many of the considerations that must be 

included in model development. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 

Acknowledgments 

Xiaoming Yang georeferenced the land-use database, did all 

of the GIs programming, and assisted with meteorological 

data handling and statistical analysis. Edith Makra served as 

liaison with the City of Chicago and arranged for use of 

meteorological sites. Greg McPherson and Dave Nowak 

handled many of the logistical concerns in Chicago. Students 

at the SUNY College of Environmental Science and Forestry, 

Kenneth Livingston, Joesph Weyl, Matt Penrod, and Craig 

Vollmer assisted with data analysis. Assistance in the field 

was provided by Steve Wensman, Scott Pritchard, Mark 

Demanes, D. Dishman, D. Horowitz, A. Johnson, and J. 

Southworth. Dave Nowak, Scott Robeson, and Jim Simpson 

reviewed an earlier draft of this paper. Assistance was also 

provided by the staff of the North Park Village Nature Center, 

Chicago Department of the Environment, Henry Henderson 

Commissioner. We thank the many homeowners who donated the use of their property for our measurements. 

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winds in cities. In: Proceedings, 8th conference on biometeorology and aerobiology; 1987 September 14-1 8; W. Lafayette, 

IN. Boston, MA: American Meteorological Society: 356-359. 

Huang, Y. J.; Akbari, H.; Taha, H.; Rosenfeld, A. H. 1987. The 

potential of vegetation in reducing summer cooling loads in 

residential buildings. Journal of Climate and Applied Meteorology. 26:1103-1116. 

McGinn, C. E. 1983. The microclimate and energy use in suburban 

tree canopies. Davis, CA: University of California. 299 p. Ph.D. 

dissertation. 

McNaughton, K. G. 1989. Micrometeorology of shelter belts and 

forest edges. Philosophical Transactions of the Royal Society 

of London, B. 324: 351-368. 

WlcPherson, E. G. 1994. Energy-saving potential of trees in 

Chicago. In: McPherson, E. G., Nowak, D. J. and Rowntree, R. 

A. eds. Chicago's urban forest ecosystem: results of the Chicago 

Urban Forest Climate Project. Gen. Tech. Rep. NE-186. Radnor, 

PA: US. Department of Agriculture, Forest Service, Northeastern 

Forest Experiment Station. 

McPherson, E. G.; Heisler, G. M.; and Herrington, L. P. 1988. 

Impacts of vegetation on residential heating and cooling. 

Energy and Buildings 12(1988): 41 -51. 

Nowak, D. J. 1994a. Urban forest structure: the stateof Chicago's 

urban forest. In: McPherson, E. G., Nowak, D. J. and Rowntree, 

R. A. eds. Chicago's urban forest ecosystem: results of the 

Chicago Urban Forest Climate Project. Gen. Tech. Rep. NE-186. 

Radnor, PA: U.S. Department of Agriculture, Forest Service, 

Northeastern Forest Experiment Station. 

Nowak, D. J. 1994b. Air pollution removal by Chicago's urban 

forest. In: McPherson, E. G., Nowak, D. J. and Rowntree, R. A. 

eds. Chicago's urban forest ecosystem: results of the Chicago 

Urban Forest Climate Project. Gen. Tech. Rep. NE-186. Radnor, 

PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 

National Climatic Center. 1981. Typical meteorological year user's 

manual. Ashville, NC: National Climatic Center TD-9734. 25 p. 

Neter, J.; Wasserman, W.; Kutner, M. H. 1985. Applied linear 

statistical models. Homewood, IL: Irwin. 1127 p. 

Oke, T. R. 1987. Boundary layer climates. New York: Methuen. 

435 p. 

Oke, T. R. 1989. The micrometeorology of the urban forest. 

Philosophical Transactions of the Royal Society of London, 6. 

324: 335-349. 

Panofsky. H.; Dutton, J. A. 1984. Atmospheric turbulence: models and methods for engineering applications. New York: 

John Wiley and Sons. 397 p. 

Simpson, J. R.; Levitt, D. G.; Grimmond, C. S. B.; McPherson, E. G.; 

Rowntree, R. A. 1994. Effects of vegetative cover on climate, 

local scale evaporation and air conditioning energy use in 

urban southern California. In: Proceedings 11 th conference on 

biometeorology and aerobiology; 1994 March 7-1 0; San Diego, 

CA. Boston, MA: American Meteorological Society. (in press). 

Slade, D. H. ed. 1968. Meteorology and atomic energy. Oak 

Ridge, TN: US. Atomic Energy Commission, Division of Technical Information. 445 p. 

Wilkinson, L. 1990. SYSTAT: the system for statistics. Evanston, 

IL: SYSTAT, Inc. 676 p. 

40 Chapter 3 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chapter 4 

Local Scale Energy and Water Exchanges in a 

Chicago Neighborhood 

Sue Grimmond, Assistant Professor, Climate & Meteorology Program, Department of Geography, 

Indiana University, Bloomington, IN 

Catherine Souch, Assistant Professor, Department of Geography, Indiana University, Indianapolis, IN 

Richard Grant, Associate Professor, Department of Agronomy, Purdue University, West Lafayette, IN 

Gordon Heisler, Meteorologist, USDA Forest Service, Northeastern Forest Experiment Station, Syracuse, NY 

Abstract 

Outlines the methods of measurement and analysis of "abovecanopy" meteorological measurements undertaken to 

investigate the nature of surface controls on energy and 

water exchanges at the local scale. Observations were made 

over two periods: "intensive" (July 1992), and "extensive" 

(July 1992 through June 1993). During the intensive measurements, the vertical fluxes of sensible and latent heat 

were measured by eddy correlation methods at one abovecanopy site. By combining these with measurements of net 

radiation and storage heat flux and detailed characterization 

of urban surface materials and morphology, a general 

understanding of energy exchanges of the urban surface at 

the local scale (100 to 1000 m) was obtained. Means of 

energy-balance values over the study period and their 

variability are presented and compared with results from other 

cities. Additional analyses to be conducted are described. 

Introduction 

Urban areas represent locations where a large and ever 

increasing proportion of the world's population lives, and 

where a disproportionate share of natural resources is used. 

Urbanization brings about significant changes in land-cover. 

The replacement of natural surface materials (the substitution of concrete, asphalt, trees, etc. for the natural vegetation) significantly alters the aerodynamic, radiative, thermal, 

and moisture properties of the surface. In turn the pre-urban 

balances of energy, mass, and momentum are altered. This 

leads to the modification of the atmosphere and the 

generation of an "urban climate" commonly characterized by 

enhanced temperatures, the "urban heat island" (Ackerman 

1985, 1987), poorer air quality (Hanna 1971 ; Wadden et al. 

1979; Sexton and Westberg 1980; Swinford 1980; Scheff et 

al. 1984), and other effects. 

Increasing attention is being directed toward strategies that 

mitigate negative, inadvertent environmental effects of 

urbanization. For example, strategically planting trees or 

lightening building and pavement surfaces have been suggested as alternate ways to reduce the summertime urban 

heat island and thus reduce energy demand for cooling 

(Heisler 1974; Akbari et al. 1992). These strategies entail 

some alteration of the morphology or material properties of 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 

the urban surface, that have an effect through the alteration 

of surface energy and water exchanges. Relatively little 

research has been conducted to quantify these effects. 

Hence, we cannot make informed decisions about planning 

or directing urban morphological changes, as we do not 

know how such changes would affect the local environment 

and its inhabitants. 

More fundamentally, our understanding of the biophysical 

processes involved in the generation of urban climates is 

limited. Direct observations of energy and mass exchanges 

in urban areas have been collected only in a restricted 

number of cities, with a small range of surface morphologies 

and climates (Oke 1988; Grimmond and Oke 1994). Thus, 

results of model simulations and predictions on the effects of 

changing the urban surface must be used with caution. To 

understand how urban morphology influences local climate 

(energy and water exchanges) it is necessary to undertake 

detailed investigations of local meteorology in conjunction 

with an understanding of urban surfaces. This paper reports 

on research conducted to study energy and water exchange 

processes in a neighborhood of Chicago. In addition to 

enhancing our understanding of biophysical processes, these 

data are to be used to evaluate ~hvsicah based meteorological models, which, in turn, wili be used-to investigate the 

effects of proposed changes in urban morphology on the 

urban climate. 

The surface-energy balance provides a framework with which 

to study energy and water exchanges at a range of spatial 

scales. It can be expressed: 

where Q* is the net all wave radiation (net available energy 

from solar and terrestrial radiation); QF is the anthropogenic 

heat flux (heat generated from fuel combustion); QH is the 

sensible heat flux (energy for heating the air); QE is the 

latent heat flux (energy for evapotranspiration); AQs is the 

net storage heat flux (energy for heating the urban fabric); 

and AQA is the net horizontal heat advection. QE, the term 

that links the energy and water balances, is the energy 

equivalent of evapotranspiration, a mass (water) term. If 

temperature is known, it is possible to convert between 

energy and mass (water) equivalents using the latent heat of 

vaporization. Thus, QE provides information about both 

energy and mass (water) exchanges. The surface energy 

balance concept, and the history of its application for an 

urbanized surface, was reviewed by Oke (1 988). 

Chapter 4 41 
Urban effects on climate are forced at a range of scales: Methodology: Meteorology from the urban canopy layer (UCL) where microclimates are 

determined by buildingltree size and spacing, to the To understand the nature of surface controls on energy and 

land-use scale, to the whole city. Table 1 (adapted from Oke water exchanges, detailed measurements of local scale 

1984) illustrates this range of scales and associated atmo- meteorology and surface conditions were conducted for one 

spheric processes in urban areas. The Chicago study was area within the City of Chicago. 

conducted at three scales: micro (length scale 10-1-1 01 m), 

local (1 02-1 03 m) and meso (1 04 m) (Figure 1 ; Table 2). Measurement Program 

We report on the local scale above-canopy studies (i.e., 

those representative of areas the size of city blocks to 

land-use zones) and outline the methodology used to select 

the study sites and collect meteorological data and 

information about the urban surface. The surface-energy 

balance provides the methodological framework (for 

measurement and modeling) for the local scale research. 

Using this framework, the partitioning of energy in Chicago 

is studied and compared with that in other cities, and 

research directions are described. The methodology 

and preliminary results from microscale "below-canopy" 

studies are presented in Heisler et al. 1994: Chapter 3, 

this report. 

The meteorological measurements were conducted over two 

periods, referred to here as intensive (July 1992) and 

extensive (July 1992 through June 1993) (Table 2). The shortterm intensive measurements were taken to collect direct 

observations of the energy and water fluxes from a 

representative neighborhood within Chicago. The extensive 

measurements were taken to provide data input for numerical modeling for all seasons; to aid in the development of 

relationships between routinely measured data at the 

National Weather Service (NWS) airport site and "urban" 

values representative of specific neighborhoods to allow 

NWS data to be extrapolated to urban sites; and to study 

relations between local scale and microscale conditions. 

Table 1. -Framework for urban climate classification adapted from Oke (1 984) 

Turbulent Boundary Layers 

Layer Flow characteristics Dimensionsa Scale 

Urban canopy layer (UGL) Highly turbulent, controlled by Same as H~ typically 10 m Micro 

roughness elements 

Roughness sub layer Highly turbulent, wakes and 2D - 3~~ 

plumes, transition zone 

Micro 

I I Urban boundary layer Turbulent, includes surface Depends on surface fluxes of heat Local 

(UBL) and mixed layers and momentum (typically 1 km day; 0.2 km night) 

Urban Morphology 

DimensionsD 

Urban unit Urban features Urban climate phenomena H D L Scale 

Building Single building, tree or Wake, plume, shadow 10 m 10m 10 m Micro 

garden 

Canyon Urban street and bordering Canyon shelter, shade 10 m 10 rn 10 m Micro 

buildings or trees bioclimate 

Block City block, park, factory Climates of parks, building 

complex clusters cumulus, rninibreezes 

Land-use zones Residential, commercial Local climates, winds, cloud 

industrial modification 

0.5 km 0.5 km Micro 

5km 5 km Local 

City Urban area Heat island, urban circulation, 25 km 25 km Meso 

urban effects in general 

a Dimensions of boundary layers are depths of affected atmosphere; dimensions of uh units are those of urban structures or plan area 

H is building height; D is building spacing; L is building length. 

42 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
%b+ RE I NAL 

RURAL 

/ URBAN 

/ BOUNDARY / LAYER //--- 

/ 

/' 

/ eon Lake-land circulation 

--. '. 

f 

Qi 1). 11 Roughness rublayer / Transition layer 

Building /Canopy layer 

Micro- Loca . Local/Mesoscale scale scale tower 

tower tower 

Figure 1 .-Schematic representation of spatial scales and atmospheric processes in urban areas (adapted 

from Oke 1984; Oke et a1.1989). 

Table 2. --Scales of meteorological measurements in the Chicago Urban Forest Climate Project 

Scale Urban features Urban climate phenomena Tower sites Measurement period 

Re ional Cook and DuPage counties. Lake-land breeze Belmont Harbor July 92 to June 93 

,$to lo5 rn Chicago Metmpolitian area. lSPT3 

Lake Michigan O'Hare airport 

Local City-blocks, land-use Above canopy local scale ISPT3 July 92 to June 93 

lo2 tolo3m zones, neighborhoods, climates, constant flux layer. Pneumatic flux tower July 92 

community areasa urban boundary layer 

Micro Individual properties Below canopy, shading, Below canopy 1 July 92 to June 93 . . 

10-1 tolOlm buildings, gardens shelter Below canopy 2-5 < 1 month at a site 

rotated between sites 

a Community area numbers referred to correspond to Figure 18 in McPherson et al. (1993): 9,10-12~, 13,14, 15T, 16.17-1 gT, 20-23,25, 76T, 87-91,11!3~ 

Community areas completely within 13 x 13 krn study area (see Figure 3), remainder are partially in area. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Chapter 4 
Selection of Study Sites 

Chicago is located along the southwest shore of Lake Michigan and occupies a plain which for the most part is only 

meters above the lake (Figure 2). The lake does not thermally 

modify the predominant synoptic-scale flow from the west, 

but it does generate a mesoscale breeze (lake-land breeze) 

as a result of differential heating between land and water. 

This effect decreases with distance due to the modification 

of airflow by the underlying urban surface. In this study it was 

essential to identify the effect of the lake on micro- and local 

scale climates from other controls. This required careful 

selection of study sites. Additional constraints on measurement locations were imposed by logistics, primarily by the 

location of pre-existing towers on which equipment could be 

mounted and where access was permitted. 

The extensive meteorological measurements were conducted 

from three towers: City Parks Board tower at Belmont 

Harbor; Illinois State Police District 3 tower (ISPT3) near the 

intersection of Forest Preserve, Harlem, and Irving Park; and 

next to the NWS climate station at O'Hare International 

Airport (Figure 2). The intensive flux measurements were 

conducted on the grounds of the Read Mental Health Center, 

directly adjacent to ISPT3 (Figure 2). The sites are aligned 

along a transect east-west across the city, from the lake, 

past the intensive-flux site to the O'Hare station (Figure 2). 

The area surrounding the ISPT3 and intensive-flux towers 

includes the neighborhoods of Harwood Heights and Norridge, 

Chicago. It has predominately two-storied densely packed 

houses and a large number of mature deciduous trees with 

many greenspaces (parks, cemeteries, etc.). In the immediate 

vicinity of the towers are large greenspaces (cemetery and 

grounds of the mental health facility) to the east, northeast, 

and west; a shopping mall and garages to the north and 

northwest; and houses to the south. 

Meteorological Measurements 

Intensive observations 

The intensive observations consisted of direct measurements 

of sensible and latent heat flux, and net all-wave radiation 

(Table 3). The convective fluxes (QH and QE) were measured 

using eddy correlation techniques (Lenschow 1986; Oke 

1987). All of the equipment was installed on a pneumatic 

tower that could be lowered when rainfall, high winds, and/or 

thunderstorms were anticipated. A Campbell Scientific Inc. 

(CSI) one-dimensional sonic anemometer and fine-wire 

thermocouple system (SAT: CA27) was used to measure 

vertical wind velocity and temperature; a CSI krypton 

hygrometer (KH20) was used to measure the absolute 

humidity. Fluctuations in the vertical wind velocity, air temperature and humidity were sampled at 5 Hz and the 

covariances determined over 15-minute periods. Flux 

corrections were made for oxygen absorption by the sensor 

and air density (Webb et al. 7 980; Tanner and Greene 1989). 

Corrections were not made for frequency response and 

spatial resolution of the eddy correlation sensors, which 

probably would increase QE by 1 percent (M. Roth 1992 

pers. commun.; Grimmond et al. 1993). All times have been 

corrected to Local Apparent Time. 

Net all-wave radiation was measured at two levels (Table 3). 

It is not practical to measure AQs directly at urbanlsuburban 

sites due to the complexity of the materials and morphology 

of the urban surface (Oke and Cleugh 1987; Grimmond et al. 

1991). Hence AQs is determined as a residual in the energy 

balance (a*-(QH+QE)) if QF and AQA are neglected. This 

approach has the inherent problem that all measurement 

errors of other energy balance fluxes are accumulated in the 

AQs term. 

QF has not been determined for this site. Grimmond (1992) 

calculated the size of this flux for a suburban area of 

Vancouver, British Columbia, based on combustion from 

stationary and mobile sources and metabolic rates. The 

magnitude of this flux is dependent on the spatial pattern of 

the sources (Schmid et al. 1991). In residential areas, the 

most notable influences on QF are major roadway systems 

and significant non-residential stationary anthropogenic heat 

sources, for example, strip malls with energy-intensive 

users. Given the location of the local anthropogenic heat 

sources relative to the measurement sites, summertime airconditioning, and the magnitude of QF calculated by various 

authors (Oke 19881, the peak diurnal values of QF at the 

study site probably were about 20 Wm-2 (4.5 percent of 

mean Q* values). 

Spatial differences in surface cover across the city result in 

differential heating and the lateral movement of energy 

(advection). The horizontal advection term (AQA) is difficult 

to determine. The observation site was located in an area 

that was extensively suburbanized, but, as discussed 

earlier, there are known regional scale circulations that are 

generated due to differential heating patterns between land 

and Lake Michigan (e.g. Hall 1954; Lyons 1972). The intensive flux-tower and ISPT3 site are less than 15 km from the 

lake (Figure 2), without intervening topographic barriers. 

Following an analysis in the Sunset neighborhood in 

Vancouver, where there is also a large water body which 

generates a sea-breeze circulation, Steyn (1 985) concluded 

that advection could be neglected at the local scale when 

working under similar land-use conditions. For this report, 

AQA has been ignored, so the energy balance residual (AQs) 

should be interpreted accordingly. The influence of advection is the subject of further investigation. 

Extensive observations 

The instrumentation used in the extensive measurements, 

and the heights at which it was mounted, are listed in Table 

3. A full description of ventilated temperature systems 

developed for the Chicago Urban Forest Climate Project is 

presented by Grant and Heisler (1994). All instruments used 

in the local scale study and the below-canopy study were 

inter-compared before and after the measurement campaigns 

(May 1992, July 1993). Appropriate corrections were made 

for inter-instrument differences. 

Methodology: Surface Controls 

Rationale 

The active surface of any system is one of the most important determinants of climate because it is the primary site of 

44 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Figure 2.-Location of the local scale measurement sites across the city of Chicago 

(Chicago is identified with the darker shading). 

Table 3. -Instrumentation used on pneumatic tower during intensive measurements and on fixed towers for extensive 

measurement period (July 1992 to June 1993) 

Intensive Measurements 

Variable Instrumentation Level instalIed (m) 

Sensible heat flux (QH) CSI sonic anemometer and fine wire 18 

thermocouple 

Latent heat flux (QE) CSI krypton hygrometer 18 

Net all wave radiation (Q*) Swissteco miniature net radiometer 18 

Soil heat flux (QG) REBS Soil heat flux plates -0.08 

Extensive Measurements 

Variable Instrumentation Level installed (m) 

Illinois 

StatePolice 

Tower ISPT3 Belmont Harbor O'Hare 

Air temperature 

Relative humidity 

Wind speed 

Wind direction 

Net all-wave radiation 

Solar radiation 

Precipitation 

Surface moisture status 

Vaisala HMP35C 

YSl thermistor 44020 

Vaisala HMP35C 

R.M. Young Wind Sentry 

R.M. Young Wind sentry 

REBS Net radiometer 

Li-cor pyranometer 

Texas Instruments rain gauge 

Weiss type wetness sensor 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 4 45 
transfer and transformation of energy, mass and momentum. 

Climatological and meteorological measurement and modeling studies require the surface datum to be defined and 

described to characterize the site where measurements have 

been conducted; provide input for numerical models; or 

ensure spatial consistency between measured and modeled 

data. In model evaluations, it is essential that surface 

parameters (the model domain) represent the same surface 

area for which the measurements were conducted (the 

measurements' source area) (Grimmond and Souch 1994). 

In this study the nature of surface controls on energy and 

water exchanges is of primary interest. 

The source area for meteorological measurements is 

dependent on the physical process involved, the instrumentation used, and the meteorological conditions under which 

the measurements occurred. For radiant fluxes, the source 

area is fixed in time by the field of view of the instruments 

(i.e., by geometry). This source area can be determined 

using procedures outlined by Reifsnyder (1 967) and Schmid 

et al. (1991). For turbulent fluxes, the source area is not fixed 

but varies through time as a sensitive function of sensor 

height, atmospheric stability, and surface roughness (in that 

order of importance). Numerical models, based on boundarylayer diffusion theory have been developed to determine the 

dimensions, weighting, and areal extent of the source area 

of turbulent measurements (e.g., Gash 1986; Schuepp et al., 

1990; Leclerc and Thurtell 1990; Schmid and Oke 1990; 

Horst and Weil 1992). 

In this study, a methodology to link a source area model for 

turbulent fluxes (based on Schmid and Oke 1990) to a surface 

database within a geographic information system (GIs) was 

developed (Grimmond and Souch 1994). This surface database in conjunction with the flux data will provide a basis for 

assessing the relationship between energy and water fluxes 

and vegetation (Demanes 1994). 

Surface Database 

Preliminary calculations based on the Schmid and Oke (1990) 

source area model for turbulent fluxes were used to identify 

the approximate dimensions of the source areas for the 

convective flux (QH and QE) measurements during the 

intensive study period. Based on these calculations a square 

approximately 13 km by 13 km, centered on the ISPT3 tower 

site, was delineated (Figure 3). A three-tier surface database 

was developed for this area, bounded by Touhy Avenue to 

the north, Chicago Avenue to the south, Mannheim Road to 

the west, and Pulaski Road to the east (Table 4). At the 

regional scale the spatial distribution of land use (Table 5) 

was mapped from aerial photographs. Given the focus of the 

Local 

Micro 

N 

A 

City \ of 

Chicago i 

5 10 krn. 

o 3 6 mi. 

Figure 3.-Schematic representation of the structure of the surface 

database (adapted from Grimmond and Souch 1994). 

Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
study on the effects of vegetation on urban climate, the two 

primary criteria for identifying the land-use categories were 

building dimensions and density, and vegetation dimensions 

and density. The digitized, geo-corrected map contains more 

than 2500 polygons (Figure 4). 

At the local scale (Figure 3), 200 m x 200 m grid squares 

were located randomly on a second set of more detailed 

(1 :4800) aerial photos (Table 4). For each square the percent 

cover of building, grass, trees, pavement, and other 

variables (Table 6) was estimated. Based on replicates within 

each land-use category, means and standard deviations 

were calculated for building and vegetation densities and 

percent plan-area surface type (Table 6). These data were 

linked to the regional digital land-use map to allow the areal 

distribution of attributes to be illustrated. 

At the microscale (Figure 3), field surveys were conducted to 

provide detailed information on surface cover at the scale of 

the individual lot in residential neighborhoods or 1/10 acre 

plot (0.04 ha) in non-residential areas. Weighted stratified 

random sampling was used to select sample plots within 

each land-use category to obtain detailed information on 

specific surface characteristics (Table 7). Data from 147 

plots (87 residential, 60 nonresidential) were collected within 

the study region, 47 surveys conducted as part of the survey 

on urban forest structure (see Nowak 1994: Chapter 2, this 

report) and 100 supplementary sites. The additional surveys 

were conducted to ensure there were replicate surveys for 

each general land-use class. Field data stored in database 

files are linked to the regional scale land-use database to 

provide information on the attributes within land-use 

categories. These include building heights (of interest in the 

calculation of roughness length); surface materials (important for albedo, emissivity, drainage properties, storage heat 

flux modeling, etc.); and tree species and tree density (which 

aid in calculating leaf area index, important in evaporation 

modeling) (Grimmond and Souch 1994). 

Figure 5 illustrates the spatial variability of vegetative cover 

and built impervious surfaces across the study region. 

Impervious surfaces are important in defining retention and 

detention storage capacities which are used in both runoff 

and evaporation modeling. Vegetative cover is important for 

defining surface resistances for evaporation and air quality 

modeling. When these figures are compared with the landuse map (Figure 4), differences in surface properties among 

the classes, which influence the energy and water exchanges 

become clear. For example, note the differences in surface 

cover within the residential A classes (A to A4) and how the 

city generally becomes more impervious toward the east. 

Results 

Representativeness of the Measurement Periods 

Analysis of synoptic classifications during the study period 

show that the weather the Chicago area experienced was 

similar to that of the prior 10 years (Grant 1993). Cold fronts 

and warm sectors passed-through the Chicago area 25 and 

12 percent of the study period respectively; within 2 percent 

of the occurrence during the prior 3 years, and within the 

range of percent occurrence over the past 10 years. Chicago 

experienced fewer warm fronts during the study period than 

in the recent past, but experienced as many as have 

occurred in two of the last ten years. Polar high pressure was 

the dominant synoptic feature during the study period (35 

percent of the time north, west or east of Chicago, and 11 

percent of the time south of Chicago). The frequency of 

occurrence of the polar high located north, west, or east of 

Chicago equaled the occurrences in 3 of the past 10 years. 

The frequency of occurrence of the polar high south of 

Chicago exceeded the highest frequency of occurrence in 

the prior ten years. The presence of more frequent polar high 

pressure systems to the south of Chicago helps explain the 

relatively cold temperatures experienced during the study 

period (Table 8). 

At O'Hare Airport a total of 95.8 mm of rain fell on 23 days 

during July 1992 (normal: 92.2 mm); longest period without 

rainfall was 2 days. Consequently, the surface was almost 

continuously wet throughout the study period (Figures 6 

and 8). The range of general climatic conditions measured 

from the ISPT3 site in July 1992 (the intensive period) are 

presented in Figure 6. 

Table 4. -Information source for surface database at each scale (See Figure 3 for scale dimensions) 

Scale Method Area covered Output 

Regional Land-use mapping on air photos Geonex 13 krn x 13 km square centered on ISPT3 Land-use 

Chicago Aerial Survey(CAS), Des Plaines Area bounded by Touhy Ave, Chicago categories 

Flown: March 2. 1992 scale: 1 : 24000 Ave., Mannheim Rd. & Pulaski Rd. (see Table 5) 

Local Detailed photo analysis Sidwell Company, Randomly located replicates within each Attributes for each 

West Chicago: Flown: Spring 1987 scale: land-use category land-use 

1 : 4800 Geonex CAS: March 24, (see Table 6) 

1990,1:4800 

Micro Field surveys 147 randomly located points and Surface details 

immediate surrounding area within region (see Table 7) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 4 47 
Table 5. -General land-use categories for Chicago 

- 

General Land-use Categories and description 

Residential (Single) 

A 

MH 

Apartments 

AA 

Commercial-Industrial 

CB 

CC 

CS 

I 

Institutional 

HS 

S 

U 

Transportation 

MRI 

rn 

VacanWild 

Dl 

Vegetated 

VG 

VGR 

VM 

VPC 

vr 

Impervious Surfaces 

CN 

I P 

IS 

Water 

wm 

High density housing, A1-A4 differentiated by shape of buildings and whether 

attached or not. Yards small, mainly grass, few trees. 

Moderate density housing, small houses with trees 

Moderate density housing, small houses, large yards. C1-C3 differentiated by 

size of houses. All have many treeslextensive landscaping 

Large houses. small grass yards with some trees and shrubs 

Large houses, large yards, yards landscaped with shrubs and trees 

Mixture of "A' and "E" type housing 

Houses equally spaced, large grass yards, few trees, F1 and F2 differentiated 

on housing density 

Mobile homes 

5-6 stories, U-shaped, distinguished from AA2 based on arrangement of 

parking 

Square shaped buildings 

L-shaped buildings, 7 stories tall, no trees 

Rectangular shape 

Duplexes 

Mixture of AR1 and A type houses 

Highly mixed 

Low-level apartments (2 stories), rectangular shape. BB1, BB2 and BB3 

distinguished on height and size 

Large commercial buildings - c 6 stories 

Very tall commercial buildings - > 15 stories 

Small commercial buildings 

Industrial - large low level buildings or many small buildings 

High school - large building, few trees, medium size parking lot 

Elementary/ Junior High school - much smaller buildings than HS 

University - large buildings, parking lot, vegetated grounds 

Major roads e.g. interstates 

Railroad tracks or side/yards: 

Dirt 

Golf course 

100% grass 

50% grass/50% tree and shrub 

Cemetery 

Trees and shrubs 

Concrete 

Parking lot (impervious) 

Tennis court 

48 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
General land use classes 

Residerdial 

npartments 

Commercial and industrial 

InatiMiond 

impennous 

Transportation 

Vegetation 

Water 

Residential classes 

A 

A1 

A2 

A3 

A4 

81 

C 

C1 

c2 

C3 

D 

E 

El 

E A 

F 

Fl 

F2 

MH 

Transportatiin & rater Apartments 

Rernalnder 

Figure 4.- a) General land-use classes across the study area b) Residential land-use classes (see 

Table 5 for descriptions) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 4 
Table 6.- Attributes determined for each land-use category 

Densities (number per area) 

Buildings 

Trees 

Roads 

Percent areal cover 

Buildinas 

~ara~& 

Grass 

Treeslshrubs 

Parking lot 

Main road 

Water 

Dirt 

Sand 

Pavement (non parking lot) 

Table 7. -Information collected in the field survey 

Non residential (0.1 acre, 0.04 ha plots) 

Landscape: Managed1 unmanaged and condition 

Land-use: Residential, commercial etc. and % of plot covered 

Ground cover: % cover by: building, structures, cement, tar, wood, other 

impervious, soil, rock, dufflmulch, herbaceouslivy, grass, 

wild grass, water, shrubs 

Building attributes: Type, length, width, material, azimuth from front door 

outward, age. height, number of floors, roof color, wall color. 

% wall glass, average distance to nearest building, height of 

nearest building 

Structure shrub and Full listing of species and size of each tree and shrub, 

trees: condition of tree, % beneath canopy of artificial surfaces, 

d.b.h, height, height to lower crown, crown width, crown 

shape, percent of crown volume occupied by leaves, tree 

Residential (variable size based on lot size; from mid-street to mid-alley or back of lot) 

Road: Width of road, length of road in front of property, type, width 

of curb to sidewalk, % of strip covered by cement 

Alley: Width, length, surface type 

Length: Length of front part of lot, width of front part of lot, presence, 

type and height of any overhead obstructions 

Irrigation: % vegetation irrigated 

Structure: Length, width, height of structure, % plot occupied by 

structure, type of structure, material, structure of roof 

Shrubs: Species, length and height of shrub mass, % shrub volume 

occupied by leaves, density of leaf mass, number of stems 

in mass, average diameter of stems in mass 

Trees: Species, number of stems, d.b.h., tree height, bole height, 

crown width, crown shape, percent of crown volume 

occupied by leaves, crown density 

Positions: Sketch and photo of building and tree locations referenced to 

tree information 

50 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

Table 8.-Meteorological conditions during extensive study 

period (July 1992 to June 1993) and departures from Normal 

(1951 -80). Source of data NOAA (National Climate Data 

Center. Local Climatological Data. Chicago O'Hare station). 

(SP study period; D departure from Normal). 

Month Temp ('C) Precip (mm) 

SP D SP D 

July 20.7 -2.1 95.8 3.6 

August 19.4 -2.7 90.4 0.8 

September 17.1 -1.1 109.5 24.4 

October 10.2 -1.7 45.5 -12.4 

November 3.5 -0.8 137.4 85.1 

December -1.9 0.5 63.3 9.9 

January -3.2 2.9 97.3 58.4 

February -4.2 -0.6 20.8 -13.7 

March 1.2 -1.7 114.8 46.5 

April 7.2 -2.0 116.1 23.6 

May 8.8 0.4 46.5 -37.8 

June 24.8 -1.2 253.0 157.0 

The climatological conditions experienced during the extensive study period are summarized in Table 8 and Figure 7. 

Overall, the period was slightly cooler and wetter than normal. 

Energy Balance Fluxes 

During the intensive measurement period 127 hours of eddy 

correlation flux measurements were collected. Because the 

measurements were conducted during a period with a high 

frequency of rainfall, there are many breaks in the data 

(Figure 8). The mean value for each of the fluxes for each 

hour and their variability is shown in Figure 9. From Figures 

8 and 9 it can be noted that clouds occurred throughout the 

day during the measurement period. The maximum output 

flux (i.e., removal of energy from the surface) was QE 

followed very closely by QH and AQs. The convective fluxes 

(QE and QH) peak at solar noon whereas AQs peaks about 

11 00 Local Apparent Time, with a marked hysteresis pattern 

(values higher in the morning and lower in the afternoon). 

To allow direct comparisons of flux partitioning\from day to 

day (i.e., to remove the effect of the available en&rgy varying 

from day to day), each of the fluxes are normaliked by net 

radiation to calculate ratios: x(QH/Q*), Y (QE/Q*) and A (AQs/ 

Q*) (Figures 10 and 11). The ratio of the two convective 

fluxes, the Bowen ratio: 13 = QH/Q~ (i.e., the amount of 

energy warming the air relative to that evaporating water), 

also is calculated. The mean daytime Bowen ratio for the 

observations, determined from the mean daytime fluxes, is 

0.87. Thus, more energy is being removed from the surface 

by the latent heat flux than sensible heat flux (i.e., more 

energy during this period was going into drying the surface 

than into warming the air). The mean ratios of x , Y, and A 

are 0.32, 0.38, and 0.30 respectively for the daytime (Q*>O) 

(32 percent of the energy going into heating the air, 38 

percent into the evaporation of water, and 30 percent into 

heating the urban fabric), and 0.35,0.49 and 0.1 6 for the day 

(24 hours) (35 percent heating the air, 49 percent evaporating water, and 16 percent heating the urban fabric). These 

results are biased to slightly higher Bowen ratios than the 

true average for the period as measurements were restricted 

to times when rainfall was neither occurring nor imminent 

(i.e., evaporation may have been more significant at the 

other times). 

The variability of the fluxes from day to day can be seen by 

the ranges on Figure 10. It is notable that the data are 

remarkably consistent except for one day (Yearlday; 92/21 0) 

when Bowen ratios were 3 to 5 (i.e., much greater QH than 

QE). This day was at the end of one of the slightly longer 

intervals between rainfall events (Figure 8). The high Bowen 

ratios were associated with a suppressed QE, while QH 

remained similar to that of previous days (Figure 8). Instead 

the energy went into storage heat flux (AQs) (heating the 

urban fabric). On the previous day (92/209), the largest QE 

fluxes in the measurement period were observed. By 92/21 0 

there had been a significant reduction in availability of 

surface moisture (Figure 8: surface moisture sensors), so 

the surface was starting to exert a more significant control on 

energy partitioning. Throughout July 1992 in Chicago, it is 

probable that the influence of surface morphology on flux 

partitioning is not as evident as it may be at other times 

because of the frequency of rainfall events. 

The Bowen ratio determined in this study, 0.87, is lower than 

the "typical" value of 1.0 suggested by Oke (1982) for 

suburban areas. It also is considerably lower than values 

observed in the summertime in Tucson, Sacramento, and 

Los Angeles (1.80, 1.40, and 1.38 respectively for daytime 

values) (Grimmond and Oke 1994). However, the value is 

not physically unrealistic given the conditions in Chicago in 

1992. As was noted, flux measurements were restricted as 

to the time period for which they were conducted and the 

range of conditions experienced. 

The x ratio expresses how much energy is going into 

warming the air rather than drying the surface or warming the 

urban fabric. The x ratio in Chicago behaves in a similar 

manner to that in other urban areas, showing an increase 

through the day (Q*>O time period) (Grimmond and Cleugh 

1994). The mean daytime ratio (0.32) (daily value 0.35) is 

lower than the typical (0.39) values suggested by Oke (1982), 

and lower than those reported for Tucson, Sacramento and 

Los Angeles (0.46, 0.40 and 0.36) (Grimmond and Oke 1994). 

Given the prevailing meteorological conditions in Chicago 

during the study period, it is likely that more energy than 

usual was used to dry surfaces rather than warm the air or 

the urban fabric, i.e., the I3 and x ratios are lower than would 

have been obtained under drier periods, and Y is higher. 

To obtain an idea of the variability of energy partitioning 

between seasons and years, it is useful to consider the data 

from Vancouver (Table 9). The Sunset neighborhood in 

Vancouver is one of the few urban sites where energy 

balance studies have been conducted over a number of 

years and thus under a range of synoptic conditions. There 

is considerable variability among seasons both within and 

across years able 9). However, it is important to note that 

52 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
0 2 4 6 8 10 12 14 16 18 20 22 

Time (h) 

Maximum 

75 percentile 

Median 

25 percentile 

Minimum 

11 LA 0~"'"''""1 

0 2 4 6 8 10 12 14 16 18 20 22 

Time (h) 

Figure 6.-Range of meteorological conditions during the intensive study period: Diurnal ranges of temperature, relative humidity, windspeed and wind direction. Minimum (O), 25, 50 (median), 75 and I00 (maximum) 

percentiles plotted. Median plotted as a diamond, and minimum, 25 and 75 percentile values, and maximum 

plotted as horizontal lines). 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 4 
Solar (short-wave) radiation Net all-wave radiation Pressure (mb) Wind speed (m s-l) Relative Humidity (%) Temperature (OC) 

(W m-2) (W m-2) 

-L I a -L A -L -L 

N 8 g; AN W a Ul b) -4(OV,(D(O(DU)OOOOO 0 0 0 0 0 0 0ddO)mroU)oo~~N 4 N g, O) ; 110 L -'MU o o o o o ooooo o o o o ooulouloolooloulooruea,~ oroemo o o o o o o o o o o o 
200 201 202 203 204 205 206 207 208- 209 210 21 1 212 

Time (d) Time (d) 

Figure 8.-Time series of energy balance fluxes, precipitation and surface moisture status. (Note the surface moisture 

status is a relative index, wet 1 .O, dry 0.0, MSl and MS2 are moisture sensors on vegetation and impervious surfaces 

respectively. These respond to both precipitation events and dew). (From July 20th (day 200) to July 31st (212), 1992). 

Note break in data 201-202. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 4 
-100 1 

0 2 4 6 8 10 12 14 16 18 20 22 

Time (h) 

- (b) QH 

Maximum 

75 percentile - 

25 percentile - 

T Minimum 

- (d) AQs 

0 2 4 6 8 10 12 14 16 18 20 22 

Time (h) 

Figure 9.-Ensemble diurnal energy balance fluxes for Chicago, July 1992. Mean, 0 (minimum), 25, 50 (median), 75 and 

100 (maximum) percentiles plotted. Mean values joined with a dashed line, median plotted as a diamond, and minimum, 

25 and 75 percentile values, and maximum plotted as horizontal lines. The number of hours used in the analysis is 

indicated on Figure 11. 

56 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
0 2 4 6 8 10 12 14 16 18 

Time (h) 

. . il"'''' 

(b) X . . . . . . .: 

Maximum 

25 percentile 

Minimum 

0 2 4 6 8 10 12 14 16 18 20 22 

Time (h) 

Figure 10.-Diurnal patterns and variability of B, x , I' and A ratios (see text for explanation) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 1 Chapter 4 
0 2 4 6 8 10 12 14 16 18 20 22 

Time (h) 

Figure 11 .-Ensemble energy balance fluxes and ratios of flux partitioning for Chicago, July 

1992. The numbers on the Figure #h indicate the number of hours used in analyses. 

58 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
not all the results of the studies are directly comparable 

because of differences in instrumentation and methods 

between years (Table 9). As in this study, only Roth and Oke 

(in press) used eddy correlation techniques to measure 

directly both convective fluxes (QE and QH). Roth (1991) 

intercompared Bowen ratios determined from a Bowen ratio 

system Re (a reversing-temperature difference system) and 

from eddy correlation techniques (DEC). He concluded that 

the OEc generally were lower in the daytime than the OB. The 

data from Chicago fall within the range of observations for 

Vancouver. 

Future Directions 

An issue that needs further study is the representativeness 

of the observations reported here. This requires consideration of both the climatological and morphological conditions 

of the study period and site. There are obvious advantages 

to supplementing these data with further direct observations 

and data analysis to document the spatial and temporal 

variability of fluxes for this metropolitan area and to investigate further the role of advection. 

Work is in progress to correlate fluxes (QE and QH) with 

tree-cover density (Demanes 1994), with the intention of 

investigating the influence of trees on flux partitioning, for 

example, the ratio T. The hypothesis is that greater Y and 

smaller I3 ratios are associated with more heavily treed source 

areas; this would imply that energy is going into evaporation 

so that air below might be expected to be cooler. The GIs 

system will provide a basis for interpreting flux measurements in terms of the surface features influencing them and 

their spatial representativeness, and for objectively 

determining model input for surface parameters which are 

spatially consistent with the measured data used to evaluate 

numerical boundary layer models. These numerical models 

will be used to predict the effects of different tree-planting 

scenarios on local scale energy and water exchanges. 

Acknowledgments 

We thank Marva Arnold, Read Mental Health Center, City of 

Chicago Parks Board, O'Hare International Airport, National 

Weather Service, Illinois Department of Transportation, and 

Illinois State Police for making the measurement sites 

available. Assistance in the field was provided by M. Demanes, 

D. Dishman, D. Horowitz, A. Johnson, K. Richards, S. Prichard, 

J. Southworth, and S. Wensman. Assistance in Chicago was 

provided by G. McPherson, D. Nowak, and E. Makra. We 

also acknowledge: A. Johnson for photo interpretation and 

mapping for the GIs database, X. Yang who georeferenced 

the database, J. M.-Hollingsworth for cartography, and D. 

Nowak and J. Simpson for reviewing this paper. This 

research was supported by funds provided by the U.S. 

Department of Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 

Table 9.-Variability of ratios determined in the Sunset Neighborhood of Vancouver, British Columbia 

Daytime Daily Methods 

Reference Period f3 X r~f3 X -r A AQ,' conv2 

Kalanda (1979)~ 77IAug 19 to Oct 3 1.03 A a 

Oke and McCaughey 80lJul to mid Aug 0.16 0.11 0.67 0.23 0.14 0.1 0.73 0.20 A a 

(1 98314 

Cleugh & Oke (1986) 8WJul 18 to Sep 22 1.28 0.44 0.34 0.22 A b 

Cleugh (1 990) 86lApr 5 to Oct 2 2.15 0.50 0.26 0.24 B c 

Grimmond (1992)~ 87lJan 21 to Feb 28 0.80 0.36 0.45 0.19 0.69 0.59 0.85 -0.44 B c 

871 Mar 1 to 31 1.29 0.42 0.32 0.26 1.19 0.53 0.45 0.02 B c 

871 Apr 1 to 30 0.87 0.35 0.40 0.25 0.85 0.42 0.49 0.09 B c 

871 May 1 to 31 1.26 0.40 0.33 0.29 1.36 0.48 0.36 0.16 €3 c 

871 Jun 1 to 28 1.40 0.42 0.30 0.29 1.47 0.50 0.34 0.17 B c 

Roth and Oke (1 994)6 891July 1.97 B d 

'AQ~: A= Oke et al. 1981 ; Fk Grimmond et al. 1991. 

*~onv: Method of convective flux determination: a= Bowen ratidenergy balanweversing temperature difference system; b= Q,, SAT and QE 

residual: c= Bowen ratio and SAT; & KH20 and SAT eddy correlation systems. 

3~ean of daytime B values (rather than determined from the mean oi the fluxes for the period); median 0.77, range of daytime values 0.3 to 2.39. 

4~e~ wet spring. 

5~atios are over Q*+QF rather than Q' only. 

6~ean of daytime hourly mean 8, median 1.85, range of mean hourly values during the daytime 1.25 to 3.0. Also determined using Bowen ratio 

methods; 8 was smaller using eddy correlation techniques. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 4 
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60 Chapter 4 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

- 
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USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 4 

Chapter 5 

Air Pollution Removal by Chicago's Urban Forest 

David J. Nowak, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Chicago, IL 

Abstract 

In 1991, trees in the City of Chicago (1 1 percent tree cover) 

removed an estimated 15 metric tons (t) (1 7 tons) of carbon 

monoxide (CO), 84 t (93 tons) of sulfur dioxide (SOz), 89 t (98 

tons) of nitrogen dioxide (NO2), 191 t (210 tons) of ozone 

(03), and 212 t (234 tons) of particulate matter less than 10 

microns (PMI 0). Across the study region of Cook and DuPage 

Counties, trees (in-leaf season) removed an average of 1.2 t/ 

day (1.3 tonslday) of CO, 3.7 t/day (4.0 tonslday) of SO2, 4.2 

tlday (4.6 tonslday) of NO2, 8.9 tlday (9.8 tonslday) of PM10 

and 10.8 tlday (1 1.9 tonstday) of 03. The value of pollution 

removal in 1991 was estimated at $1 million for trees in 

Chicago and $9.2 million for trees across the study area. 

~verage hourly improvement (in-leaf season) in air quality 

due to all trees in the study area ranged from 0.002 percent 

for CO to 0.4 percent for PM10. Maximum hourly improvement was estimated at 1.3 percent for SO2, though localized 

improvements in air quality can reach 5 to 10 percent or 

greater in areas of relatively high tree cover, particularly 

under stable atmospheric conditions during the daytime 

(in-leaf season). Large, healthy trees remove an estimated 

60 to 70 times more pollution than small trees. This paper 

discusses the ways in which urban trees affect air quality, 

limitations to estimates of pollution removal by trees in the 

Chicago area, and management considerations for improving 

air quality with urban trees. 

Introduction 

Air pollution is a multibillion dollar problem that affects most 

major U.S. cities. Air pollution is a significant human health 

concern as it can cause coughing, headaches, lung, throat, 

and eye irritation, respiratory and heart disease, and cancer. 

It is estimated that about 60,000 people die annually in the 

United States from the effects of particulate pollution 

(Franchine 1991). In addition, air pollution damages vegetation 

and various anthropogenic materials. In some of the more 

heavily polluted areas of the world, observed material deterioration rates are 10 to 100 times faster than those in the 

preindustrial age (NAPAP 1991). Air pollution also reduces 

visibility. In the rural mountainldesert areas of the Southwest, 

the standard visual range is about 130 to 190 km. In rural 

areas south of the Great Lakes and east of the Mississippi 

River, the standard visual range is about 20 to 35 km. Aerosol 

data indicate that this difference is due to greater sulfate 

concentrations in the East (and the interaction of sulfates 

with the higher humidity of the East) (Trijonis et al. 1990). Air 

pollution also contributes to acidic deposition (Smith 1990). 

Major air pollutants in urban areas are carbon monoxide 

(CO), predominantly from automobiles in urban areas; nitrogen oxides (NOx), mainly from automobiles and stationary 

combustion sources; ozone (03), formed through chemical 

reactions involving the principal precursors of NO, and 

volatile organic compounds; sulfur dioxide (SO2), emissions 

mostly from stationary combustion sources and smelting of 

ores; and particulate matter. 

Small particulate matter (PM10: particulate matter less than 

10 pm) results from local soils, industrial processes, combustion products, and chemical reactions involving gaseous 

pollutants. Small particles can have significant health effects 

because particles less than 5 pm may escape the defense 

mechanisms of the upper respiratory tract and enter the 

lungs. Particles 0.5 to 5 pm may be deposited as deep as the 

bronchioles in the lung but usually are removed by cilia 

within a few hours. Particles less than 0.5 pm may reach and 

settle in the lung alveoli, remaining for weeks, months or 

years (Stoker and Seager 1976). 

Air pollution is removed from the air primarily by three 

mechanisms: wet deposition, chemical reactions, and dry 

deposition. (Rasmussen et al. 1975; Fowler 1980). Wet deposition involves precipitation scavenging that includes "rainout" 

(transfer of pollutants to cloud droplets before they begin to 

fall) and "washout" (transfer of pollutants to falling raintsnowdrops) mechanisms. Gas phase reactions in the atmosphere 

can create aerosols that are removed by wet or dry deposition 

or produce oxidized products such as carbon dioxide (COa) 

and water vapor. Dry deposition is the mechanism by which 

gaseous and particulate pollutants are transported to and 

dry deposited on various surfaces, including trees. 

Gaseous Pollutants 

Dry deposition of gases to trees occurs predominantly through 

the leaf stomates, though some deposition occurs on the plant 

surface (Fowler 1985; Murphy and Sigmon 1990; Smith 1990). 

During daylight hours when plant leaves are transpiring water 

and taking up COP, other gases including pollutants are taken 

up into the leaf. Once inside the leaf, these gases diffuse into 

intercellular spaces and can be absorbed by water films on 

inner-leaf surfaces. Pollutant uptake by plants is highly variable 

as it is regulated by numerous plant, pollutant, and environmental forces (e.g., plant water deficit, light intensity, windspeed, 

gas solubility in water, leaf size and geometry) (Smith 1990). 

Once the gas reacts with the tree and is absorbed, it is 

removed from the atmosphere. However, plants also emit 

various compounds that can contribute to air pollution. The 

following sections outline plant-pollutant interactions for 

significant gaseous air pollutants in urban areas. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 5 
Carbon Monoxide 

Carbon monoxide is harmful principally to animals due to its 

affinity for hemoglobin. When CO reacts with hemoglobin it 

reduces the ability of blood to transport oxygen (Ziegler 

1973; Stoker and Seager 1976). It has been hypothesized 

that CO inhibits N2-fixation in plants (Ziegler 1973). Most CO 

absorbed by plants is reduced and incorporated into serine, 

which is subsequently converted to sucrose (Bidwell and 

Fraser 1972). 

Trees emit volatile organic compounds such as isoprene 

and monoterpenes into the atmosphere. These compounds 

are natural chemicals that make up essential oils, resins, 

and other plant products, and may be useful in attracting 

pollinators or repelling predators (Kramer and Kozlowski 

1979). Complete oxidation of volatile organic compounds 

ultimately produces C02, but CO is an intermediate compound 

in this process. Oxidation of volatile organic compounds is 

an important component of the global CO budget (Tingey 

et al. 1991); CO also can be released from chlorophyll degradation (Smith 1990). 

Nitrogen Dioxide 

After nitrogen dioxide is absorbed through leaf stomates, it 

can react with water on the moist surfaces of the inner leaf 

to form nitrous (HN02) and nitric (HN03) acids. Pollutant 

interactions and altering of pH in the leaf can lead to altered 

plant metabolism (e.g., inhibition of C02 fixation, suppressed 

growth) (Ziegler 1973; Smith 1990). Visible leaf injury would 

be expected at concentrations around 1.6 to 2.6 pprn for 48 

hours, 20 pprn for 1 hour, or a concentration of 1 pprn for as 

many as 100 hours (Natl. Acad. of Sci. 1977a). Concentrations 

that would induce foliage symptoms would be expected only 

in the vicinity of an excessive industrial source (Smith 1990). 

Trees generally are not considered as a source of atmospheric 

nitrogen oxides, though plants, particularly agricultural crops, 

are known to emit ammonia (NH4). Emissions occur primarily 

under conditions of excess nitrogen (e.g., after fertilization) 

and during the reproductive growth phase (Schjoerring 1991); 

NH4 in the atmosphere can be converted to NG. 

Ozone 

Ozone has low solubility in water but readily diffuses into 

stomatal cavities. The reactive nature of O3 causes it to react 

rapidly on inner-leaf surfaces (Smith 1984). Eastern deciduous species are injured by exposures to O3 at 0.20 to 0.30 

pprn for 2 to 4 hours (Natl. Acad. of Sci. 1977b). The threshold for visible injury of eastern white pine is approximately 

0.15 pprn for 5 hours (Costonis 1976). Sorption of 03 by 

white birch seedlings shows a linear increase up to 0.8 ppm; 

for red maple seedlings the increase is up to 0.5 pprn 

(Townsend 1974). Severe 03 levels in urban areas can 

exceed 0.3 pprn (Off. Technol. Assess. 1989). Injury effects 

can include altered photosynthesis, respiration, growth, and 

stomatal function (Shafer and Heagle 1989; Smith 1990). 

Trees can contribute to O3 formation by emitting volatile 

organic compounds (Brasseur and Chatfield 1991). Because 

these emissions are temperature dependent and trees generally lower air temperatures, it is believed that increased 

tree cover lowers overall volatile organic emissions and O3 

levels in urban areas, but additional research is needed 

(Cardelino and Chameides 1990). Volatile organic emissions of urban trees generally are less than 10 percent of 

total emissions in urban areas (Nowak 1991). 

Sulfur Dioxide 

Following absorption through leaf stomates, SO2 is presumed 

to be dissolved in moisture films on inner-leaf cell walls. 

Eventually, sulfurous acid (H2SO3) and, following oxidation, 

sulfuric acid (H2SO4) are formed. Toxic effects of SO2 may 

be due to its acidifying influence and/or the sulfite (S032-) 

and sulfate (S042-) ions that are toxic to a variety of 

biochemical processes (Smith 1990). Stomata may exhibit 

increases in either stomatal opening or stomatal closure 

when exposed to SO2 (Smith 1984; Black 1985). Acute SO2 

injury to native vegetation does not occur below 0.70 pprn for 

1 hour or 0.18 pprn for 8 hours (Linzon 1978). A concentration 

of 0.25 pprn for several hours may injure some species 

(Smith 1990). 

Trees can make minor contributions to SO2 concentration by 

emitting sulfur compounds such as hydrogen sulfide (H2S) 

and SO2 (Garsed 1985; Rennenberg 1991). H2S, the predominant sulfur compound emitted, is oxidized in the atmosphere to SO2. Higher rates of sulfur emissions from plants 

are observed in the presence of excess atmospheric or soil 

sulfur. However, sulfur compounds also can be emitted with 

a moderate sulfur supply (Rennenberg 1991). 

Particulate Pollution 

Particles can be dry deposited on plant surfaces through 

sedimentation under the influence of gravity or through 

impaction under the influence of wind. Particles hitting the 

tree may be retajned on the surface, rebound off it, or be 

retained temporarily and subsequently removed (resuspended 

into air or transported to soil or other surface) (Smith 1990). 

The interception and retention of particles by plants is highly 

variable -smaller leaves and/or leaves with a rough surface 

are more efficient in collecting particles than larger and/or 

smoother leaves. Also, larger particles are deposited on 

leaves more rapidly than smaller particles (Smith 1984; 

Davidson and Wu 1990). Particle resuspension after 1 hour 

of initial retention varies from 91 percent for oak leaves to 10 

percent for pines (Witherspoon and Taylor 1969). 

Thus, vegetation generally is only a temporary retention site 

for atmospheric particles as particles can be resuspended to 

the atmosphere, be washed off by rain, or drop to the ground 

through leaf and twig fall. Trees can store various trace 

metals in their tissue, but the mechanisms and pathways of 

incorporation into trees needs to be clarified (Rolfe 1974; 

Baes and Ragsdale 1981 ; Baes and McLaughlin 1984). However, it is known that heavy metals can be absorbed directly 

through the cuticle (Ziegler 1973). 

Trace metals can be toxic to plant leaves (Darley 1971 ; 

Smith 1990). The accumulation of particles on leaves also 

can reduce photosynthesis by reducing the amount of light 

reaching the leaf (Darley 1971; Ziegler 1973). Damage to 

plant leaves can occur from the deposition of acidic droplets 

(pH < 3.0) (Smith 1990). Acidic rain can be a source of the 

essential plant nutrients of sulfur and nitrogen, but also can 

64 Chapter 5 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
reduce soil nutrient availability through leaching or toxic soil 

reactions (Shriner et al. 1990). Particles can also affect tree 

pestldisease populations (Darley 1971 ; Smith 1990). Trees 

can contribute to particle concentrations in urban areas by 

releasing pollen and emitting volatile organic and sulfur compounds that serve as precursors to particle formation (Smith 

1990; Sharkey et al. 1991). 

Effect of Urban Trees on Air Quality 

Urban trees influence local air quality in various ways. First, 

trees can reduce or increase building energy use by shading 

buildings, altering air flows and lowering air temperatures 

through transpiration (e.g., Heisler 1986). In turn, this change 

in building energy use affects pollution emissions from power 

plants. By lowering air temperatures, trees also can affect O3 

photochemistry and O3 precursor emission rates, thus 

influencing O3 formation (Cardelino and Chameides 1990). 

Various tree configurations can alter wind profi[es or create 

local inversions to trap pollutants such that the removal of 

local pollutants is enhanced (McCurdy 1978). As mentioned 

previously, trees emit volatile organic and other compounds 

that can contribute to pollution formation (Sharkey et al. 

1991). Finally, trees can intercept atmospheric particles and 

absorb various gaseous pollutants. 

There has been little research on the removal of atmospheric 

pollution by urban trees. Street trees in the St. Louis area 

have been estimated to remove approximately 3.1 kglday 

(2.75 Iblacrelday) of particles for each hectare of land covered 

by street trees (DeSanto et a[. 1976b). Other particle-removal 

estimates for individual trees are 1.5 to 4.4 kglday for each 

hectare of land covered by trees (1.3 to 3.9 Iblacrelday); 1.5 

to 4.7 kglhalday (1.3 to 4.2 Ib/acre/day) for CO; 1.3 to 4.1 kg1 

halday (1.2 to 3.6 Iblacrelday) for nitrogen oxides; 22.7 to 

74.4 kglhalday (20.2 to 66.3 Ib/acre/day) for SO2; and 34.7 

to 11 1.5 kglhalday (30.9 to 99.5 Iblacrelday) for O3 (DeSanto 

et al. l976a). 

Some of these estimates are higher than expected under 

typical urban conditions because average removal rates in 

~glrn2 of leaf aredhr for vegetation were used. These rates 

are dependent on the pollutant concentrations used in the 

studies from which the average removal rate was derived. 

Often such concentrations in the literature are high so 

that plant responses to a pollutant can be studied under 

laboratory conditions. Thus, the removal rates are higher 

than would be expected under typical urban conditions. Other 

removal rates for SO2 and NO2 are given in Table 1. 

The objective of this study was to estimate air pollution 

removal (dry deposition) of CO, NO2, 03, SOa, and PMlO by 

trees in the Chicago region during 1991. The computations 

used to estimate pollution removal by urban trees should be 

considered a first-order approximation of a highly complex 

deposition system. Many factors influence dry-deposition 

removal rates, including aerodynamic roughness, atmospheric 

stability, pollutant concentration, solar radiation, temperature, 

turbulence, wind velocity, particle size, gaseous chemical 

activity and solubility, and vegetative surface characteristics 

(e.g., stomata1 activity and resistances, leaf surface area) 

(Sehmel 1980). 

Methods 

Study Area 

The study area (Figure 1 in Chapter 2) was fragmented into 

11 7 community areas for detailed analyses of tree canopy 

cover (McPherson et al. 1993),'pollution concentrations and 

total pollutant flux (Figure 1). Tree cover averages 11 percent in Chicago, 23 percent in suburban Cook County (i.e., 

Table 1. -Pollution-removal values (kg/ha/day) from the literature (divide removal rate by 1.12 to calculate Iblacrelday) 

Pollutant Removal rate Site 

Pollutant 

concentration (opm) Reference - - 

SO, 0.59 1.723 km2 forest dominated area on 0.01 5 Murphy et al. 1977 

Long Island. NY 

So, 0.20 Argonne National Laboratory. ILa Wesely and Lesht 1988 

SO, 0.15 778 km2 forest dominated area at 0.008 Murphy et al. 1977 Savannah River Plant, SC 

SO, 0.04 Loblolly pine plantation at Savannah 0.003 Lorenz and Murphy 1985 River Plant, SC 

SO, 0.03 Loblolly pine plantation in Alamance tt Hicks et al. 1982 

County, NC 

SO, 0.03 Argonne National Laboratory. ILa *I* Wesely and Lesht 1988 

N02 0.1 8 Salt Lake Valley. UT estimateb 0.02 Heggestad 1972 

NO, 0.04 Salt Lake Valley, UT estimateb 0.005 Heggestad 1972 

% percent white oak, 50 percent grass. 

b85 percent covered by vegetation. 

Peak modeled deposition in 1986 in-leaf season; " Daytime peak removal extrapolated to entire day, therefore removal rate listed is an overestimate of the actual daily removal rate; 

*'Minimum modeled deposition in 1986 in-leaf season. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 5 
Cook County exclusive of Chicago), 19 percent in DuPage 

County, and 19 percent for the entire study area (McPherson 

et al. 1993). 

Pollutant concentrations in Illinois in 1991 were typical 

of concentrations found in the mid-1980s through 1990; the 

exceptions were PM10 and nitrogen oxides, which were slightly 

below average (IEPA 1992). The average concentration of 

CO in the study area was 0.88 ppm. Peak hourly averages 

occurred in May (1 -03 ppm) and minimum hourly concentrations occurred in June (0.65 pprn). The National Ambient Air 

Quality Standard (NAAQS) of 9 pprn (8-hr average) was not 

exceeded in the study area in 1991. Concentration levels 

cycled throughout the day (Figure 2). 

Average hourly levels of NO2 were highest in August (0.025 

ppm) and lowest in November (0.019 pprn); the annual 

average in the study area was 0.021 ppm. Average levels of 

NO2 varied through the day (Figure 3). During the in-leaf 

season, O3 levels averaged 0.027 ppm; levels were highest 

in June (0.038 ppm) and lowest in October (0.013 ppm). 

Average hourly O3 levels peaked at 2 p.m. (Figure 4). Levels 

of O3 exceeded the NAAQS level of 0.12 pprn (I -hr average) 

on June 1, 18, 20, and 21 at four stations in Chicago and 

suburban Cook County (IEPA 1992). 

The average concentration of SO2 in the study area was 

0.0084 ppm. Hourly averages were highest in January (0.01 1 

ppm) and lowest in December (0.0062 ppm). Average hourly 

concentration peaked at 9 a.m. and 10 a.m. (Figure 5). The 

24-hr average NAAQS level of 0.14 pprn was exceeded in 

the study area on October 16-17, November 14-15, and 

November 17-1 9 at one monitoring station in suburban Cook 

County (IEPA 1992). 

The average level of PMIO in the study area was 34 pgIrn3. 

Levels were highest in July (45 pgIm3) and lowest in December (27 yglms). The 24-hr average NAAQS level of 150 pgl 

ma was exceeded on August 2 for one monitoring station in 

suburban Cook County (IEPA 1992). Regional air quality 

concentrations in 1991 probably were not high enough to 

induce visible damage to vegetation in the Chicago area. 

Algorithms for Estimating Pollution Removal 

To estimate pollutant flux to trees it is necessary to know the 

deposition velocity of each pollutant to trees and the local 

pollutant concentration (e.g., Hicks et al. 1987; Baldocchi 

1988; Smith 1990). The deposition velocity may be thought 

of as the rate at which the surface "cleans" a pollutant from 

the air. If the deposition velocity of a pollutant is 1.0 cmlsec, 

then the surface is completely removing the pollutant from a 

layer of air 1 .O cm thick each second (Smith 1990). The 

pollutant flux (F) is calculated as the product of the deposition velocity (Vd ) and the pollutant concentration (C) : 

The pollutant flux is multiplied by the area of the surface 

(cm2) over time periods for which the pollutant concentration 

is known around that surface (e.g., 1 hour: 3600 sec) to 

0. 

icago 

Percent Tree Cover 

A 0 5 miles 

NU 

Figure 1. -Percent tree cover by community area. 

66 Chapter 5 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Hour 

Figure 2. -Average hourly concentrations of GO calculated from Seven IEPA monitoring sites in study 

area in 1991. 

Hour 

Figure 3. -Average hourly concentrations of NO2 calculated from eight IEPA monitoring sites in study 

area in 1991. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 5 
Hour 

Figure 4. -Average hourly concentrations of O3 calculated from 13 IEPA monitoring sites in study area 

during in-leaf season (May-October) of 1991. 

Hour 

Figure 5. -Average hourly concentrations of SO2 calculated from 10 IEPA monitoring sites in study 

area in 1991. 

68 Chapter 5 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

."*, 
estimate total pollutant flux to the surface (e.g., glhr). These 

hourly fluxes can be summed to estimate total daily, monthly, 

or yearly fluxes. 

Deposition Velocities 

The rate at which pollutants are transferred onto or into 

various surfaces is influenced by a series of resistances to 

pollutant transfer. Deposition velocity is calculated as the 

inverse of the sum of the aerodynamic (R,), quasi-laminar 

boundary layer (Rb) and canopy (Rc) resistances (Vd = l/(Ra 

+ Rb + Rc)). The aerodynamic resistance is associated with 

atmospheric turbulence, the quasi-laminar boundary-layer 

resistance is influenced by the diffusivity of the material 

being transferred, and the net canopy resistance is dominated by surface factors (Baldocchi et al. 1987). As the rate 

of turbulent mixing becomes high, pollutant transport to the 

surface is rapid as the resistance to transport through the 

boundary layer approaches zero and the resistance to deposition is limited by the surface resistance (Killus et al. 1984). 

Aerodynamic and Quasi-laminar Boundary-Layer 

Resistances 

Meteorological data from Chicago's O'Hare airport (3-hr 

averages) were used in estimating R, and Rb. The aerodynamic and quasi-laminar boundary-layer resistances were 

estimated for the Chicago area with a method similar to that 

used in the Urban Airshed Model (Killus et al. f 984). 

where u(z) is the wind speed at height z (m/sec) and u, is the 

frictional velocity (mlsec). 

where k = von Karman's constant (0.40), d = displacement 

length (m), z, = roughness length (m), v, = stability function 

for momentum, and L = Monin-Obuhkov stability length (van 

Ulden and Holtslag 1985). L was estimated by classifying 

hourly local meteorological data into stability classes using 

Pasquill's (1961) stability classification scheme and then 

estimating 11L as afunction of Pasquill classes and z, (Golder 

1970). When L<O (unstable): 

Wm = 2 In [(I + X)/2] + In [(I + X2)/2] - 2 tam1 (X) + d2 

(van Ulden and Holtslag 1985) 

where X = (1 - 28 z/L)0.25 (Dyer and Bradley 1982). When 

L>O (stable conditions): 

v, = -17 (1 - exp(-0.29(z-d)/L) 

(van Ulden and Holtslag 1985). 

The quasi-laminar boundary-layer resistance was estimated as: 

where B-1 = 2.2u,-1/3 (Killus et al. 1984). 

R, and Rb were calculated for every three hours throughout 

1991 based on Chicago meteorological data. Each estimate 

of R, and Rb was used to represent the corresponding 3-hr 

period of the day. These hourly values were combined to 

yield the average daily conditions for each month in 1991. 

Canopy Resistance 

The tree canopy resistances for each of the pollutants was 

estimated by averaging the Rc values derived from literature 

on individual trees and forests. Rc estimates were categorized by in-leaf season daytime, in-leaf season nighttime, 

and out-of-leaf season using a distribution of 90 percent 

deciduous and 10 percent coniferous leaf surface area (Nowak 

1994: Chapter 2, this report) (Table 2). Rc estimates for 

particles and CO could not be found in the literature, so 

average deposition velocity minus average R, and Rb for 

Chicago was substituted as the Rc for these pollutants. Fifty 

percent of the particles being deposited to trees were assumed to be resuspended from the trees to the atmosphere. 

Particle collection by deciduous trees in winter assumed a 

surface-area index for bark of 1.7 (m2 of barklm2 of ground 

surface covered by tree crown) (Whittaker and Woodwell 

1967). In-leaf daylight ranged from 11 hrlday in October to 

15 hrlday in June. The in-leaf season for deciduous trees in 

the Chicago area was modeled as May 1 to October 31 

based on local observation of foliation periods. 

Hourly canopy resistances of trees were calculated for each 

hour in 1991 based on in-leaf vs. out-of-leaf season and day 

Table 2.-Average canopy-resistance values (seclcm) for trees in the Chicago area (90 percent deciduous; 10 percent 

coniferous leaf-surface area); values are estimates derived from the literature 

Pollutant In-leaf daytime In-leaf nighttime Out-of-leaf season 

Carbon monoxide 500 500 10,000 

Nitrogen dioxide 3.01 7.54 88.3 

Ozone 1.74 17.2 ---a 

Particulate matter 0.78 0.78 2.39 

Sulfur dioxide 1.87 9.54 58.2 

a no pollutant concentrations collected during out-of-leaf season (November-April). 

Sources: Bidwell and Fraser 1972; Roberts 1974; Fritschen and Edrnonds 1976; Garland 19T7; Garland and Bmson 1977; Little 1977; McMahon and 

Denison 1979; Rogers et al. 1979; Sheih et al. 1979; Wesely and Hicks 1979; Galbally and Roy 1980; Sehmel1980; Lindberg and Haniss 1981 - Hicks 

et al. 1982; Hofken and Gravenhorst 1982; Granat and Jd.lansswr 1983, Gravenhorst et al. 1983; Greenhut 1983, Hofken et al. 1983; Lindberg anh 

Lovett 1983; Wesely 1983; Wesely et al. 1983; Lindbe et al. 1984; Lovett and Lindberg 1984; Fowler 1985; Lorenz and Murph 1985; Wesely et al. 

1985; Voldner et al. ,986; Walcek et al. 1986; Dasch 1387; Dasch 1989; Shanley 1989; Wesely 1989; Davidson and Wu 1990; huphy and S~gmon 

1990. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 5 
vs. night. Tree-canopy resistance was combined with Ra and 

Rb to produce hourly estimates of deposition velocities 

to trees in the Chicago area. To limit deposition estimates 

to periods predominated by dry deposition, deposition 

velocities were set to zero during and immediately following 

periods of precipitation (1 hr). 

Pollution Concentration 

Hourly pollution concentrations (ppm) were obtained from 

the Illinois Environmental Protection Agency (IEPA) for CO 

(7 monitoring sites in study area), NO2 (8 sites), O3 (1 3 sites) 

and SO2 (10 sites). Average daily concentrations of PM10 

(pg/m3) also were obtained from the IEPA (14 sites). No 

concentration data for O3 were obtained for the out-of-leaf 

season (November-April). 

Each of the 11 7 community areas were assigned the average hourly concentrations for each month from the closest 

monitoring station for each pollutant. The average hourly 

pollutant flux for each month of 1991 was calculated for each 

pollutant in each community area using equation (1). Hourly 

pollutant flux (glm2 of tree canopy coverage) for each 

community area was multiplied by the amount of tree canopy 

cover (m2) in the community area to estimate total pollutant 

flux per hour for the average day in each month. These 

values were combined to yield estimates of daily, monthly, 

and yearly pollution flux to trees (for each pollutant) for 

Chicago, suburban Cook County, DuPage County, and the 

entire study area.1 

Total pollutant flux also was calculated for the individual days 

that had the highest hourly reading of the year: CO (August 

2), NO2 (June 21), O3 (June 18-21), SO2 (October 16-1 7) and 

PM10 (July 17). Because of a lack of variance information on 

some of the averages used in the calculations, no error 

bounds could be computed for the removal estimates. 

Boundary-Layer Height 

The boundary layer is the atmospheric layer characterized 

by well-developed mixing (turbulence). The height of the 

boundary layer is not constant over time. By day, thermal 

mixing enables the boundary-layer height to extend to about 

1 to 2 km. At night, mixing tends to be suppressed and the 

boundary-layer height can shrink to less than 100 m (Oke 

1987). The height of the boundary layer is important 

because the deeper the boundary layer, the less the relative 

effect of trees on reducing overall concentrations of air 

pollutants given a well-mixed boundary layer. 

To approximate boundary-layer heights in the study area, 

average mixing heights from the closest station to the study 

1 5 12 24 117 

F = X E E C ((l/Ra+Rb+Rc) X C) 

p=l m=l h=l ca=l 

where F = total annual pollution removal for five pollutants; p = 

pollutant species; m = month; h = hour; ca = community area (i.e., 

specific tree-cover data); R, and Rb =aerodynamic and quasi-laminar 

boundary-layer resistances, respectively (calculated from local meteorological data for 3-hr periods); R, = canopy resistance (varies by 

day, night, precipitation, and season); and C = average hourly pollutant concentration for each month (PMI 0 concentrations based on 

daily average). 

70 Chapter 5 

area (Peoria, IL) were used. Readings of average daily 

morning and afternoon mixing heights were extrapolated 

throughout the day to estimate the diurnal cycle of the 

boundary-layer height for each month (e.g., Holzworth 1972). 

The mixing heights used ranged from a low of 300 m in early 

morning (6 a.m.) to a high of 1,600 m for midafternoon (4 

p.m.) in June. Average hourly mixing heights for each month 

were used in conjunction with data on pollution concentrations for each community area to calculate the amount of 

pollution within the mixing layer. This extrapolation from 

ground-layer concentration to total pollution within the 

boundary layer assumes a well-mixed boundary layer. The 

amount of pollution in the air was contrasted with the amount 

of pollution removed by trees to calculate the relative effect 

of trees in reducing local pollution concentrations: 

where E = relative reduction effect (%); R = amount removed 

by trees (kg); A = amount of pollution in the atmosphere (kg). 

Effect of Individual Trees 

The ability of individual trees to remove pollutants was 

estimated for each diameter class using the formula: 

where I, = pollution removal by individual trees in diameter 

class x (kghree); Rt = total pollution removed for all diameter 

classes (kg); LA, = total leaf area in diameter class x (me); 

LAt = total leaf area of all diameter classes (ma); and Nx = 

number of trees in diameter class x. This formula yields an 

estimate of pollution removal by individual trees based on 

leaf-surface area (the major surface for pollutant removal) 

and a distribution of approximately 90 percent deciduous 

and 10 percent coniferous leaf-surface area (Nowak 1994: 

Chapter 2, this report). 

Estimated Monetary Value of Pollution Removal 

To estimate the monetary value of pollution removal by trees, 

current costs for emission control were used. The cost (dollarslmetric ton) of preventing the emission of a similar amount 

of pollutant using these control strategies was multiplied by 

the metric tons of pollutant removed by trees to yield an 

indication of the pollution removal value of trees2 Dollar 

values (1 990) per metric ton of pollutant removed were $5401 

t ($490/ton) for 03, $1,014/t ($920/ton) for CO, $1,44l/t ($1,3071 

ton) for PM10, $1,801lt ($1,634/ton) for SO2 and $4,863/t 

($4,412lton) for NO2 (California Energy Commission 1992). 

Potential Future Effects of Tree Planting 

To analyze the potential effects of future tree planting, available growing space (i.e., grass and soil area) was analyzed 

by land-use type throughout the study area. The future 

scenario assumed that none of the available space in agricultural or transportation (predominantly airport) would be planted 

with trees due to land-use limitations. Five percent of available 

2 The estimation of value is approximate as emission control 

strategies prevent the emission of pollution while trees remove pollution that already is in the atmosphere. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
space was assumed to be planted and covered with trees 

on large commercial-industrial areas and institutional land 

dominated by vegetation (e.g., parks, forest preserves, cemeteries, golf courses). Ten percent of available space was 

assumed to be planted and covered with trees on institutional 

lands dominated by building (e.g., schools); 15 percent in 

residential areas, 20 percent in landscaped commercial 

complexes, and 25 percent on vacant lands and freeways. 

Removal of pollutants by the additional trees was calculated 

based on average removal per acre of existing tree cover 

times the number of new acres of tree cover that result from 

the new plantings. This removal was subtracted from the 

amount of pollution in the atmosphere to calculate a new 

atmospheric concentration. Because the atmospheric concentration would be lower due to the additional trees, overall 

uptake per acre of trees also drops due to the lower 

concentrations. The new pollutant flux for all trees (original 

plus new trees) with a lower pollutant concentration was 

contrasted with the original flux rate to calculate the effect of 

the new tree plantings. 

Results 

In 1991, total estimated pollutant removal by trees in the 

study area was 5,575 t (6,145 tons) with PMlO and 03 

removed the most by trees (Table 3). Monthly removal rates 

varied, peaking in May for CO (41 t, 45 tons), in June for O3 

(498 t, 549 tons), in July for PMlO (348 t, 383 tons) and in 

August for NO2 (1 52 t, 168 tons) and SO2 (132 t, 145 tons). 

Minimum removal in the study area occurred in March for 

PMlO (30 t, 33 tons), in April for CO (1.6 t, 1.8 tons), in 

October for O3 (1 17 t, 129 tons) (in-leaf season data only), in 

November for NO2 (4.9 t, 5.4 tons) and in December for SO2 

(4.0 t, 4.4 tons) (Figure 6, Table 4). Monthly patterns of 

removal were similar in Chicago, suburban Cook, and DuPage 

Counties (Figures 7-9, Table 4). 

Removal occurred mostly during the in-leaf season with daily 

in-leaf removal rates ranging from 1,155 kglday (2,545 Ibl 

day) for CO to 10,819 kglday (23,850 Ibfday) for O3 (Table 

5). Total removal per hectare of tree cover ranged from 3.4 

kglyr (3.1 Iblacrelyr) for CO to 30.7 kglyr (27.4 Iblacrelyr) for 

O3 (Table 5). Total removal per hectare of trees was 85.7 kg/ 

yr (76.5 Iblacrelyr) for all five pollutants. 

Maximum daily effects of pollution removal by trees in the 

study area was approximately 1.4 t (1.5 tons; 0.02 kglha of 

tree coverlday) for CO; 4.9 t (5.4 tons; 0.08 kglha of trees1 

day) for NO2; 10.7 t (1 1.8 tons; 0.1 6 kglha of treeslday) for 

SO2; 21.6 t (23.8 tons; 0.33 kglha of treeslday) for PMIO; and 

24.4 t (26.9 tons; 0.38 kglha of treeslday) for 03. Peak-day 

effects (based on the day with highest hourly concentration) 

were lower than average-day effects for CO and NO2 due to 

relatively low concentrations during nonpeak hours. Peak 

daily effects for these pollutants were based on peak averageday effects for a month (CO: September; NO2: August). 

The maximum hourly reduction in pollutant concentrations 

due to trees across the study area ranged from 0.007 percent 

for CO to 1.3 percent for SO2 (Table 6). Average hourly 

reduction in concentrations during the in-leaf season ranged 

from 0.002 percent for CO to 0.4 percent for PM10. In large 

areas of 100-percent tree cover, reductions in concentrations 

due to trees likely reached 7 percent for sulfur dioxide 

(Table 6). 

Under typical in-leaf daytime conditions in 1991, a hectare of 

urban tree cover would be expected to remove 0.0008 kglhr 

(0.0007 Iblacrelhr) of CO, 0.0041 kglhr (0.0037 Iblacrelhr) of 

SO2, 0.0045 kglhr (0.004 Iblacrelhr) of NO2, 0.0056 kg/hr 

(0.005 Iblacrelhr) of PMIO, and 0.01 23 kg/hr (0.01 1 Iblacrel 

hr) of 03. For concentrations at the NAAQS level, a hectare 

of tree cover would be expected to remove 0.007 kglhr 

(0.006 Iblacrelhr) of CO (at 8-hr NAAQS); 0.067 kglhr (0.06 

Ib/acre/hr) of SO2 (at 24-hr NAAQS); 0.012 kglhr (0.01 Ibl 

acrelhr) of NO2 (at annual NAAQS); 0.031 kglhr (0.028 Ib/ 

acrelhr) of PMlO (at 24-hr NAAQS); and 0.046 kglhr (0.041 Ibl 

acrelhr) of O3 (at 1 -hr NAAQS). These removal rates should 

be considered high and of relatively short term. 

Large individual trees have the greatest estimated pollution 

removal due to their relatively large leaf surface area. Trees 

larger than 76 cm (30 inches) in diameter at breast height 

(d.b.h. at 1.37 m or 4.5 ft) removed an estimated 1.4 kg (3.1 

Ib) of pollution in 1991; trees less than 8 cm (3 inches) in 

d.b.h. removed approximately 0.02 kg (0.05 Ib) (Table 7). 

The monetary value of pollution removal in 1991 was approximately $1 million in Chicago ($151/ha of tree coverlyr; 

$6l/acre of tree coverlyr); $5.8 million in suburban Cook 

County ($1 37lha of treeslyr; $55/acre of treeslyr); $2.4 milTable 3.-Total pollutant removal (tlyr) and removal per hectare of land (kgfhafyr) in Chicago, suburban Cook County, DuPage 

County, and study area (multiply t by 1.102 to convert to tons; divide kglha by 1.12 to convert to Ib/acre) 

Chicago Cook County DuPage County Study area 

Pollutant Total per ha Total per ha TOM per ha ~otat per ha 

CO 15 0.3 147 0.8 61 0.7 223 0.7 

SO, 84 1.4 520 2.8 102 1.2 706 2.1 

N02 89 1.5 470 2.5 248 2.9 806 2.4 

PMlO 212 3.5 1.1 79 6.3 449 5.2 1,840 5.5 

O3 191 3.1 1,328 7.1 481 5.6 2.000 6.0 

Total 591 9.7 3,844 19.4 1,340 15.5 5,575 16.7 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 5 71 
.... 

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

I 

Month 

Figure 6. -Monthly estimates of pollution removal by trees in study area in 1991. Ozone removal 

estimates are for May-October only. Particulate removal assumes 50 percent resuspension back to 

the atmosphere. 

......... 

PMlO 

CO .-.....- _................. , K ................ - ............-. --.. 

L 

f ... .... I I I I I 

i;1\1IA&f S A S: 

Month 

Figure 7. -Monthly estimates of pollution removal by trees in Chicago in 1991. Ozone removal 

estimates are for May-October only. Particulate removal assumes 50 percent resuspension back to 

the atmosphere. 

72 Chapter 5 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
. -. .. \ 

.. .--.,.. ,. , \... ................ 

'% :\ 

!?,. g 

PMlO 

\ .. \ -. j -. , . 

Month 

Figure 8. -Monthly estimatesof pollution removal bytrees in suburban Cook County in 1991. Ozone 

removal estimates are for May-October only. Particulate removal assumes 50 percent resuspension 

back to the atmosphere. 

Month 

Figure 9. -Monthly estimates of pollution removal by trees in DuPage County in 1991. Ozone 

removal estimates are for May-Octoberonly. Particulate removal assumes 50 percent resuspension 

back into the atmosphere. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 5 
Table 4.-Total monthly removal rates (Vmonth) for pollutants by study area sector in 1991 (multiply t by 1.102 to convert to tons) 

Month CO SO, NO2 PMlO O, 

CHICAGO 

January 

February 

March 

April 

May 

June 

July 

August 

September 

October 

November 

December 

SUBURBAN COOK COUNTY 

January 

February 

March 

April 

May 

June 

July 

August 

September 

October 

November 

December 

DUPAGE COUNTY 

January 

February 

March 

April 

May 

June 

July 

August 

September 

October 

November 

December 

STUDY AREA 

January 

February 

March 

April 

May 

June 

July 

August 

September 

October 

November 

December 

na - not analyzed. 

74 Chapter 5 USDA Forest Service Gen. Tech: Rep. NE-186. 1994. 
Table 5.-Average daily pollutant removal during in-leaf and out-of-leaf seasons (kglday); total yearly removal per hectare of 

tree canopy cover (kglhdyr); and average daily pollutant removal during in-leaf and out-of-leaf seasons per hectare of tree 

canopy cover (kg/ha.day) in Chicago, suburban Cook County, DuPage County and entire study area (multiply kg by 2.204 to 

convert to pounds; divide kglha by 1.1 2 to convert to Iblacre) 

Average daily removal Removal per hectare of tree cover 

Sector In-leaf a out-of-leafb Total year In-leaf a out-of-leafb 

CO 

Chicago 

Cook County 

DuPage County 

Study Area 

so2 

Chicago 

Cook County 

DuPage County 

Study Area 

'"02 

Chicago 

Cook County 

DuPage County 

Study Area 

PMlO 

Chicago 

Cook County 

DuPage County 

Study Area 

03 

Chicago 

Cook County 

DuPage County 

Study Area 

%lay - October; kgfday 

b~overnber - April; kglday 

Table 6.-Estimated maximum and average in-leaf reduction in hourly pollution concentration (in percent) by trees in the 

Chicago area in 1991 

Study area 100-percent forested area 

Pollutant Maximum Average Maximum Average 

CO 0.007 0.002 0.03 0.01 

a daily percent reduction 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

- 

Chapter 5 
lion in DuPage County ($147/ha of treeslyr; $59/acre of 

treeslyr); and $9.2 million in the study area ($141/ha of 

treeslyr; $57/acre of treeslyr) (Table 8). The highest value 

was for NO2 removal (43 percent of total monetary value), 

followed by PM10 (29 percent), SO2 (14 percent), O3 (12 

percent) and CO (2 percent). Monetary values for individual 

trees in the study area ranged from $0.04/tree/yr for small 

trees to $2.3l/tree/yr for large trees (Table 7) . 

The proposed tree-planting scenario that would fill available 

grass and soil space on various land uses from 0 to 25 

percent with trees would increase overall tree cover in the 

study area by 4.1 percent (from 19.4 to 23.5 percent tree 

cover). This additional cover likely would have removed an 

additional 1,180 t (1,300 tons) of pollution in 1991 (CO: 45 t, 

50 tons; SO2: 150 t, 165 tons; N02: 170 t, 185 tons; PM10: 

390 t, 430 tons; 03: 425 t, 470 tons) and reduced pollution 

concentrations by another 0.05 percent. 

Discussion 

The removal estimates in this paper are approximations 

based on computations that incorporate measured local 

urban tree canopy surface, local pollution concentrations, 

and local meteorology in diurnal and annual patterns. Average in-leaf pollution removal per hectare of tree cover per 

day for 1991 in the Chicago area was significantly less than 

estimated by DeSanto et al. (1976a) for all pollutants (from 

1 1 to 32 times less for particles to 400 to 1,300 times less for 

SO2). The estimates of DeSanto et al. are higher than those 

for the Chicago area because of high pollution concentrations 

in some of the studies used to determine removal rates and 

because diurnal leaf stomatal functions were disregarded. 

In-leaf daily removal of SO2 per hectare of tree cover in the 

Chicago area was about half of that estimated by Murphy et 

al. (1977) and Lorenz and Murphy (1985) for equal pollutant 

concentration. 

Results for the Chicago area improve on earlier estimates of 

pollution removal for urban trees. However, there remain 

many limitations to the Chicago results that have unknown 

bounds on the error of estimation. Thus, the results should 

be considered first-order approximations of pollution removal 

by urban trees. Additional research is needed to better determine various aspects of the calculations, and to test results 

under urban field conditions. 

Factors influencing Pollution Removal Estimates 

Because tree-canopy resistances generally decrease from 

morning to midday and then increase until night (Grimmond 

and Oke 1991), the use of average in-leaf daytime Rc values 

likely overestimates pollution removal during the early morning and late evening, and underestimates removal during 

midday. Unfortunately, it is not known where the average R, 

value from the literature falls within the diurnal resistance 

cycle. Research is needed to evaluate the diurnal cycle of tree 

canopy resistances to pollution deposition in urban areas. 

The overall removal rate for trees is greater than reported in 

this study as results were limited to dry deposition. In periods 

76 Chapter 5 

after rain or during periods when dew collects on vegetation 

removal rates for urban trees increase as trees offer a large 

wet surface area upon which water-soluble pollutants can 

readily dissolve (e.g., SO2, NO2). 

Estimates of particle removal also may be conservative as 

the model assumed 50 percent resuspension of deposited 

pollutants. This rate was estimated as a midvalue based on 

limited literature. Zinke (1967) estimated that retention of 

airborne materials ranged from 17 to 57 percent in pine 

stands and 82 to 86 percent in hardwood stands. For the 

Chicago area's urban forest, which is approximately 90 percent hardwoods, a resuspension rate of 20 percent would be 

reasonable given Zinke's estimates. However, due to the 

more open nature of urban forests relative to more natural 

forest stands, higher resuspension would be expected due 

to the increased probability of wind resuspension in 

tree canopies. Research is needed on the resuspension of 

particles in urban areas. 

Average canopy-resistance values obtained from the literature probably are too high (leading to conservative deposition 

velocities) for SO2 (average in-leaf daytime R, = 1.9 seclcm) 

and O3 (average in-leaf daytime Rc = 1.7 seclcm). Daytime 

tree-canopy resistances could be as low as 0.5 seclcm for 

SO2 and 0.4 seclcm for 03.3 Average daytime in-leaf deposition velocities for forests and trees in the literature typically 

range from 0.2 to 2 cmlsec and average around 1 .O cmlsec 

for SO2 (e.g., Garland 1977; McMahon and Denison 1979; 

Fowler and Cape 1983; Lovett and Lindberg 1984; Fowler 

1985; Lorenz and Murphy 1985; Murphy and Sigmon 1990). 

Daytime deposition velocities for O3 in the literature normally 

range from 0.3 to 1 cmlsec and average around 0.7 cmlsec 

(e.g., Greenhut 1983; Colbeck and Harrison 1985; Davidson 

and Wu 1990). 

The deposition velocities used in this study were lower than 

averages in the literature (study SO2 average in-leaf daytime 

Vd = 0.52 cmlsec; 03 average in-leaf daytime Vd = 0.55 cml 

sec) and are thought to be conservative (Wesely 1993, pers. 

commun.). Through the use of average R, values, deposition 

velocities and pollution removal may be underestimated by a 

factor of 1.9 for SO2 and a factor of 1.3 for 03. Research is 

needed on improving R, and Vd estimates for urban vegetation 

and other urban surfaces. The average deposition velocity of 

NO2 was within the range of velocities in the literature. 

The location of pollution monitors in the city can lead to 

an overestimation of pollution removal by urban trees. These 

monitors tend to be located in areas that are expected 

to have relatively high concentrations of pollution. Thus, 

extrapolations of these concentrations to larger areas may 

result in inflated concentration estimates. Detailed variations 

3 Based on minimum stomatal and mesophyll resistance of 

rsDH,o/Dx + r,, where r, is minimum stomatal resistance, DH20 is the 

molecular diffusivity of water vapor, D, is the molecular diffusivity of 

gas xin air, and r,, is mesophyll resistance of gas ~(Wesely 1989). 

Minimum stomatal resistance was assumed to be 1.5sec/cm (Baldocchi 

1988). Leaf area index of urban forests was estimated to be 6 (see 

Nowak 1994: Chapter 2, this report). 

USDA Forest Sewice Gen. Tech. Rep. NE-186. 1994. 
Table -/.--Estimated removal rate per tree by d.b.h. class (kg/yr) and total annual dollar value per tree for removal of 

pollutants (see Table 8); particulate removal assumes 50 percent resuspension back to the atmosphere (multiply kg by 2.204 

to convert to pounds) 

D.b.h. class CO SO, NO2 PMlO O3" Total Dollars 

0-7 cm 0.001 0.003 0.003 0.007 0.008 0.021 0.04 

8-1 5 cm 0.003 0.008 0.009 0.021 0.023 0.064 0.10 

16-30 cm 0.007 0.021 0.024 0.055 0.060 0.166 0.27 

31 -46 cm 0.017 0.054 0.062 0.141 0.153 0.428 0.70 

47-61 cm 0.033 0.104 0.118 0.270 0.294 0.81 9 1.34 

62-76 cm 0.043 0.136 0.155 0.355 0.385 1.074 1.76 

77+ cm 0.056 0.178 0.204 0.465 0.505 1.409 2.31 

a May-October only. 

Table 8.-Total yearly monetary value (thousands of dollars) of pollutant removal and average daily monetary value (dollars) 

during in-leaf season for Chicago, suburban Cook County, DuPage County, and entire study area; estimated tons of poliutant 

removed by trees was multiplied by 1990 cost of preventing emission of similar amount of pollutant using current -&on 

control strategies ($It): CO = 1,014; SO2 = 1,801; NO2 = 4.863; PM10 = 1,441; O3 = 540 (California Energy Camissian 1992) 

Chica~o Cook County DuPa~e County Study area 

Pollutant Total Day Total Day Total Day Total Day 

CO 16 80 149 770 62 320 227 1,170 

SO, 152 790 937 4,860 1 83 940 1,272 6.590 

NO, 431 2.250 2,287 11,910 1,204 6,290 3,922 20,450 

PMlO 306 1,470 1,699 8.1 90 646 3,140 2,651 12,800 

O3 103 560 71 7 3,880 260 1,410 1.080 5,850 

Total 1,008 5.150 5,789 29,610 2,355 12,100 9.152 46,860 

in pollution concentrations across a city need to be investigated more fully to better understand the limitations of 

extrapolating concentrations from limited monitoring points. 

Boundary Layer 

Current estimates of percent reduction in pollution concentrations in the Chicago area likely are conservative due to the 

effect of the breeze off Lake Michigan and the assumption of 

a well-mixed boundary layer. The lake breeze reduces mixing 

depths (Lyons and Olsson 1973), thus, increasing the relative 

effect of trees in reducing air pollution. The assumption of a 

well-mixed unstable atmosphere presumed little variation in 

pollution concentration with height (e.g., Colbeck and Harrison 

1985). However, there are times, particularly at night, when 

there is limited mixing (van Dop et al. 1977; Colbeck and 

Harrison 1985). During these times of limited mixing, the 

effect of trees and other surfaces in removing pollutants is 

concentrated in the lower boundary layer, so trees have a 

greater relative effect on pollution reduction near the ground. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 

This effect is of particular importance as this is the layer in 

which humans reside. 

The depth of the boundary layer.has an immense effect on 

the percent reduction in pollution concentration. Maximum 

tree effects occurred in early morning when stomates were 

assumed open and transpiring and the boundary-layer height 

still was relatively low. Research is needed on variations in 

stomata1 resistances and boundary-layer heights in the 

Chicago region to improve the estimates of reductions in 

pollution concentration by Chicago's trees. 

Emission Effects 

Another factor that is not considered in estimates of pollution 

removal is that trees emit compounds that can increase local 

concentrations of pollution. These emissions offset some of 

the removal effects of trees. The relatively low removal of CO 

by trees likely is offset by their emission of volatile organic 

compounds, which can increase CO concentrations. It is 

Chapter 5 77 
possible that urban trees may be an overall source of CO; 

this sinklsource relationship in urban areas needs further 

study. If trees are a source of CO, the source amount probably would be insignificant relative to automobile emissions. 

Emissions of volatile organic compounds by trees can 

contribute to the formation of O3 (Brasseur and Chatfield 

1991). However, because these emissions are temperature 

dependent and trees generally lower air temperatures, it is 

believed that increased tree cover would lower overall volatile organic emissions and O3 levels in urban areas (Cardelino 

and Chameides 1990). 

Pollen $missions by trees can contribute significantly to local 

concentrations of total particles. However, tree pollen often is 

greater than 10 pm (Smith 1990) and likely contributes little to 

PMlO concentrations. Inhalation of noninfectious allergens 

can cause disease, the major response being allergic rhinitis, 

including seasonal hay fever and bronchial asthma (Smith 

1978). Emissions of H2S by trees generally occur in connection with moderate to high concentrations of sulfur in the 

atmosphere or soil. Thus, removal of SO2 by trees under 

moderate to high SO2 concentrations likely will be offset 

some by sulfur emissions by trees to the atmosphere. 

Depending on their configuration around buildings, trees can 

increase or decrease building energy use. Trees generally 

conserve energy use in the summer but often increase use 

in the winter in colder climates (e.g., tree branches shade 

residences). This change in energy use alters pollutant 

emissions from local power plants. Thus, there are many 

interactive factors involving urban trees and air quality that 

remain to be investigated to more fully understand the 

impact of urban trees on air quality. 

Model estimates of pollution removal by trees are specific to 

1991 conditions in the Chicago area. Extrapolations to other 

years or other cities must consider specific pollution concentrations, tree configuration, and local meteorotogy. 

Management Considerations 

The majority of pollution removal by trees occurs under in-leaf 

daytime conditions as this is the time when leaf surfaces are 

actively transpiring and pollution concentrations can reach 

their maximum. The size of individual trees also affects total 

removal per tree. Large trees can remove 60 to 70 times more 

pollution a year than small trees. Thus, to maximize pollution 

removal by trees and other environmental benefits (e.g., 

reductions in air temperature), it is important to sustain healthy, 

functional (i.e., transpiring) trees, particularly large ones. 

Future tree plantings can further enhance the air quality 

benefits of the urban forest and should be concentrated in 

polluted areas. When pollution concentrations become high, 

it is likely that stomates partially or fully close, reducing or 

eliminating most of the potential for pollution reduction of 

urban trees. However, tree response to pollutants varies by 

species and pollutant. Pollution-tolerant species (Kozlowski 

1980) should be selected to enhance survival and subsequent 

air quality benefits. 

78 Chapter 5 

Planting to reduce building energy use (McPherson 1994: 

Chapter 7, this report) also will improve air quality by reducing 

power plant emissions. Mass plantings can act as buffers 

from pollution sources (McCurdy 1978). Ample water should 

be supplied to enhance stomatal removal of pollution. Conifers 

should be planted to enhance particle removal, particularly 

in winter. 

Monetary Value 

Typical monetary values per tree are relatively small, ranging 

from $0.04/yr for small trees to more than $2/yr for large 

trees. These estimates are based on the cost of preventing 

the emission of a similar amount of pollutant with current 

control strategies. It is important to note that emission controls prevent pollution from entering the air while deposition 

to trees removes air pollutants already in the air. Using 

emission-control values likely overestimates the value 

generated by reducing pollutant concentrations after emission because once the pollutant is emitted, it can increase 

atmospheric concentrations and pollution effects around all 

surfaces, adversely affecting human health, materials, and 

visibility before being removed. 

These estimates also do not fully incorporate the effects of 

trees on human health, materials, or visibility received through 

improvements in air quality. Other benefits and detriments 

not considered in this monetary valuation include possible 

lower concentrations of O3 due to lower air temperatures, 

altered power plant emissions due to changes in building 

energy use, and changes in human perceptions of air quality. 

Perceptions can change through the production of pleasant 

odors, screening views from polluted air, and vegetation 

damage from pollution. 

Research Issues 

Continued research and field studies are needed to better 

evaluate and quantify aerodynamic and quasi-laminar boundary-layer resistances in urban areas. The R, and Rb estimates 

in this study are minimal and in the range expected for 

forests (Fowler 1985). Considering that the stomatal influence 

on pollution removal is large, additional research is needed 

to investigate urban evapotranspiration (e.g., Grimmond and 

Oke 1991), particularly, urban tree transpiration, tree-canopy 

resistances to various pollutants, and the effect of pollutants 

on stomatal functioning (e.g., Baldocchi et al. 1987). Although 

advances are being made continually in these areas, particularly for forests and agricultural crops, field studies 

are needed to quantify pollution deposition in urban areas 

to begin to understand how various urban surfaces and 

combinations of surfaces influence pollution deposition and 

concentrations. 

The study calculations are the first in a series to be developed 

to estimate pollution deposition in urban areas. Future calculations will incorporate all urban surfaces in a multi-layer 

model (e.g., Baldocchi 1988). Field measurements of urban 

tree stomatal resistance are planned to help improve these 

estimates. In addition, eddy-correlation estimates of pollutant 

deposition in urban areas are planned to test the removal 

estimates under summer field conditions. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Conclusion Literature Cited 

Urban trees can improve air quality, removing approximately 

590 metric tons (650 tons) of pollution in Chicago and 5,600 

metric tons (6,100 tons) in Cook and DuPage Counties in 

1991. These amounts relate to an average air quality 

improvement of approximately 0.3 percent, peaking at around 

one percent. These removal estimates are likely conservative, 

particularly for SO2 and 03. Further air quality improvement 

(reaching 5 to 10 percent or greater) can be obtained by 

increasing and sustaining healthy tree cover, particularly 

under stable atmospheric conditions. The majority of pollution removal by trees occurs during daylight in-leaf hours 

with the greatest overall removal effects for PMIO and 03. 

Relatively minor removal was estimated for CO and urban 

trees may be an overall source of CO via tree volatile organic 

emissions. Research is needed to investigate the interactive 

relationships of pollution removal, trace-gas emissions, and 

air temperature and building energy use effects of urban 

trees on overall air quality. 

Providing ample water to facilitate tree transpiration is critical 

to maximizing gaseous pollutant removal. Maximum percent 

reduction in pollution concentrations near the ground can be 

expected when trees are transpiring under stable atmospheric 

conditions and/or the boundary-layer height is relatively low. 

Trees offer both an active (via transpiration) and passive surface for gaseous and particulate pollutant removal, decreasing 

the amount of pollution inhaled by humans, deposited on 

anthropogenic material and available to decrease visibility. 

Trees should not be viewed as a substitute for emission 

controls, but rather as a supplement. Reduction of pollution 

emissions prevents possible pollution damage, reduction in 

ambient concentrations (e.g., via trees) only reduces the 

likelihood of possible damage. The effect of typical urban 

tree configurations on pollution emissions from both anthropogenic and biogenic sources remains to be investigated. 

Acknowledgments 

I sincerely thank Robert Swinford and the Illinois Environmental Protection Agency for providing pollution-concentration 

data; Dennis Baldocchi, John Dwyer, Sue Grimmond, Gordon 

Heisler, Tilden Meyers, Susan Sherwood, Gerald Walton, 

and Marvin Wesely for technical assistance in model development; Henry Henderson, Dave Inman, Edith Makra, and 

the Chicago Department of Environment for local technical 

assistance; Sue Grimmond, Marty Jones, and Paul Miller for 

reviewing this article; Marcia Henning and Scott Prichard for 

meteorological and pollution-concentration data entry; and 

Una Arnold and Jack Stevens for literature assistance. 

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Chapter 5 

Chapter 6 

Atmospheric Carbon Dioxide Reduction by 

Chicago's Urban Forest 

David J. Nowak, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Chicago, IL 

Abstract 

In terms of reducing atmospheric carbon dioxide (C02), trees 

in urban areas offer the double benefit of direct carbon 

storage and the avoidance of C02 production by fossil-fuel 

power plants through energy conservation from properly 

located trees. In the City of Chicago, trees store an estimated 

855,000 metric tons (t) of carbon (942,000 tons), and trees 

throughout the study area of Cook and DuPage Counties 

store about 5.6 million t (6.1 million tons). Carbon storage by 

shrubs is approximately 4 percent of the amount stored by 

trees. Total carbon storage and annual sequestration are 

greatest on 1-3 family residential lands, institutional lands 

dominated by vegetation (e.g., parks, forest preserves) and 

vacant lands. Net carbon sequestration in the study area is 

estimated at 140,600 t (155,000 tons). Carbon storage by 

urban forests nationally likely is between 400 and 900 million 

t (440 to 990 millions tons). 

Storage by individual trees is up to 1,000 times greater in 

large than in small trees, with sequestration rates up to 90 

times greater for healthy large than healthy small trees. 

Estimated carbon emissions avoided annually due to energy 

conservation from existing trees throughout the study area is 

approximately 11,400 t (1 2,600 tons). Total carbon stored by 

trees in the study area, which took years to store, is equivalent to the amount of carbon emitted from the residential 

sector in the study area during a 5-month period. Net annual 

sequestration equals the amount of carbon emitted from 

transportation use in the study area in 1 week. The amount 

of carbon sequestered annually by one tree less than 8 cm (3 

inches) in trunk diameter (d.b.h.) equals the amount emitted 

by one car driven 16 km (1 0 mi). Reasonable additional tree 

planting, in conjunction with efforts to sustain existing tree 

cover could increase carbon storage in the study area by 

another 1.2 million t (1.3 million tons), or the amount of 

carbon emitted by transportation use in the study area in less 

than 2 months. The advantages and limitations of urban 

trees in reducing atmospheric COe are discussed. 

Introduction 

Increasing levels of atmospheric carbon dioxide (C02) and 

other "greenhousengases (e.g., methane, chlorofluorocarbons, 

nitrous oxide) are thought by many to be contributing to an 

increase in atmospheric temperatures by the trapping of 

certain wavelengths of heat in the atmosphere. Climate models 

indicate that the probable doubling of C02 within the next 

century would increase average global surface temperatures 

by 1.5" to 4.5"C (2.7" to 8.1°F) (US. National Research Council 1983). While no single gas is likely to have the direct impact 

on climate expected from Con, the sum of the radiative effects 

from other trace gases could effectively double the climatic 

impact of projected C02 increases (Wuebbles et al. 1989). 

The observed increases in atmospheric concentrations 

of C02, methane (CH4), chlorofluorocarbons (CFC's), and 

nitrous oxide (N20) during the 19801s, which resulted from 

human activities, contributed to the greenhouse effect by 56, 

15, 24 and 5 percent, respectively (IPCC 1991). During this 

period, the contribution of different human activities to the 

change in the greenhouse effect is an estimated 46 percent 

from energy production and use; 24 percent from the 

production and use of CFC's and other halocarbons (e.g., 

from refrigerants, aerosol sprays); 18 percent from deforestation, biomass burning, and other changes in land use 

practices; 9 percent from agriculture (e.g., methane from rice 

cultivation and livestock and N20 release from nitrogenous 

fertilizers); and 3 percent from other sources (e.g., methane 

from landfills) (IPCC 1991). 

Urban Trees and Carbon Dioxide 

Increased atmospheric C02 is attributable mostly to fossil fuel 

combustion (about 75 percent) and deforestation (Schneider . 

1989). Atmospheric carbon is estimated to be increasing by 

approximately 2.6 billion metric tons (t) (2.9 tons) annually 

(Sedjo 1989). By storing carbon through their growth process, 

trees act as a sink for atmospheric COP. Thus, increasing the 

number of trees can potentially slow the accumulation of 

atmospheric carbon (e.g., Moulton and Richards 1990). 

In reducing atmospheric Con, trees in urban areas offer double 

benefits. First, they directly sequester and store atmospheric 

carbon. Second, when located properly, urban trees conserve 

energy, which results in lower C02 emissions from fossil-fuel 

power plants. Properly located trees shade residences in 

summer (reducing air-conditioning energy use), but also allow solar access and/or block winds in winter to reduce heating needs (Heisler 1986). Tree transpiration also reduces 

local air temperatures, which can affect local energy use. 

There has been little research on the amount of carbon that 

urban forests store, or on the effect of energy conservation by 

trees on the amount of carbon released to the atmosphere. 

Biomass (dry weight) of trees in Shorewood, Wisconsin, a 

suburb of Milwaukee, has been estimated at 35.7 t per 

hectare (ha) of above-ground biomass (1 5.9 tons/acre) (Dorney 

USDA Forest Service Gem Tech. Rep. NE-186. 1994 Chapter 6 83 
et al. 1984). Biomass was calculated using a generalized 

formula from Whittaker et al. (1974). This biomass estimate 

converts to approximately 22.8 t/ha of carbon (10.2 tons/ 

acre) (above and below ground). Shorewood's tree cover has 

been liberally estimated at 39 percent, with approximately 67 

percent of the trees less than 15 cm (6 inches) in trunk 

diameter (d.b.h.) at 1.37 m (4.5 ft) (Dorney et al. 1984). 

Estimated carbon storage by trees in Oakland, California, 

(21 percent tree cover) is 145,800 t or 11.0 tfha (160,700 

tons or 4.9 tonsfacre) (Nowak 1993). 

Carbon storage by urban forests in the United States has 

been estimated at 350 to 750 million t (385 to 825 million 

tons) (Rowntree and Nowak 1991 ; Nowak 1993). It has been 

estimated that the establishment of 10 million urban trees 

annually over the next 10 years would sequester and offset 

the production of 363 million t (400 million tons) of carbon 

over the next 50 years, 77 million t (85 million tons) due to 

direct sequestration and 286 million t (315 million tons) due 

to avoided carbon emissions from power plants (Nowak 

1993). This estimate assumes that the 100 million trees 

survive the 50-year period and were planted in optimal 

positions for energy conservation. Even so, this total is less 

than 1 percent of the amount of carbon emissions projected 

for the United States over the same 50-year period. 

The purpose of this paper was to estimate total carbon 

storage, annual carbon sequestration, and carbon emissions 

avoided from power plants through energy conservation by 

trees in the Chicago area. 

Met hods 

Ground Sampling of Trees 

Data on 8,996 trees were collected on 652 randomly located 

plots throughout the study area (see Figure 1 in Chapter 2). 

0.04-ha (0.1 acre) plots were used for all land uses except 1-3 

family residential, where information on the entire residential 

lot was collected. Tree data collected included d.b.h., tree 

height, and species. Total shrub area was measured on 

each plot; on every tenth plot, diameters for individual shrubs 

were measured at 15 cm (6 inches) above groundline (see 

Nowak 1994: Chapter 2, this report). 

Carbon and Tree Biomass 

Biomass for each measured tree was calculated using allometric equations from the literature (Table 1). If no allometric 

equation could be found for an individual species, the genera 

average was substituted. If no genera equations were found, 

biomass was computed separately for each hardwood and 

conifer equation and the average result from the hardwood 

or conifer group was used. 

To help determine whether allometric equations for forestgrown trees were applicable for urban trees, above-ground 

total fresh-weight biomass was collected for 30 street trees 

in Oak Park, Illinois. As the trees were removed, tree limbs 

were chipped and bagged and larger stems cut into logs. 

Logs and chips were weighed using a truck scale. Decay 

84 Chapter 6 

was evident in 10 trees but was not considered significant 

(Mike Stankovich, 1993, Village of Oak Park, pers. commun.). 

Measured trees ranged in d.b.h. from 20 to 99 cm (8 to 39 

inches). Included were nine silver maple, eight American 

elm, four Norway maple, three ash, two pin oak, one elm, 

one linden, one tulip poplar and one sugar maple. Measured 

weight was matched against predicted weight using 

appropriate allometric equations. A pair-wise t-test was used 

to determine if significant differences existed between actual 

and predicted weights. 

Measured biomass from street trees in Oak Park was significantly lower than that predicted from allometric equations 

from natural forest stands (alpha = 0.05). Biomass estimates 

of more open-grown trees were multiplied by a factor 0.8 to 

account for the discrepancy. No adjustment was made for 

trees found in more natural stand conditions (e.g., on vacant 

lands or in forest preserves). 

Biomass equations differ in the portion of tree biomass that 

is calculated; whether fresh or oven-dry weight is estimated, 

and in the diameter ranges used to devise the equations 

(Table 1). Below-ground biomass of trees averages approximately 22 percent of total tree biomass (Bray 1963; Ovington 

1965; Young and Carpenter 1967; Whittaker and Woodwell 

1968; Andersson 1970; Woodwell and Botkin 1970; King and 

Schnell 1972; Whittaker and Marks 1975; Harriss et al. 1977; 

Hermann 1977; Husch et al. 1982; Raile and Jakes 1982; 

Czapowskyj et al. 1985; Harmon et al. 1990; Little and 

Shainsky 1992). 

Average biomass per square meter of shrub cover was estimated for each land-use type by calculating the above-ground 

biomass (kg) using formulas in Smith and Brand (1 983) and 

dividing the calculated biomass by individual shrub cover (W). 

Below-ground biomass of small shrubs averaged approximately 61 percent of total shrub biomass (Whittaker 1962; 

Whittaker and Woodwell 1968; Woodwell and Botkin 1970). 

Many shrubs in the study area were larger than found in the 

literature, so a more conservative estimate of 40 percent of 

total biomass was used in converting above-ground shrub 

biomass to total shrub biomass. Equations that compute 

above-ground biomass were divided by 0.78 for trees and 

0.6 for shrubs to convert to total biomass. 

Equations that compute fresh-weight biomass were multiplied by species or genera specific conversion factors to yield 

dry-weight biomass. These conversion factors, derived from 

average moisture contents of species given in the literature, 

averaged 0.48 for conifers and 0.56 for hardwoods (U.S. 

Dept. Agric. 1955; Young and Carpenter 1967; King and 

Schnell 1972; Wartluft 1977; Stanek and State 1978; Wartluft 

1978; Monteith 1979; Clark et al. 1980; Ker 1980; Phillips 

1981 ; Husch et al. 1982; Schlaegel 1984a-d; Smith 1985). 

For dead and dying trees, leaf biomass was removed from 

the estimate of total tree biomass using leaf biomass formulas derived as part of the Chicago Urban Forest Climate 

Project. Total biomass of dead trees was reduced by approximately 4 percent. 

USDA Forest Service Gen. Tech. Rep. NE-I 86. 1994. 
Table 1 .-Attributes of biomass equations used to calculate tree biomass 

Species Tree p& weightb D.b.h. rangeC Reference 

American beech Dry 3-56 Tritton and Hombeck 1982 

American beech 

American beech 

Aspen 

Aspen 

Balsam fir 

Balsam fir 

Balsam fir 

Black cherry 

Black oak 

Chestnut oak 

Douglas-fir 

Eastern hemlock 

Eastern hemlock 

Eastern hemlock 

Eastern hemlock 

Eastern hemlock 

Eastern white-cedar 

Green ash 

Hickory 

Hickory 

Jack pine 

Jack pine 

Lodgepole pine 

Longleaf pine 

Norway spruce 

Overcup oak 

Paper birch 

Paper birch 

Pin cherry 

Red maple 

Red maple 

Red maple 

Red oak 

Red oak 

Red oak 

Red pine 

Red pine 

Red/white spruce 

Scarlet oak 

Shortleaf pine 

Slash pine 

Spruce 

Spruce 

Sugarberry 

Sugar maple 

Sugar maple 

Sugar maple 

Sweetgum 

Tulip-poplar 

Above 

Above 

Above 

Above 

Total 

Total 

Above 

Total 

Above 

Total 

Above 

Total 

Total 

Above 

Above 

Above 

Total 

Above 

Ab-If 

Total 

Above 

Above 

Total 

Total 

Total 

Above 

Ab-If 

Total 

Above 

Above 

Above 

Above 

Above 

Ab-If 

Above 

Above 

Above 

Total 

Total 

Ab-If 

Total 

Total 

Above 

Above 

Ab-If 

Above 

Above 

Total 

Ab-If 

Ab-If 

Dry 

Dry 

Dry 

Fresh 

Dry 

Dry 

Fresh 

Dry 

Dry 

Dry 

Dry 

Fresh 

Dry 

Dry 

Dry 

Fresh 

Dry 

Dry 

Fresh 

Dry 

Dry 

Fresh 

Dry 

Fresh 

Dry 

Dry 

Fresh 

Dry 

Dry 

Dry 

Dry 

Dry 

Dry 

Dry 

Dry 

Dry 

Fresh 

Fresh 

Dry 

Fresh 

Fresh 

Dry 

Dry 

Dr 

Dry 

Dry 

Fresh 

Dry 

Dry 

Tritton and Hombeck 1982 

Tritton and Hombeck 1982 

Tritton and Hornbeck 1982 

Wenger 1984 

Stanek and State 1978 

Tritton and Hombeck 1982 

Wenger 1984 

Tritton and Hornbeck 1982 

King and Schnell1972 

Tritton and Hombeck 1982 

Wenger 1984 

Stanek and State 1978 

Tritton and Hornbeck 1982 

Tritton and Hornbeck 1982 

Tritton and Hombeck 1982 

' Wenger 1984 

Ker 1980 

Schlaegel l984a 

Wenger 1984 

Tritton and Hornbeck 1982 

Stanek and State 1978 

Wenger 1984 

Stanek and State 1978 

Wenger 1984 

Jokela et al. 1986 

Schlaegel1984b 

Stanek and State 1978 

Tritton and Hombeck 1982 

Tritton and Hombeck 1982 

Tritton and Hombeck 1982 

Tritton and Hornbeck 1982 

Tritton and Hombeck 1982 

Clark et ai. 1980 

Tritton and Hombeck 1982 

Tritton and Hombeck 1982 

Tritton and Hombeck 1982 

Wenger 1984 

Wenger 1984 

Clark et al. 1980 

Wenger 1984 

Wenger 1984 

Tritton and Hombeck 1982 

Tritton and Hombeck 1982 

Schlaegel 1984c 

Tritton and Hombeck 1982 

Tritton and Hombeck 1982 

Wenger 1984 

Schlaegel1984d 

Clark and Schroeder 1977 

USDA Forest Service Gem Tech. Rep. NE-186. 1994. Chapter 6 
Table 1 .-continued 

Species Tree parta weightb D.b.h. rangeC Reference 

Tulip-poplar Above Dry 3-51 Tritton and Hornbeck 1982 

Tulip-poplar Above DV 5-51 Tritton and Hornbeck 1982 

Western redcedar Above Dry 3-1 19 Stanek and State 1978 

White ash Above Dry 5-51 Tritton and Hombeck 1982 

White oak Above DV 5-51 Tritton and Hombeck 1982 

White pine Above DV 3-56 Tritton and Hombeck 1982 

White pine Above Dry 3-66 Tritton and Hornbeck 1982 

White pine Total Fresh 3-66 Wenger 1984 

Yellow birch Above Dry 3-56 Tritton and Hombeck 1982 

Yellow birch Above Dry 3-66 Tritton and Hombeck 1982 

Yellow birch Above Dry 5-51 Tritton and Hornbeck 1982 

Yellow birch Total Fresh 3-66 Wenger 1984 

aAbove = above-ground biomass; Ab-If = above ground biomass excluding leaves; Total = total tree biomass (including roots). 

%esh or ovendry weight. 

cm 

Total tree and shrub dry-weight biomass was converted to 

total stored carbon by multiplying by 0.5 (For. Prod. Lab. 

1952; Millikin 1955; Ovington 1957; Reichle et al. 1973; 

Pingrey 1976; Ajtay et al. 1979; Chow and Rolfe 1989; Koch 

1989). Total carbon storage by trees and shrubs was calculated by land-use type for each sector of the study area. 

Because of a lack of information on errors in the basic 

formulas from which the projections were made and the 

various adjustment factors that were used, standard errors 

report sampling error rather than the error of estimation. 

Sampling errors underestimate the actual standard errors. 

Urban Tree Growth and Carbon Sequestration 

To estimate the amount of carbon sequestered annually by 

trees, urban tree-growth was estimated from measurements 

of radial growth increments. Sections cut at d.b.h. were 

obtained for 543 trees - 223 elms, 171 maples, 78 ash, 13 

poplar, and 58 other (10 species) removed from Chicago, 

Oak Park, Glen Ellyn, and Bloomingdale during 1991-92. A 

radial line was marked across the section where average 

growth occurred (not compressed or elongated tree rings). 

To avoid measuring tree growth that might be affected by the 

condition of the removed trees (i-e., many trees were declining 

or dead), radial growth and tree cumulative radius to 0.05 cm 

(1150 inch) were measured for each ring developed between 

1965 and 1985. Average annual growth by diameter class 

was calculated for major genera. Average diameter growth 

from the appropriate genera and diameter class was added to 

the existing tree diameter (year x) to estimate tree diameter in 

year x+1. Average height growth was assumed to be 0.15 ml 

yr (0.48 ftfyr) (Fleming 1988). The difference in estimates of 

carbon storage between year x and year x+l is the amount 

of carbon sequestered annually. 

86 Chapter 6 

Tree death will lead to the eventual release of stored carbon. This release is hastened when wood is burned or 

allowed to decay (e.g., not stored in durable wood products 

or landfills). To calculate the potential release of carbon 

due to tree death, estimates of annual mortality rates by 

diameter class were derived from a study of street-tree 

mortality (Nowak 1986). Annual mortality was estimated as 

2.9 percent for trees 0 to 7 cm (0 to 3 inches) in diameter; 8 

to 15 cm (3.1 to 6 inches) = 2.2 percent; 16 to 46 cm (6.1 to 

18 inches) = 2.1 percent; 47 to 61 cm (18.1 to 24 inches) = 

2.9 percent; 62 to 76 cm (24.1 to 30 inches) = 3.0 percent; 

and 77+ cm (30+ inches) = 5.4 percent. The amount of 

carbon sequestered due to tree growth was reduced by the 

amount lost due to tree mortality to estimate the net carbon 

sequestration rate. 

Energy Conservation 

Total distribution of residential natural gas in Chicago in 

1992 was 4.16 billion m3 (147 billion ft3) (Peoples Energy 

Corp. 1993). In Dupage County, residential gas use in 1991 

was 861 million m3 (30.4 billion ft3) (Northern Illinois Gas, 

1992, pers. commun.). Cook County's estimated natural gas 

use, based on per capita consumption in Chicago and DuPage 

County, is 3.27 billion m3 (115.6 billion ft3). Natural gas 

consumption was converted to heating energy use by multiplying by 0.78 (Peoples Gas, 1992, pers. commun.); thousand 

m3 of natural gas was converted to million Btu by multiplying 

by 36.55 (Energy Information Administration 1993). Total 

carbon emissions from natural gas were estimated based 

on the rate of 14.2 t (15.7 tons) of carbon per billion Btu 

for natural gas (Citizens Fund 1992). Total conservation of 

heating energy due to existing tree configurations (i.e., 

shading, wind modification) at 50 residences in Chicago has 

been estimated at 0.04 percent (Jo and Wilkin,1994). This 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
value was used to estimate carbon emissions avoided due to 

the effects of existing trees on heating energy. 

Total electrical energy generation by Commonwealth Edison 

in 1992 was 79.9 billion kwh with Con emissions of 15.0 

million t (1 6.5 million tons) (Commonwealth Edison, 1993, 

pers. commun.). Considering that 68 percent of Commonwealth Edison sales are in Cook and Dupage Counties 

(McPherson et al. 1993), 26.7 percent of sales are to residences (Commonwealth Edison, 1993, pers. commun.) and 

approximately 75 percent of residential energy use is for air 

conditioning (Greg McPherson, 1993, pers. commun.), it is 

estimated that air-conditioning energy use in the study area 

is 2.2 billion kwh. Commonwealth Edison's C02 emission 

rate is 0.051 t (0.056 tons) of carbon1MWh. Total conservation of air-conditioning energy use due to existing tree configurations at 50 residences in Chicago has been estimated 

at 8.4 percent (Jo and Wilkin 1994). This value was used to 

estimate carbon emissions avoided due to the effect of existing trees on air conditioning energy use. 

Future Tree Planting 

To analyze the potential effect of future tree plantings, available growing space (grass and soil area) was analyzed by 

land-use type throughout the study area. A reasonable treeplanting scenario assumes that none of the available space 

in agricultural or other transportation (predominantly airport) 

uses would be planted with trees due to land-use limitations. 

Five percent of available space could readily be planted and 

covered with trees on large commercial-industrial areas and 

institutional land dominated by vegetation such as parks, 

cemeteries, golf courses, and forest preserves. Ten percent 

of available space could be planted and covered with trees 

on institutional lands dominated by building such as schools, 

15 percent in residential areas, 20 percent in landscaped 

commercial complexes, and 25 percent on vacant lands and 

along freeways. 

Results 

Total carbon storage by trees in the study area was about 

5.6 million t or 85.7 t/ha of tree cover (6.1 million tons or 38.2 

tonslacre). Trees in Chicago store 0.9 million t of carbon or 

128.0 ffha of tree cover (0.9 million tons or 57.1 tonslacre); 

suburban Cook County trees store 3.2 million tor 75.5 t/ha of 

tree cover (3.5 million tons or 33.7 tons/acre) and DuPage 

County trees store 1.5 million t or 95.0 t/ha of tree cover (1.7 

million tons or 42.4 tonslacre) (Table 2). The most carbon 

stored by trees was on residential land and the least on 

agricultural lands. Total carbon stored by shrubs in the study 

area is estimated at 216,000 t (238,000 tons). 

Tree carbon stored per ha in the study area averaged 16.7 t 

(7.4 tonslacre) and ranged from 14.1 t/ha (6.3 tonslacre) in 

Chicago to 17.7 tlha (7.9 tonslacre) in DuPage County (Table 

3). The highest carbon storage per ha was on institutional 

lands dominated by vegetation and least on agricultural lands 

(Table 3). 

Average carbon storage by individual trees was 3 kg (7 Ib) 

for a tree less than 8 cm (3 inches) d.b.h. to more than 3,100 

kg (7,000 Ib) for a tree greater than 76 cm (30 inches) d.b.h. 

(Figure 1, Table 4). Average carbon sequestration by individual trees ranged from 1.0 kglyr (2.3 Iblyr) for a tree less 

than 8 cm d.b.h. to 93 kg Iyr (204 Iblyr) for a tree greater than 

76 cm d.b.h. (Figure 2, Table 4). 

Table 2.-Total carbon stored (in thousands of metric tons) in Chicago, suburban Cook County, DuPage County, and entire 

study area (multiply thousands of metric tons by 1.102 to convert to thousands of tons) 

Chicago Cook Co. DuPage Co. Study area 

Land use Total SE Total SE Total SE Total SE 

Agriculture 0.0 0.0 0.0 0.0 2.9 2.6 2.9 2.6 

~rans~ortation~ 40.5 25.5 0.0 0.0 19.7 19.7 60.2 32.2 

Institutional (bldg.)= 28.7 25.9 0.0 0.0 42.1 31.6 70.7 40.9 

~ultiresidential~ 100.9 87.8 24.0 11.6 7.0 1.7 131.9 88.5 

Vacant 66.2 25.9 191.1 128.8 198.3 68.6 455.5 148.2 

Institutional (~eg.)~ 198.2 46.1 1.308.4 192.6 31 0.6 66.4 1,817.2 208.9 

I3esidentialf 420.1 69.6 1,659.8 210.2 936.8 146.6 3,016.7 265.6 

Total 854.8 129.1 3,192.2 313.1 1,525.9 178.9 5,572.9 383.0 

SE = standard error (based on sampling emr. not the em of estimation. Sampling errors underestimate the actual standard em). 

aCommercialtindustrial. 

b~irport, freeways, etc. 

Clnstiiutionalands dominated by buildings, e-g., schools, churches. 

d~partments with four or more units. 

elnstitutional lands dominated by vegetation, e.g., parks, cemeteries, forest preserves, golf courses. 

1-3 family residential buildings. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 6 

- 
Table 3.--Carbon storage per hectare (metric tons) in Chicago, suburban Cook County, DuPage County, and entire study 

area (divide Wha by 2.24 to convert to toridacre) 

Chicago Cook Co. DuPage Co. Study area 

Land use Total SE Total SE Total SE Total SE 

Agriculture 0.0 0.0 0.0 0.0 0.2 0.2 0.1 0.1 

Commercial 

Transportation 

Institutional (bldg.) 

Multiresidential 

Vacant 

Institutional (veg.) 

Residential 

All uses 

Table 4.-Average carbon stored (kg/tree) and sequestered (kgltree/yr) in study area by d.b.h. class (multiply kg by 2.204 to 

convert to pounds) 

Carbon stored Carbon sequestered 

D.b.h. class (cm) Mean SE Mean SE 

0-7 3 0.05 1 .O 0.02 

8-1 5 24 0.3 4.4 0.05 

16-30 105 1.4 9.4 0.1 

31-46 399 6 19.1 0.3 

47-61 962 19 34.6 0.8 

62-76 1,808 5 1 55.3 1.8 

77+ 3,186 153 92.7 4.0 

" 

0-7 8-15 16-30 31-46 47-61 62-76 77+ 

D.B.H. Class (cm) 

Figure 1. -Average carbon stored in individual urban trees by d.b.h. class (kg). 

88 Chapter 6 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
0-7 8-15 16-30 31-46 47-61 62-76 77+ 

D.B.H. Class (cm) 

Figure 2. -Average annual carbon sequestration by individual urban trees by d.b.h. 

class (kglyear). 

Average urban tree growth ranged from 0.78 to 1.02 cmlyr 

(0.31 to 0.40 inchlyr) (Table 5). Maximum total sequestration 

by trees in the study area (no tree mortality) is estimated at 

315,800 t (348,000 tons) of carbon, ranging from 40,100 t 

(44,200 tons) in Chicago to 186,500 t (205,500 tons) in 

suburban Cook County (Table 6). Loss of carbon due to tree 

mortality in the study area (2.6 percent average annual 

mortality rate) is estimated at 175,200 t (193,000 tons) - 55 

percent of the carbon sequestered -for a net sequestration 

rate of 140,600 t (155,000 tons) of carbon. This amounts to 

0.4 t/ha of land and 2.2 tlha of tree cover (0.2 tonlacre and 

0.9 tonslacre). At an average mortality rate greater than 4.8 

percent per year (assuming the same relative difference in 

mortality rates among the d.b.h. classes), more carbon would 

be lost due to tree mortality than would be sequestered by 

existing living trees. 

Carbon emissions due to heating energy use in the study 

area total about 3.3 million tlyr (3.7 million tonslyr). Avoided 

carbon emissions due to savings in heating energy use from 

existing trees are estimated at 1,300 tlyr (1,500 tonslyr). Total 

carbon emissions due to air-conditioning use in the study 

area are approximately 109,900 ffyr (1 21,100 tonslyr). Avoided 

carbon emissions due to savings in air-conditioning use from 

existing trees are estimated at 10,100 t/yr (1 1,100 tonslyr). 

If 0 to 25 percent of the available grass and soil space on 

various land uses were planted with trees, overall tree cover 

in the study area would increase from 19.4 to 23.5 percent. 

This planting assumes a tree-diameter structure comparable 

to what exists today and probably would take 40 to 80 years 

to become established. This tree establishment likely would 

store an additional 1.2 million t (1.3 million tons) of carbon. 

These trees also could reduce carbon emissions from power 

plants by lowering air temperatures through transpiration 

and by properly shading buildings and blocking winter winds. 

Discussion 

There are limitations to estimating carbon storage and 

sequestration by urban trees. Preliminary indications are that 

biomass equations derived from forest stands overestimate 

biomass from open-grown urban trees by a factor of 1.25. 

Open-grown trees typically are shorter but often have larger, 

more branchy crowns than forest-grown trees (Spurr and 

Barnes 1980). However, urban tree crowns often are pruned, 

which removes stored carbon. These differences in tree height 

and pruning likely contribute to the discrepancy between 

forest derived equations and measured biomass of urban 

trees. Pruning practices vary by location but street trees 

usually are well maintained; thus, the biomass equation 

adjustment factor (derived from street trees) likely is near 

maximum. Research is needed to further test the applicability 

of existing biomass equations to urban trees, and on how 

biomass-equation estimates vary by land-use type and associated maintenance practices. 

D.b.h. ranges for biomass equations used in this study generally ranged from 3 to 66 cm (1 to 26 inches). The degree of 

error in predicting biomass outside of regression formula 

d.b.h. ranges is unknown, but visual inspection of biomass 

estimates for large trees (greater than 66 cm d.b.h.) indicates 

the estimates appear reasonable. Research is needed on 

root-shoot relationships of open-grown urban trees. 

In U.S. forest ecosystems, 59 percent of the total carbon 

stored is in soils (Birdsey 1990). Estimates of carbon storage 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Chapter 6 89 
Table 5.-Average tree-diameter growth rates (cdyr), from a sample of street trees in the Chicago area, used for estimating 

carbon sequestration; dead and dying trees were given a growth rate of 0.0 cdyr (divide cm by 2.54 to convert to inches) 

- D.b.h. - . . . . class - . -. - - fcm) ,----, 

Genera 0-7 8-1 5 1 6-30 31 -46 47-61 62-76 77+ 

Ash 0.90 0.99 0.85 0.64 0.68 0.70 0.44 

Elm 

Maple 

Other 

~~ ~- 

Average 0.85 1.02 0.90 0.79 0.78 0.84 0.95 

Table 6.-Total carbon sequestered annually (in thousands of metric tons) in Chicago, suburban Cook County. DuPage 

County. and entire study area; estimates of sequestration are high because they do not account for tree mortality (multiply 

thousands of metric tons by 1.102 to convert to thousands of tons) 

Chicago Cook Co. DuPage Co. Study area 

Land use Total SE Total SE Total SE Total SE 

Agriculture 0.0 0.0 0.0 0.0 0.8 0.7 0.8 0.7 

Commercial 0.1 0.1 2.2 1.4 0.7 0.3 2.9 1.4 

Transportation 2.5 1.6 0.0 0.0 0.7 0.7 3.1 1.8 

Institutional (bldg.) 1.2 1.0 0.0 0.0 1.7 1.1 3.0 1.5 

Multiresidential 3.1 2.2 2.0 0.8 0.9 0.2 6.1 2.4 

Vacant 4.4 1.6 13.5 5.9 21.3 6.6 39.2 9.0 

Institutional (veg.) 10.7 2.2 94.4 12.4 17.9 3.4 123.0 12.8 

Residential 18.2 2.7 74.4 8.0 45.1 6.3 137.7 10.5 

Total 40.1 4.9 186.5 16.0 89.2 9.9 31 5.8 19.4 

Table 7.-Average carbon stored (metric tons) per hectare of land in Oakland, CA. Chicago, suburban Cook County, and 

DuPage County; Oakland estimate is adjusted to meet same assumptions of biomass and carbon used in Chicago area 

estimates; land-use classes are combined to allow for equal comparison with Oakland estimates (Nowak 1993) (divide tha by 

2.24 to convert to tondacre) 

Land use Oakland Chicago Cook County DuPage County - 

Commercial 0.6 0.0 0.3 1 .O 

Transportation 0.8 7.2 0.0 9.0 

Residentiala 10.4 17.2 21.6 24.4 

lnstitutional/Wildlandb 26.0 27.8 21.9 15.0 

All uses 12.5 14.1 17.0 17.7 

alncludes street trees that were categorized separately in Oakland. 

b~ildlands, institutional and miscellaneous land uses, including agriculture. 

90 Chapter 6 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
for the Chicago area's urban forest include only carbon stored 

by trees and shrubs. Research is needed on carbon storage 

by soil, grass, and other components of the urban-forest 

ecosystem. Carbon storage by shrubs in the study area is 

approximately 4 percent of the amount stored by trees. 

Estimates of carbon storage for the Chicago area differ from 

those for Oakland, California (Table 7). There are various 

factors that contribute to the differences observed among 

Oakland, Chicago, and Cook and DuPage Counties. One 

factor is the difference in land-use distribution among these 

areas. Oakland is relatively high in transportational land 

uses while Chicago is relatively high in commercial-industrial 

uses, and DuPage County is relatively high in agricultural 

use. As land-uses change, so does the amount of trees and 

associated tree biomass. 

Land-use distribution affects overall tree density. Chicago had 

the lowest tree density with 68 treeslha (28 trees/acre), 

followed by Oakland with 120 treeslha (49 treeslacre), suburban Cook County with 169 treeslha (68 treeslacre) and 

DuPage County with 173 treeslha (70 treeslacre) (Table 3 , 

Chapter 2). The greater the tree density, the more biomass 

that is stored per ha given an equal diameter distribution. 

Other factors that greatly influence carbon storage are tree 

species and diameter distribution. Tree species will differ in 

growth characteristics, so estimates of carbon storage can 

vary among trees of the same diameter. Chicago had relatively more large trees than other urban areas: 7.5 percent of 

Chicago's trees were larger than 46 cm (18 inches) d.b.h. 

compared with 4.5 percent for Oakland, 4 percent for DuPage 

County, and 3.5 percent for suburban Cook County. Cook 

and DuPage Counties had relatively more small trees with 

78.7 and 76.7 percent of the trees less than 15 cm (6 inches) 

d.b.h. respectively. This compares with 63.5 percent in Chicago and 60.9 percent for Oakland (Table 9, Chapter 2). 

Carbon stored per ha of tree cover was highest in Chicago at 

128 Wha (57 tonslacre), followed by DuPage County at 95.0 

Wha (42 tonslacre), suburban Cook County at 75.5 Wha (34 

tonslacre), and Oakland at 59.6 tlha (27 tonslacre). Both tree 

density per ha of tree cover and tree-diameter distribution 

affect estimates of carbon storage per ha of tree cover. 

DuPage County had the highest density per ha of tree cover 

at 927 (375 treeslacre), followed by Cook County at 752 (304 

treeslacre), Chicago at 61 9 (250 treeslacre), and Oakland at 

571 (231 treeslacre). The estimate for Chicago may be too 

high due to the probability of a conservative estimate of tree 

cover from aerial photographs. The large amount and size of 

buildings in Chicago obscure small trees, so tree cover likely 

is underestimated and the amount of carbon stored per ha of 

tree cover probably is overestimated. 

U.S. forest ecosystems store approximately 52.5 billion t 

(57.9 billion tons) of carbon, with 31 percent in live trees 

(Birdsey 1990). This estimate converts to 55 t of carbonlha 

(24.5 tons/acre) of land in live trees in U.S. forests - 3 to 4 

times greater than storage estimates for urban forests. This 

live-tree forest estimate of 55 tlha is less than urban forest 

carbon storage estimates per ha with 100 percent tree cover 

because the former estimate is not based on 100 percent 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

tree cover and the latter estimate includes dead trees (about 

3 percent of total biomass). In the Chicago area, total carbon 

and residential carbon storage per ha appears to decrease 

with an increase in the density of urban development. 

Carbon storage in urban forests nationally (28 percent tree 

cover) is estimated at 600 to 900 million t (660 to 990 million 

tons). This estimate falls at the upper end and beyond the 

estimated range (350 to 750 million t) of total carbon storage 

by US. urban forests (Nowak 1993). 

Carbon Sequestration by Urban Trees 

Total carbon stored by trees in the study area (5.6 million t), 

which took years to store, equals the amount of carbon 

emitted from the residential sector (including transportation 

use) in the study area during a 5-month period.1 Net annual 

sequestration for all trees in the study area (140,600 t of 

carbon) equals the amount of carbon emitted from transportation use in the study area in one week2 The amount of 

carbon sequestered annually by one tree less than 8 cm 

d.b.h. is equivalent to the amount of carbon emitted by 

driving one car 16 km (1 0 mi). Annual sequestration by one 

tree greater than 77 cm d.b.h. is equivalent to driving one car 

approximately 1,460 km (900 mi).3 

Carbon storage by individual trees is as much as 1,000 times 

greater in large than small trees, with sequestration rates as 

much as 90 times greater for healthy large than healthy small 

trees. Thus, to maximize carbon storage and sequestration 

from urban trees, it is necessary to ensure the survival and 

vigor of large trees and establish small ones. 

The net sequestration rate is highly sensitive to mortality as 

tree death ultimately leads to the release of Con. An annual 

mortality rate of 2.6 percent was assumed in the estimate of 

net sequestration. This mortality rate is relatively low compared to that for newly planted street trees (Nowak et al. 

1990). However, there is limited information on urban tree 

mortality, particularly for larger trees and nonstreet trees. If 

actual annual mortality of urban trees exceeds approximately 

5 percent in the Chicago area (with no replacement plantings), 

it is likely that the urban forest will be a source of atmospheric 

C02. There will be a delay in the emission of C02 depending 

on the method of tree disposal (e-g., burning facilitates early 

emissions of GO2). Trees removed today will contribute to 

Con levels in the future, just as trees removed in the past are 

contributing to concentrations of CO2 today. The cycle of 

carbon emissions due to urban tree removal needs further 

investigation. 

1 2.24 t (2.47 tons) of carbon were emitted in 1991 from the 

residential sector (including transportation use) per capita in Illinois 

(Citizens Fund 1992). With 5.88 million people in the study area, an 

estimated 13.2 million t (14.5 million tons) of carbon are released 

annually from residences. 

2 1.30 t (1.43 tons) of carbon were emitted on average in 1991 

from all transportation uses per capita in Illinois (Citizens Fund 1992). 

With 5.88 million people in the study area, an estimated 7.6 million t (8.4 

million tons) of carbon are released annually due to transportation use. 

3 0.0636 kg of carbon emitted per vehicle km (0.226 Iblmi) 

(Citizens Fund 1992). 

Chapter 6 91 
Average diameter growth of urban trees in this study ranged 

from 0.78 to 1.02 cm/yr (0.31 to 0.40 inlyr), within the range 

of average growth rates for street trees in New Jersey (0.58 

to 1.09 cmlyr; 0.23 to 0.43 inchlyr) (Fleming 1988) but higher 

than those for trees in New York's Central Park (0.36 to 0.86 

cmlyr; 0.14 to 0.34 inch/yr) (deVries 1987). The rates also 

are higher than those for forest trees in Illinois, which average 0.38 cmlyr (0.15 inchlyr) (Smith and Shifley 1984). 

Thus, the net sequestration rate is likely liberal as trees in 

more closed-canopy positions have slower growth rates than 

those in this study. 

Energy Effects of Urban Trees 

Estimated carbon emissions avoided annually due to energy 

conservation from existing trees throughout the study area 

total 11,400 t (12,600 tons). This amounts to about 8 percent 

of the net carbon sequestration rate. However, the heating 

energy conservation value (0.04 percent) likely is conservative as most of the sample buildings analyzed for energy use 

had a north-south orientation. Shading from trees on the 

south side of residences can increase winter heating use 

(Heisler 1986). If heating energy savings reached 3 percent 

(McPherson 1994: Chapter 7, this report), 113,600 t (125,200 

tons) of carbon emissions would be avoided annually. More 

research is needed to evaluate the effect of existing tree 

configurations on residential energy use. Most studies to 

date have evaluated optimal tree configurations. A national 

average ratio of 4:l carbon emissions avoided to carbon 

sequestered by urban trees has been estimated for optimal 

locations of urban trees (Nowak 1993). The actual ratio for 

existing urban tree configurations in the study area is probably much lower. Ratios can be higher in regions with little 

winter heating needs, but also can be negative in certain 

locations due to increased energy consumption from shading 

of homes in winter. 

Avoided carbon emissions due to savings in air-conditioning 

energy use probably would be higher in other cities given the 

same energy savings as 83 percent of the study area's 

electricity is generated from nuclear sources. 

Maximizing CO, Reduction with Urban Trees 

There are two primary strategies for maximizing the effect of 

urban trees on atmospheric GO2. The first is to sustain or 

enhance existing tree health to maximize sequestration while 

minimizing losses due to tree mortality. The net effect of 

existing trees is relatively minimal. However, due to the large 

amount of carbon stored in trees, existing trees could become a source of C02 through increased tree mortality in 

conjunction with minimal replanting to offset tree losses. A 

loss of urban trees without replacement is a net source of 

carbon to the atmosphere both directly and indirectly (loss of 

energy conservation around buildings). 

The second strategy is to establish more properly chosen and 

located urban trees in available planting spaces. Planting 

trees to maximize building energy conservation will yield the 

greatest relative carbon benefit. A reasonable tree-planting 

program in conjunction with efforts to sustain existing tree 

cover could increase carbon storage in the study area by 

another 1.2 million t (1.3 million tons). This additional storage, 

which will take years to accrue, is the amount of carbon 

emitted through transportation use in the study area in less 

than 2 months. Future tree plantings must survive to ensure 

that they act as carbon sinks and not sources, that is, trees 

must live long enough to compensate for the COz emitted 

due to planting and maintenance. Research is needed to 

analyze the carbon budget of urban trees. 

Because trees are only a short term reservoir of carbon, 

future planting structures must be sustained to ensure that 

newly treed areas remain long-term carbon sinks. Although 

the benefit of carbon sequestering by trees will eventually be 

lost and the trees will need to be replanted, C02 emissions 

avoided by properly located urban trees are avoided forever. 

Conclusion 

Average carbon storage by trees in the Chicago area is 

between 14 and 18 tlha (6 and 8 tons/acre), with more 

intensely urbanized areas having lower carbon storage. 

Estimates of carbon storage vary widely by land-use type 

and city depending on urban forest structure (e.g., species 

composition, tree density, diameter distribution). Estimates 

of carbon storage by urban forests nationally likely is 

between 400 and 900 million t (440 and 990 million tons). 

However, research is needed to refine this estimate and 

investigate urban forest characteristics and their influence 

on atmospheric GO2. This research would include understanding variations in urban forests across the United States, 

carbon cycling and anthropogenic carbon emissions due 

to vegetation management, tree energykarbon emission 

effects, and urban tree growth, mortality, and biomass. 

Although urban trees can help in reducing atmospheric C02, 

their effect is minimal relative to the magnitude of emissions 

in urban areas. The principal ways to decrease C02 emissions 

are increasing energy conservation and efficiency and converting to non-carbon or low-carbon fuels. 

Acknowledgments 

I sincerely thank Steve Bylina Jr., Jerry Dalton, Bill Brown, 

Timothy Vaughan, Ray Toren, Jr. and the Chicago Bureau of 

Forestry, Mike Stankovich and the Village of Oak Park, 

Peggy Young and the Village of Glen Ellyn, and Larry Slavicek 

and the City of Bloomingdale for providing tree disks for 

growth analyses; Mike Stankovich and the Village of Oak 

Park for providing tree weight data from removed street 

trees; John Dwyer, Hyan-kil Jo, Greg McPherson, and Gerald 

Walton for technical assistance in model development; Richard Birdsey, Richard Pouyat and Gerald Walton for reviewing this paper; Scott Prichard for tree-ring measurements 

and data entry; and Una Arnold and Jack Stevens for literature assistance. 

92 Chapter 6 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
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Ajtay, G. L.; Ketner, P.; Duvingneaud, P. 1979. Terrestrial primary 

production and phytomass. In: Bolin, B.; Degens, E. T.; Kempe, 

S; Ketner, P. eds. The global carbon cycle. SCOPE Rep. 13. 

New York: John Wiley and Sons: 129-1 81. 

Andersson, F. 1970. Ecological studies in Scanian Woodland 

and meadow area, southern Sweden. II. Plant biomass, primary 

production and turnover of organic matter. Botaniska Notiser. 

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94 Chapter 6 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chapter 7 

Energy-Saving Potential of Trees in Chicago 

E. Gregory McPherson, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Davis, CA 

Abstract 

Parametric computer simulations of microclimates and 

building energy performance were used to investigate the 

potential of shade trees to save residential heating and 

cooling energy use in the City of Chicago. Prototypical buildings included one-, two-, and three-story brick buildings similar 

to residences in the Chicago area, and one-and two-story 

wood-frame buildings representing suburban construction. 

To validate the energy performance of prototypes, building 

performance indices of reference buildings were calculated, 

in some cases using whole-house metered data, and compared with indices of the prototypes. Increasing tree cover by 

10 percent (corresponding to about three trees per building) 

could reduce total heating and cooling energy use by 5 to 10 

percent ($50 to $90). On a per-tree basis, annual heating 

energy can be reduced by about 1.3 percent ($10, 2 MBtu), 

cooling energy by about 7 percent ($15, 125 kilowatt-hours), 

and peak cooling demand by about 6 percent (0.3 kilowatts). 

Simulation results were used in a 20-year economic analysis 

of costs and benefits associated with a hypothetical shadetree program. Benefit-cost ratios of 1.35 for trees planted 

around typical two-story residential buildings and 1.90 for 

trees near energy-efficient wood-frame buildings indicate 

that a utility-sponsored shade-tree program could be costeffective for both existing and new construction in Chicago. 

Introduction 

This study provides information to utilities, policy makers, 

planners, urban foresters, arborists, and landscape professionals in the Chicago area on the potential impacts of trees 

on energy use for residential space conditioning. Based on 

results of computer simulations, the cost-effectiveness of tree 

planting for energy conservation around typical residential 

buildings is evaluated and landscape design guidelines are 

presented. These findings can be used to: I) evaluate energyefficient landscape design incentives for new and existing 

residential construction; 2) conduct a broader analysis of 

benefits and costs associated with tree planting and care; 

and 3) educate residents and landscape professionals regarding energy-efficient landscape design. Effects of tree shade, 

cooler summertime temperatures due to evapotranspirational 

(ET) cooling, and reduced windspeeds were simulated using 

Chicago weather data and two computer programs: the Shadow 

Pattern Simulator and Micropas 4.01. Energy savings were 

calculated for three brick buildings (one, two and three story) 

typical of residences in the City of Chicago and older suburban communities, as well as two wood-frame buildings (one 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 

and two story) representative of housing products built in 

suburban Chicago. This study builds on previous simulations 

of potential energy savings from trees in Chicago (Akbari 

et al. 1988; Huang et al. 1990) by incorporating additional 

building types, a variety of tree sizes and locations, and ET 

cooling effects. 

Background 

Chicago area residents spend about $660 million annually 

for natural gas to heat their homes, and $216 million for 

air conditioning (McPherson et al. 1993). Approximately 93 

percent of all households use natural gas for space heating, 

40 percent use electricity for central air conditioning, and 38 

percent use electricity for room air conditioning (Bob 

Pendlebury, Peoples Gas, 1994, pers. commun.; Tom 

Hemminger, Commonwealth Edison, 1991, pers. commun.). 

Each year, the typical Chicago household with central air 

conditioning pays $755 for heating (151 million Btu or MBtu) 

and $216 for cooling (1,800 kilowatt-hours or kwh). 

The need for summertime cooling is greatest in Chicago's 

most densely developed areas, where paving and buildings 

absorb and trap heat to create mini-heat islands. Air temperatures can be 5" to 10°F (2" to 6°C) warmer in these "hot 

spots" than in cooler park or rural areas (Landsberg 1981). A 

study of air temperatures measured at Midway Airport and 

rural Argonne National Laboratory found temperature 

differences between city and rural sites of 54°F (3°C) or 

more in August 20 percent of the time (Ackerman 1985). A 

substantial amount of air conditioning is required just to 

offset increased temperatures associated with localized heat 

islands (Akbari et al. 1992). 

The potential of trees to mitigate urban heat islands and 

conserve heating and cooling energy has not been well 

documented in Chicago, but studies have been conducted in 

other cities with a similar climate (Akbari et al. 1992; Akbari 

and Taha 1992; McPherson and Rowntree 1993). Large 

numbers of trees and parks can reduce local air temperatures by lo to g°F(0.50 to 5OC), and the advection of this cool 

air can lessen the need for air conditioning. Results of 

computer simulations of three trees around an unshaded 

well-insulated house in Chicago showed that shade alone 

reduced annual and peak cooling energy use by 31 percent 

(583 kwh) and 21 percent (0.67 kW), respectively (Akbari et 

al. 1988). ET by trees lowers air temperatures and results in 

additional cooling energy savings. There is considerable 

uncertainty as to the magnitude of this ET cooling effect, but 

findings from several simulation studies suggest that it can 

produce savings greater than those from direct shade of 

buildings (Huang et al. 1987; McPherson and Rowntree 1993). 

Chapter 7 
Scattered trees throughout a neighborhood increase surface 

roughness, thereby reducing windspeeds by as much as 50 

percent (Heisler 1990). Trees and shrubs located slightly 

upwind of buildings provide additional protection that reduces 

the amount of cold outside air that infiltrates. Lower windspeed 

results in reduced infiltration of outside air. Reduced infiltration is beneficial during both the heating and cooling seasons. 

However, lower windspeed is detrimental during the cooling 

season when natural ventilation can reduce reliance on air 

conditioning. Reduced infiltration from wind shielding by three 

trees around a well-insulated Chicago residence was simulated to reduce heating energy use by 16 percent (16.8 

MBtu) or about $84 (Huang et al. 1990). In the same study, 

wind shielding reduced annual air-conditioning energy use by 

9 kwh (0.03 GJ), suggesting that the benefit from reduced 

infiltration is slightly greater than the detrimental effect of 

lower windspeeds on natural ventilation. Other computer 

simulations and building energy measurements confirm that 

windbreaks can reduce annual heating costs by 10 to 30 

percent (DeWalle et al. 1983, Heisler 1991). Proper placement and tree selection is critical in Chicago because winter 

shade on south-facing surfaces increases heating costs 

in mid- and high-latitude cities (Heisler 1986a; McPherson 

and Rowntree 1993; Sand and Huelman 1993; Thayer and 

Maeda 1985). 

Methods 

Building Energy Analysis 

Micropas and the Shadow Pattern Simulator (SPS) were the 

two computer programs used to project the effects of trees 

on heating and cooling energy use (McPherson and Dougherty 

1989; McPherson and Rowntree 1993; McPherson and 

Sacamano 1992). Micropas 4.01 provides hour-by-hour estimates of building energy use based on the building's thermal 

characteristics, occupant behavior, and specific weather data 

(Nittler and Novotny 1983). It is used widely by engineers, 

architects, and utilities to evaluate building energy performance. Micropas algorithms have been validated and found 

to agree closely with data from occupied houses and passive 

test cells (Atkinson et al. 1983). The California Energy Commission (1 992) has certified Micropas for checking building 

compliance with state energy-efficiency standards. 

In this study, Micropas simulations used Chicago weather 

data for each unshaded base case building. Two additional 

simulations use a modified weather file and adjusted shielding class to account for energy savings due to the reductions 

in air temperature and windspeed associated with trees. 

Information on how Micropas estimates solar heat gains, 

infiltration, natural ventilation, and internal heat gains is contained in the footnote to Table 1. 

SPS quantifies the effects of each shading scenario on 

solar-heat gains (McPherson et al. 1985). SPS uses sunplant-building geometry, tree size, shape, and crown density 

to compute hourly surface shading coefficients for the 21st 

day of each month. Micropas was modified to accept output 

from the SPS files to account for tree shade on each of eight 

possible building surfaces (four wall and four roof orienta96 Chapter 7 

tions). Micropas multiplies the hourly shading coefficients by 

direct and diffuse radiation values to reduce solar-heat gains 

on opaque and glazing surfaces, 

Energy savings are calculated as the difference between the 

unshaded base case and results from each of the shading, 

ET cooling, and windspeed-reduction scenarios. Standardized reports in Appendixes C and D include the following 

information: 

-Heating, cooling, and total annual energy use (kBtu1sf). 

-Total annual electricity (kwh) use for air conditioning. 

-Summer peak (kW) energy use for air conditioning. 

-Total annual natural gas (MBtu) use for space heating. 

-Hours of air conditioning use. 

Base Case Buildings 

Energy simulations are applied to five base case buildings: 

three brick buildings typical of construction in Chicago 

and nearby communities, and two wood-frame buildings characteristic of suburban residential development. The brick 

buildings are one, two, and three stories and the wood-frame 

houses are one and two stories. Because Chicago streets 

are laid out in a grid pattern and building orientation influences energy use, brick buildings are modeled with their 

long walls facing north-south and east-west. This was not 

necessary for the wood-frame buildings because the window 

area is identical for all walls. The following characteristics of 

each base case building are detailed in Table 1. 

1. One-story brick. One family and three occupants, 2,125 ft2 

(1 97 m2) of floor area, constructed during 1950's with 8-inch 

(20-cm) brick walls (gypsum lath and plaster, plus 1-inch 

blanket insulation) (R-7), gypsum lath (318 inch) and plaster 

ceiling below an unheated attic with 6 inches (15 cm) of attic 

insulation (R-19), wood floor over enclosed unheated basement with 4 inches (1 0 em) of insulation (R-4), double-hung, 

wood-sash, single-pane windows with storms, and moderately efficient heating and cooling equipment. 

2. Two storv brick. Two households and six occupants, 

3,562 ft2 (331 m2) of floor area (1,781 ft2 per household), 

constructed during the 1950's with materials and equipment 

similar to the one-story brick building. 

3. Three storv brick. Six households, 18 occupants, 6,048 ft2 

(562 ma) of floor area (1,008 ft2 per household), constructed 

during the 1930's with materials similar to those for the oneand two-story brick buildings, but no storm windows, loose 

construction, and relatively inefficient heating (e.g., boiler 

instead of furnace) and cooling equipment. 

4. One-storv wood frame. One household, three occupants, 

1,500 ft2 (139 mz) of floor area, constructed during 1950's 

with 2 by 4-inch (5 by 10-cm) studs on 76-inch (40 cm) 

centers, hardboard siding, sheathing, and drywall (R-7), 

drywall ceiling below an unheated attic with 6 inches of attic 

insulation (R-19), wood floor over enclosed unheated 

basement with 4 inches of insulation (R-4), single-pane metal 

slider windows with storms, and moderately efficient heating 

and cooling equipment. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Table 1 .-Base case building characteristics and Micropas simulation assumptions 

Construction type 

Date built 

No. units (occupants) 

Floor area (@) 

Volume (e) 

Front orientation 

Window area (it2) 

North 

East 

South 

West 

Total 

floor area (%) 

Window panes (No. and u-value) 

Window shading ~oef.~ 

Glass only 

Drapes or blinds 

Duct insulation (R-value) 

Duct 

CVCrawl 

Wall insulation (R-valuelb 

Attic insulation (R-valuelb 

Crawlspace/basement 

Floor (R value) 

Stem wall (R value) 

Air exchange 

Ventilation (a~h)~ 

Infiltration (achld 

Local shielding classd 

Latent heat fraction 

Glazing obstructiona 

Wind correction factore 

Internal gain (~tu/day)~ 

Gas furnace efficiency 

Air conditioner (SEER) 

Thermostat settings 

Summer cooling 

Brick 

1950-60 

1 (3) 

2,125 

19.125 

North (East) 

' 79 (28) 

96 (79) 

67 (96) 

28 (67) 

270 

12.7 

2, 0.60 

0.88 

0.78 

4.2 

4.2 

7 

19 

4 

5 

1.39 

0.58 

3 

0.1 

0.7 

0.25 

51,875 

0.6 

7.8 

No setback 

78 

Brick 

1950-60 

2 (6) 

3,562 

33,858 

South (East) 

136 (105) 

105 (98) 

98 (214) 

214 (136) 

553 

15.5 

2, 0.60 

0.88 

0.78 

2.0 

4.2 

7 

19 

4 

5 

2.80 

0.62 

3 

0.1 

0.75 

0.4 

73,430 

0.58 

6.7 

No setback 

80 

Brick 

1930 

6 (18) 

6.048 

54,432 

South [East) 

90 (200) 

200 (200) 

200 (200) 

200 (90) 

690 

11.4 

1, 0.88 

1 .oo 

0.78 

4.2 

4.2 

7 

19 

4 

5 

2.32 

0.75 

3 

0.1 

0.75 

0.5 

21 0,720 

0.5 

6.5 

No setback 

78 

wood 

1950-60 

1 (3) 

1,500 

12,500 

South 

75 

75 

75 

75 

300 

20.0 

1, 0.88 

1 .oo 

0.78 

4.2 

4.2 

7 

19 

4 

5 

2.17 

0.67 

3 

0.1 

0.75 

0.25 

42,500 

0.7 

7.5 

No setback 

78 

wood 

1990 

1 (3) 

1,761 

15,588 

West 

75 

75 

75 

75 

300 

17.0 

2, 0.44 

0.88 

0.78 

4.2 

4.2 

13 

30 

11 

5 

2.66 

0.48 

3 

0.1 

0.75 

0.4 

46,415 

0.78 

10 

Setback 

78 

Winter heating 70 72 70 70 68 day, 60 night 

a Shading coefficients are fraction of irradiance transmitted. Micropas simulations assume drapes are drawn. when air conditioning was on the 

previous hour. Glazing obstruction is a shading coefficient that applies at all times to all windows to approximate irradinace reductions fmm shade 

cast by nearby buildings and vegetation (Enercomp 1992). 

Solar absorptance of walls and roof assumed to be 0.5 corresponding to a medium gray color. 

Micropas simulations assume that the buildings are naturally cooled and ventilated by opening the windows whenever the outside temperature and 

windspeeds allow such natural cooling to occur. The average hourly ventialtion rate during summer (June-August) is shown as air changes per hour 

(ac h) . ti ~hk hourly infiltration rate is simulated to va with outdoor air temperature and windspeed and is calculated using estimates of the building's total 

effective leakage area (ASHR* ,989). ~oca7shieldin~ classes are used to amount for windspeed reductions associated with increased tree cover 

(see text). The average hourly infiltration rate during wmter (November-April) is shown as air changes per hour (ach). 

The wind-reduction factor is a fraction of airport windspeed that accounts for windspeed differences between the building site and measurement 

instrument, which is typically 30 feet above the ground. ' Daily internal heat gains are assumed constant year mud Hourly gains are simulated using a research-based schedule (CEC 1992). 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 7 
5. Two-story wood frame. One household, three occupants, 

1,761 ft2 (1 64 me) of floor area, constructed during 1990's 

with 2 by 4-inch (5 by 10-cm) studs on 16-inch (40 cm) 

centers, hardboard siding, sheathing, insulation, and drywall 

(R-13), drywall ceiling below an unheated attic with 6 inches 

of attic insulation (R-30), wood floor over enclosed unheated 

basement with 4 inches of insulation (R-11), double-pane 

metal slider windows with storms, and very efficient heating 

and cooling equipment. 

Calibration 

To ensure that the energy performance of each base case 

building is reasonably similar to actual buildings in Chicago, 

building performance targets were established with data from 

real reference buildings. A close match between building 

performance of the base case building and its reference 

indicates that simulations produce realistic data on energy 

use. To achieve similitude, various input parameters for 

each base case building are adjusted in an iterative process. 

Comparisons of similitude are made using a Heating Performance lndex (HPI) and Cooling Performance lndex (CPI) 

that partially normalize for different weather conditions and 

building sizes (Mahajan et al. 1983). The HPI and CPI are 

calculated as: 

HPI = Btu I HDD 1 FA CPI = Wh I CDD / FA 

where Btu = British thermal units of natural gas consumed 

for space heating, Wh = watt-hours of electricity consumed 

for air conditioning, HDD = heating degree-days-(one HDD 

accumulates for every degree that the mean outside temperature is below 65°F (18.3"C) for a 24-hr period), CDD = 

cooling degree days-(one CDD accumulates for every degree that the mean outside temperature is above 65°F (1 8.3%) 

for a 24-hr period) and FA = conditioned floor area (ftz). 

Indices for target building performance for the one-and 

two-story brick buildings were calculated using metered data 

from a sample of 18 residences in a two-block area in Chicago 

(Wilkin and Jo 1993). These buildings are part of another 

Chicago Urban Forest Climate Project study, and are representative of the brick bungalows and two-story houses that 

were built throughout Chicago soon after World War 11. Data 

on monthly metered electricity and bimonthly natural gas, as 

well as data on heating and cooling degrees were obtained 

with the residents' approval from the local utilities for April 

1991 through March 1993. Energy consumed for space heating 

(SH) and cooling (SC) for each bimonthly and monthly 

period was estimated by the base-load method (Linaweaver 

et al. 1967): 

SH = TG - BLG SC = TE - BLE 

where TG and TE are total metered gas and electric consumption, respectively, and BLG and BLE are base-load gas 

and electric consumption. BLG is defined as the lowest 

consumption of natural gas during the summer cooling season 

(May through September); BLE is defined as the lowest 

consumption of electricity during the winter heating season 

(October through April). Use of base loads to calculate SH 

and SC assumes that base-load consumption remains constant throughout the year. Base loads can vary monthly 

and seasonally (e.g., less electricity used for lighting during 

summer than winter due to shorter nights). Another limitation 

98 Chapter 7 

to the base load method is that the use of degree-days may 

not fully normalize energy use for different weather conditions. For example, when there are high amounts of wind 

or irradiance, the temperature-based cooling degree-day 

approach becomes a less accurate indicator of heating and 

cooling energy use. Also, the assumption of constant base 

loads becomes increasingly less tenable as weather conditions deviate from normal (e.g., during very hot periods 

people may use less electricity for cooking). Annual HDD 

and CDD from 1991 to 1992 and from 1992 to 1993 indicate 

that while HDD for both periods are within 10 percent of the 

30-year normal for Chicago, there are 56 percent more CDD 

than normal during the first year and 39 percent fewer than 

normal during the second year (Table 2). Although average 

annual HDD and CDD for the 2-year period (1991-93) are 

within 10 percent of normal, the extremely warm summer of 

1991 and cool summer of 1992 are likely to reduce the 

reliability of estimates of air-conditioning energy use. Although these building performance indices provide only rough 

approximations of energy consumed for space heating and 

air conditioning, they serve as a basis for simulating effects 

of vegetation on building energy performance in Chicago. 

Separate average monthly CPl's and HPl's for the 2 years 

were calculated for the one-and two-story brick buildings 

using data from the four one-story and 14 two-story reference 

buildings. Separate target CPl's and HPl's were established 

for the one-and two-story buildings using the mean values 

for each building type. The one and two story brick buildings 

with building performance indices closest to the overall mean 

were selected for use as the base case buildings in this 

study. To gather information for modeling energy use of 

these buildings, an informal energy audit was conducted 

by the Center for Neighborhood Technology and detailed 

building measurements were taken. 

Because there are no three-story buildings in the sample of 

actual houses, building features and performance targets were 

based on results of numerous energy audits of three-story 

and four-story buildings conducted by the Center for Neighborhood Technology (John Katrakis 1993, pers. commun.). 

To facilitate comparisons of potential energy savings from 

trees in Chicago with studies in other cities, the characteristics of the two wood-frame buildings used in this study are 

similar to those used in previous simulations (McPherson 

and Rowntree 1993). The base cases were calibrated so that 

their performance indices are similar to the target indices 

of reference buildings used in a previous simulation study 

for Chicago conducted by scientists at Lawrence Berkeley 

Laboratory (LBL) (Huang et al. 1990). LBL developed two 

wood-frame reference buildings, the "pre-?973 house" had 

little insulation and was not energy efficient, while the "1 980's 

house" was highly efficient. The CPI and HPI of the LBL 

reference buildings served as targets for evaluating the energy 

performance of the two wood-frame base case buildings 

used in this study. 

Shading Scenarios 

Two sets of shading scenarios account for different treebuilding juxtapositions in Chicago and suburban areas. In 

Chicago, front yards and narrow side yards seldom have 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 2.-Number of heating and cooling degree-days for Chicago 

Period Heating degree-days Cooling degree-days 

April 1 991 - March 1992 5.928 1.154 

April 1993 - March 1993 6,746 457 

Average annual (1 991 -93) 6,337 806 

30-vear normal 6,455 740 

trees. Therefore, street trees located 20 to 35 ft (6 to 11 m) 

from the front of buildings are a major source of shade. In 

suburban areas, larger lots and wider side yards provide 

more opportunities for locating trees to optimize summer 

shade. This section describes one set of shading scenarios 

applied to the brick buildings typically found in Chicago, and a 

second set of scenarios applied to the wood-frame buildings 

often seen in suburban Chicago. 

Brick Buildings 

Shading scenarios were developed to estimate the positive 

and negative impacts of shade from trees of different sizes, at 

different distances from the building, and at different aspects 

around the building. Tree heights of 24, 36, and 50 feet (7.3, 

1 1 .O, 15.3 m) roughly correspond with sizes of trees at 20,30, 

and 45 years (Table 3). All trees are assumed to be deciduous, blocking 85 percent of total irradiance during summer 

(May-October) and 25 percent during winter (November-April). 

Tree crowns are assumed to have a paraboloid shape. 

Trees are located at three distances from the building walls: 

12, 22, and 34 feet (3.7,6.7,10.4 m). A distance of 12 feet 

usually is about as close to a building that a tree is placed. 

Distances of 22 and 34 feet correspond with potential locations of backyard and street trees. In Chicago, street trees 

are seldom farther than 34 feet from the front of buildings 

because of building setback and right-of-way configurations. 

Four shading scenarios account for these tree size and 

distance factors: 

-One 24-foot-tall tree sequentially located 12 feet 

from the east, south, and west walls. 

-One 36-foot-tall tree sequentially located 22 feet 

from the east, south, and west walls. 

-One 50-foot-tall tree sequentially located 22 feet 

from the east, south, and west walls. 

-One 50-foot-tall tree sequentially located 34 feet 

from the east, south, and west walls 

To account for shade from trees located at different aspects 

around the building, the four scenarios listed are repeated 

for trees centered and opposite the east, south, and west 

walls of each brick building. These scenarios allow a comparison of cooling savings associated with trees opposite 

west- and east-facing walls, as well as of increased heating 

costs associated with reduced winter solar-heat gain from 

trees opposite south-facing walls. Fifteen shading scenarios 

are run for each base case building orientation. Because the 

orientation of each brick building is rotated 90 degrees 

USDA Forest Service Gen. Tech. Rep. NE-186.1994. 

to account for dissimilar window distributions, 90 shading 

scenarios are simulated. 

Wood-Frame Buildings 

Shading scenarios for the wood-frame buildings were developed to supply information to utilities interested in evaluating 

the cost-effectiveness of yard trees for demand-side management (DSM). Cost-effectiveness analysis for DSM options 

usually require's annual estimates of energy savings over a 

20-year period (McPherson 1993). Shading scenarios should 

reflect near optimum tree placement for energy savings, Le., 

if trees are not cost-effective in the best locations, they will 

not be cost-effective elsewhere. 

To provide data for annual estimates of energy savings, 

shading scenarios occur at 5-year intervals for 20-years. 

Tree dimensions at years 5, 10, 15, and 20 are based on a 

typical growth curve for a deciduous tree assumed to be 6 

feet (1.8 m) tall when planted (Table 3). The rate of growth 

reaches a maximum of 1.5 feet (0.5 m) per year several 

years after planting, then slows until a height and spread of 

25 feet (7.6 m) is obtained 20 years after planting. Crown 

density, shape, and foliation periods are assumed to be the 

same as for trees shading the brick buildings (Table 3). 

Computer simulation results suggest that in mid- and highlatitude cities like Chicago, tree shade on west walls 

is beneficial but detrimental on the south walls because 

increased heating costs outweigh cooling savings (Thayer 

and Maeda 1985; Heisler 1986a). Shade from trees to the 

east may increase heating, but net savings are likely due to 

substantial cooling benefits. Therefore, four shading scenarios were developed to assess potential energy savings 

from trees opposite east and west walls: one tree opposite 

the west wall; two trees opposite west wall; one tree opposite 

east wall; and three trees, two opposite the west wall and 

one the east wall. 

Single trees are placed opposite the middle of the wall to 

maximize the area shaded. All trees are 12-feet from the 

walls (Figure 1). 

ET Cooling and Reduction in Windspeed 

Reductions in windspeed and summertime air temperatures 

cannot be simulated as accurately as the effects of direct 

shade on buildings. The former reflect the aggregate effect 

of trees in the local area, which makes it difficult to isolate 

Chapter 7 
Table 3.-Tree dimensions for shading scenarios in feet 

Building Crown diameter Bole height Crown height Tree height 

Brick buildings 

Small 12 6 18 24 

Medium 24 8 28 36 

Large 36 12 38 50 

Wood buildings 

Yr. 5 13 4 9 13 

Yr. 10 19 6 13 19 

Yr. 15 24 6 18 24 

Yr. 20 25 6 19 25 

mmm 

mmm 

Figure 1 .-Plan view and section showing simulated tree growth over the 20-year period for 

two trees opposite the west wall and one opposite the east wall of the two-story wood-frame 

base case building. 

100 Chapter 7 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
the role of any single tree. Yet, they are important because 

their effect can be substantial (Akbari et al. 1992; Huang et 

al. 1987; McPherson 1993). Further analysis of weather data 

collected at backyard locations throughout Chicago will 

reduce uncertainty about the relative impact of reductions in 

windspeed and summertime air temperature. 

Reductions in Air Temperature 

The method used by Huang et al. (1987) was followed to 

ascribe cooling energy savings associated with modeled 

reductions in air temperature for individual trees. Assuming 

a typical lot size of 7,000 ft2 (650 mn), each tree (24-foot 

crown diameter) adds 7-percent tree cover to the lot (450 ft2 

per tree). Adding three trees around the residence increases 

tree cover by about 20 percent, but in reality the presence 

of other trees on or near the lot diminishes the marginal 

contribution of each new tree. Therefore, it is conservatively 

assumed that the simulated cooling savings associated with 

three trees is due to about half of the new tree cover they 

represent, or 10 percent. 

To determine how a 10-percent increase in tree cover influences outside air temperatures in Chicago, limited data from 

local measurements, previous studies, and the literature 

were consulted. Measurements of air temperature taken 

between 12 noon and 5 p.m. during a summer day in Chicago were lo to 2°F (0.5" to 1 .O°C) cooler in a city block with 

59-percent tree cover than in a nearby block with 36-percent 

tree cover (Wilkin and Jo 1993). A similar cooling effect was 

found in Bloomington, Indiana, where midday temperatures 

measured under the canopy of trees over grass were 1.3" to 

2.3"F (0.7" to 1.3"C) cooler than at an open reference site 

(Souch and Souch 1993). Other findings (Huang et al. 1987; 

Profous 1992) suggest that there is a lo to 2°F (0.5" to 

1 .O°C) decrease in temperature for every increase of 10- 

percent in vegetation cover. On the basis of these data, 

an empirical model was developed that reduced hourly 

summertime temperatures in a graduated manner to account 

for diurnal differences. Nighttime temperatures are altered 

the least because evapotranspiration is small, while midafternoon temperatures are reduced by as much as 1.8 

percent (Figure 2). In all cases, winter temperatures are 

unaltered. Thus, a maximum hourly reduction in temperature of 2°F (1 .l°C) is modeled that corresponds to what 

might be associated with an increase in local tree cover 

of about 10 percent. 

Reductions in Windspeed 

Results from studies of wind reduction in residential neighborhoods suggest that a 10-percent increase in tree canopy 

cover is associated with a reduction in wind speed of 5 to 15 

percent (Heisler 1990; Myrup et al. 1993). The magnitude of 

windspeed reduction associated with a 10-percent increase 

in tree cover is greater for neighborhoods with relatively low 

tree canopy cover than for areas with high tree cover. 

Micropas uses local shielding classes to incorporate the 

effects of buildings and vegetation on air infiltration rates in 

houses. Reductions in windspeed of approximately 5 to 15 

percent are simulated by modifying the building shielding 

class from 3 or moderate local shielding (some obstructions 

within two house heights, thick hedge, solid fence, or one 

neighboring house) to 4 or heavy shielding (obstructions 

around most of perimeter, buildings or trees within 30 feet in 

most directions; typical suburban shielding). Savings in heating 

energy associated with increased shielding are conservatively attributed to the aggregate effects of three trees on site 

or a 10-percent increase in local tree cover. 

1 -- Unaltered -- Altered 

Figure 2.-Modeled outside air temperature reductions associated with a 10- 

percent increase in neighborhood tree-canopy cover are shown as the altered 

temperature curves for July 1 and 2. ( In the simulation model, 4 p.m. on July 1 

is when peak air-conditioning energy demand occurs.) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 7 
Micropass Simulations 

Effects of air temperature and reductions in windspeed are 

simulated separately with Micropas. The combined savings 

due to direct and indirect effects of trees is calculated by 

adding the savings due to shade, ET cooling, and wind 

reductions. Simulations were run to determine if there were 

interactions among these three factors, but none were observed. The presence of tree shade had little effect on the 

indirect effects and indirect effects did not alter the impact of 

shade. Savings due to ET cooling and wind shielding are 

calculated on a per-tree basis as one-third of the savings 

attributed to a 10-percent increase in tree cover associated 

with the addition of three trees. Savings from shade cast by a 

tree on the west wall is added to the ET cooling and wind 

shielding savings to calculate total savings per tree. 

Results and Discussion 

Base Case Building Validation 

To determine if simulated energy use is realistic the HPl's 

and CPl's of the base case buildings were compared with 

those of their respective reference buildings. The HPl's of 

the base case buildings are within 6 percent of their respective targets except for the two-story wood-frame building, 

which is less energy efficient than the LBL reference building 

(Table 4). Although less efficient than its reference, the twostory wood-frame base case consumes less than one-half 

the amount of natural gas used to heat a typical Chicago 

residence (1 51 MBtu). The CPl's of the base case buildings 

also are within 7 percent of their respective targets except 

for the one-story brick building, which is about 15 percent 

less energy efficient (Table 4). However, total electricity 

used to air condition this building is similar to that of typical 

Chicago households (1,800 kwh). 

Relations among annual energy costs for heating and cooling each base case building are shown in Figure 3. Because 

Table 4.-Targeted and base case building performance indices 

the two- and three-story brick buildings contain two and six 

households, respectively, costs for the typical Chicago household are multiplied by 2 and 6 as a basis for comparison with 

the base cases. Total costs for the one-story brick building 

are similar to those of the typical Chicago household ($971). 

Costs for the two-story brick buildings, each containing two 

dwelling units, are about $400 (20 percent) greater than the 

costs of a building containing two households with typical 

energy consumption for heating and cooling. Annual costs 

for the three-story base case containing six dwelling units 

are about $1,400 (24 percent) less than projected for six 

typical households. This result is not surprising because 

smaller households often use less energy than larger households and the average dwelling unit size in the six-unit base 

case is only 1,008 ft2 (94 m2). Energy costs for the poorly 

insulated one-story wood-frame building are $30 (3 percent) 

greater than for the typical household. Annual costs for the 

single-family, two-story wood-frame building are $390 (40 

percent) less than the typical residence due to its insulative 

properties and tight construction. 

Effects of Tree Shade 

Effects of tree shade on heating and cooling energy use vary 

with building type, building orientation, and tree type and 

location. Results from simulations using more than 100 shading 

scenarios provide a basis for examining relations among 

these variables. 

Building Type and Orientation 

Street trees are a major source of building shade within 

Chicago (Nowak 1994: Chapter 2, this report). Therefore, 

relations among building type, building orientation, and energy savings are shown for a large street tree (50-feet-tall 

and 36-feet-wide) located 34 feet (10 m) from the east, 

south, and west walls of each brick-base case building (Figure 4). Because winter irradiance is primarily from the south, 

street trees to the south reduce solar-heat gain and increase 

Item One-story bricka Two-story brickb Three-story brick'= One-story woodd Two-story woodd 

Heating HPI~ MBtu HPI MBtu HPI MBtu HPI MBtu HPI MBtu 

Target 13.3 17.2 18.0 14.2 5.3 

N-S facing 13.3 173.4 17.6 385.1 19.1 711.7 14.0 129.7 

E-W facing 13.0 170.1 17.1 375.5 19.2 715.6 6.6 71.5 

Cooling CPle kwh CPI kwh CPI kwh CPI kwh CPI kwh 

Target 0.82 1.06 1.20 1.71 0.94 

N-S facing 0.92 1,795 1.12 3,682 1.29 7,199 1.75 2,941 

E-W facing 0.98 1,928 1.13 3,725 1.25 6,970 0.94 1,853 

a Targets based on wholehouse metered data for four Chicago residences. 

Targets based on whole-house metered data for 14 Chicago residences. 

Targets based on energy audit results fmm the Center for Neighborhood Technology. 

Targets based on perfomlance of similar Chicago buildings in Huang et al. 1990. 

Units for HPI and CPI are: Btuheating degreeday# conditioned floor area and Wh/cooling degree-day# conditioned floor area. 

102 Chapter 7 USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Heating 0 Cooling 

Figure 3.-Simulated annual heating and cooling costs are shown for 

each base case building, where the number corresponds to the number 

of stories and the letter corresponds to the brick building's front orientation (e.g., l-N is one-story brick building facing north, l-Wood is the onestory wood-frame base case). For comparison, average costs per 

Chica~o household have been extrapolated for buildings with one, two, 

and six dwelling units. 

heating costs (Figure 4a). Street trees usually are too far 

from the building to block much summer irradiance, so cooling savings do not offset increased heating costs (Figure 

4b). Trees to the south are projected to increase total annual 

heating and cooling costs by $5 to $1 3 compared to unshaded 

base cases. These results suggest selecting trees with open 

crowns during the leaf-off period and/or species that drop 

their leaves relatively early during the fall and leaf out in 

late spring. These traits minimize the obstruction of irradiance during the heating season. Tree species identified 

as "solar friendly" and well adapted to growing conditions in 

the Chicago area are listed in Appendix 6. Information in 

Appendix B was adapted from Watson (1991) and Ames 

(1987). It should be noted that energy penalties from trees 

south of buildings can be offset to some extent by other 

energy benefits such as shading of streets, ET cooling, and 

wind shielding. 

Annual energy savings from a large street tree to the east 

range from $7 to $13, while savings from a tree to the west 

range from $5 to $26 (Figure 4a). Differences in savings 

among buildings are largely due to differences in the relative 

amount of window area shaded by the tree. For example, 

energy savings from a tree to the east of the one-story brick 

building facing north are more than twice that from a tree to 

the west, but the building has 96 ft2 (8.9 m2) of window area 

facing east and only 28 ft2 (2.6 m2) facing west. When the 

building is rotated 90 degrees (facing east), 79 ft2 (7.3 m2) of 

window area face east and 67 ft2 (6.2 m2) face west. Given 

this comparable distribution of window area, the savings 

from a tree to the east and west are nearly equal. Similarly, 

when the three-story building is rotated to face east, the 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

west-facing window area decreases from 200 to 90 ft2 (1 9 to 

8 m2) and savings from a west tree drops from $21 to $14. 

When only the beneficial aspects of shade on annual airconditioning energy use are considered, a large street tree to 

the east or west provides savings of 2 to 8 percent in total 

cooling energy use (Figure 4b). Cooling savings are greatest 

(6 to 8 percent) for a tree to the east of the one-story brick 

buildings and west of the two-story building facing south. A 

tree opposite the three-story building provides the least cooling savings on a percentage basis, but the most savings on 

an absolute basis (kwh) due to overall building size. 

Air-conditioning energy use at the building peak (4 p.m., July 

1) is not influenced by shade from trees to the east and 

south. A large tree to the west reduces peak cooling energy 

demand by 2 to 6 percent (Figure 4c). Savings are greatest 

for buildings with relatively large amounts of west-facing 

window area. 

Tree Size and Distance from Building 

Energy savings are related to the amount of window and wall 

area that a tree shades. Generally, larger trees produce 

more building shade than smaller trees in the same location. 

Also, the closer a tree is to a building the more wall area it 

shades. Using the two-story brick building facing south as an 

example, shade from the 50-foot-tall tree (large) located 22 

feet from the building walls produces greater total annual 

energy savings than the other shading scenarios (Figure 5). 

Savings are about 40 percent less for the same size tree 

located 34 feet away from the buildings, the typical distance 

of a street tree in Chicago. The 36-foot-tall tree (medium) 

Chapter 7 103 
-20 -1 I I - North I -East 2-South 2-East 3-South 3-East 

Brick Buildings 

Tree to East 0 Tree to South Tree to West 

2-South 2-East 3-South 3-East 

Brick Buildings 

I-North I-East >South 2-East 3-South 3-East 

Brick Buildings 

Figure 4.-Annual savings in space conditioning savings due 

to shade from a single deciduous tree (50 feet tall and 36 feet 

wide) located 34 feet from each brick building. The shading 

scenario is representative of a mature street tree in Chicago. 

Figures 4b and 4c show the simulated effects of tree shade 

as percentages of annual and peak air-conditioning savings. 

104 Chapter 7 

located 22 feet from the building produces about one-third 

the savings as the 50-foot tree at the same location. Savings 

from the 24-foot tree (small) located 12 feet away from the 

west wall are about half the savings produced by the 36-foot 

tree at 22 feet. The 24-foot tree opposite the east wall 

produces no net savings because cooling savings are offset 

by increased heating costs due to winter shading. These 

relations betweenenergy savings and tree size and distance 

are consistent across building types. 

Annual cooling savings divided by heating costs produces a 

ratio with a value greater than 1.0 when savings from tree 

shade exceed costs. Ratios for trees to the south are less 

than 1.0 for all size-distance combinations (Figure 6). Ratios 

for trees to the east range from 1.0 to 2.2, while ratios for 

trees to the west range from 4.5 to 7.5. Lower ratios for trees 

to the east are due to shade during the spring-fall transition 

months when large amounts of irradiance strike the east wall, 

but nighttime temperatures are cool and heating is required. 

Early morning shade extends the hours of heating demand, 

whereas shade in the late afternoon from a tree to the west 

may be beneficial because air has warmed and cooling is 

needed. These data suggest that for similar buildings in Chicago, a tree located to the west provides about 2 to 4 times 

greater net energy savings than a similar tree located to the 

east. The use of so[ar friendly trees to the east can increase 

their cooling-heating ratio and net energy savings produced. 

Tree Growth 

Tree growth influences the amount of wall area shaded and 

resulting cooling and heating energy savings. In shading 

scenarios for the wood-frame buildings, wall area shaded 

increases with tree age. As expected, the incremental increase in energy savings follows the incremental increase in 

crown size and area of wall surface that is shaded (Figure 

7a). For all shading scenarios, savings increase most from 

years 5 to 15 when crown diameters increase from 13 feet at 

year 5 to 19 feet at year 10 to 24 feet at year 15. The 

marginal savings from years 15 to 20 result from a small 

increase in tree growth (24 to 25 feet) and area shaded. 

Thus, growth rate has a direct influence on the rate of return 

on investment provided that tree shape and location are 

such that increased size results in greater building shade. 

Annual heating and cooling energy savings from the 25- 

foot-tall tree on the west are $20 and $13 for the one- and 

two-story buildings, respectively. Marginal savings from the 

second 25-foot tree on the west are $14 and $7, respectively. Hence, marginal savings per tree diminish by about 

30 to 50 percent for the second tree opposite the west wall 

compared to savings from the first tree (Figure 7a). Adding 

the second tree results in more overall shade, but each tree 

is less efficient because it shades more nonbuilding surface 

than when centered opposite the wall as a single tree. 

Energy savings from the 25-foot tree opposite the east wall 

are $1 6 and $9 for the one- and two-story buildings, respectively, or 20 to 30 percent less than savings from the same 

tree to the west. 

Smaller absolute savings from tree shade are noted for the 

energy-efficient two-story building than the inefficient onestory base case. The former consumes 42 percent less 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
energy each year for space heating and cooling, and receives 30 to 45 less energy savings from shade. Despite 

differences in energy consumption and absolute savings 

between the two building types, savings in air-conditioning 

energy as a percentage are similar (Figure 7b). Single 25- 

foot trees to the west and east reduce annual cooling energy 

use by about 7 and 5 percent, respectively. Two trees on the 

west lower annual air-conditioning energy use by about 11 

percent. Electricity savings for peak cooling also are similar 

for the two buildings, though the savings are about double 

those noted for annual cooling (Figure 7c). Analogous percentage cooling savings for the two wood-frame buildings 

are not surprising since they have similar ratios of window 

area to floor area, and window area is distributed equally on 

each wall (Table 1). 

Maximum Air-conditioning Energy-Savings 

If trees are not cost-effective when they are located optimally 

and near mature size, they will not be cost-effective when 

smaller and in less optimal sites. The maximum savings in 

air-conditioning due to shade from a single tree is listed in 

Table 5 for each base case building. Maximum savings for 

-10 1 I I I 

Small at 12-ft Med. at 223 Large at 22-17 Large at 3447 

@# Tree to East Tree to South 0 Tree to West 

Figure 5.-Effects of shade from trees of different size and location on 

annual energy savings are for two-story brick building facing south. 

W East South 0 West 

Figure 6.-Ratios depict net impact of energy penalties from tree shade 

during winter and savings from shade during summer on annual heating and cooling energy costs for two-story brick building facing south. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 7 
1 Eas 

story 1 East 

L 

Two-Story 

1 West 2 West 1 East 1 West 2 West 1 East 

One-Story Two-Story 

One-Story Two-Story 

Figure 7.-Annual savings in space conditioning savings 

due to shade from deciduous trees (25 feet tall and 25 feet 

wide) located 12 feet from one- and two-story wood-frame 

buildings. Shading scenarios are one tree to the west, one 

tree to the east, and two trees to the west. Figures 7b and 7c 

show the simulated effects of tree shade as percentages of 

annual and peak air-conditioning savings. 

the brick buildings resulted from a large tree (50 feet and 36 

feet wide) located 22 feet from the west wall, while a 25-foot 

tree located 12 feet from the west wall produced maximum 

savings for the wood-frame buildings. 

Annual savings in air-conditioning energy range from 126 to 

399 kwh (0.45 to 1.43 gigajoules, GJ) per tree ($15 to $49). 

Absolute savings are greatest for the two- and three-story 

buildings. However, percentage savings, which range from 3 

to 11, are least for the three-story buildings, probably because a relatively large amount of the wall area is unshaded 

by the single tree. Peak cooling savings range from 0.3 to 

1.3 kW per tree (4 to 17 percent). Percentage peak cooling 

savings vary among building types, increasing in buildings 

with relatively large amounts of west-facing glass and high 

ratios of window to floor area. Solar-heat gain through windows accounts for the greatest proportion of heat gain in all 

buildings, but is especially important in the wood-frame and 

two-story brick buildings, which have ratios of window to 

floor area ranging from 16 to 20 percent (Table 1). Since 

solar gain has a strong influence on the demand for peak 

cooling, tree shade on the buildings with large amounts of 

west-facing glass results in a relatively greater percentage 

savings in peak cooling energy than was observed for the 

other buildings. 

Effects of Air Temperature and Reductions in 

Wind Speed 

Cooler summertime (outside) air temperatures due to ET 

cooling and lower windspeeds associated with increased 

surface roughness produced by trees are simulated assuming effects associated with a 10-percent increase in neighborhood tree-canopy cover. The savings from these indirect 

effects plus shade produced by a 25-foot wide tree opposite 

the west wall are shown on a per-tree basis in Figures 8a-c 

and Table 6. 

Annual heating savings per tree from wind shielding range 

from $5 (0.96 MBtu, 1.3 percent) for the well-insulated wood 

frame-building to $52 (10.3 MBtu, 1.5 percent) for the loosely 

constructed three-story brick buildings (Figure 8a). Although 

savings in heating energy vary little on a percentage basis 

per tree, absolute savings increase with size of the brick 

building (Table 6). Annual savings in space heating due to 

wind shielding increase from $13 (2.5 MBtu) to $26 (5.1 

MBtu) to $52 (10.3 MBtu) per tree for the one-, two-, and 

three-story buildings, respectively. Shade on the west wall 

results in a small penalty in heating energy (up to 0.7 MBtu 

or $3.50), there is virtually no savings or penalty from ET 

cooling during the heating season. 

Annual cooling savings per tree from wind shielding range 

from $1 (5 kwh, 0.3 percent) for the wood-frame building to 

$3 (29 kwh, 0.4 percent) for the three-story brick buildings 

(Figure 8b). Given the building characteristics and modeling 

assumptions used here, this result confirms that cooling 

savings due to reduced infiltration in summer can offset 

increased reliance on mechanical cooling due to lower 

windspeeds and reduced natural ventilation. 

106 Chapter 7 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 5.-Per-tree maximum annual savings in air-conditioning (AC) from tree shadea 

Base case AC AC saved Peak AC saved 

Base case buildings kwh $ Peak kW "LO $ kW =lo 

1-story brick north facing 1.795 21 5 4.2 187 10.4 22.85 0.3 6.2 

1-story brick east facing 1,928 231 4.5 149 7.7 18.21 0.5 10.5 

2-story brick south facing 3.682 442 10.6 399 10.8 48.76 1.3 12.3 

2-story brick east facing 3.725 447 10.1 297 8.0 36.29 1 .O 9.7 

3-story brick south facing 7,199 864 16.7 345 4.8 42.16 1 .O 5.8 

3-story brick east facing 6,970 836 16.1 245 3.5 29.94 0.7 4.4 

1 -story wood poorly insulated 2,941 353 7.4 187 6.4 22.85 1.1 15.5 

2-story wood well insulated 1,858 223 5.1 126 6.8 15.40 0.9 17.1 

a Savings for brick buildings due to shade from one 50-foot-tall and 36-foot-wide tree at 22 feet from the west wall and savings for wood-frame buildings due to shade from one 25-foot-tall and 25-foot-wide tree at 12 feet from the west wall. 

Table 6.-Per-tree annual savings in heating and cooling energy from shade, ET cooling and reductions in windspeeda 

-- 

Heating Cooling Total Peak Cooling 

Base case buildings MBtu YO kwh "LO $ % kW % 

1 -story brick east base case 

Shade 

ET cooling 

Wind-shield 

Total 

2-story brick south base case 

Shade 

ET cooling 

Windshield 

Total 

3-story brick south base case 

Shade 

ET cooling 

Wind-shield 

Total 

1 -story wood base case 

Shade 

ET cooling 

Wind-shield 

Total 

2-story wood base case 

Shade 

ET cooling 

Wind-shield 

Total 0.5 0.7% 170 9.1% 22.78 3.9% 0.93 18.2% 

a ET cooling and wind-shielding effects correspond to lower air temperatures and windspeeds associated with a 10-percent increase in neighbomood 

tree canopy cover. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 7 107 
Base Case Buildings 

86 

I- North I-East 2-South 2-East 3-South 3-East I-Wood 2-Wood 

Base Case Buildings 

Base Case Buildings 

Shade ET Cooling 0 Wind Reduction 

Figure 8.-Annual savings in heating (a), cooling (b), and 

total (c) space conditioning due to shade, ET cooling, and 

reductions in windspeed on a per tree basis. Shading savings are from a deciduous tree opposite the west wall of 

each base case building. Reductions in ET cooling and 

windspeed are assumed to be associated with a 10-percent 

increase in overall neighborhood tree-canopy cover. 

108 Chapter 7 

The relative magnitudes of cooling savings from shade and 

ET cooling vary with building type and orientation. Annual 

savings from shade range from $4 (37 kwh, 2 percent) per 

tree for the one-story brick building facing north to $22 (186 

kwh, 6.3 percent) per tree for the one-story wood-frame 

building (Table 6). Annual savings in air-conditioning attributed to shade are 2 to 3 times greater than savings from ET 

cooling for buildings with large amounts of solar-heat gain 

through west-facing windows (i.e., wood-frame houses and 

two-story brick building facing south). This trend is more 

pronounced for savings in peak air-conditioning due in part 

to the influence of solar-heat gain on peak demand in late 

afternoon (Table 6). Annual savings in ET cooling range 

from $5 (39 kwh, 2.1 percent) per tree for the two-story 

wood-frame building to $20 (168 kwh, 2.3 percent) per tree 

for the three-story brick building. 

Total annual savings in heating and cooling energy range 

from 2 to 4 percent of total heating and cooling costs, or $20 

to $35 per tree for the single-family detached homes, about 

$50 per tree for the two-story brick buildings, and $85 per 

tree for the three-story brick buildings (Figure 812). Savings 

due to indirect effects are considerably greater than from 

direct shade for the brick buildings. Indirect effects account 

for 70 to 90 percent ($19 to $75 per tree) of total energy 

savings for the brick buildings, and about 45 percent ($10 to 

$16 per tree) of the savings for wood-frame buildings (Table 

6). This finding is in general agreement with results of other 

simulation studies, but differences in percentage savings 

attributed to each indirect effect reflect the uncertainty associated with modeling these complex meteorological processes. 

For example, simulation results from this study, as well as for 

residences in Minneapolis (Sand and Huelman 1993) and 

Toronto (Akbari and Taha 1992), estimate an annual heating 

savings from wind shielding of 1 to 1.5 percent per tree. 

Simulated heating savings per tree from wind shielding for a 

well-insulated building in Chicago was 7 percent in another 

study (Huang et al 1990). On a per tree basis, simulated 

annual ET cooling savings ranged from 7 to 8 percent for 

buildings in Toronto (Akabari and Taha 1992) and Minneapolis (McPherson and Rowntree 1993), but are estimated as 

about 3 percent in this study. Thus, indirect savings are lower 

end estimates compared to those from several other studies. 

Simulation results suggest that in Chicago, the amount and 

type of energy savings associated with trees are sensitive to 

building characteristics. On a percentage basis per tree, 

total dollar savings in heating and cooling are greatest for 

the energy-efficient, two-story wood-frame building ($23, 4 

percent). This indicates that shade trees could be costeffective as an energy conservation measure associated 

with new home construction. Also, it is important to reiterate 

that the magnitude of annual and peak cooling savings, as 

well as heating costs associated with direct shading by trees, 

depends largely on the relative area and orientation of windows that are shaded. In absolute dollar savings, substantiai 

savings ($75 per tree) for the three story brick buildings is 

attributed to ET cooling and wind shielding because trees 

reduce heat exchange by conduction and infiltration, the 

primary heat transfer pathways in these large, old buildings. 

Savings in heating energy from wind protection is especially 

large because of the buildings' relatively loose construction, 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
high rates of air infiltration, and inefficient heating equipment 

(Table 1). This means that trees in Chicago not only can 

mitigate summer heat islands but also provide sizable annual savings in heating energy, especially for older buildings 

in areas where tree cover is relatively sparse. Since nearly 

every household in Chicago is heated with natural gas, substantial heating savings could result from neighborhood tree 

plantings that increase tree cover by 10 percent or more. 

Effect of Trees on Peak Demand 

Trees can help defer the construction of new electric generating facilities by reducing the peak demand for building air 

conditioning and shifting the hour of building peak to reduce 

the total system peak. Commonwealth Edison is a summer 

peaking utility, with electricity demand usually greatest in 

July or August. In 1992, peak demand for electricity occurred 

on July 22 (Claire Saddler,,Marketing, Commonwealth Edison, 

1993, pers. commun.). Electricity demand by residential customers peaked from 6 to 7 p.m. (7.64 GW), while the total 

system peak occurred at 4 p.m. (17.73 GW) (Figure 9). 

Midday peaking by commercial and industrial users shifted 

the system peak from late to mid-afternoon. 

The simulated peak demand for air conditioning for the twostory brick building is 10 to 11 kW between 3 and 5 p.m. 

Direct shading and indirect effects associated with a 10 

percent increase in cover reduce the peak demand by 2 kW 

(1 9 percent) at 5 p.m. The effect of trees is to shave the peak 

between 4 and 6 p.m. and to shift the building peak from 5 to 

3 p.m., or 1 hour before the system peak. A similar peak 

savings is noted for the two-story wood-frame base case. 

Trees reduce the peak by 1 kW (20 percent) at 5 p.m., but 

the time of building peak remains 5 p.m. The brick building's 

responsiveness to tree shade and dry-bulb temperature depression between 4 and 6 p.m. is largely due to its relatively 

large amount of west-facing window area (25 percent of net 

wall area) and low amount of insulation compared to the 

wood-frame building. 

Cost-Effectiveness of Shade Trees in Chicago 

Utilities apply economic analyses to determine if conservation measures such as shade trees can meet their need 

for clean and efficient power as cost-effectively as other 

supply-side and demand-side options. Tree planting and 

care programs sponsored by electric utilities in Washington, 

D.C., Minnesota, Iowa, Arizona, and California suggest 

that shade-tree programs can be cost-effective in certain 

markets. Simulation results for Chicano indicate that trees 

near residential buildings can produce substantial energy 

savings if selected and located judiciously. Although an exhaustive accounting of all benefits and costs associated with 

a utility-sponsored shade tree program in Chicago is beyond 

the scope of this study, an initial analysis is undertaken. 

Assumptions 

This simplified analysis accounts for selected costs and 

benefits over 20 years associated with the planting and 

3-year follow-up care of "typical" trees near two "typical" 

buildings. The annual stream of benefits is derived from 

energy savings previously modeled around the two-story 

brick building (south-facing) and the energy-efficient twostory wood-frame building. It is assumed that the annual 

savings for the 20-year-old tree are 266 kwh (0.96 GJ) and 

0.64 kW for the brick building and 169 kwh (0.61) and 0.93 

kW for the wood building. The energy-savings pattern is 

linked to tree growth using an S-shaped growth curve for 

liIiiIiiiIiiIIIIiIiiIiiii12000 

1 3 5 7 9 11 13 15 17 19 21 23 

Hours 

-- Brick Base -- Brick Tree -- Wood Base 

-- Wood Tree -- CommEd Resid. -- CommEd Total 

Figure 9.-Commonwealth Edison profiles residential and total peak summer demand for July 22, 1992, as well as simulated peak-day cooling 

electricity demand (July 1) for two-story brick (south facing) and two-story 

wood-frame base case buildings, with and without a deciduous tree. 

USDA Forest Sewice Gen. Tech. Rep. NE-186. 1994. Chapter 7 
years 1 to 20 (Appendix E). It is assumed that one typical tree 

is planted for energy savings near each typical building in 

1993, with a total of 10,000 trees shading 10,000 brick buildings, and 10,000 trees shading 10,000 wood buildings. The 

typical tree is 3 feet tall and wide when planted and costs $50 

to plant. This includes the cost of the tree, stakes and other 

planting materials, program administration, overhead, and 3 

years of follow-up care and public education. It also assumes 

that the residents plant the trees. As a comparison, the estimated costs of the Sacramento Tree Foundation's Shade Tree 

Program to the Sacramento Municipal Utility District (SMUD) 

have dropped from $49 per tree planted in 1990-91 to $35 per 

tree in 1993-94 (Richard Sequest, SMUD, 1993, pers. commun.). 

Two adjustments are made to estimates of avoided energy 

and capacity. First, it is assumed that trees die at a rate of 5 

percent a year during the first two years of establishment. A 

1-percent annual mortality rate is assumed for the remaining 

18 years. Over the 20-year planning horizon, 25 percent of 

the planted trees are expected to die. Second, it is assumed 

that only half of the houses that receive a tree have a space 

cooling device. Both of these adjustments reduce estimated 

energy savings. 

The analysis assumes Commonwealth Edison's current avoided 

energy and capacity costs of $0.01 5 per kwh and $89 per kW 

yr-1, as well as the 11 -percent discount rate and 4.5 percent 

inflation rate typically used in their economic analyses (Gary 

Rehof, Commonwealth Edison, 1994, pers. commun.). 

Results 

Cost-effectiveness is evaluated by comparing the present 

value of estimated program costs with estimated benefits. 

The net present value, or benefits minus costs, is $176,928 

for the brick building and $447,588 for the wood building. 

Capacity benefits account for more than 90 percent of the 

total benefits in both cases. The benefit-cost ratio, or benefits divided by costs, is 1.35 for the brick building, and 1.90 

for the wood building (Appendix E). Both measures indicate 

that the benefits derived from such a shade-tree program 

would outweigh costs incurred to Commonwealth Edison. 

This analysis assumes a single tree located optimally to 

shade each building. Benefits per tree would be less if several trees were planted for each building, as noted in results 

from the multiple-tree shading simulations for the woodframe buildings. However, program costs may be less if 

fewer customers are receiving trees. Also, this analysis does 

not incorporate the value of other benefits that shade trees 

can provide, such as removal of atmospheric carbon and 

other air pollutants, heating energy savings, reduced 

stormwater runoff, and increased property values, scenic 

beauty, and biological diversity. The following chapter explores these benefits, as well as many other costs associated with the planting and care of trees in Chicago. 

Energy-Efficient Landscape Design 

There are a number of good references on the topic of 

energy-efficient landscape design that Chicagoans can use 

to save energy dollars (Akbari et al. 1992; Foster 1978; 

Heisler 1986b; McPherson 1984; Moffat and Schiler 1981 ; 

110 Chapter 7 

Robinette 1977; Sand 1991; Sand 1993a; Sand 199313). In 

this section, general guidelines for energy-efficient residential landscape design in the Chicago area are summarized. 

Appendix B contains information on recommended trees. 

Generally, the best place to locate the first (and perhaps 

second) tree for energy savings is opposite west-facing windows and walls. This suggests that a tree to the west provides the greatest peak cooling energy savings, and greater 

net annual energy savings than a tree to the east unless 

large amounts of window area face east. Also, trees to the 

west provide the most protection from winter winds, which 

prevail from the west and northwest during the coldest months 

(Sand and Huelman 1 993). Select evergreens if space permits, or low branching deciduous trees with broad crowns for 

extensive shading during summer (Figure 10). Locate trees 

within 30 feet (9 m) of the building to increase the amount of 

shade. Evergreen vines and shrubs are good plants for solar 

control on west walls (Hudson and Cox 1985; Parker 1987). 

Where feasible, shading the air conditioner improves its 

efficiency and can save electricity. 

The next best place for a tree in Chicago is opposite the east 

wall, where shade reduces annual cooling demand and does 

not obstruct winter solar gain as much as a tree to the south. 

Select solar friendly deciduous trees with broad spreading 

crowns and relatively short foliation periods (May-October 

rather than April-November) for east shade. Keep trees pruned 

high to maximize the flow of cool breezes during summer, 

which prevail from the south and southwest except near 

Lake Michigan, where breezes move inland from the east. 

Figure 10.-Energy-efficient residential landscape desigrl 

with east and west shade as well as wind protection to the 

west and northwest (from Sand and Huelman 1993). 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Deciduous vines and shrubs can provide both summer shade 

and winter solar access. 

South shade can reduce summer peak cooling demand more 

than east shade, especially for taller residential and commercial buildings (McPherson and Sacamano 1992). However, shade from trees located south of buildings in Chicago 

usually increases heating costs more than it reduces airconditioning costs. If trees are required to the south, select 

large solar friendly ones that will eventually branch above the 

windows to provide winter solar access and summer shade 

(McPherson 1984). South trees should be located fairly close 

(8 to 20 feet) to the building for optimum energy savings. 

Cool breezes can improve comfort and reduce cooling energy use during hot muggy days if natural ventilation is used 

and outside temperatures are below 90°F (32°C) (Givoni 

1981). Whether you live near Lake Michigan or further inland will influence the direction of cooling breezes, but in 

either case avoid hedges that restrict natural ventilation. 

Dense plantings to the west are needed to protect from 

winter winds and summer solar-heat gain. Windbreak 

plantings located 30 to 50 feet upwind of the building can 

provide savings once they grow about as tall as the building 

(Heisler 1984). Select trees that will grow to about twice the 

height of the building they protect, and plant staggered rows 

where possible. Windbreak plantings should be longer than 

the building for protection as wind directions shift. Because 

cooling breezes are from the east and southeast while winter winds usually are from the west and northwest, it is 

possible to use shade trees and evergreen windbreaks for 

wind and solar control without obstructing solar access to 

the south side of buildings (Figure 10). 

Summary and Conclusion 

The following are key findings of this study. 

-Shade trees in Chicago can provide substantial energy 

savings. A single 25-foot tree is estimated to reduce annual 

heating and cooling costs by 2 to 4 percent, or $23 to $86. 

Three such trees located for maximum summer shade and 

protection against winter wind could save a typical Chicago 

homeowner about $50 to $90 per year (5 to 10 percent of the 

typical $971 heating and cooling bill). 

-Results of an economic analysis indicate that a utilitysponsored shade-tree program could be cost-effective in Chicago. Benefit-cost ratios of 1.35 for trees planted near typical 

two-story brick buildings and 1.90 for trees planted near 

energy-efficient wood-frame buildings suggest that avoided 

energy and capacity benefits can outweigh costs incurred. 

-Street trees are a major source of building shade within 

Chicago. Shade from a large street tree located to the west of 

a typical brick residence can reduce annual air-conditioning 

energy use by 2 to 7 percent (1 38 to 205 kwh or $1 7 to $25) 

and peak cooling demand by 2 to 6 percent (0.16 to 0.6 kW). 

Street trees that shade the east side of buildings can produce similar cooling savings, have a negligible effect on 

peak cooling demand, and can slightly increase heating 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

costs. Shade from large street trees to the south increase 

heating costs more than they decrease cooling costs for the 

buildings studied. Planting solar friendly trees to the south 

and east can minimize the energy penalty associated with 

blocking irradiance during the heating season. 

-For typical suburban wood-frame residences, shade from 

three trees reduces annual heating and cooling costs 10 

years after planting by $15 to $31, and 20 years after planting 

by $29 to $50. Savings in annual and peak air-conditioning 

energy per tree range from 126 to 187 kwh (0.45 to 0.67 GJ) 

(6 to 7 percent, $15 to $23) and 0.9 to 1.1 kW (16 to 17 

percent), assuming a 25-foot-tall tree opposite the west wall. 

-The amount and type of energy savings associated with 

trees are highly sensitive to building characteristics. Effects 

of ET cooling and reductions in windspeed associated with 

increased tree cover account for an estimated 70 to 90 

percent of the total annual savings for the older brick buildings, with heating savings exceeding cooling savings. Trees 

that provide mitigation of summer heat islands in Chicago 

also can provide sizable annual savings in heating energy, 

especially for older buildings in areas where tree cover is 

relatively low. Strategic landscaping for maximum shading is 

especially important with new construction because solarheat gains through windows strongly influence cooling loads. 

-Features of energy-efficient residential landscapes in the 

Chicago area include: 1) shade trees, shrubs, and vines 

located for shade on the west and southwest windows and 

walls; 2) solar friendly deciduous trees to shade the east and 

an open understory to promote penetration of cool breezes; 

3) evergreen windbreaks to the northwest and west for protection from winter winds; and 4) shade on the air conditioner 

where feasible. 

Although the effect of Chicago's existing urban forest on 

climate and energy use is difficult to quantify precisely, 

it appears to be substantial. Resources invested in the 

maintenance and upgrade of Chicago's trees will provide 

direct benefits to residents in energy savings and a more 

hospitable outdoor climate. Thus, maintaining the health and 

longevity of trees in areas where canopy cover is relatively 

high should be a top priority. 

The potential for energy savings from new tree p[antings 

is greatest in areas where tree cover is relatively low, such 

as public housing sites and new suburban development. 

Residents in public housing often spend a relatively large 

portion of their income for space conditioning, and these 

buildings seldom are energy efficient. Tree planting could be 

a new type of "weatherization" program, largely carried out by 

the residents themselves. In addition to direct energy savings, 

other social, environmental, and economic benefits would 

accrue to the community (see section on benefits and costs of 

volunteered-based tree planting and care in public housing 

sites). Demonstration projects are needed to evaluate the 

long-term cost-effectiveness of public investment in tree 

plantings for energy conservation and other benefits. Chicago 

is an ideal location for innovative projects aimed at promoting 

energy efficiency and forging new partnerships among residents, government, utilities, and nonprofit organizations. 
Acknowledgments 

This study would not have been possible without information 

and assistance provided by Hyun-kil Jo and Chicago residents in his study area, Claire Saddler and Tom Hemminger 

of Commonwealth Edison, Gene Waas and Bob Pendlebury 

of Peoples Gas, Ray Lau of the Center for Neighborhood 

Technology and John Katrakis, consultant to the Center for 

Neighborhood Technology. Danny Parker (Florida Solar Energy Center), Peggy Sand (Minnesota Department of Natural Resources), and Jim Simpson (USDA Forest Service) 

provided helpful reviews of this manuscript. 

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IN. Journal of Arboriculture. 19(5): 303-312. 

Thayer, R. L.; Maeda, B. 1985. Measuring street tree impact on 

solar performance: a five climate computer modeling study. 

Journal of Arboriculture. 11 : 1-12. 

Watson, G. W. ed.. 1991. Selecting and planting trees. Lisle, IL: 

Morton Arboretum. 24 o. 

Wilkin, D.; Jo, H. K. 1993. Landscape carbon budgets and planning guidelines for greenspaces in urban residential lands. 

Tech. Res. Rep. ~ucson, AZ: University of Arizona, School of 

Renewable Natural Resources. 205 p. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 7 

Chapfer 8 

Benefits and Costs of Tree Planting and Care in Chicago 

E. Gregory McPherson, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Davis, CA 

Abstract 

Benefit-cost analysis is used to estimate the net present 

value, benefit-cost ratio, and discounted payback periods of 

proposed tree plantings in the City of Chicago. A "typical" 

tree species, green ash (Fraxinus pennsylvanica), was located in "typical" park, residential yard, street, highway, and 

public housing sites. The 30-year stream of annual costs and 

benefits associated with planting 95,000 trees was estimated 

using a computer model called Cost-Benefit Analysis of Trees 

(C-BAT) and discount rates of 4, 7, and 10 percent. NPV 

were positive and projected benefit-cost ratios were greater 

than 1 at all discount rates. Assuming a 7-percent discount 

rate, a net present value of $38 million or $402 per planted 

tree was projected. Benefit-cost ratios were largest for trees 

planted in residential yard and public housing sites (3.5), and 

least for park (2.1) and highway (2.3) sites. Discounted 

payback periods ranged from 9 to 15 years. Expenditures for 

planting alone accounted for more than 80 percent of projected costs except at public housing sites, while the largest 

benefits were attributed to "other" benefits (e.g., scenic, 

wildlife, improved water quality, noise abatement, and social 

values) and energy savings. Considerations for planting and 

managing Chicago's urban forest to maximize return on 

investment are presented. 

Introduction 

Trees have a long and rich tradition in Chicago. This tradition 

can be seen today as the formal elm bosques in Grant Park, 

Chicago's many majestic tree-lined boulevards, its extensive 

forest preserves, and the informal plantings of hawthorns, 

hackberry, oak, and other natives that grace its many parks 

(McPherson et al. 1993a). In Chicago and most surrounding 

communities, trees have long been recognized as valuable 

community assets. First-rate urban forestry programs abound 

as evidence of commitment to the perpetuation of healthy 

community forests. However, dwindling budgets for planting 

and care of street and park trees are creating new challenges for urban forestry. Community officials are asking if 

trees are worth the price to plant and care for them over the 

long term. Urban forestry programs now must prove their 

cost-effectiveness. 

Similarly, some residents wonder whether it is worth the 

trouble of maintaining street trees in front of their home or in 

their yard. Certain species are particularly bothersome due 

to litterfall, roots that invade sewers or heave sidewalks, 

shade that kills grass, or sap from aphids that fouls cars and 

other objects. Branches broken by wind, ice, and snow can 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 

damage property. Thorns and low-hanging branches can be 

injurious. These problems are magnified when trees do not 

receive regular care, or when the wrong tree was selected 

for planting. 

The purpose of this analysis is to quantify some of the 

benefits and costs associated with tree planting and care in 

Chicago. In previous sections of this report, existing and 

potential benefits of Chicago's urban forest have been outlined with respect to climate, air quality, atmospheric carbon, 

and energy used for space heating and cooling. Relations 

between these functions and the composition and distribution of tree species have been discussed. In this study, 

benefit-cost analysis was used to estimate the annual dollar 

value of benefits and costs over a 30-year period associated 

with the planting and care of 95,000 new trees in Chicago. 

The estimated number of new trees is based upon interviews 

with entities responsible for much of the tree planting and 

care in the city and covers projected plantings between 1992 

and 1997 as follows: 

-12,500 trees planted and maintained in parks by the 

Chicago Park District. 

-25,000 trees planted by residents in their yards with 

maintenance by professional arborists beginning 15 

years after planting. 

-50,000 trees planted along residential streets and 

maintained by the Bureau of Forestry. 

-5,000 trees planted along expressways under the 

auspices of Gateway Green and the Illinois Department of Transportation, with maintenance by volunteers and city personnel. 

-2,500 trees planted in public housing sites by local 

residents under the direction of the Openlands Project, 

with initial maintenance by residents and Openland's 

TreeKeepers and professional maintenance of larger 

trees. 

Quantifying benefits and costs associated with these plantings 

will provide initial answers to the following questions: 

1) Are trees worth it? Do their benefits exceed their 

costs? If so, by how much? 

2) In what locations do trees provide the greatest net 

benefits? 

3) How many years does it take before newly planted 

trees produce net benefits in Chicago? 

4) What tree-planting and management strategies will 

increase net benefits derived from Chicago's urban 

forest? 

This analysis is complicated by incomplete information on 

such critical variables as tree growth and mortality rates, the 

value of social, aesthetic, and economic benefits that trees 

Chapter 8 115 
produce, and costs associated with infrastructure repair, 

litigation, and program administration. When data from local 

sources were unavailable, it was necessary to use the best 

available data. As a result, some variables were excluded 

from this analysis (e.g., costs of litter clean-up and health 

care benefits and costs). Estimating the value of social, 

aesthetic, and economic benefits, called "other benefits" in 

this study, is uncertain because we have yet to identify the 

full extent of these benefits or their implications. Additional 

problems emerge since many of these benefits are not 

exchanged in markets and it is often difficult to estimate 

appropriate dollar values. This lack of data required the 

development of several assumptions about the planting and 

care of a "typical" tree species in "typical" locations. To 

simplify the analysis it was necessary to limit its scope to the 

planting of trees over a 5-year period and their care over a 

30-year period. Benefit-cost data were gathered in 1992 and 

1993 from local contacts and used to estimate future values. 

Therefore, this study provides an initial approximation of 

those benefits and costs for which information is available. 

As our understanding of urban forest structure, function, and 

values increases, and we learn more about urban forestry 

programs and costs, these assumptions and the methods 

used to estimate benefits and costs will be improved. 

Background 

Urban trees provide a range of services for community 

residents that can influence the quality of our environment. 

As illustrated elsewhere in this report, trees in the Chicago 

area can moderate local climate, reduce building energy use 

(Akbari et al. 1992), improve air quality (McPherson and 

Nowak 1993), and sequester and avoid carbon dioxide (Nowak 

1993, Rowntree and Nowak 1991). Other studies have found 

that urban forests reduce stormwater runoff (Lormand 1988; 

Sanders 1986), increase property values (Anderson and 

Cordell 1988), and provide a connection to nature, relaxation, 

or spiritual joy (Dwyer et al. 1992). Quantifying the value of 

these and other benefits and the costs associated with urban 

trees can assist planners and managers optimize their return 

on investment in Chicago's urban forest. 

Current efforts to determine the value of greenspace do not 

include the broad range of important benefits and costs or 

how they vary across time and location. Nor do they allow 

comparison of future cost-benefit relationships associated 

with alternative management scenarios (McPherson 1992). 

In response to these limitations, the Cost-Benefit Analysis of 

Trees (C-BAT) computer model was developed to quantify 

various management costs and environmental benefits. 

C-BAT as applied here quantifies annual benefits and costs 

for a 30-year period associated with the establishment and 

care of trees in Chicago. 

Approch 

C-BAT 

C-BAT estimates annual benefits and costs for newly planted 

trees in different locations over a specified planning horizon. 

C-BAT is unique in that it directly connects tree size with 

the spatial-temporal flow of benefits and costs. Prices are 

116 Chapter 8 

assigned to each cost (e.g., planting, pruning, removal, 

irrigation, infrastructure repair, liability, waste disposal) and 

benefit (e.g., heatinglcooling energy savings, absorption of 

air pollution, reduction in stormwater runoff) through direct 

estimation and implied valuation of benefits as environmental externalities. This makes it .possible to estimate the net 

benefits of plantings in typical locations and with typical tree 

species. C-BAT incorporates the different rates of growth 

and mortality as well as different levels of maintenance 

associated with typical trees. Hence, this greenspace accounting approach "grows trees" in different locations and 

directly calculates the annual flow of benefits and costs as 

trees mature and die (McPherson 1992). 

Although Chicago's urban forest is planted with many tree 

species (Nowak 1994a: Chapter 2, this report), the scope of 

this analysis is limited to planting and care of a single typical 

tree species, green ash (Fraxinus pennsylvanica), in each of 

five typical locations: parks, residential yards, residential 

streets, highways, and public housing sites. Typical locations 

were selected to represent the types of trees, management 

approaches, socio-economic situations, and growing conditions that influence tree health and productivity in Chicago. 

Green ash was selected as the typical species because it is 

one of the most widely planted and successful tree species in 

Chicago (Nowak 1994a: Chapter 2, this report). 

In this study, trees are "planted" during the first 5 years and 

their growth is assumed to follow an S-shaped curve that 

incorporates a slow start after transplanting. As trees age, 

their numbers decrease. Transplanting-related losses occur 

during the first 5 years after planting, and age-independent 

losses occur over the entire 30-year analysis period. Transplanting-related losses are based on annual loss rates 

reported by local managers and other studies (Miller and 

Miller 1991 ; Nowak et al. 1990). Age independent losses are 

assumed to be equally likely to occur in any year (Richards 

1979). Tree growth and mortality rates reflect rates expected 

for the green ash on each type of site. 

Each year, C-BAT calculates total leaf area for each age 

class by multiplying the number of live trees times the typical 

tree's leaf-area (LA). LA is calculated using the typical tree's 

leaf-area index (LAI) and ground projection (GP) term, where 

GP is the area under the tree-crown dripline: 

The LA1 of a tree varies with species, size, and condition. In 

this study, the LA1 of green ash trees in Chicago is assumed 

to be 5 based on data presented in Chapter 2. 

C-BAT directly connects selected benefits and costs with 

estimated leaf area of the planted trees. Because many 

functional benefits of trees are related to leaf-atmosphere 

processes (e.g., interception, transpiration, photosynthesis), 

benefits increase as leaf-surface area increases. Similarly, 

pruning and removal costs usually increase with tree size. 

To account for these time-dependent relationships, benefits 

and costs are assumed to vary with leaf area. 

For most costs and benefits, prices are obtained for large 

trees (assumed to be 20-inches in d.b.h. or about 45-feet tall 

and wide) and estimated for trees of smaller size using 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
different functions (e.g., linear, sine, cosine). For parameters 

such as sidewalk repair, costs are small for young trees but 

increase relatively rapidly as tree roots grow large enough to 

heave pavement. For other parameters such as rainfall interception, benefits are directly proportional to leaf area (Aston 

1979). In this study, a linear function is used to estimate all 

benefits and costs with the exception of infrastructure repair 

and litigation costs (cosine function) and benefits related to 

energy savings (sine function). These prices are divided by 

the tree's leaf area to derive a base price per unit LA for 

different tree size classes (e-g., $2011 0,000 ft2 LA = $0.0021 

ft2 LA). C-BAT multiplies the base price times the total LA of 

trees in that size class to estimate the total annual nominal 

value of each benefit and cost. Once the nominal values are 

calculated for each year into the future, they can be adjusted 

to account for future inflation and discounted to a present 

value. Thus, both tree size and the number of live or dead 

trees influence the dollar value of each benefit and cost. 

Most benefits occur on an annual basis, but some costs are 

periodic. For instance, street trees are pruned on yearly 

cycles and removed when they pose a hazard or soon after 

they die. C-BAT calculates tree and stump removal costs for 

the same year as each tree dies. Pruning costs are average 

annual costs based on average tree size. 

Generally, benefits directly related to leaf-surface area increase yearly as trees grow larger and add more leaves 

each spring. However, two benefits are more directly related 

to the annual change in tree girth than to the increase in 

leaf area: "other benefits" (i.e., social, aesthetic, and other 

environmental benefits not explicitly accounted for); and the 

storage of atmospheric carbon in tree biomass. ,The annual 

value of these benefits is proportional to the increase in 

d.b.h. for that year. Relations between tree d.b.h., age, and 

crown dimensions are based on findings reported by Nowak 

(1994~: Chapter 6, this report) and data from Churack and 

Miller (1992, Univ. of Wisconsin-Stevens Point, pers. 

commun.), Fleming (1 988), and Frelich (1 992). 

In this study, both direct estimation and implied valuation 

are used to assign values. Much of the cost data for tree 

management were directly estimated based on interviews 

with local contact persons. Findings from energy simulations 

presented by McPherson (1994: Chapter 7, this report) are 

used in this study to directly estimate energy savings due to 

shading, temperature modification, and wind speed reductions from trees. Other benefits are estimated using implied 

valuation, which relies on the costs of required or anticipated 

environmental control measures or regulations. For instance, 

if society is willing to pay $1 per pound for current or planned 

air-pollution control, then the air-pollution mitigation value of a 

tree that absorbs or intercepts 1 pound of air pollution should 

be $1 (Chernick and Caverhill 1991 ; Graves et al. 1987). 

Tree Planting and Care 

Contact persons from each organization (Table 1) were 

interviewed to estimate the number of trees to be planted 

annually over a 5-year period (1 992 to 1997), growth and 

mortality rates, and planting and management practices and 

costs. Costs summarized in Table 2 and described in the 

section that follows are for the typical large tree (45-feet tall, 

20-inch d.b.h.) and adjusted downward for smaller trees 

using functions noted previously. 

Trees in Parks 

There are about 250,000 trees in Chicago parks that receive 

regular care from the Chicago Park District. On average, the 

Park District expects to plant 2,500 trees per year for the 

next 5 years. About 30 varieties will be planted, with an 

average planting height of 15-feet (4-inches d.b.h.). Total 

planting costs average $470 per tree, including $100 for 

watering during the establishment period. The typical green 

ash is assumed to have a life-span of 30 to 50 years after 

planting mortality and an average annual height growth rate 

of O.&feet (0.4-inch d.b.h.). It is expected to attain a height of 

39 feet (16-inch d.b.h.) 30 years after planting. Mortality 

during the 5-year establishment period is assumed to be 16 

percent, with an overall loss rate of 39 percent for 30 years. 

The cost to prune a large park tree is assumed to be $160, 

and the typical tree is pruned four times over 30 years. Large 

tree and stump removal costs are assumed to be $900 and 

$1 10, respectively, with 80 percent of all dead trees and 

stumps removed. Sixty percent of the removed wood is 

recycled as mulch and the remainder is taken to a landfill, 

where the dumping fee is $40 per ton. Each year the Park 

Table 1. -'Typicaln locations, planting sizes, and organizational roles 

Tree location Planting sizea Organization and assumed tree plantingicare activity 

Park 15 ft, 4-inch caliper Chicago Park District plant and maintain 

Residential yard 12 ft. 2-inch caliper Residents plant and maintain while trees are small; arborists 

maintainlremove large trees 

Residential street 12 ft, 2-inch caliper Bureau of Forestry plant and maintain 

Highway 14 ft, 3-inch caliper Gateway Green, Illinois Dept. of Transportation, and arborists plant 

and maintain 

Public housing 13 ft, 2.5-inch caliper Openlands, TreeKeepers, and residents plant and maintain while 

young; professional maintenance of larger trees 

a Tree height in feet and caliper (trunk diameter) in inches measured 6 inches (15 crn) above the ground. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 8 117 
Table 2.-Estimated tree planting and management costs 

Tree location 

Cost category a Park Yard Street Highway Housing 

Planting 

Cost per tree (dollars) 470 250 1 62 250 150 

Pruning 

Cost per tree (dollars) 

Frequency (# in 30 yrs) 

Tree removal 

Cost per tree (dollars) 

Frequency (% removed) 

Stump removal 

Cost per tree (dollars) 

Frequency (% removed) 

Waste disposal 

Cost (dollars per ton) 

Infrastructure repair 

(dollars per tree per year) 

Walk, curb, gutter cost 0.62 0.62 2.49 0.25 0.62 

Sewer and water cost 0.38 1.15 0.76 0.12 0.76 

Litigation and liability 

Cost (dollars per tree per year) 0.01 0.50 1 

Inspection 

Cost (dollars per tree per year) 0.19 0 0.35 

Program administration 

Cost (dollars per tree per year) 0.94 0 0 2.63 32.78 

a Cost estimates given as dollars per year per tree (454 tail, 20-inch d.b.h.1 unless shown otherwise. 

District spends about $75 per tree on the Grant Park elm 

program to control Dutch elm disease, but other expenditures for pest and disease control are minimal. The annual 

program administration cost is assumed to be $0.94 per 

large tree, while costs for litigationlliability and infrastructure 

repair are negligible. 

Residential Yard Trees 

Eight local garden centers were surveyed to estimate the 

number of trees planted annually in Chicago's residential 

landscapes. Questions were asked regarding numbers of 

trees sold, most popular species and sizes, and average 

cost. Based on the response, an estimated 5,000 trees will 

be planted each year in residential yards at an average 

planting height of 12-feet ( 2-inches d.b.h.). The average 

cost of this size tree is assumed to be $250. The typical 

green ash in yards is assumed to grow at an average annual 

rate of 0.8 feet in height (0.4-inch d.b.h.), reaching a height 

of 36 feet (14-inches d.b.h.) 30 years after planting. Due to 

the relatively favorable growing conditions in yards, low mortality rates are expected. Only 4 percent of the transplants 

are assumed to die during the first 5 years; a mortality rate of 

18 percent is assumed for the entire 30 years. 

118 Chapter 8 

On average, residential yard trees are assumed to be pruned 

once by a paid landscape professional over the 30-year 

analysis period at a cost of $1 96 per tree. Costs for tree and 

stump removal are assumed to be $504 and $140 per large 

tree, respectively. Costs are included for removal of all trees 

and 50 percent of all stumps. 

Tree roots can damage old sewer lines that are cracked or 

otherwise susceptible to invasion. Several local companies 

were contacted to estimate the extent to which street and 

yard trees damage sewer lines and repair costs. Respondents noted that sewer damage is minor until trees and 

sewers are more than 30 years old, and that roots from trees 

in yards usually are a greater problem than roots from street 

trees. The latter assertion may be due to the fact that sewers 

become closer to the root zone as they enter houses than at 

the street. Repair costs typically range from $1 00 for rodding 

to $1,000 or more for excavation and replacement. This 

study assumes that on average, 10 percent of all yard trees 

planted will invade sewers during the 30-year period after 

planting, each requiring repair at an average cost of $345. 

When factored over the 30-year period, this cost amounts to 

about $1.15 per year per tree. The annual costs for repair of 

sidewalks due to damage from yard trees is $0.62 per tree. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
The annual litigation or liability costs associated with property damage from yard trees is assumed to be $0.50 per tree 

based on data from other cities (McPherson et al. 1993b). 

I Residential Street Trees 

Lnlcago s mureau UI rureslry III~IIIL~III~ IIGQII~ a II~IIIIIIIIVII 

trees along city streets and boulevards. It anticipates planting 10,000 bare root trees each year for the next 5 years at 

an average planting cost of $162 each. Trees are typically 

12-feet tall (2-inches d.b.h.) when planted. Along streets the 

typical green ash is assumed to grow at an average annual 

rate of 0.67 feet (0.33-inch d.b.h.), reaching a height of 32 

feet (12-inches d.b.h.) 30 years after planting. It is assumed 

that 28 percent of the trees die during the first 5 years, with 

42 percent dying over the 30-year planning horizon. 

The Chicago Bureau of Forestry anticipates pruning street 

trees once every 6 years at an average cost of $97 per tree. 

All dead trees and their stumps are removed at a cost 

of $658 and $108 per tree, respectively. Nearly all of the 

removed wood is salvaged and used as mulch or compost. 

Roots of older street trees can cause sidewalk heaving that 

is costly to repair. In Chicago, costs for sidewalk repair are 

shared between the city and property owner. Approximately 

$3 million is spent annually for sidewalk repair (Ronny Eisen, 

City of Chicago Transportation Dept., 1993, pers. commun.). 

It is estimated that about $1 million is spent each year 

repairing sidewalk damage that is largely attributed to trees, 

or $2.1 8 each year per street tree. Data on the cost of curb 

and gutter repair due to tree damage are unavailable for 

Chicago but is asssumed to be 14 percent of sidewalk repair 

costs ($0.31 per tree per year) based on information from 

other cities (McPherson et al. 1993b). Based on data from 

several local sewer contractors, the estimated cost is $0.76 

per year per large tree. 

Data on litigation and liability costs are unavailable for Chicago, so costs are estimated as $1 annually per tree based 

on data from several other cities (McPherson et al. 1993b). 

The annual inspection cost is $0.35 per tree, while Bureau of 

Forestry program administration costs are included in the 

unit costs cited. Inspection costs cover time and expenses 

for personnel who regularly inspect trees, adjust staking, 

apply mulch, and perform other minor tree-care operations. 

Trees Along Highways 

The Chicago Gateway Green Committee is a nonprofit organization that raises funds for tree planting and care. Gateway 

Green teams with Illinois Department of Transportation (IDOT), 

Hendricken The Care of Trees, City of Chicago, and local 

volunteers to plant and care for trees along major transportation corridors. Recent plantings along the Kennedy Expressway and at the Ohio-Ontario-Orleans triangle demonstrate 

the success of this collaboration. IDOT is responsible for 

additional tree plantings associated with the reconstruction 

of expressways and highways. Planting numbers vary yearly 

depending on the construction schedule; and trees planted 

within the city limits are maintained by city personnel. 

From 1992 to 1997, about 1,000 trees will be planted annually along Chicago's expressways and major streets by IDOT 

and Gateway Green. Plantings contain many native species 

that are well adapted to local growing conditions. The typical 

green ash is assumed to be 14 feet tall (3-inches d.b.h.) with 

an average planting cost of $250 per tree. This $250 incorporates savings due to donated labor from Gateway Green 

volunteers. Green ash trees along expressways are assumed 

to grow at an average annual rate of 0.67 feet in height 

(0.33-inch d.b.h.) attaining a height of 34 feet (13-inches 

d.b.h.) after 30 years, which is about their typical life-span 

since highways are rebuilt every 25 to 30 years. It is anticipated that sixteen percent of the new trees will die during the 

first 5 years. A loss rate of 39 percent is expected over the 

30-year period. 

On average, expressway trees are pruned once every 10 

years at a cost of about $1 50 per large tree. Costs for tree and 

stump removal are assumed to be $312 and $91 per tree, 

respectively. Sixty percent of all dead trees are removed, and 

all stumps are removed. Nearly all waste wood is recycled as 

mulch used for landscaping. Because expressway trees are 

not planted close to sidewalks, curbs and gutters, and other 

built property, damage to them from trees is minimal. Program administration costs are assumed to be $2.63 annually 

per tree based largely on IDOT's projected expenses. 

Trees In Public Housing Sites 

Openlands Project is a nonprofit organization with an active 

urban forestry program called TreeKeepers, which teaches 

volunteers how to plant and maintain trees. Openlands plants 

300 to 500 trees each year at a variety of locations throughout Chicago. About half of these trees are planted at public 

housing sites with participation from local residents. Other 

planting sites include libraries, parks, and streets. Plantings 

involve TreeKeepers and other volunteers. To simplify this 

analysis, data for tree planting and care at public housing 

and similar park-like sites are used. 

During the next 5 years, Openlands expects to plant about 

2,500 balled and burlapped trees (31 1 per year) averaging 

13 feet in height (2.5 inches d.b.h.). It costs about $150 to 

plant each tree. The typical green ash is assumed to have an 

average annual growth rate of 0.8 feet in height (0.4-inch 

d.b.h.) per year and attain a height of 37 feet (14.5-inches 

d.b.h.) 30 years after planting. Mortality during the first 5 

years is assumed to be 16 percent, and estimated as 39 

percent for the entire 30 years. 

TreeKeepers and other Openlands volunteers do not prune 

or remove trees over 10 inches d.b.h. Therefore, maintenance of maturing trees is performed by local arborists or 

other landscape professionals. Pruning costs are assumed 

to be $160 per tree, with the typical tree pruned four times 

over 30 years. Large tree and stump removal costs are 

assumed to be $900 and $1 10, respectively, with 80 percent 

of all dead trees and stumps removed. Annual program 

administration costs are $32.78 per tree. Administration costs 

cover expenses for coordinating, training, and supplying volunteers with equipment needed to plant and maintain trees. 

Energy Savings 

Trees can reduce energy use for air conditioning (AC) by 

shading building surfaces and lowering air temperatures and 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 8 119 
windspeed. During winter, trees can conserve energy use for 

heating by lowering windspeeds and associated infiltration of 

cold outside air. However, even bare branches of deciduous 

trees can block winter sunlight and increase heating energy 

use (Heisler 1986). Results from energy simulations for a 

typical two-story brick building in Chicago (McPherson 1994: 

Chapter 7, this report) are used in this benefit-cost analysis. 

Specifically, a single deciduous tree 36 feet (1 1 m) tall and 

24 feet (7 m) wide was estimated to reduce annual air conditioning energy use by 266 kwh (0.96 GJ) and heating energy 

use by 4.42 MBtu (4.66 GJ). These base values represent 

maximum potential savings from a well-sited tree around a 

typical two-story residential building in Chicago. Reduction 

factors are applied to these base values to account for less 

than optimal shading and indirect effects, less than 100 

percent presence of air-conditioning and natural gas heating 

devices, and less than mature tree size (McPherson 1991). 

Electricity and natural gas prices are $0.12 per kilowatt-hour 

(kwh) and $5 per million Btu (MBtu). About 40 percent of all 

households in Chicago have central air conditioning, 36 percent have room air conditioning, and 93 percent use natural 

gas for space heating (Thomas Hemminger and Claire Saddler, Commonwealth Edison; Bob Pendlebury, People's Gas, 

1993, pers. commun.). Reduction factors that account for 

less than optimal tree placement with respect to buildings are 

based on personal observation of tree locations in Chicago 

and a previous study (McPherson 1993) (Table 3). 

Air Quality Improvement 

Although the ability of urban greenspace to mitigate air pollution through particulate interception and absorption of gases 

is recognized by many, few studies have translated this 

environmental control function into dollars and cents. This 

study uses an approach similar to that used previously by 

Chicago Urban Forest Climate Project (CUFCP) scientists to 

model the value of improvements in air quality from trees in a 

portion of Lincoln Park (McPherson and Nowak 1993). This 

analysis also includes benefits from the avoided costs of 

residual power plant emissions control due to cooling energy 

savings from trees. 

Pollutant uptake is modeled as the surface deposition velocity times the pollutant concentration. Deposition velocities to 

vegetation for each pollutant, i.e., particulate matter less than 

10 pm (PMIO), ozone (03), nitrogen dioxide (NO2), sulfur 

dioxide (SOz), and carbon monoxide (CO) are derived from 

the limited literature on this subject (Davidson and Wu 1988). 

Two scenarios with different pollution concentrations are 

used to estimate uptake rates. The first scenario uses average annual pollution concentrations during periods when 

National Ambient Air Quality Standard (NAAQS) levels are 

exceeded. The second scenario uses average pollution concentrations. Average annual pollution concentrations and 

the number of hours associated with each scenario are 

derived for in-leaf and leaf-off months from 2 years of data 

collected at Edgewater (gaseous pollutants) and the Chicago Avenue Pumping Station (particulates). All trees are 

considered to be deciduous, so annual pollutant uptake rates 

are calculated using in-leaf data only (May through October). 

Gaseous absorption is assumed to occur during daylight 

hours when stomates are open. 

Biogenic hydrocarbon emissions from planted trees can 

contribute to O3 pollution. However, as noted by Nowak 

(1994b: Chapter 5, this report), reducing city temperatures 

with trees can lower O3 production and hydrocarbon 

emission. Because much research is needed before these 

complex interactions are understood, these costs and benefits are assumed to be offsetting. 

Emissions by power plants depend on the type of technology 

used to generate electricity, fuel type, plant age, and other 

factors. Energy savings by trees will influence future 

emissions, and future emissions will be different as Commonwealth Edison begins to retire nuclear power plants. However, 

it is conservatively assumed that pollution emission rates 

will not change because advanced control technologies will 

offset an increase in the use of fossil fuels. Current emission 

rates provided by Commonwealth Edison are used for PM10 

and SO2 (Tom Hemminger, Commonwealth Edison, 1991, 

pers. commun.). Generic emission rates are used for other 

pollutants (California Energy Commission 1992). Avoided emissions are calculated by multiplying annual savings in electric 

energy from trees by the estimated power-plant emission 

rate for each pollutant (McPherson et al. 199313) (Table 4). 

The societal value of reducing air pollutants through tree 

planting is estimated using the cost of traditional air-pollution 

Table 3.-Location reduction factors for energy, hydrologic, and other benefits. in percent 

Tree location 

Category Park Yard Street Highway Housing 

Shade 30 60 50 30 50 

ET cooling 50 90 80 50 80 

Wind 50 90 80 50 80 

Hydrologic 15 30 70 25 30 

Other benefits 

Species factor 70 70 70 70 70 

Condition factor 70 70 70 70 70 

Location factor 70 75 75 65 65 

120 Chapter 8 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 4.-Assumptions for estimating implied value of air quality improvement 

Item PM~O 03 NO2 SO2 co 

Deposition velocity (cmlsec) 0.60 0.45 0.40 0.66 0.0006 

Control costs (dollarslton) 1,307 490 4,412 1,634 920 

Emission factors (IbIMWh) 0.14 0.03 2.10 6.81 0.63 

controls as proxies for the price society is willing to pay to 

reduce air pollutants. Due to the unavailability of data for 

Chicago regarding air-pollution control costs, 1990 estimates 

for the Northeastern United States are used for this analysis 

(California Energy Commission 1992). These values may not 

reflect the actual price Chicagoans are willing to pay to reduce various air pollutants. Deposition velocities, control costs, 

and emission factors for each pollutant are listed in Table 4. 

Carbon Dioxide Sequestered and Avoided 

Carbon dioxide is a major greenhouse gas that influences 

atmospheric processes and climate. As part of the CUFCP, 

the potential of urban and community forests to directly store 

carbon in their biomass has been reported in this report 

(Nowak 1994c: Chapter 6). Other studies have analyzed the 

extent to which cooling energy savings attributed to urban 

forests reduce atmospheric carbon released by power plants 

as a byproduct of electric generation (Huang et al. 1987; 

Rowntree and Nowak 1991, Sampson et al. 1992; Nowak 

1993). Generally, avoided carbon emissions are many times 

greater per tree than are amounts of carbon stored. This 

study uses an approach similar to that developed by Rowntree 

and Nowak (1 991). 

Sequestered carbon is calculated using biomass equations 

for a sugar maple (Acer saccharurn) to represent hardwood 

biomass (Wenger 1984). Hardwood dry weight is estimated 

to be 56 percent of fresh weight and carbon storage 

is approximately 45 percent of total dry-weight biomass. 

Annual carbon sequestration for a 20-inch d.b.h. (45-foot 

tall) deciduous tree is estimated to be 100 Ib (45 kg). 

Avoided carbon emissions from power plants are calculated 

using energy analysis estimates of cooling energy saved 

and Commonwealth Edison's current fuel mix. A weighted 

average carbon emission rate of 0.1 1 Ib (50 g) per kilowatthour was calculated. Estimated carbon emissions associated 

with natural gas consumed for space heating total 29.9 Ib 

(13.6 kg) per million Btu (Larry Guzy, Peoples Gas, 1993, 

pers. commun.). The implied value of stored and avoided 

carbon is assumed to be $22 per ton (California Energy 

Commission 1992). 

Hydrologic Benefits 

Rainfall intercepted and stored by the crowns of trees eventually evaporates. Findings from hydrologic simulations 

using different amounts of tree-canopy cover indicate that 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

existing tree cover reduces urban stormwater runoff by 4 to 

8 percent, and that modest increases in tree cover can 

further reduce runoff (Sanders 1986; Lormand 1988). Power 

plants use approximately 0.6 gal (2.3 1) of water to produce 1 

kwh of electricity (McPherson 1991), so trees that provide 

energy savings through cooling also reduce water use 

associated with power production. Avoided water use at 

power plants is calculated by multiplying the rate of water 

use (0.6 gal) and kilowatt-hours of annual cooling energy 

saved. According to the Chicago Water Collection Division, 

the value of this water is estimated using a local retail water 

price of $0.001 75 per gallon. 

Most jurisdictions in the Chicago area require on-site 

retention-detention basins or other control devices to ensure 

that off-site flow does not exceed predevelopment rates. 

Costs for land acquisition, basin excavation, landscaping, 

and maintenance were approximately $0.02 per gallon of 

water retained (McPherson et al. 1993b). This price is used 

to establish a base implied value for rainfall interception and 

consequent avoided costs for stormwater control. 

The amount of rainfall intercepted annually by trees is calculated as a linear function of tree size (Aston 1979). The value 

of tree-crown interception for retention-detention begins to 

accrue after the storage capacity of soil and other surfaces is 

filled and runoff commences. For example, storm events 

less than 0.1 inch seldom result in runoff. For this study, it is 

assumed that 80 percent of annual rainfall results in runoff. 

Interception equations for leafless and in-leaf periods (Hamilton 

and Rowe 1949) are used to estimate annual interception 

volumes for trees with different crown spreads. 

In urban areas, land-cover characteristics dominate runoff 

processes and overland flow. Runoff from parking lots will 

exceed runoff from lawns under similar storm conditions. 

Thus, the potential effect on runoff of rainfall interception by 

trees can vary according to land cover characteristics associated with each planting location. To calculate net avoided 

runoff, land-cover reduction factors are incorporated and are 

assigned to each location based on the rational method for 

estimating runoff (Dunne and Leopold 1978) (Table 2). 

Other Benefits 

There are many environmental and aesthetic benefits 

provided by trees in Chicago that should be included in any 

benefit-cost analysis. Environmental benefits from trees not 

accounted for thus far include noise abatement, soil conserChapter 8 
vation, water-quality effects, increased human thermal comfort, and wildlife habitat. Although such benefits are more 

difficult to quantify than those described previously, they 

can be just as important. 

Research shows that humans derive substantial pleasure 

from trees, whether it be feelings of relaxation, connection to 

nature, or religious joy (Dwyer et al. 1992). Trees provide 

important settings for recreation in and near cities. Research 

on the aesthetic quality of residential streets has shown that 

street trees have the single strongest positive influence on 

scenic quality. 

Research comparing variations in sales prices over a large 

number of residential properties with different tree resources 

suggests that people are willing to pay 3 to 7 percent more 

for residential properties with ample tree resources versus 

few or no trees (Morales et al. 1983; Payne 1973). One of the 

most comprehensive studies of the influence of trees on 

residential property values was based on actual sales prices 

for 844 single-family homes in Athens, Georgia (Anderson 

and Cordell 1988). Each large front-yard tree was associated with about a 1-percent increase in sales price ($336). A 

value of 9 percent ($15,000) was determined in a U.S. Tax 

Court case for the loss of a large black oak on a property 

valued at $1 64,500 (Neely 1988). 

Several approaches can be used to estimate the value 

of "other" benefits provided by trees. The hedonic pricing 

approach relies on differences in sales prices or property 

values of similar houses with good tree cover and no or little 

tree cover. The dollar difference should reflect the willingness 

of buyers to pay for the economic, social, and environmental 

benefits that trees provide. Some limitations to using this 

approach for this study include the difficulty associated with 

determining the value of individual trees on a property; the 

need to extrapolate results from studies done years ago in 

the east and south to Chicago; and the need to extrapolate 

results from trees on residential properties to trees in other 

locations (e.g., streets, parks, highways, public housing). 

A second approach is to estimate the compensatory value 

of a tree using techniques developed by the Council of 

Landscape and Tree Appraisers and described by Neely 

(1992). Tree valuation is used by appraisers to calculate the 

replacement cost of a tree of similar size and kind as one 

that has been damaged or destroyed. The replacement value 

of smaller trees is estimated using local market prices for a 

transplantable tree of similar size and species. For larger 

trees, a basic value is calculated based on the local market 

price for the largest normally-available transplantable tree. 

This value is then adjusted downward to account for the 

species, condition, and location. A trunk adjustment factor is 

applied to trees larger than 30 inches d.b.h. based on the 

premise that a mature tree will not increase in value as 

rapidly as its trunk area will increase (Figure 1). 

A good overview of the tree valuation method is provided by 

Miller (1 988). The approach is used with street tree inventory 

data to estimate the asset value of street tree populations. 

The tree valuation was used in an economic analysis of the 

optimum pruning cycle for Milwaukee, Wisconsin by comparing the marginal cost of pruning to its marginal return (Miller 

and Sylvester 1981). Street tree inventory data regarding 

0 00 

0 I0 20 30 40 50 60 70 80 90 100 

Year 

+ Trunk Diameter -- Trunk Area -- Adjusted Trunk Area 

Figure 1. -Trunk area is adjusted for trees greater than 30 inches d.b.h. to more 

realistically estimate their replacement value. Estimated trunk diameter for a typical 

green ash used to calculate trunk area and tree replacement value is shown. 

122 Chapter 8 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
pruning intervals and tree condition were used with regression 

analysis to determine relations between pruning and condition class. Marginal costs were calculated as the loss 

in tree value associated with lower condition classes and 

extended pruning cycles. Thus, Miller and Sylvester (1981) 

applied the tree valuation formula to estimate the economic 

value of benefits forgone as tree condition deteriorates. This 

study adopts a similar approach to estimate the total value of 

benefits trees produce at a given time. Then the value of 

energy, air quality, carbon, and hydrologic benefits are subtracted from this total to calculate the remaining "other benefits". Tree replacement value (Neely 1988) is estimated as: 

Replacement Value = Basic Value x Species Factor x 

Condition Factor x Location Factor 

where Basic Value = $27 x (0.789 x dn) and d is tree d.b.h. in 

inches. Because in this analysis benefits begin accruing in 

1992, basic value is calculated using $27 per square inch of 

trunk area, the value used in 1992 (Neely 1988). Currently, it 

costs about $33 to $35 per square inch of trunk area to 

purchase and install a typical 4-inch (10 cm) tree in the 

Chicago area (George Ware, Morton Arboretum, 1993, pers. 

commun.). Species and condition factors are assumed to be 

70 percent for all trees, corresponding with species that are 

fairly well adapted to local growing conditions and in fair to 

good condition (Table 3). Locations factors range from 65 

percent for highway and pubtic housing trees to 75 percent 

for street trees based on the site context, functional contribution of trees, and likely placement (Table 3). 

As described previously, annual tree-replacement value is 

calculated as the incremental value associated with the yearly 

increase in trunk diameter of each age class. To avoid 

double-counting the environmental benefits already discussed 

(e.g., energy and carbon savings, improvement in air quality, 

hydrologic benefits), these benefits are totaled and subtracted 

from the incremental tree replacement value each year. Theoretically, the amount remaining after the environmental benefits already accounted for are deducted represents the value 

of benefits such as aesthetic value, improved health, wildlife 

value, and social empowerment. 

Discount Rates 

C-BAT was designed to estimate annual costs and benefits 

over a 30-year period. This is long enough to reflect benefits 

from maturing trees and still be within the planning horizon 

of policymakers. With a tree-planting and care program, 

benefits and costs are incurred at various points in time. 

Because decisionmakers have other uses for the dollars that 

they invest in the tree program as well as the ones they 

receive, it is important that the analysis reflect the cost of 

other foregone investment opportunities. This usually is done 

by discounting all benefits and costs to the beginning of the 

investment period using a rate of compound interest. The 

discount rate incorporates the time value of money and 

inflation. The former refers to the fact that a dollar received in 

the future is worth less than one received in the present 

since the present dollar can earn interest. Inflation is the 

anticipated escalation in prices over time. For studies such 

as this, selecting a discount rate is problematic because 

the cost of capital for a municipality is different than for a 

resident or a nonprofit organization, all of whom are investing in the planting and care of trees. The net present value 

(NPV) of investments will be higher for decisionmakers with 

lower discount rates, but lower for those who face a higher 

cost of capital. At higher discount rates, NPV decrease 

several fold because most costs are incurred during the first 

five years when trees are planted, while most benefits accrue later as the trees mature and are discounted heavily. To 

assess how C-BAT findings change in response to different 

discount rates simulations were conducted using rates of 4, 

7, and 10 percent. The NPV estimates (benefits minus costs) 

in this study can be interpreted as yield on the investment in 

excess of the cost of capital (discount or interest rate). 

Investment in tree planting is evaluated using NPV and benefit-cost ratios. The former is the present value of benefits 

minus the present value of costs; the latter is the ratio of the 

present value of benefits and costs. If the benefit-cost ratio is 

greater than one, net benefits are produced. Higher ratios 

and NPV indicate greater returns relative to dollars invested. 

Model Limitations 

The application of C-BAT yields results that must be interpreted with care because of the limitations associated with 

the available data and with C-BAT itself. There is considerable variability in the quality of information upon which 

C-BAT results are based. For instance, cost data for tree 

planting, pruning, and removal are thought to be quite 

reliable, but information on litigation/liability, infrastructure 

repair, and administration costs was difficult to obtain and is 

less reliable. Second, there is a high degree of uncertainty 

associated with some parameters used to model benefits. 

For example, a stronger empirical basis is needed to estimate benefits not explicitly accounted for, such as "other" 

benefits. Limitations of the tree valuation method include 

1) the need to extrapolate value to large trees for which 

transplants of similar size are unavailable, 2) the lack 

of research-based guides for adjusting the basic value by 

species, condition, and location, and 3) the fact that the 

amount one demands as compensation for a damaged or 

destroyed tree may be greater than what one is willing to pay 

for the same tree prior to the casualty (Randall 1981). 

Limited urban forest research makes it necessary to base 

some assumptions on professional observation and data 

from forest trees rather than on research results for urban 

trees. Carbon sequestration benefits may be understated if 

open-growing urban trees have relatively more biomass than 

forest trees. 

C-BAT accounts for only a few of the many benefits and 

costs associated with trees. For example, some benefits and 

costs not explicitly considered in this study include effects of 

trees on human health and wildlife habitat, as well as costs 

of pick-up and disposal of tree litter. 

This is pioneering research that awaits thorough testing and 

validation with field data. Results are first-order approximations and some error is to be expected. As our understanding 

of urban forestry increases better methods will be available 

to estimate benefits and costs. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 8 123 
Results and Discussion 

Growth, Mortality, and Leaf Area 

Growth curves for the typical trees are shown in Figure 2. 

The green ash in park, yard, and public housing sites display 

similar growth rates. Growth rates for trees along highways 

and residential streets are slower because less favorable 

growing conditions are assumed. 

Mortality rates reflect anticipated loss associated with growing conditions, care, and likely damage from cars, vandalism, 

pestldisease, and other impacts. Loss rates are projected to 

be greatest along residential streets (42 percent), where 

trees are exposed to a variety of human and environmental 

abuse (Table 5). A 39-percent loss rate is projected for trees 

planted in parks, on public housing sites, and along highways. 

About 18 percent of the trees planted in residential yards are 

expected to die. Of the 95,000 trees planted, 33,150 (35 

percent) are projected to die, leaving 61,850 trees alive at 

the end of the 30-year analysis (Figure 3). 

The total amount of leaf area varies according to tree numbers and size. Although twice as many trees are projected to 

be planted along residential streets than in yards, total leaf 

area is similar because yard trees are faster growing (i.e., 

larger trees) and have a lower mortality rate (Figure 4). 

Because relatively few trees are projected to be planted in 

highway and public housing locations, their projected total 

leaf area is small. 

Future Tree Cover 

Patterns of growth and mortality that influence total leaf area 

have a similar impact on new tree cover (Table 5). Planting 

of 95,000 trees is projected to add approximately 1,204 

acres (487 ha) of future tree cover 30 years after planting 

began. Yard trees account for 26 percent of all trees planted 

and 36 percent of new tree cover. Together, park and streettree plantings contribute 56 percent of total future tree cover; 

trees planted along highways and on public housing sites 

account for the remaining 6 percent. 

To place the magnitude of future tree cover in perspective it 

was compared to the amounts of current tree cover and total 

land area of Chicago. Based on our analysis of aerial photographs, trees and shrubs cover about 18,608 acres (7,530 

ha) or 11.1 percent of total land area in Chicago (McPherson 

et al. 1993a). The addition of 1,204 acres (487 ha) of new 

tree cover due to planting of 95,000 trees increases overall 

tree cover by about 1 percent, assuming no other change in 

land cover. This future tree cover amounts to 7 percent of 

existing tree cover, so it is not an insignificant contribution. 

Another way to assess the relative impact of these proposed 

plantings is to project their effect on the current canopystocking levels. We found that about 32 percent of land in 

Chicago that is actively managed is Available Growing Space 

(AGS), meaning land that can be planted with trees because 

it is not covered with paving and buildings (McPherson et al. 

1993a). The proportion of AGS occupied by trees is called 

the Canopy Stocking Level (CSL), and is about 25 percent in 

Chicago. By comparison, CSL for 12 other U.S. cities ranged 

from 19 to 65 percent (McPherson et al. 1993b). The relatively 

low CSL for Chicago implies that there is space available for 

new tree planting, though some of this space should not be 

planted with trees (e.g., prairie, playfields). The additional 

1,204 acres (487 ha) of future tree cover would increase 

CSL from 25 percent to 28 percent. 

Net Present Values and Benefit-Cost Ratios 

The NPV reflects the magnitude of investment in tree planting 

and care at each location, as well as the flow of benefits and 

costs over time. The projected NPVs were positive at all 

Table 5.---C-BAT results 

No. trees Mortality New tree NPV in Benefit Per planted tree dollar^)^ 

Tree location planted rate (%)a $1,00oC PV benefit PV cost NPV 

Park 12,500 39 1 90 5,592 2.14 840 393 447 

Yard 25,000 18 433 14,637 3.51 81 8 233 585 

Street 50.000 42 489 15,160 2.81 471 168 303 

Highway 5.0(30 39 58 1,606 2.32 564 243 32 1 

Housing 

Total 95,000 35 1.204 38,150 2.83 621 219 402 

a Percentage of trees planted expected to die during %year planning period. 

Estimate of new tree cover in acres provided by plantings in 30 years (2022) assuming listed mortality and no replacement planting after 5 

years. 

Net present values assuming 7-percent discount rate and 30-year analysis period. 

Discounted benefitcost ratio assuming 7-percent discount rate and =year analysis period. 

Present value of benefits and costs per planted tree assuming 7-percent discount rate and 30-year analysis period. 

124 Chapter 8 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
- Park -- Yard + Street 

-t Highway -* Public Housing 

Figure 2. -Growth curves modeled for the typical green ash tree at each 

planting location. 

- Park - Yard + Street 

Highway - Public Housing 

Figure 3. -Projected number of live trees at each location, assuming planting and 

replacement during the first 5 years only. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 8 
Park - Yard + Street * Highway Housing 

Figure 4. -Projected leaf-surface area for trees at each planting location. 

discount rates, ranging from $638,153 at public housing sites 

with a 10 percent discount rate to $30.6 million for street 

trees with a 4 percent discount rate. At a 7 percent discount 

rate, the NPV of the entire planting (95,000 trees) is projected 

to be $38 million or about $402 per planted tree (Table 5). 

This means that on average the present value of the yield on 

investment in tree planting and care in excess of the cost of 

capital is $402 per tree. The NPV of street and yard trees is 

projected to be about $15 million each, while the NPV for 

park tree plantings is $5.6 million. The NPVs are lower for 

planting and care of trees along highways ($1.6 million) and 

at public housing sites ($1.2 million) because fewer trees are 

projected to be planted than in the other locations. 

The discounted benefit-cost ratio (BCR), or the present value 

of benefits divided by costs, is greater than 1.0 at all discount 

rates. The BCRs range from 1.49 for park trees with a 10- 

percent discount rate, to 5.52 for residential yard trees with a 

4-percent discount rate. At a 7-percent discount rate, the BCR 

for all locations is 2.83, meaning that $2.83 is returned for 

every $1 invested in tree planting and care in excess of the 7- 

percent cost of capital (Table 5). BCRs are projected to 

be greatest for residential plantings (3.5 for yard and public 

housing at 7-percent) and least for park trees f2.14), although 

actual BCRs will vary with the mix of species used and other 

factors influencing growth, mortality, and tree performance. 

Although NPVs and BCRs vary considerably with discount 

rate, these results indicate that economic incentives for 

investing in tree planting and care exist, even for 

decisionmakers who face relatively high discount rates. While 

the rate of return on investment in tree planting and care is 

less at higher discount rates, benefits still exceed costs for 

126 Chapter 8 

this 30 year analysis. Given this result, a 7 percent discount 

rate is assumed for findings that follow. 

The estimated present value of total benefits and costs is 

$59 and $21 million, respectively (Tables 6 -7). Expenditures 

for planting alone are projected to account for more than 80 

percent of all costs except for trees at public housing sites, 

where program administration costs are substantial. "Other" 

scenic, social, and ecological benefits represent 52 to 78 

percent of total benefits. Energy savings, removal of atmospheric Con, and hydrologic benefits are tho next most 

important benefits produced by the trees. 

Heating savings associated with reductions in windspeed 

from the maturing trees are projected to account for about 70 

percent of total energy savings (Table 6). This trend, noted 

in the previous section of this report, can be attributed to 

Chicago's relatively long heating season and the pervasiveness of space-heating devices compared to air conditioners. 

The present value of carbon emissions avoided due to heating and cooling energy savings is about 3 to 6 times the 

value of carbon sequestered by trees (Table 6). In several 

other studies, savings from avoided emissions were 4 to 15 

times greater than savings from direct carbon uptake and 

storage in tree biomass (Huang et al. 1987; Nowak 1993; 

Sampson et al. 1992). Smaller avoided emissions for Chicago 

can be explained by several factors. First, 80 percent of 

Chicago's base-load electricity is generated by nuclear power, 

with relatively little emissions of C02. Second, Chicago has 

a short cooling season, so savings in air-conditioning energy 

are less than the national average or regions with warmer 

weather.Third, although heating savings are substantial in 

Chicago, natural gas is a relatively clean burning fuel, so 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 6.-Projected present value of benefits for tree plantings in Chicago (30 year analysis, 7-percent discount rate, in 

thousands of dollars) 

Tree location 

Benefit category Park Yard Street Highway Housing Total 

Energya 

Shade 

ET cooling 

Wind reduction 

Subtotal 

Air qualityb 

PMlO 

Ozone 

Nitrogen dioxide 

Sulfur dioxide 

Carbon monoxide 

Subtotal 

Carbon dioxideC 

Sequestered 37 65 82 12 5 201 

Avoided 92 359 465 37 27 980 

Subtotal 129 424 547 49 32 1,181 

Iiydrologicd 

Runoff avoided 46 170 494 24 15 749 

Saved at power plant 6 26 32 3 2 69 

Subtotal 52 196 526 27 17 818 

Other benefitse 8,242 11,854 12,262 1,926 923 35,207 

Total 10,501 20,458 23,549 2,820 1,614 58,942 

a Net heating and cwling savings estimated using Chicago weather data and utility prices of $0.12 per kwh and $5 per MBtu. Heating costs due 

to winter shade from trees are included in this analysis. 

Implied values calculated using traditional costs of pollution control (see Table 4). 

Implied values calculated using traditional costs of control ($0.01 1Ab) and carbon emission rates of 0.1 1 IMcWh and 29.9 Ib per MBtu. 

Implied values calculated using typical retentionldetention basin costs for stonnwater runoff control ($0.02/gal) and potable water cost of 

($0.001 75lgal) for avoided power plant water consumption. 

Based on tree replacement costs (Neely 1988). 

carbon savings are not great. Thus, care must be taken in 

comparing results from Chicago with other communities. 

Savings in air-conditioning energy and associated removal 

of atmospheric C02 could be higher in communities served 

by utilities more reliant on coal, oil, and gas than Commonwealth Edison, or in cities with longer cooling seasons. 

Present Values of Costs and Benefits Per 

Planted Tree 

Differences in return on investment can be understood by 

examining the present value of costs and benefits per planted 

tree at different planting locations (Figures 5-6). Despite the 

fact that trees of similar size and wholesale price are projected 

for planting in all locations, the present value of planting costs 

varies markedly, ranging from $109 per tree at public housing 

sites where volunteer assistance kept costs down to $341 in 

parks where costs for initial irrigation added to planting expenditures. Participation by residents of public housing in tree 

planting and care can reduce initial tree loss to neglect 

vandalism. Similarly, initial watering of park trees can increase 

survival rates by reducing tree loss to drought. 

The present value of pruning costs is only $12 per planted 

street tree even though trees are assumed to be pruned more 

frequently along streets than at other locations (every 6 years). 

In fact, the present value of total costs is only $168 per tree 

for street trees (Figure 5). Cost-effective planting and care of 

street trees is important because they account for about onethird of Chicago's overall tree cover (McPherson et al. 1993a). 

The present value of removal costs is projected to be highest 

for trees planted in parks and public housing sites ($16 to 

$22 per tree). Costs for infrastructure repair, pest and disease control, and liabilityllitigation are relatively small. The 

present value of program administration costs for tree plantings 

by Openlands and trained volunteers is $35 per planted tree. 

A similar finding was noted for other U.S. cities (McPherson 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 8 127 
Table 7.-Projected present value of costs for tree plantings in Chi'cago (30 year analysis, -/-percent discount rate, in 

thousands of dollars) 

Tree location 

Cost category Park Yard Street Highway Housing Total 

Plantinga 4,258 5,484 7,107 1,097 272 18.21 8 

pruningb 346 192 585 75 57 1.255 

Removalc 

Tree 22 1 105 547 18 36 927 

Stump 27 15 90 9 4 145 

Subtotal 248 120 637 27 40 1,072 

Tree waste disposald 3 1 0 0 0 0 31 

Inspectione 3 0 13 0 1 17 

Infrastructure repairf 

Sewerhater 

Sidewalk/curb 

Subtotal 

Liabilityllitigationg 0 6 11 1 0 18 

Program administrationh 15 0 0 13 87 11 5 

Total 4,909 5,823 8,388 1,214 459 20,793 

a Reported cost of trees, site preparation, planting, and initial watering (see Table 2). 

Reported cost of standard Class II pruning. Pruning frequency varied by locattion (see Table 2). 

Reported cost of tree and stump removal. Frequency of removals varied by location (see Table 2). 

Tree waste disposal fee $4O/ton. Value of wood waste recycled as compost and mulch assumed to offset recycling costs where no net cost shown. 

Reported labor and material costs for systematic tree inspection (see Table 2). f Cost of infrastructure repair due to damage from tree roots assumed to vary by location (see Table 2). 

Cost of litigatiodiability as reported or based on data from other cities (McPherson et al. 1993) when unavailable. 

Salaries of administrative personnel and other program administration expenditures. Administrative costs were incorporated in other reported costs 

for residential street trees. 

Park Yard Street Highway Housing 

Planting C] Pruning Removal Other Care 

Infrast. Repair [mm] Liability Program 

Figure 5. -Present value of costs per tree planted at each location, assuming a 

30-year analysis period and -/-percent discount rate. 

Chapter 8 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Park Yard Street Highway Housing 

Air Quality Carbon Dioxide 

Other 

Figure 6. -Present value of benefits per tree planted at each location, assuming a 30-year analysis period and 7-percent discount rate. 

et al. 199313). Generally, nonprofit tree groups have higher 

administrative costs than municipal programs using in-house 

or contracted services because of their small size and amount 

of funds spent organizing and training volunteers. These 

additional expenditures somewhat offset savings associated 

with reduced labor costs for planting and initial tree care 

compared to municipal programs. 

The projected present value of benefits per planted tree is 

$471 and $564 for street and highway plantings, respectively, $645 for public housing sites, and more than $800 for 

trees planted in parks and residential yards (Figure 6). Lower 

benefits for street and highway trees can be attributed to 

their slower growth (Figure 2), smaller total leaf area (Figure 

3), and relatively smaller energy and other benefits due to 

locational factors. 

The amount of annual benefits the typical tree produces 

depends on tree size as well as relations between location 

and functional performance. Larger trees can produce more 

benefits than smaller trees because they have more leafsurface area. Because yard trees exert more influence on 

building energy use than highway trees, they produce greater 

energy savings per unit leaf area. To illustrate how these 

factors influence benefits, nondiscounted annual benefits 

are estimated for the typical tree at year 30 in each typical 

location (Table 8). Estimated savings in annual air-conditioning energy from the 36-foot tall (14-inches d.b.h.) yard tree 

are 201 kwh (0.7 GJ) ($24 nominal) compared to 102 kwh 

(0.4 GJ) ($12 nominal) for a 34-foot tall (13-inches d.b.h.) 

tree along a highway. Differences in benefits from the uptake 

of air pollutants by trees, including carbon sequestered, are 

assumed to be solely due to differences in tree size, because little is known about spatial variations in pollution 

concentrations that influence rates of vegetation uptake. 

However, location-related differences in cooling energy savings translate into differences in avoided emissions and 

water consumed in the process of electric power generation. 

For instance, trees are projected to intercept more particulate 

matter and absorb more O3 and NO2 directly than in avoided 

power-plant emissions. But energy savings from the same 

trees result in greater avoided emissions of SO:!, CO, and CO:! 

than is gained through direct absorption and sequestration. 

Street trees are projected to provide the greatest annual 

reductions in avoided stormwater runoff, 327 gallons (12.4 

kl) for the 32-foot tall tree (12-inches d.b.h.) compared to 104 

gallons (3.9 kl) avoided by a park tree of larger size. More 

runoff is avoided by street trees than by trees at other sites 

because street tree canopies intercept rainfall over mostly 

paved surfaces. In the absence of street trees, rainfall on 

paving begins to runoff quickly. Trees in yards and parks 

provide less reduction in avoided runoff because in their 

absence, more rainfall infiltrates into soil and vegetated areas; 

thus, less total runoff is avoided. Assumed differences in 

economic, social, aesthetic, and psychological values attached 

to trees in different locations are reflected in the projected 

value of "other" benefits (Table 8). 

Discounted Payback Periods 

The discounted payback period is the number of years before the benefit-cost ratio exceeds 1.0 and net benefits begin 

to accrue. Assuming a 7 percent discount rate, projected 

payback periods range from 9 years for trees planted and 

maintained at public housing sites to 15 years for plantings in 

parks and along highways (Figure 7). Yard and street trees 

are projected to have 13- and 14-year discounted payback 

periods, respectively. As expected, payback periods are 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Chapter 8 
Table 8.-Projected annual benefits produced 30 years after planting by the typical green ash tree at typical locations 

Tree location 

Benefit category - - Tree size (height in feet) 

d.b.h. (inches) 

Energy 

Cooling (kwh) 

Heating (MBtu) 

PM10 (Ib) 

Direct uptake 

Avoided emissions 

Ozone (Ib) 

Direct uptake 

Avoided emissions 

Nitogen dioxide (Ib) 

Direct uptake 

Avoided emissions 

Sulphur dioxide (Ib) 

Direct uptake 

Avoided emissions 

Carbon monoxide (Ib) 

Direct uptake 

Avoided emissions 

Carbon dioxide (Ib) 

Direct uptake 

Avoided emissions 

Hydrology (gal) 

Runoff avoided 

Water saved 

Other benefits (dollars) 

Park Yard Street Highway Housing 

39 36 32 34 37 

16 14 12 13 14.5 

- Park 1- Yard + Street: 

t Highway -c Public Housing 

Figure 7. -Discounted payback periods depict the number of years before the 

benefit-cost ratio exceeds 1 .O. This analysis assumes a 30-year planning period and 

-/-percent discount rate. 

Chapter 8 USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
slightly longer at the 10 percent discount rate (11 to 18 

years), and shorter at most locations with a 4-percent discount rate (9 to 13 years). 

Early payback at public housing sites can be attributed to 

several factors. Trees are projected to add leaf area at a 

relatively rapid rate due to low initial mortality and fast growth 

compared to trees at other locations. These trees are relatively inexpensive to plant and establish due to participation 

by residents and volunteers. Thus, the payback period is 

shortened because upfront costs, which are heavily discounted compared to costs incurred in the future, are low. 

Conclusions 

Are trees worth it? Do their benefits exceed their costs? If 

so, by how much? Our findings suggest that energy savings, 

air-pollution mitigation, avoided runoff, and other benefits 

associated with trees in Chicago can outweigh planting and 

maintenance costs. Given the assumptions of this analysis 

(30 years, 7-percent discount rate, 95,000 trees planted), 

the projected NPV of the simulated tree planting is $38 

million or $402 per planted tree. A benefit-cost ratio of 2.83 

indicates that the value of projected benefits is nearly three 

times the value of projected costs. 

In what locations do trees provide the greatest net benefits? 

Benefit-cost ratios are projected to be positive for plantings 

at park, yard, street, highway, and public housing locations 

at discount rates ranging from 4 to 10 percent. Assuming a 

7-percent discount rate, BCRs are largest for trees in residential yard and public housing (3.5) sites. The following 

traits are associated with trees in these locations: relatively 

inexpensive to establish, low mortality rates, vigorous growth, 

and large energy saving. Because of their prominence in 

the landscape and existence of public programs for their 

management, street and park trees frequently receive more 

attention than yard trees. By capitalizing on the many opportunities for yard-tree planting in Chicago, residents can gain 

additional environmental, economic, social, and aesthetic 

benefits. Residents on whose property such trees are located 

receive direct benefits (e.g., lower energy bills, increased 

property value), yet benefits accrue to the community as 

well. In the aggregate, private trees improve air quality, 

reduce stormwater runoff, remove atmospheric C02, enhance 

the local landscape, and produce other benefits that extend 

well beyond the site where they grow. 

How many years does it take before trees produce net 

benefits in Chicago? Payback periods vary with the species 

planted, planting location, and level of care that trees receive. 

C-BAT findings suggest that discounted payback periods for 

trees in Chicago can range from 9 to 18 years. Shorter 

payback periods are obtained at lower discount rates, while 

higher rates lengthen the payback periods. These payback 

periods compare favorably with those for similar plantings in 

other U.S. cities (McPherson et al. 1993b). ' 

What tree planting and management strategies will increase 

net benefits derived from Chicago's urban forest? Findings 

from the C-BAT simulations suggest several strategies to 

USDA Forest Service Gen. Tech. Rep. NE-186.1994. 

maximize net benefits from investment in Chicago's urban 

forest. These concepts are not new and many currently 

are being applied in Chicago. Most of the following recommendations also have application in communities outside 

Chicago as well. 

1. Select the right tree for each location. Given that planting 

and establishment costs represent a large fraction of total 

tree expenditures, investing in trees that are well suited to 

their sites makes economic sense. Matching tree to site 

should take advantage of local knowledge of the tolerances 

of various tree species. Species that have proven to be well 

adapted should be selected in most cases, though limited 

testing of new introductions increases species diversity and 

adds new horticultural knowledge (Richards 1993). When 

selecting a tree an important first question is: will this tree 

survive the first 5 years after transplanting? A second question is: what are the long-term maintenance requirements of 

this tree and do they match the level of maintenance likely to 

be delivered? Fast starters that have short life spans or high 

maintenance requirements are unlikely to maximize net benefits in the long term. A third question is: what functional 

benefits does a tree produce and will this species provide 

them? For example, if summer shade and winter sunlight are 

desired benefits, then a "solar friendly" species should be 

given high priority (McPherson 1994: Chapter 7, this report). 

2. Weigh the desirabilit~ of controllina initial planting costs 

with the need to provide arowing environments suitable for 

healthv. lona-lived trees. Because the costs of initial investments in a project are high, ways to cut up-front costs should 

be considered. Some strategies include the use of trained 

volunteers, smaller tree sizes, and follow-up care to increase 

survival rates. When unamended growing conditions are 

likely to be favorable, such as yard or garden settings, it may 

be cost-effective to use smaller, inexpensive stock that reduces planting costs. However, in highly urbanized settings, 

money may be well spent creating growing environments 

to improve the long-term performance of trees. Frequent 

replacement of small trees in restricted growing space may 

be less economical than investing initially in environments 

conducive to the culture of long-lived, vigorous shade trees. 

3. Plan for long-term tree care. Benefits from trees increase 

as they grow, especially if systematic pruning and maintenance result in a healthy tree population (Miller and Sylvester 

1981). The costs of providing regular tree care are small 

compared to the value of benefits forgone when maturing 

trees become unhealthy and die (Abbott et al. 1991). Efficiently delivered tree care can more than pay for itself by 

improving health, increasing growth, and extending longevity. 

A long-term tree care plan should include frequent visits to 

each tree during the first 10 years after planting to develop a 

sound branching structure and correct other problems, and 

less frequent but regular pruning, inspection, and treatment 

as needed. Mature trees in Chicago provide substantial 

benefits today. Maintenance that extends the life of these 

trees will pay dividends in the short term, just as routine 

maintenance of transplants will pay dividends in the future. 

Clearly, a healthy urban forest can produce long-term benefits 

that all Chicagoans can share. This study has developed 

Chapter 8 
initial estimates of the value of some of these benefits, as 

well as the costs. To improve the health and increase the 

productivity of Chicago's urban forest will require increased 

support from agencies and local residents. lnformation from 

this chapter could be part of a public education program 

aimed at making more residents aware of the value their 

trees add to the environment in which they live. 

Acknowledgments 

I wish to thank Paul Sacamano, Steve Wensman, Scott 

Prichard, and Lisa Blakeslee for their assistance with data 

collection and C-BAT modeling. lnformation on tree-planting 

and care programs in Chicago was provided by Geri Weinstein 

and Robert Megquier (Chicago Park District), Larry Hall 

(Hendricksen The Care of Trees), Steve Bylina (Bureau of 

Forestry), Vince Pagone (Chicago Gateway Green Comrnittee), Rick Wanner (Illinois Department of Transportation), 

and Suzanne Malec Hoerr (Openlands). The author thanks 

George Ware (Morton Arboretum), Robert Miller (University 

of Wisconsin-Stevens Point), and John Dwyer (USDA Forest 

Service) for helpful reviews, but he alone is responsible for 

the contents of the article. 

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Chicago's urban forest ecosystem: results of the Chicago Urban 

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USDA Forest Service Gem Tech. Rep. NE-186.1994. Chapter 8 

Chapter 9 

Sustaining Chicago's Urban Forest: 

Policy Opportunities and Continuing Research 

E. Gregory McPherson, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Davis, CA 

David J. Nowak, Research Forester, USDA Forest Service, Northeastern Forest Experiment Station, Chicago, IL 

Rowan A. Rowntree, Program Leader, USDA Forest Service, Northeastern Forest Experiment Station, Berkeley, CA 

Abstract 

Chicago's trees are a community resource that provide a 

myriad of benefits. Obtaining and sustaining higher levels of 

net benefits from Chicago's urban forest will require more 

active participation by residents, businesses, utilities, and 

governments. Opportunities for policies and programs that 

forge new links between city residents and city trees are 

outlined. They address issues such as economic development, environmental planning, public housing, energy conservation, and management of the region's air, water, and 

land resources. 

Although this report marks completion of the 3-year Chicago 

Urban Forest Climate Project, scientists will continue to study 

many aspects of Chicago's urban environment. Ongoing 

research that measures and models the effects of trees on 

urban climate, air quality, and carbon flux is summarized. A 

book that will document results of this research is planned 

for publication in 1996. 

Introduction 

Research findings presented in this report describe relations 

between the structure of Chicago's urban forest and environmental and ecological processes that influence hydroclimate, 

carbon flux, energy use, and air quality. The value that 

Chicagoans' place on tree-related services is estimated by 

accounting for annual benefits and costs associated with 

their planting and long-term care. Strategies are presented 

that can maximize return on investment. 

Chicago's trees are a community resource that provide a 

myriad of benefits. Obtaining and sustaining higher levels 

of net benefits from Chicago's urban forest will require 

more active participation by residents, businesses, utilities, 

and governments. Whether they know it or not, each of 

these entities has a vested interest in Chicago's urban 

forest and stands to gain from the increased benefits it can 

produce. Policies and programs that could expand the 

current role of these participants in the planning and management of Chicago's future urban forest are described in 

the following section. 

Policy and Program Opportunities 

Green Infrastructure and Development 

The 1909 Plan of Chicago envisioned a continuous greenbelt 

of forest preserves, parks, and boulevards around the city. As 

this "green infrastructure" developed, it added value to nearby 

properties, provided accessible recreational opportunities, 

improved local environments, guided growth, and contributed 

to Chicago's unique character as a "City in a Garden." Today, 

Chicagoans enjoy many of the benefits that this greenspace 

provides. As Chicago evolves into the 21 st century, the green 

infrastructure can continue to play a prominent role. Urban 

forest planning and management can address issues such as 

job training, conservation education, neighborhood revitalization, mitigation of heat islands, energy conservation, stormwater 

management and water quality, biological diversity, wildlife 

habitat, and outdoor recreation. 

A comprehensive set of urban forest planning principles could 

position greenspace once again as a value-adding magnet for 

economic development. Through planning, greenspaces created as a part of development can be linked and connected to 

Chicago's historic network of greenbelts and the region's 

system of greenways. The design of Chicago's new green 

infrastructure can integrate values that residents demand of 

greenspace with the most recent advances in urban forest 

science. In this way, Chicagoans can redefine the greenspace 

legacy they have inherited to fit the social, economic, and 

environmental needs of current and future generations. 

Partnerships for Tree Planting and Care at 

Public Housing Sites 

CUFCP research results suggest great potential net benefits 

from tree planting and care at public housing sites. Relatively large energy savings could accrue to persons in lowincome areas who now spend larger than average percentages of their income to heat and cool their homes. Because 

residents of public housing incur a disproportionate health 

risk due to exposure to air pollution, tree plantings designed 

to improve air quality could provide substantial health benefits. Also, local residents who participate in the planting and 

care of trees can strengthen bonds with both neighbors and 

nature. Seasonal job training in arboriculture and full-time 

employment opportunities could result from a substantial 

commitment to the restoration of urban forests in areas with 

USDA Forest Service Gen. Tech. Rep. 186. 1994. Chapter 9 135 
the greatest need for increased tree cover. Finally, business 

opportunities for local entrepreneurs might be increased in a 

more serene and attractive retail environment associated 

with a healthy urban forest. 

Potential partners for shade tree programs in public housing 

sites include the Chicago Housing Authority, Chamber of 

Commerce, Openlands, Commonwealth Edison, People's 

Gas, Center for Neighborhood Technology, and other local, 

state, and federal organizations that manage public housing, 

energy, water, and air resources. 

Urban Forest Stewardship Program 

Chicago's street and park trees account for more than onethird of the city's tree cover. The health, welfare, and productivity of these public trees is important to the health, 

welfare, and productivity of all city residents. The responsibility for stewardship of street and park trees rests with 

Chicago's Bureau of Forestry and the Chicago Park District. 

To increase and sustain benefits from public trees, these 

organizations require adequate funding for tree care operations. Other partners can assist with an urban forest stewards hip effort. For example, urban greenspace influences 

the quantity and quality of stormwater runoff. Thus, there are 

opportunities for water resource agencies to expand their 

role from management of local restoration sites to stewardship of the urban-forest canopy. Stewardship programs supported by organizations responsible for managing water, air, 

and energy resources could provide financial assistance for 

professional care of existing trees and funds to develop and 

distribute educational materials for use by residents and 

design professionals. 

Yard-Tree Planting Program 

Electric utilities are beginning to factor the external costs of 

supplying power into their resource planning process. External costs are costs for reclaiming land, cleaning air, and 

mitigating other impacts of power production that are not 

fully reflected in the price of electricity. As generating stations come due for replacement, more utilities are evaluating 

the potential of shade trees to cool urban heat islands and 

reduce the demand for air conditioning. Utilities such as 

Potomac Electric Power Company, Tucson Electric Power, 

and the Sacramento Municipal Utility District have initiated 

shade-tree programs because the value of energy saved 

exceeds the cost of generating new electricity. Each of these 

programs is a joint effort between the utility and a local 

nonprofit tree group. The utility provides funding to the group, 

which implements the yard-tree planting and care program. 

Urban foresters are employed and trained to ensure that 

trees are selected and planted where they will provide the 

greatest energy savings. To save money and promote interactions at the neighborhood level, each planting usually 

involves residents in the same block or neighborhood. Workshops and educational materials are used to train residents 

in proper planting and tree-care practices. 

Initial economic analyses described by McPherson (Chapter 

8, this report) suggest that the present value of benefits 

136 Chapter 9 

produced by yard trees in Chicago can be 3 1/2 times their 

cost. Trees provide benefits other than energy savings that 

should interest utilities, such as removal of air pollutants and 

atmospheric carbon dioxide (Chapters 5 and 6, this report). 

Such economic incentives can provide new opportunities for 

local utilities to take a more active role in the planting and 

care of Chicago's urban forest. 

In Chicago and surrounding communities steps have been 

taken to make the most of funds available for urban forestry. 

Partnerships like Gateway Green bring together municipal 

foresters, representatives of highway departments and nonprofit tree groups, and professional arborists to create and 

share resources in new ways. Volunteer-based groups like 

TreeKeepers work with local residents to ensure that trees 

receive the care they need to survive after planting. The 

Chicago Bureau of Forestry has invested in a training program and now employs more than 100 certified arborists, 

each more knowledgeable than ever about tree care. The 

Chicago Park District is systematically inventorying trees 

and developing urban-forest management plans for its historic parks. However, the continued support of all Chicagoans is needed to forge new links between city residents and 

city trees. A public education program that informs residents 

about the benefits of a healthy and productive urban forest is 

one way to strengthen this connection. 

Continuing Research 

The CUFCP has created an extensive database on urban 

forest structure and function. Although completion of the 3- 

year CUFCP is marked by this report, scientists will continue 

to study many aspects of Chicago's urban environment. A 

book that will document results of CUFCP work is planned 

for publication in 1996. Also, methods and tools developed 

as part of the CUFCP are being improved and disseminated 

to address urban-forest planning and management issues in 

other U.S. cities. A brief description of on-going research in 

Chicago follows. 

Modeling the Effect of Urban Trees on Ozone 

Concentrations 

This cooperative research with the Lake Michigan Air Directors Consortium is investigating the effect of increasing or 

decreasing the amount of urban trees in Cook and DuPage 

Counties on concentrations of ozone in the Chicago area. 

This research will incorporate data on emissions of volatile 

organic compounds by trees, as well as information on ozone 

deposition and modifications in air temperature due to trees. 

Emissions of Volatile Organic Compounds by 

Vegetation 

This research is estimating the amount of isoprene, monoterpenes, and other volatile organic compounds emitted by 

vegetation in the Chicago area in 1991 and comparing these 

emissions with anthropogenic emissions in the same area. 

Results will be used to help quantify the overall effect of 

urban trees on ozone and test the applicability of the US. 

Environmental Protection Agency's Biogenic Emission Inventory System in two heavily urbanized counties. Many 

USDA Foresrt Service Gen. Tech. Rep. NE-186. 1994. 
organizations use the Biogenic Emission Inventory System 

to estimate emissions of non-methane hydrocarbons as part 

of state implementation plans. 

Measuring and Modeling the Effect of Urban Trees 

on Microclimate 

Research continues to analyze microclimatic data collected 

at 39 sites to better understand tree influences on climate as 

a function of area-wide tree and building attributes, nearby 

tree and building characteristics, and general weather conditions. Validated mathematical models will predict how different building and tree configurations affect air temperature 

and wind speed in Chicago. Input for the models will consist 

of hourly weather data from an airport and estimates of 

characteristics of tree and building structure. The models will 

be applied to evaluate further how trees influence energy 

use in houses, air quality, and human comfort outdoors. 

Modeling the Effect of Urban Trees on Local Scale 

Hydroclima te 

This study continues to investigate relations between observed fluxes, in particular latent heat flux (energy going into 

evaporation) and sensible heat flux (energy going into warming the air) with tree-cover density. A geographic information 

system, which has been developed, will provide a basis for 

interpreting the representativeness of flux measurements 

and for objectively determining model input for surface parameters. Numerical boundary layer models will be used to 

predict the effects of different tree-planting scenarios on 

local scale energy and water exchanges. 

Landscape Carbon Budgets and Planning Guidelines 

This study quantifies landscape-related carbon storage and 

annual carbon fluxes for two residential blocks in Chicago. 

Landscape planting and management guidelines based on 

increased rates of carbon removal due to direct sequestration by trees and reduction of indirect emissions associated 

with energy savings for residential heating and cooling will 

be presented. 

Use of Airborne Videography to Describe Urban 

Forest Cover in Oak Park, Illinois 

Computer image processing technologies provide new tools 

for assessing urban forest structure and health. This study 

compares data on land cover from two types of airborne 

videography in terms of accuracy, cost, and compatibility 

with geographic information systems. Information on forest 

cover obtained from black and white and color infrared photographs also are being compared. Potential uses and limitations associated with each type of imagery will be outlined. 

USDA Forest Service Gen. Tech. Rep. 186. 1994 Chapter 9 137 

Appendix A 

Supplemental Tables for Chapter 2 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 
Table 1. -Average shading coefficients (percentage of 

sunlight intercepted by foliated tree canopies) used in 

regression model for leaf-surface area of individual urban 

trees (derived from McPherson 1984) 

Common name Shading coefficient 

American elm 0.87 

Amur maple 

Ash (average) 

Beech 

Birch 

Catalpa 

Cottonwood 

Crabapple 

Elm (average) 

Ginkgo 

Golden-rain tree 

Green ash 

Hackberry 

Hawthorn 

Honeylocust 

Horsechestnut 

Kentucky coffeetree 

Linden 

Maple (average) 

Norway maple 

Oak (average) 

Pear 

Pin oak 

Poplar (average) 

Red maple 

Red oak 

Russian olive 

Serviceberry 

Shagbark hickory 

Siberian elm 

Silver maple 

Sugar maple 

Sycamore 

Tuliptree 

Walnutlhickory 

White oak 

140 Appendix A USDA Forest Service Gen. Tech. R~D. NE-186. 1994. 
Table 2. --Scientific names of tree species or genera 

Common name Scientific name Common name Scientific name 

Ailanthus Ailanthus altissima Magnolia Magnolia spp. 

Akler 

American elm 

Amur maple 

Apple 

Arborvitae 

Ash (other)= 

Austrian pine 

Basswood 

Beech 

Black locust 

Blue spruce 

Boxelder 

Buckthorn 

Bur oak 

Catalpa 

Chinese elm 

Cottonwood 

Crabapple 

Cypresslcedar 

Doswood 

Elm (otherlb 

Euonymus 

Fir 

Ginkgo 

Greedwhite ash 

Golden-rain tree 

Hackberry 

Hawthom 

Hemlock 

Hickory 

Honeylocust 

Honeysuckle 

Horsechestnut 

Ironwood 

Jack pine 

Juniper 

Kentucky coffeetree 

Larch 

Lilac 

Linden 

Alnus spp. 

UImus amencana 

Acer ginnala 

Malus pumila 

Thuja occidentalis 

Fraxinus spp. 

Pinus nigra 

Tilia amencane 

Fagus grandifolia 

Robinia pseudoacacia 

Picea pungens 

Acer negundo 

Rhamnus spp. 

Quercus macrocarpa 

Catalpa speciosa 

Ulmus parvifolia 

Populus deltoides 

Malus spp. 

Cupressocyparis spp J 

Chamaeqpatus spp. 

Cornus spp. 

UImus spp. 

Euonymus spp. 

Abies spp. 

Ginkgo biloba 

Fraxinus pennsylvanicat 

F. americana 

Koelreuteria paniculata 

Celtis occidentalis 

Crataegus spp. 

Tsuga canadensis 

Carya spp. 

Gleditsia triacanthos 

Lonicera spp. 

Aesculus spp. 

Ostrya virginiana 

Pinus banksiana 

Juniperus spp. 

Gymnocladus dioica 

Larix spp. 

Syringa spp. 

Tilia spp. (exclusive of 

T. akericana) 

Po~ulus niara italica 

Maple (~ther)~ 

Mountain ash 

Mulberry 

Norway maple 

Norway spruce 

Oak (otherld 

OtheP 

Pear 

Pin oak 

Poplar (other)' 

Prunus spp.g 

Redbud 

Red maple 

Red/black oak 

Red pine 

Redhlack spruce 

River birch 

Russian olive 

Sassafras 

Scotch pine 

Serviceberry 

Shagbark hickory 

Siberian elm 

Silver maple 

Slippery elm 

Smoketree 

Spruce (otherlh 

Sugar maple 

Sumac 

Swamp white oak 

Sycamore 

Tuliptree 

Vibemum 

Walnut 

White birch 

White oak 

White pine 

White poplar 

White spruce 

Willow 

Acef spp. 

Sorbus spp. 

Moms spp. 

Acer platanoides 

Picea abies 

Quercus spp. 

Pyms spp. 

Quercus paIustris 

Populus spp. 

Prunus spp. (including 

Amygdalus persica) 

Cercis canadensis 

Acer rubrum 

Quercus rubra/O. velutina 

Pinus resinosa 

Picea rubens/P. manana 

Betula nigra 

Elaeagnus angustifolia 

Sassafras albidum 

Pinus sylvestrls 

Amelanchier spp. 

Carya ovata 

UImus pumila 

Acer saccharinum 

Ulmus rubra 

Cotinus spp. 

Picea spp. 

Acer saccharurn 

Rhus spp. 

Quercus biwlor 

Platanus spp. 

Liriodendron tulipifera 

Vibernum spp. 

Juglans spp. 

Betula papyrifera 

Quercus alba 

Pinus strobus 

Populus alba 

Picea glauca 

Salix spp. 

Lombard poplar - Yew TZIXUS SDD. . . 

a Exclusive of Frawius pennsyivanica and F. americana 

Exclusive of Ulmus amerimna, U. paM'foIia, U. pudfa, and U. rubra. 

Exdusive of Acerginnala, A. negundo, A. platamids, A. tubmm, A. sadmum, and A. saccharinom. 

Exclusive of Quercus macnxarpa, Q. rubra, Q, velutina, Q. bicdor, and Q. alb~ 

Includes 12 minor individual species (sample size = 1) and unknown species that are not included in other species-identification categories. 

Exclusive of Populus deltoides, P. alba, and P. nip italics. 

fJ Cherries, plums, peaches. 

Exclusive of Pba abies, P. tubens, P. rnarkm, and P. &ma. 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Appendix A 141 
Table 3. -Tree composition in Chicago based on number and percentage of trees, and species dominance based on percentage 

of total leaf-surface area 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

Cottonwood 

Greedwhite ash 

American elm 

Prunus spp. 

Hawthorn 

Buckthorn 

Honeylocust 

Boxelder 

Mulberry 

Silver maple 

Notway maple 

Yew 

Ash (other) 

Ailanthus 

Crabapple 

Elm (other) 

Hackberry 

Chinese elm 

Blue spruce 

White oak 

Swamp white oak 

Siberian elm 

Walnut 

Honeysuckle 

Hickory 

Norway spruce 

Redtblack oak 

Basswood 

Arborvitae 

Shagbark hickory 

Linden 

Lilac 

Sugar maple 

Pear 

White pine 

Other 

Juniper 

Catalpa 

White spruce 

Austrian pine 

142 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 3.--continued 

Species 

Tree population Species dominance 

Number SE Percent Rank Percent Rank 

White birch 9,600 9,600 0.2 41 0.5 30 

Golden-rain tree 8,700 8,700 0.2 42 0.2 37 

Poplar (other) 8,700 8.700 0.2 43 0.2 39 

Red maple 8,700 8,700 0.2 43 0.0 52 

Horsechestnut 8.200 6,200 0.2 45 0.2 38 

Willow 7,800 7,800 0.2 46 0.1 45 

Cypress /cedar 6,700 6,700 0.2 47 0.3 34 

Bur oak 6,500 6,500 0.2 48 1 .O 24 

Black locust 5,200 5,200 0.1 49 0.2 41 

Dogwood 5,200 3,600 0.1 49 0.0 54 

Euonymus 5,200 5,200 0.1 49 0.0 49 

Sumac 4.500 4,500 0.1 52 0.0 57 

Apple 3,800 3,800 0.1 53 0.0 53 

Spruce (other) 2,600 2.600 0.1 54 0.0 55 

Viburnum 2,600 2,600 0.1 54 0.0 48 

Red pine 2.000 2,000 0.0 56 0.0 5 1 

Fir 1,500 1,500 0.0 57 0.0 56 

White poplar 1,300 1,300 0.0 58 0.0 58 

I 

USDA Forest Sewice Gem Tech. Rep. NE-186. 1994. Appendix A 
Table 4. -Tree composition in suburban Cook County based on number and percentage of trees, and species dominance 

based on percentage of total leaf-surface area 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

Buckthorn 

Greedwhite ash 

Prunus spp. 

American elm 

Boxelder 

Hawthorn 

Alder 

Silver maple 

Redhlack oak 

Poplar (other) 

Black locust 

Slippery elm 

Cottonwood 

Sugar maple 

White oak 

Crabapple 

Honeylocust 

Mulberry 

Bur oak 

Norway map!e 

Basswood 

Juniper 

Arborvitae 

Shagbark hickory 

Blue spruce 

Willow 

Ash (other) 

Hickory 

Other 

Elm (other) 

Siberian elm 

Apple 

Maple (other) 

Norway spruce 

Lilac 

Dogwood 

River birch 

Swamp white oak 

Scotch pine 

Red maple 

144 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 4. -continued 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

Linden 

White birch 

Yew 

Pin oak 

Red pine 

Pear 

Ironwood 

White spruce 

Hackberry 

Sycamore 

Redbud 

Honeysuckle 

Magnolia 

Amur maple 

Sassafras 

Walnut 

Austrian pine 

Catalpa 

Spruce (other) 

Russian olive 

Smoketree 

Larch 

White poplar 

White pine 

Fir 

Lornbardi poplar 

Cypresslcedar 

Kentucky coffeetree 

Oak (other) 

Sumac 

Viburnum 

Ginkgo 

Tuliptree 

Euonymus 

Serviceberry 

Horsechestnut 5,500 5,500 0.0 76 0.3 43 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 
Table 5. -Tree composition in DuPage County based on number and percentage of trees, and species dominance based on 

percentage of total leaf-surface area 

Willow 

Boxelder 

Buckthorn 

Prunus spp. 

Greedwhite ash 

Cottonwood 

Hawthorn 

Shagbark hickory 

American elm 

Mulberry 

Redlblack oak 

Blue spruce 

Silver maple 

Bur oak 

Basswood 

Black locust 

Jack pine 

White oak 

Crabapple 

Walnut 

Arborvitae 

Norway maple 

Sumac 

Honeylocust 

Pin oak 

Elm (other) 

Slippery elm 

Austrian pine 

Other 

Honeysuckle 

Norway spruce 

Sugar maple 

Hackberry 

Siberian elm 

Magnolia 

Apple 

Chinese elm 

Juniper 

White pine 

Red pine 

146 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Table 5. --continued 

Tree population Species dominance 

Number SE Percent Rank Percent Rank 

Scotch pine 

Red maple 

Linden 

White birch 

Pear 

White spruce 

Hickory 

Yew 

Poplar (other) 

Viburnum 

D0Sw-c' 

Red spruce 

Amur maple 

Redbud 

River birch 

Russian olive 

Lilac 

Fir 

Euonymus 

Maple (other) 

Ash (other) 

Tuliptree 

Hemlock 

Horsechestnut 

Catalpa 

Oak (other) 

White poplar 

Mountain ash 

Kentucky coffeetree 

Sycamore 

Alder 

Beech 

Serviceberry 

Spruce (other) 

Swamp white oak 

Ginkgo 

Smoketree 

Ailanthus 500 500 0.0 77 0.0 78 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 
Table 6. -Tree composition in study area based on number and percentage of trees, and species dominance based on 

percentage of total leaf-surface area 

Tree population 

Soecies Number SE Percent Rank 

Buckthorn 

Greedwhite ash 

Prunus spp. 

Boxelder 

American elm 

Hawthorn 

Willow 

Cottonwood 

Silver maple 

Redlblack oak 

Alder 

Black locust 

Poplar (other) 

Mulberry 

Shagbark hickory 

Slippery elm 

White oak 

Crabapple 

Honeylocust 

Norway maple 

Bur oak 

Sugar maple 

Blue spruce 

Basswood 

Arborvitae 

Elm (other) 

Juniper 

Ash (other) 

Other 

Hickory 

Siberian elm 

Norway spruce 

Walnut 

Yew 

Jack pine 

Apple 

Pin oak 

Hackberry 

Honeysuckle 

Lilac 

Swamp white oak 

Species dominance 

Percent Rank 

148 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 6. --continued 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

Dogwood 

Linden 

Red maple 

Scotch pine 

Maple (other) 

Sumac 

Austrian pine 

River birch 

White birch 

Red pine 

Pear 

White spruce 

Chinese elm 

Magnolia 

Ailanthus 

White pine 

Redbud 

Amur maple 

Ironwood 

Sycamore 

Catalpa 

Viburnum 

Russian olive 

Sassafras 

Fir 

Red spruce 

Euonymus 

Spruce (other) 

Horsechestnut 

White poplar 

Smoketree 

Tuliptree 

Larch 

Cypresskedar 

Oak (other) 

Kentucky coffeetree 

Lombardi poplar 

Hemlock 

Golden raintree 

Serviceberry 

Ginkgo 

Mountain ash 

Beech 3,400 2,900 0.0 84 0.0 82 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 149 
Table 7. -Tree composition on institutional lands dominated by buildings in Chicago. DuPage County and entire study area (no 

trees were sampled for this land use in suburban Cook County) based on number and percentage of trees, and species 

dominance based on total leaf-surface area in each sector 

Species 

Tree population Species dominance 

Number SE Percent Rank Percent Rank 

CHICAGO 

Greenlwhite ash 45,600 45,600 62.5 1 36.8 2 

Honeylocust 18,200 18,200 25.0 2 24.5 3 

Hawthorn 9,100 9,100 12.5 3 38.6 1 

DUPAGE COUNTY 

White oak 14,300 14,300 25.0 1 60.0 1 

Cottonwood 14,300 14,300 25.0 1 35.4 2 

Boxelder 14,300 14,300 25.0 1 4.5 3 

Other 14,300 14,300 25.0 1 0.0 4 

STUDY AREA 

Greedwhite ash 45,600 45,600 35.0 1 8.5 4 

Honeylocust 18,200 18,200 14.0 2 5.6 5 

White oak 14,300 14.300 11 .O 3 46.3 1 

Cottonwood 14,300 14,300 11.0 3 27.3 2 

Boxelder 14,300 14.300 11 .O 3 3.5 6 

Other 14,300 14.300 11.0 3 0.0 7 

Hawthom 9.100 9.100 7.0 7 8.9 3 

150 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 8. -Tree composition on transportational lands in Chicago, DuPage County and entire study area (no trees were sampled 

on transportational lands in suburban Cook County) based on number and percentage of trees, and species dominance based 

on total leaf-surface area in each sector 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

CHICAGO 

Yew 86,700 86,700 38.5 1 25.2 2 

Greenlwhite ash 86,700 86,700 38.5 1 61.7 1 

Chinese elm 26,000 26,000 11.5 3 5.5 3 

Honeylocust 17,300 11,8DO 7.7 4 2.1 5 

Silver maple 8,700 8,700 3.8 5 5.5 4 

DUPAGE COUNTY 

Sumac 13,900 13,900 50.0 1 1.1 2 

White oak 6,900 6,900 25.0 2 98.1 1 

Buckthorn 6,900 6,900 25.0 2 0.8 3 

STUDY AREA 

Yew 86,700 86,700 34.2 1 17.1 3 

Greenlwhite ash 86,700 86.700 34.2 1 41.9 1 

Chinese elm 26,000 26,000 10.3 3 3.8 4 

Honeylocust 17,300 11.800 6.8 4 1.4 6 

Sumac 13,900 13.900 5.5 5 0.4 7 

Silver maple 8,700 8,700 3.4 6 3.7 5 

Buckthorn 6,900 6,900 2.7 7 0.2 8 

White oak 6.900 6,900 2.7 8 31.4 2 

Table 9. -Tree species composition on agricultural lands in DuPage County (no trees were sampled on agricultural lands in other 

sectors of the study area) based on number and percentage of trees, and species dominance based on total leaf-surface area 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

Prunus spp. 138.200 1 38,200 31 -3 1 11.5 3 

Mulberry 1 10.600 75,400 25.0 2 33.7 2 

Other 55,300 55,300 12.5 3 2.9 6 

Hackberry 55.300 55,300 12.5 3 7.4 4 

Chinese elm 27,600 27.600 6.3 5 5.2 5 

Boxelder 27.600 27.600 6.3 5 2.6 7 

Silver maple 27.600 27.600 6.3 5 36.8 1 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 
Table 10. -Tree composition on multifamily residential lands in Chicago, suburban Cook County, DuPage County, and entire 

study area based on number and percentage of trees, and species dominance based on percent of total leaf-surface area in 

each sector 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

CHICAGO 

Boxelder 

Cottonwood 

Greedwhite ash 

Honeylocust 

Crabapple 

Norway maple 

SUBURBAN COOK COUNTY 

Honeylocust 

Boxelder 

Lilac 

Blue spruce 

Norway maple 

Redblack oak 

Hawthorn 

Siberian elm 

Crabapple 

Mulberry 

DUPAGE COUNTY 

Blue spruce 

Crabapple 

Red pine 

Honeylocust 

Greedwhite ash 

White pine 

Austrian pine 

Scotch pine 

Jack pine 

Norway spruce 

Boxelder 

Hemlock 

Buckthorn 

Maple (other) 

Norway maple 

Arborvitae 

STUDY AREA 

Boxelder 

Honeylocust 

Crabapple 

Greedwhite ash 

Blue spruce 

Norway maple 

Cottonwood 

Lilac 

Red pine 

Hawthorn 

Siberian elm 

Mulberry 

Redlblack oak 

White pine 

Austrian pine 

Norway spruce 

Arborvitae 

Scotch pine 

Maple (other) 

Hemlock 

Buckthorn 

Jack pine 4,900 41900 0.8 16 0.8 . . 

152 Appendlx A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 11. -Tree composition on commercial/industriaI lands in Chicago, suburban Cook County, DuPage County, and entire 

study area based on number and percentage of trees, and species dominance based on percent of total leaf-surface area in 

each sector 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

CHICAGO 

Cottonwood 

Ailanthus 

SUBURBAN COOK COUNTY 

Greedwhite ash 

Poplar (other) 

Boxetder 

Other 

Prunus spp. 

DUPAGE COUNTY 

Russian olive 

Siberian elm 

Norway maple 

Greedwhite ash 

Magnolia 

STUDY AREA 

Greedwhite ash 

Boxelder 

Poplar (other) 

Other 

Prunus spp. 

Ailanthus 

Cottonwood 

Russian olive 

Siberian elm 

Norway maple 

Magnolia 16,300 16,300 1.4 8 1 .O 9 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 
Table 12. --Tree composition on vacant lands in Chicago, suburban Cook County, DuPage County, and entire study area 

based on top 20 species in number and percentage of trees, and species dominance based on percent of total leaf-surface area 

in each sector 

Tree population Species dominance 

S~ecies Number SE Percent Rank Percent Rank 

CHICAGO 

Cottonwood 

Ash (other) 

Elm (other) 

Walnut 

Mulberry 

American elm 

Buckthorn 

Greedwhite ash 

Ailanthus 

Chinese elm 

Hawthorn 

Poplar (other) 

Siberian elm 

Red maple 

Honeylocust 

Silver maple 

SUBURBAN COOK COUNTY 

Poplar (other) 

Black locust 

Cottonwood 

Prunus spp. 

Greedwhite ash 

Boxelder 

American elm 

Buckthorn 

Silver maple 

Willow 

Ash (other) 

Rediblack oak 

Dogwood 

White oak 

Pin oak 

Siberian elm 

Other 

DUPAG E COUNTY 

Willow 

Boxelder 

Greenlwhite ash 

Buckthorn 

Cottonwood 

Shagbark hickory 

Pnrnus spp. 

Rediblack oak 

Basswood 

Black locust 

American elm 

Bur oak 

Walnut 

-'Hawthorn 

Slippery elm 

154 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 12. --continued 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

Elm (other) 78,600 56.900 1.2 16 0.6 2 1 

Honeysuckle 

Sumac 

Austrian pine 

Pin oak 

Mulberry 

Linden 

STUDY AREA 

Willow 

Boxelder 

Cottonwood 

Greedwhite ash 

Buckthorn 

Black locust 

Prunus spp. 

Poplar (other) 

Shagbark hickory 

American elm 

Redlblack oak 

Silver maple 

Ash (other) 

Walnut 

Basswood 

Elm (other) 

Bur oak 

Hawthom 

Slippery elm 

Dogwood 

Pin oak 

Austrian oine 39.300 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 
Table 13. -Tree composition on residential lands in Chicago, suburban Cook County. DuPage County, and entire study area 

based on top 20 species in number and percentage of trees, and species dominance based on percent of total leaf-surface area 

in each sector 

Tree population Species dominance 

Soecies Number SE Percent Rank Percent Rank 

CHICAGO 

Greedwhite ash 

Mulberry 

Hone ylocust 

Norway maple 

Silver maple 

Prunus spp. 

Blue spruce 

Ailanthus 

American elm 

Swamp white oak 

Honeysuckle 

Ash (other) 

Crabapple 

Noway spruce 

Boxelder 

Yew 

Arborvitae 

Chinese elm 

Lilac 

Pear 

Cottonwood 

Sugar maple 

Linden 

White oak 

White birch 

Basswood 

Bur oak 

SUBURBAN COOK COUNlY 

Silver maple 

Greenlwhite ash 

Crabapple 

Buckthorn 

Prunus spp. 

Juniper 

Mulberry 

Arborvitae 

Blue spruce 

Norway maple 

American elm 

Honeylocust 

Siberian elm 

Boxelder 

Apple 

Norway spruce 

White oak 

Lilac 

Red maple 

Willow 

Sugar maple 

Other 

Hackberry 

Swamp white oak 

Catalpa 

Appendix A USDA Forest Service Gen. Tech. Reu. NE-186. 1994. 
Table 13. --continued 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

DUPAGE COUNTY 

Buckthorn 

Blue spruce 

Silver maple 

Greenlwhite ash 

Prunus spp. 

Crabapple 

Arborvitae 

Norway maple 

Red/black oak 

White oak 

Mulberry 

Hawthorn 

American elm 

Bur oak 

Shagbark hickory 

Honeylocust 

Boxelder 

Black locust 

Norway spruce 

Pin oak 

Siberian elm 

Willow 

Red maple 

White pine 

Poplar (other) 

Cottonwood 

STUDY 

Buckthorn 

Silver maple 

Greenlwhite ash 

Prunus spp. 

Blue spruce 

Crabapple 

Mulberry 

Norway maple 

Arborvitae 

Honeylocust 

American elm 

Juniper 

Boxelder 

White oak 

Norway spruce 

Siberian elm 

Apple 

Hawthorn 

Redblack oak 

Yew 

Willow 

Red maple 

Bur oak 

Pin oak 

Swamp white oak 

Other 

' AREA 

Cottonwood 44,500 1 7,500 0.4 50 1.5 19 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 157 
Table 14. -Tree composition on institutional lands dominated by vegetation in Chicago, suburban Cook County, DuPage 

County, and entire study area based on top 20 species in number and percentage of trees, and species dominance based on 

percent of total leaf-surface area in each sector 

Tree population Species dominance 

Species Number SE Percent Rank Percent Rank 

CHICAGO 

Cottonwood 

American elm 

Hawthom 

Buckthorn 

Greedwhite ash 

Prunus spp. 

Boxelder 

Hackberry 

White oak 

Silver maple 

Redblack oak 

Siberian elm 

Crabapple 

Shagbark hickory 

Ash (other) 

Hickory 

Honeylocust 

Basswood 

Mulberry 

Other 

Linden 

Norway maple 

Sugar maple 

Swamp white oak 

SUBURBAN COOK COUNTY 

Buckthorn 

Prunus spp. 

Greedwhite ash 

Hawthorn 

American elm 

Alder 

Boxelder 

Redlblack oak 

Slippery elm 

Sugar maple 

Silver maple 

Bur oak 

Basswood 

White oak 

Cottonwood 

Shagbark hickory 

Hickory 

Elm (other) 

Black locust 

Ash (other) 

Norway maple 

Willow 

Pin oak 

Red pine 

Appendix A USDA Forest Sewice Gen. Tech. ReD. NE-186. 1994. 
Table 14. --continued 

Tree ~ooulation 

. . Soecies dominance - - 

Species Number SE Percent Rank Percent Rank 

DUPAGE COUNTY 

Prunus spp. 

Boxelder 

Hawthorn 

Buckthorn 

Jack pine 

American elm 

Cottonwood 

Sumac 

Greenlwhite ash 

White oak 

Basswood 

Mulberry 

Bur oak 

Walnut 

Sugar maple 

Crabapple 

Honeylocust 

Arborvitae 

Scotch pine 

Viburnum 

Shagbark hickory 

Norway maple 

Siberian elm 

STUDY AREA 

Buckthorn 

Prunus spp. 

Hawthorn 

American elm 

Greedwhite ash 

Boxelder 

Alder 

Red/black oak 

Cottonwood 

Slippery elm 

Sugar maple 

White oak 

Silver maple 

Basswood 

Bur oak 

Shagbark hickory 

Hickory 

Elm (other) 

Jack pine 

Black locust 

Mulberry 

Norway maple 

Willow 

Walnut 

Pin oak 

Red pine 27.1 00 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 159 
Table 15. -Distribution of tree diameters in Chicago, suburban Cook County, DuPage County, and entire study area, by land use 

0-7 cm 8-1 5 cm 1630 cm 31 -46 cm 47-61 cm 62-76 ern 77+ cm 

Land use PercenP SE Percenta SE Percenta SE PercenP SE PercenP SE Percenta SE PercenP SE 

CHICAGO 

Agriculture 

Commerciallindust. 

lnstitutional (bldg.) 

lnstitutional (veg.) 

Mukiresidential 

Residential 

Transportation 

Vacant 

0.0 

50.0 

0.0 

55.2 

55.2 

22.8 

7.7 

51.8 

Overall 41.3 

SUBURBAN COOK COUNTY 

Agriculture 

Cornmercial/indust. 

lnstitutional (bldg.) 

lnstitutional (veg.) 

Muhiresidential 

Residential 

Transportation 

Vacant - 

Overall 

DUPAGE COUNTY 

Agriculture 75.0 9.1 25.0 9.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

CommercialAndust. 40.0 22.8 0.0 0.0 40.0 22.8 20.0 18.6 0.0 0.0 0.0 0.0 0.0 0.0 

Institutional (bldg.) 0.0 0.0 25.0 17.1 25.0 17.1 0.0 0.0 25.0 26.1 25.0 26.1 0.0 0.0 

Institutional (veg.) 52.2 3.7 26.2 1.6 15.0 3.4 2.9 0.8 2.0 0.7 1.6 0.6 0.1 0.1 

Muhiresidential 22.6 9.4 41.9 11.1 35.5 9.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Residential 34.6 5.3 24.3 1.8 22.1 2.9 10.0 1.3 5.1 1.0 2.6 0.5 1.2 0.4 

Trans~ortation 75.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 

vacant 

STUDY AREA 

Agriculture 

Cornmercial/indust. 

lnstitutional (bldg.) 

lnstitutional (veg.) 

Muhiresidential 

Residential 

Transportation 

Vacant 

69.5 11.3 18.5 6.8 10.2 4.6 1.2 0.7 0.6 0.3 0.0 0.0 0.0 0.0 

Overall 54.5 5.2 22.2 3.0 15.0 2.3 4.3 0.5 2.4 0.4 1.3 0.2 0.4 0.1 

75.0 4.3 25.0 4.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0,O 0.0 0.0 

71.8 8.7 23.9 7.9 2.9 3.6 1.4 2.9 0.0 0.0 0.0 0.0 0.0 0.0 

0.0 0.0 46.0 6.2 25.0 7.0 0.0 0.0 11.0 5.5 11.0 5.5 7.0 1.0 

61.9 2.6 21.3 1.2 11.6 1.4 2.9 0.5 1.6 0.3 0.5 0.1 0.3 0.1 

35.8 7.3 25.7 5.5 26.9 4.0 8.1 3.9 0.0 0.0 0.0 0.0 3.5 6.1 

30.0 2.2 24.2 1.2 22.8 1.5 12.8 1.1 5.7 0.6 2.8 0.5 1.7 0.4 

15.1 2.9 0.0 0.0 71.9 6.4 6.8 2.4 0.0 0.0 2.7 0.0 3.4 1.4 

72.5 4.9 15.9 2.7 8,6 2.0 2.2 0.9 0.6 0.3 0.0 0.1 0.2 0.3 

Overall 56.0 2.1 20.9 1.2 13.9 1.0 5.2 0.4 2.3 0.2 1.0 0.1 0.7 0.1 

a Percentage of land-use population in sector 
Table 16. -Distribution of tree condition in Chicago, suburban Cook County, DuPage County, and entire study area, by land use 

Excellent Good Moderate Poor Dying Dead 

Land use Percenta SE PenenP SE Percenta SE Percenta SE PemenP SE PercenP SE 

CHICAGO 

Agriculture 

CommerciaVindust. 

lnstitutional (bldg.) 

lnstitutional (veg.) 

Muhiresidential 

Residential 

Transportation 

Vacant 

SUBURBAN COOK COUNTY 

Agriculture 

CommerciaVindust. 

lnstitutional (bldg.) 

lnstitutional (veg.) 

Muhiresidential 

Residential 

Trans~ortation 

vacant 

Overall 

DUPAGE COUNTY 

Agriculture 

CommerciaVindust. 

lnstitutional (bldg.) 

lnstitutional (veg.) 

Muhiresidential 

Residential 

Transportation 

Vacant 

Overall 

STUDY AREA 

Agriculture 

Commercial/indust. 

lnstitutional (bldg.) 

lnstitutional (veg.) 

Muhiresidential 

Residential 

Transportation 

Vacant 9.3 2.2 62.5 4.7 13.2 3.0 5.5 1.4 1.8 0.5 7.7 1.8 

Overall 10.9 0.9 54.7 2.0 17.7 1.1 6.2 0.7 2.2 0.3 8.3 0.8 

a Percentage of landus populatbn in sector 
Table 17. -Distribution of ground-surface materials in Chicago, suburban Cook County, DuPage County, and entire study area. 

by land use 

Chicago Cook County DuPage County Study Area 

Surface type Percenta SE Percenta SE PercenP SE Percenp SE 

INSTITUTIONAL (vegetation) 

Grass (maintained) 

Herbaceous 

Shrub 

Duff 

Soil 

Grass (unmaintained) 

Tar 

Water 

Rock 

Building 

Other structure 

Cement 

Other impervious 

Wood 

All surfaces 100.0 100.0 100.0 100.0 

AGRICULTURAL 

Herbaceous 

Soil 

Grass (unmaintained) 

Grass (maintained) 

Tar 

Rock 

Duff 

Shrub 

Building 

Cement 

Other impervious 

Other structure 

Water 

Wood 

All surfaces 

INSTITUTIONAL (building) 

Grass (maintained) 

Tar 

Building 

Grass (unmaintained) 

Cement 

Herbaceous 

Rock 

Soil 

Other structure 

Duff 

Shrub 

Other impervious 

Water 

Wood 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

All surfaces 100.0 100.0 100.0 100.0 

162 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Table 17. --continued 

Chicago Cook County DuPage County Study Area 

Surface type PercenP SE PercenP SE PercenP SE PercenP SE 

COMMERCIAVINDUSTRIAL 

Tar 

Grass (maintained) 

Building 

Other impervious 

Rock 

Cement 

Other structure 

Soil 

Water 

Herbaceous 

Shrub 

Grass (unmaintained) 

wood 

Duff 

All surfaces 

MULTlRESlDENTlAL 

Building 

Grass (maintained) 

Tar 

Cement 

Shrub 

Other impervious 

Soil 

Duff 

Watar 

Rock 

Herbaceous 

Other structure 

Grass (unmaintained) 

wood 

All surfaces 

TRANSPORTATION 

Tar 

Grass (maintained) 

Cement 

Rock 

Grass (unmaintained) 

Soil 

Herbaceous 

Other structure 

Shrub 

Other impervious 

Water 

Building 

Duff 

Wood 

All surfaces 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix A 
Table 17. --continued 

Chicago Cook County DuPage County Study Area 

Surface tv~e PercenP SE PercenP SE PercenP SE PercenP SE 

VACANT 

Herbaceous 

Grass (unmaintained) 

Shrub 

Grass (maintained) 

Soil 

Duff 

Water 

Rock 

Tar 

Cement 

Wood 

Other structure 

Other impervious 

Building 

All surfaces 

RESIDENTIAL 

Grass (maintained) 

Building 

Tar 

Cement 

Other structure 

Shrub 

Soil 

Herbaceous 

Rock 

Other impervious 

Duff 

Water 

Grass (unmaintained) 

Wood - ~ --~ 0.2 0.1 0.2 0.0 

All surfaces 100.0 100.0 100.0 100.0 

a Percentage of land-use population in sector. 

164 Appendix A USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Appendix B 

Trees for Energy-Efficient Landscapes in Chicago 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix B 
Trees for energy-efficient landscapes in the Chicago area 

Tree species Solar friendly 

Small (< 20 feet) 

Dogwood. Corneliancherry Cornus mas 

Filbert. European Cotylus avellana 

Hawthorn Crataegus spp. 

Cockspur C. CNS-galli 

Dotted C. punctata 

Downy C. molls 

Lavelle C. x lavallei 

VaugRn C. 'Vaughn' 

Washington C. phaenopyrum 

Winter King C. vitidis 'Winter King' 

Lilac. Japanese Tree Syringa reticulata 

Maple, Amur Acer ginnala 

Redbud Cercis canadensis 

Smoketree, Common Cotinus coggygria 

Willow, French Pussy Salix caprea 

Crabapples Malus spp. 

Medium (20-40 feet) 

Alder Alnus spp. 

European Black A. glutinosa 

White A. incana 

Catalpa Catalpa spp. 

Chinese C. ovata 

Northern or Western C. speciosa 

Southem C. bignonioides 

Corktree, Amur Phellodendron arnurense 

Elm, Lacebark Ulmus parviflora 

Linden, Littleleaf Tilia cordata 

Maple Acer spp. 

Hedge A. campestre 

Miyabe A. rniyabei 

Tartarian A. tatancum 

Osage-orange Maclura pornifera 

Pagodatree. Japanese Sophora japonica 

Poplar, Quaking Aspen Populus tremuloides 

Yellowwood Cladrastis lutea 

Large (140 feet) 

Ash Fraxinus spp. 

Green F. pennsylvanica 

White F. americana 

Birch Betula nigra 

Coffeetree, Kentucky Gymnocladus dioica 

Elm Ulmus spp. 

English U. carpinifolia 

Regal U. 'n?gaIf 

Ginkgo Ginkgo biloba 

Hackberry, Common Celtis occiden talk 

Honeylocust. Thornless Gleditsia triacanthos v. 

inermis 

Horsechestnut, Common Aesculus hippocastanum 

Larch Larix spp. 

European L. decidua 

Japanese L. kaempfen' 

Linden Tilia spp. 

American (Basswood) T. americana 

Bigleaf T. pfatyphyllos 

NA 

NA 

Y 

Y 

N 

N 

NA 

N 

N 

Y 

Varies 

Y 

Y 

NA 

Varies 

NA 

NA 

NA 

Y 

N 

Varies 

Varies 

NA 

NA 

N A 

Y 

Y 

Y 

Form 

R 

S 

L 

L 

L 

R 

L 

v 

L 

R 

R 

B 

s 

S 

Varies 

Growth rate Longevity 

166 Appendix B USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Trees for energy-efficient landscapes in the Chicago area (continued). 

Tree species Solar friendly Form Growth rate Longevity 

Large (s40 feet) 

Maple Acer spp. 

Balck A. nigrum Y 0 M L 

Norway A. platanoides Y R M L 

Oak Quercus spp. 

Bur 0. macrocarpa N B M L 

English 0. mbur N R M L 

Pin or Swamp Q. palustfls N P R L 

Red Q. mbra N R M L 

Sawtooth Q. acutksima NA P M L 

Shingle Q. imbricaria NA P M L 

Southern Red Q. flacata NA 0 M L 

Swamp White 0. bicolor NA R M L 

White Q. alba N R M L 

Willow Q. phellos N P R L 

Persimmon, Common Diospyros virginiana Y 0 M L 

Redwood, Dawn Metasequoia Y P R L 

glyptwstrwboides 

Sourgum (Black Tupelo) Nyssa sylvatica Y P M L 

Sycamore Platanus occidentalis N 0 R L 

Medium Evergreens (c40 feet) 

Arbovitae Thuja spp. 

Oriental T. orientalis 

White Cedar T. occidentalis 

Juniper Juniperus spp. 

Chinese J. chinensis 

Eastern Redcedar J. virginiana 

Rocky Mountain J. scopulorum 

Large Evergreens (~40 feet) 

Pine Pinus spp. 

Austrian or Black P. nigra N P M I 

Red P. resinma N P M I 

White P. strobus N P M L 

Spruce, Colorado Picea pungens N P M L 

Legend 

Solar friendly Form Growth rate Longevity 

Y=Yes R=Rounded L=Layered S=Slow (cl O"/year) S=Short (c25 years) 

N=No P=Pyramidal W=Weeping M=Moderate (lo-20"/year) I=lntermediate (25-50 years) 

NA=Data not available V=Vase shaped O=Oval R=Rapid (>20"/year) L=Long (A50 years) 

Varies=with cultivar B=Broad S=Shrubby 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

1 

Appendix B 

1 Appendix C 

1 Standard Reports for Brick Base Case Buildings 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix C 
Chicago, Illinois Tree Shade Only 

1 Story, Brick Construction - 2,125 sq fl Residence (Front Facing East) 

Nat. Gas ($/them): 0.5 

Electricity ($/kwh): 0.12 

East Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

South Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

West Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Source Energy Use (kBtul sq R) Tree Height and Distance from Building % Saved from Base Case 

Small (24 fl) Med. (36 fl) Large (SO fl) Large (50 ft) Small (24 ft) Med. (36 fl) Large (SO ft) Large (SO n) 

Base Case I2 ft Away 22 fl Away 22 fl Away 34 ft Away East Tree I2 ft Away 22 fl Away 22 fl Away 34 ft Away 

82.50 82.88 82.99 83.06 82.96 -0.46 -0.59 -0.68 -0.56 

9.29 9.04 8.82 8.47 8.66 2.68 5.03 8.88 6.75 

91 63 -0.14 -0.02 0.29 0.18 

4.49 0.01 0.01 0.02 0.02 

South Tree 

83.23 -0.59 -0.86 -1.45 -0.88 

9.24 0.47 0.51 3.06 0.49 

92.48 -0.48 -0.72 -1 -0.75 

4.49 0 0 0 0 

West Tree 

82.67 -0.14 -0.19 -0.34 -0.2 

8.81 2.05 3.84 7.75 5.21 

91.47 0.08 0.21 0.48 0.35 

4.21 2.47 4.45 10.4 6.17 

Annual Energy Use 

East Tree 

Heating (kBtu) 

Cooling (kwh) 

South Tree 

Heating (kBtu) 

Cooling (kwh) 

West Tree 

Heating (kBtu) 

Cooling (kwh) 

Annual Hours of Use 

East Tree 

Heating (hrs) 

Cooling (hrs) 

South Tree 

Heating (hrs) 

Cooling (hrs) 

West Tree 

Heating (hrs) 

Tree Height and Distance from Building 

small (24 n) Med. (36 ft) Large (50 ft) Large (50 n) 

Base Case 12 ft Away 22 ft Away 22 ft Away 34 fl Away 

170101 170878 171107 171256 171051 EastTree 

1928 1876 1831 1757 1798 

Total 

170101 171106 171569 172574 171605SouthTree 

1928 1919 1918 1869 1919 

Total 

170101 170341 170430 170676 170439 W@st Tree 

1928 1889 1854 1779 1828 

Total 

$ Saved from Base Case 

Small (24 fl) Med (36 ft) Large (50 R) Large (50 fl) 

12 n Away 22 fl Away 22 fl Away 34 fl Away 

-4 -5 -6 -5 

6 12 21 16 

2 7 15 1 I 

-5 -7 -1 2 -8 

1 1 7 1 

-4 -6 -5 -7 

-1 -2 -3 -2 

5 9 18 12 

4 7 15 ?is 

Tree Heiaht and Distance from Buildina % Saved from Base Case ~ - - Small (24 fl) Med. (36 ft) Large (SO ft) Large (50 ft) Small (24 fl) Med. (36 R) Large (50 ft) Large (50 ft) 

Base Case 12 ft Away 22 ft Away 22 ft Away 34 fl Away East Tree 12 R Away 22 fl Away 22 fl Away 34 ft Away 

4310 4331 4348 4355 4349 -0.49 -0.88 -1.04 -0.9 

987 97 1 951 927 941 1.62 3.65 6.08 4.66 

South Tree 

431 0 4335 4356 4394 4360 -0.58 -1.07 -1.95 -1.16 

987 986 985 974 985 0.1 0.2 1.32 0.2 

West Tree 

4310 4317 432 1 4330 4323 -0.16 -0.26 -0.46 -0.3 

Annual Heating and Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

-10 

-20 

East South West 

24-ft tall, 1243 away 3643 tall, 22-ft away 50-ft tall, 22-ft away 50-ft tall, 3 4-ft away 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing East) 

170 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Annual Heating and Cooling Savings From Base Case 

Due to Shade from A Large Deciduous Tree - 22 ft Away 

East South West 

Heating Ris8 Cooling 0 Total Savings 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing East) 

Annual Percentage Cooling Savings From Base Case 

Due to Shade fl-om One Deciduous Tree 

East South West 

244 tall, 124 away 36-ft tall, 22-ft away 

50-ft tall, 22-ft away 50-ft tall, 343 away 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing East) 

Percentage Peak Cooling Savings From Base Case 

Due to Shade fl-om One Deciduous Tree 

East South West 

243 tall, 124 away &$8 363 tall, 22-ft away 

50-ft tall, 223 away 50-ft tall, 344 away 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing East) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix C 
Chicago, Illinois Energy Analysis 

Nat. Gas ($/them): 0.5 

1 Story. Brick Construction - 2.125 sq ft Residence (Front Facing East) Electricity ($/kwh): 0.12 

Deciduous tree, 364 tall and 244 crown spread, 22-fl away from building Avoided Peak Electricity ($/Avoid kW): 65 

Annual Unshaded Shade ET Reduced East Shade South Shade West Shade 

Energy Use Base Case East South West Cooling Wind + ET + Wind + ET + Wind + ET + Win 

Heat (MBtu) 170.10 171.11 171.57 170.43 170.10 162.49 

$ 850.50 855.55 857.85 852.15 850.50 81 2.45 

MBtu diff I tree -1.01 -1.47 -0.33 0.00 2.54 1.53 I .07 2.21 

$ diff / tree -5.05 -7.35 --l -95 0.00 f2.68 T.63 5.33 i 1 .03 

% d~ff I tree -0.60 -0.90 -0.20 0.00 1.49 0.89 0.59 1.29 

Cool (kwh) 1928 1831 1918 1854 1789 1909 

$ 231.37 21 9.74 230.19 222.49 214.65 229.02 

kWh diff I tree 97 10 74 46 7 150.00 63.00 127.00 

$ diff / tree 11.63 f -18 8.88 5.57 0.78 17.98 7.53 15.23 

% diff I tree 5.03 0.51 3.84 2.41 0.34 7.78 3.26 6.59 

Total (MBtu) 195.06 195.11 196.47 194.64 193.64 187.01 

$ 1081.87 2075.29 1088.04 1074.64 1065.15 1041.47 

MBtu diff I tree -0.05 -1.41 0.42 0.47 2.68 3.10 1.74 3.57 

$ diff 1 tree 6.58 -6.17 7.23. 5.57 f 3-47 25.62 . 12.87 26.27 

% diff I tree -0.03 -0.72 0.22 0.24 1.38 1.59 0 .90 1.84 

Peak Cool (kW 4.49 4.49 4.49 4.29 4.24 4.41 

Avoided $ 292.00 292.00 292.00 279.00 276.00 287.00 

Kw diff I tree 0.00 0.00 0.20 0.08 0.03 0.1 1 0.1 1 0.31 

Avoided $ diff I tree 0.00 0.00 13.00 5.33 I .67 7.00 7.00 20.00 

% diff I tree 0.01 0.00 4.45 1.83 0.60 2.44 2.43 6.88 

Annual Savings from Base Case - I Deciduous Tree 

Due to Shade, ET Cooling, and Reduced Wind Speed fiom 36-ft Tall and 244 Wide Tree 

-20 v / 

East South West 

Shade ET Cooling 0 Reduced Wind Total Savings 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing East) 

1 tree 22-ft ftom wall 

172 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chicago, Illinois Tree Shade Only 

I Story, Brick Construction - 2,125 sq fl Residence (Front Facing North) 

Source Energy Use (kBtul sq ft) Tree Height and Distance from Building 

East Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (MN) 

South Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

West Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Annual Energy Use 

East Tree 

Heating (kBtu) 

Cooling (kwh) 

South Tree 

Heating (kBtu) 

Cooling (kwh) 

West Tree 

Heating (kBtu) 

Cooling (kwh) 

Annual Hours of Use 

Base Case 

84.08 

8.65 

92.74 

4.20 

84.08 

8.65 

92.74 

4.20 

84.08' 

8.65 

92.74 

4.20 

Small (24 fl) 

I2 fl Away 

84.51 

8.38 

92.88 

4.19 

84.50 

8.61 

93.1 1 

4.20 

84.15 

8.56 

92.7 1 

4.13 

Med (36 fl) Large (50 fl) 

22 ft Away 22 fl Away 

84.63 84.72 

8.1 4 7.75 

92.78 92.47 

4.1 9 4.19 

Large (50 fl) 

34 n Away East Tree 

84.60 

7.96 

92.57 

4.19 

South Tree 

84.70 

8.61 

93.31 

4.20 

West Tree 

84.18 

8.40 

92.58 

4.04 

Tree Height and Distance from Building 

small (24 fl) Med. (36 fl) Large (50 ft) Large (50 ft) 

Base Case 12 fl Away 22 fl Away 22 R Away 34 fl Away 

173359 174232 174492 174677 174433 East Tree 

1795 1738 1690 1608 1652 

Total 

173359 17421 3 174599 175471 174626 South Tree 

1795 1788 1787 1748 1788 

Total 

173359 173499 173554 173698 173561 West Tree 

small (24 fl) 

12 fl Away 

-0.5 

3.17 

-0.16 

0.01 

-0.49 

0.42 

-0.41 

0 

-0.08 

1.09 

0.03 

1.44 

Nat. Gas ($/therm): 0.5 

Electricity ($/kwh): 0.12 

% Saved from Base Case 

Med. (36 ft) Large (SO fl) 

22 fl Away 22 fl Away 

-0.65 -0.76 

5.89 10.43 

-0.04 0.28 

0.02 0.03 

Large (50 fl) 

34 fl Away 

-0.62 

7.98 

0.18 

0.02 

$ Saved from Base Case 

small @4 R) Med. (36 ft) Large (50 fl) Large (SO ft) 

12 n Away 22 n Away 22 n Away 34 n Away 

-4 -6 -7 -5 

7 13 22 17 

3 7 15 12 

-4 -6 -1 I -6 

I 1 6 1 

-3 -5 -5 -5 

-1 -1 -2 -1 

1795 1776 1759 1717 1743 2 4 9 6 

Total I 3 7 5 

East Tree 

Heating (hrs) 

Cooling (hrs) 

south Tree 

Heating (hrs) 

Cooling (hrs) 

West Tree 

Heating (hrs) 

Cooling (hrs) 974 973 973 970 972 0.1 0.1 0.41 0.21 

Tree Height and Distance from Building % Saved from Base Case 

Small (24 ft) Med. (36 fl) Large (LO fl) Large (50 fl) Small (24 fl) Med. (36 fl) Large (50 fl) Large (50 fl) 

Base Case 12 fl Away 22 fl Away 22 ft Away 34 fl Away East Tree 12 fl Away 22 fl Away 22 fl Away 34 ft Away 

4395 4424 4442 4458 4445 -0.66 -1.07 -1.43 -1.14 

974 96 1 942 906 927 1.33 3.29 6.98 4.83 

South Tree 

4395 4426 4432 4478 4433 -0.71 -0.84 -1.89 -0.86 

974 973 973 961 973 0.1 0.1 1.33 0.1 

West Tree 

4395 4398 440 1 4406 4404 -0.07 -0.14 -0.25 -0.2 

Annual Heating and Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

-10 

-20 / 

East South West 

2443 tall, 12-ft away 3643 tall, 22-ft away 

50-ft tall, 224 away 50-ft tall, 34-ft away 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing North) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix C 
Annual Heating and Cooling Savings From Base Case 

Due to Shade from A Large Deciduous Tree - 22 ft Away 

- - 

East South West 

Heating Cooling i Total Savings 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing North) 

Annual Percentage Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

V 

East South West 

244 tall, 12-ft away 364 tall, 22-ft away 

50-ft tall, 22-ft away 50-ft tall, 344 away 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing North) 

Percentage Peak Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

V 

East South West 

24-ft tall, 123 away 3643 tall, 223 away 

0 SO-ft tall, 224 away 504 tall, 34-ft away 

1 Sto~y, Brick Construction - 2,125 sq ft Residence (Front Facing North) 

174 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chicago, Illinois Energy Analysis 

Nat. Gas ($ltherm): 0.5 

1 Story, Brick Construction - 2.125 sq ft Residence (Front Facing North) Electricity ($/kwh): 0.12 

Deciduous tree, 364 tall and 244 crown spread, 224 away from building Avoided Peak Electricity ($/Avoid kW): 65 

Annual Unshaded Shade ET Reduced East Shade South Shade West Shade 

Energy Use Base Case East South West Cooling Wind + ET + Wind + ET + Wlnd + ET + Wm 

Heat (MBtu) 173.36 174.49 174.60 173.55 173.36 165.70 

$ 866.80 872.45 873.00 867.75 866.80 828.50 

MBtu diff I tree -1.13 -1.24 -0.19 0.00 2.55 1.42 1 31 2.36 

$ diff I tree -5.65 -6.20 4.95 0.00 ?XT? T,12 8.57 11 82 

% diff I tree -0.70 -0.70 -0.10 0.00 1.47 0.77 0.77 1.37 

Cool (kwh) 1795 1690 1787 1759 1661 1776 

$ 215.45 202.76 21447 211.04 199.28 213.08 

kwh d~ff I tree 106 8 37 45 7 158.00 60.00 89.00 

$ diff I tree 7 2.69 0.98 4.41 5.39 0.79 1837 7.16 70.59 

% diff I tree 5.89 0.46 2.05 2.50 0.37 8.76 3.32 4.92 

Total (MBtu) 197.06 197.15 198.26 196.89 195.68 188.97 

$ 1082.25 1075.21 1087.47 1078.79 1066.08 1041 58 

MBtu diff I tree -0.09 -1.20 0.1 7 0.46 2.70 3.07 1.96 3.33 

$ diff 1 tree . T.04 -5.22. . 3.6. . - i'5,3;9: lZ.56 25.W 13.73 22.41 

% diff I tree -0.05 -0.61 0.09 0.23 1.37 1.55 0.99 1.69 

Peak Cool (kW 4.20 4 19 4.20 4.09 3.95 4.1 1 

Avoided $ 273.00 273.00 273.00 266.00 257.00 267.00 

Kw diff I tree 0.00 0.00 0.1 1 0.08 0.03 0.11 0.1 1 0 22 

Avoided $ diff I tree 0.W 0.00 7.00 5.33 2.00 7.34 7.33 14.33 

% diff I tree 0.02 0.00 2.60 I .96 0.64 2.62 2.60 5.20 

Annual Savings from Base Case - 1 Deciduous Tree 

Due to Shade, ET Cooling, and Reduced Wind Speed fi-om 364 Tall and 2443 Wide Tree 

East South West 

Shade !8$@ ET Cooling 0 Reduced Wind Total Savings 

1 Story, Brick Construction - 2,125 sq ft Residence (Front Facing North) 

1 tree 22-ft fiom wall 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix C 
Chicago, Illinois Tree Shade Only 

2 Story. Brick Construction - 3.562 sq R Residence (Front Facing East) 

Source Energy Use (kBtul sq ft) Tree Height and Distance from Building 

Nat. Gas ($/them): 0.5 

Electricity ($/kwh): 0.12 

% Saved from Base Case 

East Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

South Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

West Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Annual Energy Use 

East Tree 

Heating (kBtu) 

Cooling (kwh) 

South Tree 

Heating (kBtu) 

Cooling (kwh) 

West Tree 

Heating (kBtu) 

Cooling (kwh) 

Annual Hours of Use 

East Tree 

Heating (hrs) 

Cooling (hrs) 

South Tree 

Heating (hrs) 

Cooling (hrs) 

West Tree 

. . 

Base Case 

108.55 

10.71 

119.26 

10.09 

108.55 

10.71 

11 9.26 

10.09 

108.55 

10.71 

1 19.26 

10.10 

Small (24 fl) 

lzftnway 

108.86 

10.58 

11 9.44 

t0.09 

109.01 

10.68 

1 19.69 

10.09 

108.64 

10.56 

119.19 

9.95 

Med. (36 fl) 

22 fl Away 

108.98 

10.45 

119.42 

10.09 

109.29 

1 0.68 

1 19.97 

10.09 

108.69 

10.38 

1 19.07 

9.80 

Large (50 fl) 

22 ft Away 

109.12 

10.12 

1 19.24 

10.09 

109.96 

10.43 

120.39 

10.09 

108.82 

9.85 

118.68 

9.12 

Large (50 fl) Small (24 ft) 

34 fl Away East Tree 12 fl Away 

109.02 -0.28 

10.3f 

1 19.33 

10.09 

South Tree 

109.34 

10.67 

120.01 

10.09 

West Tree 

108.71 

10.19 

1 18.89 

Med. (36 fl) Large (50 fl) Large (50 ft) 

22 fl Away 22 fl Away 34 ft Away 

-0.4 -0.53 -0.44 

2.46 5.51 3.75 

-0.14 0.02 -0.06 

0.01 0.01 0.01 

Tree Height and Distance from Building $ Saved from Base Case 

Small (24 fl) Med (36 fl) Large (50 fl) Large (50 fl) Small (24 fl) Med (36 fl) Lage (SO fl) Large (50 fl) 

Base Case t 2 fl Away 22 fi Away 22 fl Away 34 fl Away 12 fl Away 22 fl Away 22 fl Away 34 fl Away 

37551 1 376573 377002 377485 3771 53 East Tree -5 -7 -1 0 -8 

3725 3681 3634 3520 3586 5 11 25 17 

Total 0 4 15 9 

37551 1 3771 04 378083 380400 378252 South TW -8 -13 -24 -1 4 

3725 3715 3714 3627 3712 I I 12 2 

Total -7 -12 -f 2 42 

37551 1 375812 376014 376465 376059 West Tree -2 -3 -5 -3 

3725 3673 361 1 3428 3544 6 14 36 22 

Total 4 21 3'1 I9 

Tree Heiaht and Distance from Buildina - - % Saved from Base Case 

Small (24 fl) Med. (36 fl) Large (SO fl) Lame (SO ft) Small (24 fl) Mad. (36 fl) Large (50 fl) Large (50 fl) 

Base Case 12 fl Away 22 ft Away 22 fl Away 34 fl Away East Tree 12 fl Away 22 ft Away 22 ft Away 34 fl Away 

441 9 4433 4442 4449 4442 -0.32 -0.52 -0.68 -0.52 

765 762 749 733 739 0 39 2.09 4.18 3.4 

South Tree 

4419 4439 4456 4493 4458 -0.45 -0.84 -1.67 -0.88 

765 765 765 756 764 0 0 1.18 0.13 

West Tree 

Heating (hrs) 4419 4424 4428 4437 4427 -0.1 1 -0.2 -0.41 -0.18 

Cooling (hrs) 765 765 765 757 763 0 0 1.05 0.26 

Annual Heating and Cooling Savings From Base Case 

Due to Shade fi-om One Deciduous Tree 

5 0 

-20 v 

East South West 

24-ft tall, 12-ft away 36-ft tall, 224% away 

50-ft tall, 2243 away 50-ft tall, 34-R away 

2 Story, Brick Construction - 3,562 sq I3 Residence (Front Facing East) 

176 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Annual Heating and Cooling Savings From Base Case 

Due to Shade fiom A Large Deciduous Tree - 22 ft Away 

East South West 

Heating Cooling 0 Total Savings 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing East) 

Annual Percentage Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

East South West 

244 tall, 12-ft away 36-ft tall, 22-3 away 

504 tall, 22-ft away 50-fi tall, 344 away 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing East) 

Percentage Peak Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

East South West 

244 tall, 12-ft away 36-ft tall, 22-ft away 

50-ft tall, 224 away 50-ft tall, 34-ft away 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing East) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix C 
Chicago, Illinois Energy Analysis 

Nat. Gas ($/them): 0.5 

2 Story, Brick Construction - 3.562 sq R Residence (Front Facing East) Electricity ($/kwh): 0.12 

Deciduous tree, 36-ft tall and 244 crown spread, 224 away from building Avoided Peak Electricity ($/Avoid kW): 65 

Annual Unshaded Shade ET Reduced East Shade South Shade West Shade 

Energy Use Base Case East South West Cooling Wind + ET + Wind + ET + Wind + ET + Win 

Heat (MBtu) 375.51 377.00 378.08 376.01 375.52 360.28 

$ 

MBtu diff I tree 

$ diff 1 tree 

% diff I tree 

Cool (kwh) 

$ 

kwh diff I tree 

$ diff / tree 

% diff I tree 

Total (MBtu) 

$ 

MBtu diff I tree 

$ diff 1 tree 

% diff I tree 

Peak Cool (kW 

Avoided $ 

Kw diff I tree 0.00 0.00 0.30 0 19 0.06 0 .24 0.24 

Avoided $ diff I tree 9.00 D.OO '1 9-00 92.00 3.67 15.67 15.67 

% diff I tree 0.01 0.00 2.93 1.83 0.55 2.39 2.38 5.31 

Annual Savings from Base Case - 1 Deciduous Tree 

Due to Shade, ET Cooling, and Reduced Wind Speed fkom 364 Tall and 24-ft Wide Tree 

, 

East South West 

Shade ET Cooling 0 Reduced Wind Total Savings 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing East) 

1 tree 22-ft fi-om wall 

178 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chicago, Illinois Tree Shade Only 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing South) 

Nat. Gas ($/them): 0.5 

Electricity ($/kwh): 0.12 

Source Energy Use (kBW sq fl) Tree Height and Distance from Building % Saved from Base Case 

Small (24 ft) Med. (36 ft) Large (50 ft) Large (50 fl) Small (24 ft) Med. (36 ft) Large (50 ft) Large (50 ft) 

Base Case 12 ft Away 22 fl Away 22 fl Away 34 ft Away East Tree 12 fl Away 22 ft Away 22 fl Away 34 ft Away 

111.32 111.62 111.75 111.90 111.79 -0.27 -0.38 -0.52 -0.42 

10.58 10.47 10.34 10.05 10.21 1.02 2.27 5.03 3.48 

121.91 122.10 122.09 121.95 122.01 -0.16 -0.15 -0.04 -0.08 

East Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

South Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

West Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Annual Energy Use 

East Tree 

Heating (mu) 

Cooling (kwh) 

South Tree 

Heating (Id3tu) 

Cooling (kwh) 

West Tree 

Heating (kBtu) 

Cooling (kwh) 

Annual Hours of Use 

East Tree 

Heating (hrs) 

Cooling (hrs) 

South Tree 

Heating (hrs) 

Cooling (hrs) 

West Tree 

Heating (hrs) 

South Tree 

111.79 112.22 111.82 

10.56 10.42 10.56 

122.35 122.64 122.38 

10.60 10.60 10.60 

west Tree 

111.53 111.72 111.55 

10.12 9.44 9.86 

121.65 121 .I6 121.41 

10.21 9.30 9.99 

Tree Height and Distance from Building $ Saved from Base Case 

Small (24 ft) Med. (36 ft) Large (50 ft) Large (SO fi) Small (24 ft) Med. (36 ft) Large (SO ft) Large (50 ft) 

Base Case 12 ft Away 22 ft Away 22 ft Away 34 ft Away 12 fl Away 22 ft Away 22 fl Away 34 ft Away 

385113 386152 386584 387106 386740 East Tree -5 -7 -1 0 -8 

3682 3644 3598 3496 3553 5 10 22 15 

Total 0 3 12 7 

385113 386116 386728 388208 386832SauthTree -5 -8 -1 5 -9 

3682 3673 3672 3625 3673 1 1 7 1 

Total -4 -7 -8 ' -0 

3851 13 385544 385820 386491 385882 We~l Tree -2 -4 -7 -4 

3682 3606 3521 3283 3431 9 19 48 30 

Total 7 2 5 41 26 

Tree Height and Distance fmm Building % Saved from Base Case 

Small (24 ft) Med. (36 ft) Large (SO ft) Large (50 ft) Small (24 ft) Med. (36 ft) Large (50 rt) Large (50 ft) 

Base case 12 rt Away 22 ft Away 22 n Away 34 n Away East Tree i 2 n Away 22 n Away 22 fl Away 34 n Away 

4538 4551 4560 4573 4562 -0.29 -0.48 -0.77 -0 53 

745 738 736 721 728 0.94 1.21 3.22 2.28 

South Tree 

4538 4549 4559 4580 4563 -0.24 -0.46 -0.93 -0.55 

745 744 744 740 744 0.13 0.13 0.67 0.13 

west Tree 

4538 4542 4544 4561 4548 -0.09 -0.13 -0.51 -0.22 

Cooling (hrs) 745 743 742 734 740 0.27 0.4 1.48 0.67 

Annual Heating and Cooling Savings From Base Case 

Due to Shade fiom One Deciduous Tree 

-20 v / 

East South West 

24-ft tall, 12-ft away k$@8 36-ft tall, 224 away 

0 50-ft tall, 22-ft away 50-ft tall, 34-ft away 

2 Stoly, Brick Construction - 1,562 sq ft Residence (Front Facing South) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix C 
Annual Heating and Cooling Savings From Base Case 

Due to Shade from A Large Deciduous Tree - 22 ft Away 

40 

East South West 

Heating I$Sm Coolinp Total Savings 

2 Sto~y, Brick Construction - 3.562 sq ft Residence (Front Facing South) 

Annual Percentage Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

15 

East South West 

244 tall, 124 away 36-ft tall, 224 away 

50-ft tall, 22-ft away 50-ft tall, 34-ft away 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing South) 

Percentage Peak Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

East South West 

243 tall, 124 away 364 tall, 224 away 

SO-ft tall, 22-ft away 50-ft tall, 34-ft away 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing South) 

180 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Chicago, Illinois Energy Analysis 

Nat. Gas ($ltherm): 0.5 

2 Story, Brick Construction - 3,562 sq R Residence (Front Facing South) Electricity ($/kwh): 0.12 

Deciduous tree, 36-fl tall and 24-R crown spread, 22-fl away from building Avoided Peak Electricity ($/Avoid kW): 65 

Annual Unshaded Shade ET Reduced East Shade South Shade West Shade 

Energy Use Base Case East South West Cooling Wind + ET + Wind + ET + Wind + ET + Win 

Heat (MBtu) 385.11 386.58 386.73 385.82 385.12 369.73 

$ 1925.55 1932.90 1933.65 1929.10 1925.60 1848.65 

MBtu diff I tree -1.47 -1.62 -0.71 0.00 5.13 3.66 3.51 4.42 

$ diff / tree -7.35 -8.10 . - --3.55 -0.02 25.63 18.26 37-51 - 22.06 

% diff I tree -0.40 -0.40 -0.20 0.00 1.33 0.93 0.93 1.13 

Cool (kwh) 3682 3598 3672 3521 3400 3647 

$ 441.79 431.77 440.69 422.55 407.95 437.61 

kwh diff 1 tree 84 9 160 94 12 190 .OO 115.00 266.00 

$ diff / tree , : .:::.,...:.: . . .... : : , :: ::: . 37 33197 

% diff I tree 2.27 0.25 4.36 2.55 0.32 5.14 3.12 7.22 

Total (MBtu) 259.05 259.45 259.99 258.51 257.33 249.39 

$ 2367.34 2364.67 2374.34 2351.65 2333.55 2286.26 

MBtu diff I tree -0.40 -0.94 0.54 0.57 3.22 3.39 2.85 4.33 

$ diff / tree 2.57 -7.00 15.69 7126 27.03 40.96 31 29 53.68 

% diff I tree -0 15 -0.36 0.21 0 22 1 .24 131 110 1.67 

Peak Cool (kW 10.60 f0.60 10.60 10.21 10.05 10.43 

Avoided $ 689.00 689.00 689.00 663.00 653.00 678.00 

Kw dlff I tree 0.00 0.00 0.39 0.19 0.06 0.24 0.24 0.63 

Avoided $ diff 1 tree 0.00 0.00 26.00 22.00 3.67 15.67 15.67 41.67 

% diff 1 tree 0.01 0.00 3.71 1.74 0.53 2.28 2.27 5.98 

Annual Savings from Base Case - 1 Deciduous Tree 

Due to Shade, ET Cooling, and Reduced Wind Speed fiom 36-ft Tall and 24-ft Wide Tree 

East South West 

Shade ET Cooling Reduced Wind Total Savings 

2 Story, Brick Construction - 3,562 sq ft Residence (Front Facing South) 

1 tree 22-A- fiom wall 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix C 
Chicago, Illinois Tree Shade Only 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing East) 

Source Energy Use (kBtul sq ft) Tree Height and Distance from Building 

Nat. Gas ($/them): 0.5 

Electricity ($/kwh): 0.12 

% Saved from Base Case 

East Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

South Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

West Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Annual Energy Use 

East Tree 

Heating (kBtu) 

Cooling (kwh) 

South Tree 

Heating (kBtu) 

Cooling (kwh) 

West Tree 

Heating (kBtu) 

Cooling (kwh) 

. . - 

Small (24 ft) Med. (36 ft) Lame (50 fl) Large (50 ff) Small (24 ft) Med. (36 ft) Large (50 ft) Large (50 ft) 

Base case 12 ft Away 22 n Away 22 n Away 34 ft Away East Tree 12 ft Away 22 ft Away 22 n Away 34 n Away 

121.35 121.69 121.85 122.08 121.95 -0.28 -0.41 ' -0.6 -0.49 

11.37 

133.32 

16.1 5 

South Tree 

121.83 

11.79 

133.62 

16.15 

West Tree 

121.46 

11.57 

133.02 

1 5.86 - 

Tree Height and Distance from Building $ Saved from Base Case - small (24 n) Med. (36 ft) ~arge (W ft) Large (50 ft) Small (24 ft) Med. (36 ft) Large (SO ft) Large (50 ft) 

Base case 12 ft Away 22 fl Away 22 ft Away 34 ft Away 12 fl Away 22 ft Away 22 fl Away 34 fl Away 

71 5653 717658 718598 719945 719151 East Tree -10 -1 5 -2 1 -1 7 

6970 6906 6822 6602 671 7 8 18 44 30 

Total -2 3 23 13 

715653 717130 718102 721 180 718467 South Tree -7 -1 2 -28 -1 4 

6970 6962 6961 6891 6962 1 1 10 1 

Total -6 -1 1 -1 8 -f3 

715653 715913 716141 716769 716259 westTE -1 -2 -6 -3 

6970 6937 6889 6725 6832 4 10 29 17 

Total 3 8 23 $4 

Annual Hours of Use 

East Tree 

Heating (hn) 

Cooling (hrs) 

South Tree 

Heating (hrs) 

Cooling (hn) 

West Tree 

Heating (hrs) 

Cooling (hrs) 972 972 97 1 967 97 1 0 0.1 0.51 0.1 

Tree Height and Distance fmm Building % Saved from Base Case 

Small (24 ft) Med. (36 ft) Large (SO ft) Large (SO ft) Small (24 ft) Med. (36 ft) Large (50 ft) Large (50 ft) 

Base Case 12 ft Away 22 fl Away 22 ft Away 34 ft Away East Tree 12 ft Away 22 ft Away 22 fl Away 34 ft Away 

4500 4508 4521 4535 4526 -0.18 -0.47 -0.78 -0.58 

972 964 952 93 5 943 0.82 2.06 3.81 2.98 

South Tree 

4500 4506 4514 4548 4517 -0.13 -0.31 -1.07 -0.38 

972 972 972 964 97 1 0 0 0.82 0.1 

West Tree 

4500 4500 4504 4512 4506 0 -0.09 -0.27 -0.13 

Annual Heating and Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

3 0 

9, 20 & 

10 

0 

-10 

-20 

East South West 

24-ft tall, 12-ft away 364 tall, 22-ft away 

50-ft tall, 22-fi away 504 tall, 34-ft away 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing East) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 182 Appendix C 
Annual Heating and Cooling Savings From Base Case 

Due to Shade &om A Large Deciduous Tree - 22 ft Away 

3 0 

20 

2 10 

0 

-10 

-20 

Total Savings 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing East) 

Annual Percentage Cooling Savings From Base Case 

Due to Shade from One Deciduous Tree 

East South West 

244 tall, 124 away 36-ft tall, 224 away 

i 50-ft tall, 22-ft away 503 tall, 343 away 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing East) 

Percentage Peak Cooling Savings From Base Case 

Due to Shade fi-om One Deciduous Tree 

East South West 

24-ft tall, 124 away 364 tall, 22-ft away 

50-ft tall, 224 away 504 tall, 34-ft away 

3 Story, Brick Construction - 6,048 sq A Residence (Front Facing East) 

USDA Forest Service Gen. Tech. Reu. NE-186. 1994. Appendix C 
Chicago, Illinois Energy Analysis 

Nat. Gas ($/them): 0.5 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing East) Electricity ($/kwh): 0.12 

Deciduous tree, 364 tall and 24-ff crown spread, 22-ff away from building Avoided Peak Electricity ($/Avoid kW): 65 

Annual Unshaded Shade ET Reduced East Shade South Shade West Shade 

Energy Use Base Case East South West Cooling Wind + ET + Wind + ET + Wind + ET + Win 

Heat (MBtu) 715.65 718.60 718.10 716.14 715.67 684.56 

$ 3578.25 3593.00 3590.50 3580.70 3578.35 

MBtu diff I tree -2.95 -2.45 -0.49 -0.01 

$ diff 1 tree -14.75 -3I2.25 -2.45 -0.03 

% diff I tree -0.40 -0.30 -0.10 0.00 

Cool (kwh) 6970 6822 6961 6889 6456 

$ 836.46 818.60 835.36 826.62 774.77 

kwh diff I tree 149 9 82 171 

$ diff / tree 1-7.86 1.10 9.84 20.56 

% diff / tree 2.14 0.13 1.18 2.46 

Total (MBtu) 282.96 283.48 283.81 282.84 281 11 

$ 4414.71 441 1.60 4425.86 4407.32 4353.12 

MBtu diff 1 tree -0.52 -0.85 0.12 0.62 

$ diff 1 tree 3.11 -11.15 7.39 20.53 

% diff I tree -0.18 -0.30 0.04 0.22 

Peak Cool (kW 16.1 5 16.15 16.15 15.97 15.16 

Avoided $ 1049.00 1049.00 1049.00 1038.00 986.00 

Kw diff 1 tree 0.00 0.00 0.18 0.33 

Avoided $ diff I tree 0.00 0.00 1 ? .OD 21 .OO 

% diff I tree 0 00 0.00 1.12 2.03 

Annual Savings from Base Case - 1 Deciduous Tree 

Due to Shade, ET Cooling, and Reduced Wind Speed fiom 36-ft Tall and 244 Wide Tree 

-20 v / 

East South West 

Shade ET Cooling Reduced Wind Total Savings 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing East) 

1 tree 224 &om wall 

184 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chicago, Illinois Tree Shade Only 

3 Story. Brick Construction - 6,048 sq ft Residence (Front Facing South) 

Source Energy Use (kBtul sq ft) Tree Height and Distance from Building 

East Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

South Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (MN) 

West Tree 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Annual Energy Use 

East Tree 

Heating (kBtu) 

Cooling (kwh) 

South Tree 

Heating (kBtu) 

Cooling (kwh) 

West Tree 

Heating (kBtu) 

Cooling (kwh) 

Annual Hours of Use 

East Tree 

Heating (hrs) 

Cooling (hrs) 

South Tree 

Base Case 

120.68 

12.19 

132.87 

16.69 

120.68 

12.19 

132.87 

16.69 

120.68 

12.19 

132.87 

16.69 

small (24 n) 

12 fl Away 

121.01 

12.08 

133.10 

16.69 

120.94 

12.16 

133.11 

16.69 

120.75 

12.09 

132.84 

16.57 

Med. (36 ft) 

22 ft Away 

121.16 

1 1.94 

133.1 1 

16.69 

121 .I 1 

12.16 

133.27 

16.69 

120.80 

11.98 

132.78 

16.44 

Lage (SO fl) 

22 ft Away 

121.38 

11.59 

132.97 

16.69 

121.65 

12.03 

133.68 

16.69 

120.95 

11.60 

132.55 

15.72 

Large (50 fl) 

34 fl Away East Tree 

121.25 

11.78 

133.03 

16.69 

South Tree 

121.18 

12.16 

133.34 

16.69 

Hest Tree 

120.83 

1 1.84 

132.67 

16.30 

Nat. Gas ($/them): 0.5 

Electricity ($/kwh): 0.12 

% Saved from Base Case 

small (24 ft) 

12 fl Away 

-0.27 

0.84 

-0.17 

0 

-0.21 

0.19 

-0.18 

0 

-0.05 

0.77 

0.02 

0.72 

Med. (36 ft) 

22 ft Away 

-0.4 

1.99 

-0.18 

0 

-0.36 

0.22 

-0.3 

0 

-0.1 

1.69 

0.07 

1.48 

Large (50 ft) 

22 fl Away 

-0.58 

4.87 

-0.08 

0 

-0.8 

1.28 

-0.61 

0 

-0.22 

4.79 

0.24 

5.81 

Large (50 ft) 

34 n Away 

-0.47 

3.34 

-0.12 

0 

-0.41 

0.18 

20.35 

0 

-0.12 

2.85 

0.15 

2.34 

Tree Height and Distance from Building $ Saved from Base Case 

Small (24 ft) Med. (36 ft) Large (SO ft) Large (50 ft) Small (24 ft) Med. (36fl) Large (Soft) Large @On) 

Base Case 12 ft Away 22 ft Away 22 ft Away 34 ft Away 12 ft Away 22 ft Away 22 fl Away 34 fl Away 

71 1700 713623 714521 715797 715051 East Tree -10 -1 4 -20 -1 7 

7199 71 38 7055 6848 6959 7 17 42 29 

Total -3 3 22 12 

71 1700 713229 71 4235 717403 714607 South Tree -8 -1 3 -29 -1 5 

7199 71 85 7183 7106 7186 2 2 11 2 

Total -6 -? 1 -1 8 -13 

71 1700 722062 712382 713258 712542 West Tree -2 -3 -8 4 

7199 71 43 7077 6854 6994 7 15 41 25 

Total. . . - 5 12 33 21 

Tree Height and Distance from Building % Saved from Base Case 

small (24 ft) Med. (36 n) ~arge (50 R) Large (50 R) Small (24 ft) Med~ (36 ft) Large (50 ft) Large (50 ft) 

Base Case '12 n Away 22 ft Away 22 n Away 34 n Away East Tree 12 ft Away 22 R Away 22 n Away 3d ft Away 

4470 4483 4492 4504 4497 -0.29 -0.49 -0.76 -0.6 

Heating (hrs) 

Cooling (hrs) 

West Trw 

Heating (hrs) 

Cooling (hrs) 977 974 973 968 972 0.31 0.41 0.92 0.51 

977 968 956 943 949 0.92 2.15 3.48 2.87 

South Tree 

4470 4479 4483 4519 4487 -0.2 -0.29 -1 .I -0.38 

977 975 973 964 974 0.2 0.41 1.33 0.31 

West Tree 

4470 4479 4479 4488 4482 -0.2 -0.2 -0.4 -0.27 

Annual Heating and Cooling Savings From Base Case 

Due to Shade fi-om One Deciduous Tree 

5 0 

40 

3 0 

9, 20 2 

10 

0 

-10 

-20 

East South West 

I 

24-ft tall, 12-ft away 36-ft tall, 22-ft away 

50-ft tall, 224% away @B?B 50-ft tall, 34-ft away 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing South) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Appendix C 
Annual Heating and Cooling Savings From Base Case 

Due to Shade i?om A Large Deciduous Tree - 22 ft Away 

40 

-20 

East South West 

Heating Cooling 1 Total Savings 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing South) 

Annual Percentage Cooling Savings From Base Case 

Due to Shade fkom One Deciduous Tree 

East South West 

243 tall, 124 away &% 36-ft tall, 2243 away 

50-ft tall, 22-ft away 504 tall, 344% away 

3 Story, Brick Constsuction - 6,048 sq ft Residence (Front Facing South) 

Percentage Peak Cooling Savings From Base Case 

Due to Shade &om One Deciduous Tree 

East South West 

24-ft tall, 1243 away 3643 tall, 22-ft away 

50-ft tall, 22-ft away 5043 tall, 34-ft away 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing South) 

186 Appendix C USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chicago, Illinois Energy Analysis 

Nat. Gas ($/them): 0.5 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing South) Electricity ($/kwh): 0.12 

Deciduous tree, 36-ft tall and 24-ft crown spread, 224 away from building Avoided Peak Electricity ($/Avoid kW): 65 

Annual Unshaded Shade ET Reduced East Shade South Shade West Shade 

Energy Use Base Case East South West Cooling Wind + ET + Wind + ET + Wind + ET + Win 

Heat (MBtu) 711.70 714.52 714.23 712.38 71 1.71 680.68 

$ 3558.50 3572.60 3571.1 5 3561.90 3558.55 3403.40 

MBtu diff I tree -2.82 -2.53 -0.68 0.00 10.34 7.52 7.81 9.66 

$ diff I tree -14.f0 -12.8-5 -3.40 -0.02 53.70 37.58 39.03 48.28 

% diff / tree -0 40 -0.40 -0.10 0.00 1.45 1.05 1.05 1.35 

Cool (kwh) 7199 7055 71 83 7077 6696 7111 

$ 863.85 846.63 861.92 849.25 803.53 853.34 

kwh diff I tree 143 16 122 168 29 340.00 213.00 31 9.00 

$ diff 1 tree , 17.22 3.93 14.60 20.11 3.50 40.83 25.54 3821 

% diff / tree 1.99 0.22 1.69 2.33 0.41 4.73 2.96 4.42 

Total (MBtu) 282.35 282.85 283.21 282.16 280.55 270.86 

$ 4422.35 441 9.23 4433.07 441 1 .I 5 4362.08 4256.74 

MBtu diff I tree -0.50 -0.86 0.19 0.60 3.83 3 93 3.57 4.62 

$ diff I tree 3.f2 -10.72 1-I.u) 20.09 55.20 78.47 64.57 86.49 

% diff / tree -0.18 -0.31 0.07 0.21 1.36 1.39 1.27 1.64 

Peak Cool (kW 16.69 16.69 16.69 16.44 15.71 16.36 

Avoided $ 1085.00 1085.00 1085.00 1069.00 1021 .OO 1064.00 

Kw diff I tree 0.00 0.00 0.25 0.33 0.1 1 0.44 0.44 0.69 

Avoided $ diff I tree 0.00 0.00 16.00 21 -33 7.00 28.33 28 33 44 33 

% diff / tree 0.00 0.00 I .48 1.96 0.66 2.62 2.62 4.10 

Annual Savings from Base Case - 1 Deciduous Tree 

Due to Shade, ET Cooling, and Reduced Wind Speed fiom 36-ft Tall and 2443 Wide Tree 

100 A 

-20 l" / 

East South West 

Shade ET Cooling Reduced Wind Total Savings 

3 Story, Brick Construction - 6,048 sq ft Residence (Front Facing South) 

1 tree 22-ft fiom wall 

I USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 

Appendix D 

Standard Reports for Wood-Framed Base Case Buildings 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Appendix D 
Chicago, .Illinois Tree Shade Only 

1 Story - Wood Frame Residence (1,500 sq ft) 

Space Conditioning Source Energy Use (kBtul sq ft) 

Year 5 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Year 10 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Year 15 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Year 20 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Annual Energy Use 

Year 5 

Heating (kBtu) 

Cooling (kwh) 

Year 10 

Heating (kBtu) 

Cooling (kwh) 

Year 15 

Heating (kBtu) 

Cooling (kwh) 

Year 20 

Heating (kBtu) 

Cooling (kwh) 

Base Case 1 Tree 

89.59 89.79 

20.07 19.68 

109.66 109.47 

7.43 7.03 

Base Case 1 Tree 

129735 130031 

294 1 2883 

Heating and Air Conditioning Hours of Use 

Year 5 Base Case I Tree 

Heating (hrs) 4081 4090 

Cooling (hrs) 1240 1232 

Year 10 

Heating (hrs) 4081 4099 

Cooling (hrs) 1240 1232 

Year 15 

Heating (hrs) 4081 4099 

Cooling (hrs) 1240 1232 

Year 20 

Heatina Ihrs) 4081 4099 

2 Tree 

89.83 

19.41 

109.24 

6.63 

89.96 

18.60 

108.55 

5.83 

90.03 

18.02 

108.05 

5.43 

90.09 

17.91 

108.00 

5.37 

2 Tree 

130093 

2843 

130271 

2724 

130384 

2640 

130466 

2624 

2 Tree 

4090 

1232 

4099 

1232 

41 I5 

1232 

41 15 

3 Tree 

89.92 

19-23 

109.15 

6.63 

90.1 1 

18.06 

108.17 

5.83 

90.29 

17.10 

107.39 

5.43 

90.32 

16.93 

107.25 

5.37 

3 Tree 

130214 

2818 

130498 

2645 

130752 

2506 

130803 

2480 

3 Tree 

4090 

1214 

4099 

1214 

41 15 

1206 

4115 

Nat. Gas ($/therm): 0.5 

Electricity ($/kwh): 0.12 

% Saved from Base Case 

Year 5 1 Tree 2 Tree 3 Tree 

-0.23 -0.28 -0.37 

1.95 3.32 4.17 

0.17 0.38 0.46 

5.38 10.76 10.76 

Year 10 

-0.29 -0.41 -0.59 

4 7.35 10.04 

0.49 1 .O1 1.36 

11.13 21.55 21.55 

Year 15 

-0.36 -0.5 -0.78 

5.95 10.23 14.79 

0.8 1.46 2.07 

14.74 26.93 26.93 

Year 20 

-0.37 -0.56 -0.82 

6.33 10.78 15.66 

0.85 1.51 2.19 

15.42 27.74 27.75 

1991 $ Savedfrom Base Case 

1 Tree 2 Tree 3 Tree 

Year 5 -1 -2 -2 

7 

Total @.:..: 

Year 10 -2 

14 

Total : : 52 . . 

Year 15 -2 

2 1 

Total 19. 

Year 20 -2 

22 

Total - : 20 

% Saved from Base Case 

Year 5 1 Tree 2 Tree 3 Tree 

-0.21 -0.21 -0.21 

0.69 0.69 2.1 

Year 10 

-0.42 -0.42 -0.42 

0.69 0.69 2.1 

Year 15 

-0.42 -0.83 -0.83 

0.69 0.69 2.79 

Year 20 

-0.42 -0.83 -0.83 

190 Appendix D USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
Annual Dollar Savings From Base Case 

l Tree 2 Trees 3 Trees 

Yr. 5 (13 R) m Yr. 10 (19 A) Yr. 15 (24 A) Yr. 20 (25 ft) 

1,500 sf, I story wood he home in Chiago 

Annual Space Conditioning Energy Year 20 -- 25 fi tree 

Base Case 1 Tree 2 Trees 3 Trem 

Heabng &ling 

Annual Cooling Savings from Base Case 6" 

1 Tree 2 Trees 3 Trees 

Yr. 5 (13 ft) m Yr. 10 (19 ft) 0 Yyr. 15 (24 A) Yr. 20 (25 A) 

Peak Cooling Savings from Base Case n 

1 Tree 2 Trees 3 Trees 

Yr. 5 (13 ft) Yr. 10 (19 R) 0 Yr. 15 (24 A) Yr. 20 (25 ft) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix D 
Chicago, Illinois Energy Analysis 

1 Story - Wood Frame 1500 sq ft 

Nat. Gas ($ltherm): 0.5 

Electricity ($lkW h): 0.12 

Year 20 - 25 ft trees 

Annual Unshaded 

Energy Use Base Case 1 Tree 

Heat (MBtu) 129.74 130.22 

$ 648.70 651.10 

MBtu difT -0.48 

$ diff -240 

% diff -0.40 

Cool (kwh) 2941 2754 

$ 352.87 330.53 

kwh diff 186 

$ diff 22.34 

% diff 6.33 

Total (MBtu) 164.49 163.08 

$ 1001.57 981.63 

MBtu diff 1.41 

$ diff 16.:94: 

% diff 0.86 

Peak Cool (kW 7.43 6.28 

Avoided $ 483.00 408.00 

Kw diff 1.15 

Avoided $ diff 75.DO 

% dlff 15.42 

Avoided Peak ~lectricity void k~): 65 

Shade ET Reduced 3 Tree+ET Avg. Savings 

2 Trees 3 Trees Cooling Wind +Wind TreeNr. 

130.47 130.80 129.81 124.91 

Annual Dollar Savings From Base Case - 3 Trees (25 ft tall) Due to Shade. ET Caolinp. and Rc&ccd Wind Spssd 

1.500 % 1 stoly wood he homc 

Average Annual Dollar Savings From Base Case - 1 Tree (25 ft tall) Due to St41dc. ET Coolii and Kchccd Wind Spscd 

I 

-10 

Heating Coaling 

1.500 sf. 1 story mod ihne home 

192 Appendix D USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Annual Dollar Savings From Base Case A 

-10 I- Shade 1 -Tree Shade - 2 Trees - Shade - 3 Trees ET Cooling Reduced Wind 

Heating Coolins 

1,500 sS 1 story wmd be home im Chicago 

Annual Dollar Savinas From Base Case 

-10 v , Shade - 3 Trees ET Cooling Redufed Wind 

Heating 0 Cooling Toid 

Percentage Cooling (kwh) Savings From Base Case 20 

Shade I - Tree . Shade - 2 Trees Shade - 3 Tees ET Cooling Reduced Wlnd 

Percentage Peak Cooling (kW) Savings From Base Case 30 

25 

20 

z 

s 15 

10 

5 

0 Shade I - Tree Shade - 2 Trees Shade - 3 Trees ET Cooling Reduced Wmd 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix 0 
Chicago, Illinois Tree Shade Only 

2 Story - Wood Frame Residence (1,761 sq ft) 

Space Conditioning Source Energy Use (kBtul sq ft) 

Year 5 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Year 10 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Year I5 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Year 20 

Total Heating Use 

Total Cooling Use 

Total Energy Use 

Peak Cool (kW) 

Annual Energy Use 

Year 5 

Heating (kBtu) 

Cooling (kwh) 

Year 10 

Heating (kBtu) 

Cooling (kwh) 

Year 15 

Heating (kBtu) 

Cooling (kwh) 

Year 20 

Heating (kBtu) 

Cooling (kwh) 

Base Case 1 Tree 2 Tree 3 Tree 

42.24 42.37 42.39 42.44 

10.80 10.66 10.57 10.53 

53.05 53.03 52.96 52.97 

5.10 4.93 4.78 4.78 

Base Case 1 Tree 2 Tree 3 Tree 

71538 71746 71793 71871 

1858 1834 1817 1811 

Nat. Gas ($ltherm): 0.5 

Electricity ($/kwh): 0.12 

% Saved from Base Case 

Year 5 1 Tree 2 Tree 3 Tree 

-0.29 -0.36 -0.46 

1.29 2.19 2.56 

0.03 0.16 0.15 

3.27 6.36 6.36 

Year 10 

-0.46 -0.64 -0.93 

3.5 6.28 8.04 

0.35 0.77 0.89 

9.52 17.6 17.6 

Year 15 

-0.62 -0.91 -1.39 

6.15 10.49 14.13 

0.76 1.42 1.77 

15.87 26.45 26.46 

Year 20 

-0.65 -0.91 -1.48 

6.8 10.49 15.09 

0.87 1.42 1.9 

16.98 26.45 27.66 

1991 $ Saved from Base Case 

1 Tree 2 Tree 3 Tree 

Year 5 -1 -1 -2 

3 5 6 

Total 2 4 4 

Year 10 -2 -2 -3 

8 14 18 

Total 6 12 15 

Year 15 -2 -3 -5 

Total 

Year 20 -2 -3 -5 

1858 1732 1663 1578 15 23 34 

Total 13 : . -. :20 . 29 

Heating and Air Conditioning Hours of Use 

% Saved from Base Case 

Year 5 

Heating (hrs) 

Cooling (hrs) 

Year 10 

Heating (hrs) 

Cooling (hrs) 

Year 15 

Heating (hrs) 

Cooling (hrs) 

Year 20 

Heating (hrs) 

Cooling (hrs) 

Base Case 

3281 

11 88 

1 Tree 

3289 

1179 

3298 

1179 

3306 

1179 

3306 

1171 

2 Tree 

3289 

1179 

3306 

1179 

331 5 

1171 

331 5 

1171 

3 Tree 

3289 

1179 

3306 

1170 

3323 

1153 

3323 

1127 

Year 5 I Tree 

-0.26 

0.76 

Year 10 

-0.52 

0.76 

Year 15 

-0.78 

0.76 

Year 20 

-0.78 

1.48 

2 Tree 

-0.26 

0.76 

-0.78 

0.76 

-1 -05 

1.48 

-1 .O5 

1.48 

3 Tree 

-0.26 

0.76 

-0.78 

1.5 

-1.3 

2.95 

-1.3 

5.18 

194 Appendix D USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Annual Dollar Savings From Base Case 35 

1 Tree 2 Trees 3 Trees 

Yr. 5 (13 ft) Yr. lo (19 ft) Yr. 15 (24 ft) Yr. 20 (25 A) 

1,761 sf, 2 sbo'y wood be home in Chicago 

Annual Space Conditioning Energy Year 20 -- 25 ft treefin / I 

Base Case 1 Tree Z Trees 

Heabng Cooling 

3 Trees 

Annual Cooling Savings from Base Case 

l Tree 2 Trees 3 Trees 

Yr 5 (13 A) Yr. 10 (19 ft) 0 YY~ 15 (24 A) Yr. 20 (25 ft) 

Peak Coolina Savinas from Base Case 30 

25 

.E 20 .A 5 l5 

s 10 

5 

0 

3 Trees 

YI. 5 (13 fi) Yr. 10 (19 ft) 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix D 
Annual Dollar Savincrs From Base Case 

-10 , Shade - 1 Tree Shade - 2 Trees Shade - 3 Trees ET Coohg Reduced Wind 

HeafinB Cooling 

1,761 sf, 2 story wood he home in Chicago 

Annual Dollar Savings From Base Case 

-10 / 

Shade - 3 Trees ET Cooling Reduced Wind 

Heating Cooling Td 

Percentage Cooling (kwh) Savings From Base Case 20 

15 

i: 

3 2 10 

5 

0 

Shade - 1 Tree Shade - 2 Trees Shade - 3 Trees ET Cooling Reduced Wind 

Percentage Peak Cooling (kW) Savings From Base Case 30 

25 

20 

z 

r '5 

10 

5 

0 

Shade - I Tree Shade - 2 Trees Shade - 3 Trees ET Cooling Reduced Wind 

196 Appendix D USDA Forest Service Gen. Tech. Rep. NE-186. 1994. 
Chicago, Illinois Energy Analysis Nat. Gas ($/therm): 0.5 

2 Story - Wood Frame 1761 sqft Electricity ($/kwh): 0.1 2 

Year 20 - 25 ft trees Avoided Peak Electricity ($/Avoid kW): 65 

Annual Unshaded Shade ET Reduced 3 Tree+ET Avg Savings 

Energy Use Base Case 1 Tree 2 Trees 3 Trees Coollng Wlnd + Wlnd TreeNr. 

Heat (MBtu) 71.54 72.00 72.19 72.60 71.59 68.65 

$ 357 70 360.00 360.95 363 00 357.95 343.25 

MBtu diff -0.46 -0.65 -1.06 -0.05 2.89 1 78 0 59 

$ dlff -2.30 -3.25 -5 30 -0.25 14.45 8.90 2.97 

Oh dlff -0.60 -0.90 -1.50 -0.10 4.00 2 40 0 80 

Cool (kwh) 1858 1732 1663 1578 1743 1845 

$ 222.98 207.80 199.58 189.32 209.10 221.34 

kwh dlff 126 195 280 116 14 41 0 136 67 

$ dlff 15.98 23.40 33.66 13.88 1.64 49.18 16.39 

% diff 6.81 10.50 15.09 6.22 0.73 22 05 7.35 

Total (MBtu) 93.42 92.61 92.10 91 65 92.29 90.28 

rb 580.68 567.80 560.53 552.32 567.05 564.59 

MBtu diff 0.81 1 32 1 77 1.13 3.14 6 04 2.01 

$ diff 12.88 20.15 28.36 13.63 16.09 58 08 19.36 

% dff 0 87 141 1 90 1.21 3.36 6.47 2.16 

Peak Cool (kW 5 10 4 23 3 75 3.69 4.94 5.07 

Avolded $ 331.00 275.00 244.00 240.00 321 .OO 330.00 

Kw diff 0 87 1 35 1 41 0.16 0.03 1.60 0.53 

Avoided $ d~ff 56.00 87.00 91.00 50.00 1 .OO f 02.00 34.00 

% dff 16 98 26 45 27 66 3.04 0.52 31 23 10.41 

Annual Dollar Savings From Base Case - 3 Trees (25 ft. tall) Due to Me. IZT Cooling mdRcchced Wind Speed 

70 

Shade - 3 Tress ET Cooling a Rcduccd Wind 

1.761 st 2 story wood fmnc home 

Average Annual Dollar Savings From Base Case - 1 Tree (25 ft. tall) Dus Lo Shade, Cooli md Rcducsd Wind Spad 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Appendix D 

1 Appendix E 

I 

Initial Analysis of the Cost-Effectiveness of Shade Trees in Chicago 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994. Appendix E 

-- 
CONOMIC ANALYSIS OF SHADE TREE PROGRAM IN CHICAGO, ILLINOIS 

Story Wood Frame Building (West-facing) Avoided kwh: $0.015 Adjustments: 

household, 3 occupants Avoided kW: $89.00 Tree Mortality per Year 

,761 sq R floor area Cost 1 tree: $50.00 Years 1-2 : 5% 

ooling: 1,858 kWh/yr ($223), Peak: 5.1 kW Trees Planted: 10,000 Years 3-20: I % 

eating: 71.5 MBtuIyr ($358) Discount Rate: 11% AC Present: 5056 .. . 

Inflation Rate: 4.5% 

4djusted Savings Adjusted Nominal Savings (All Trees) 

)er Planted Tree kwh Saving kW Savings Wh+W 

:Whltree kwltree Yr Total $ Total $ Total 3 

0 0.00 1 $78 $2,529 $2,607 

2 0.01 2 $294 $9,551 $9,846 

4 0.02 3 $649 $21,058 $21,707 

7 0.04 4 $1,125 $36,529 $37,655 

I1 0.06 5 $1,709 $55,461 $57,170 

15 0.08 6 $2,381 $77.275 $79,656 

20 0.1 1 7 $3,122 $101,333 $104,455 

25 0.14 8 $3,911 $126,957 $1 30,868 

30 0.16 9 $4,727 $153,441 $158,168 

35 0.19 10 $5,548 $1 80,077 $1 85,625 

41 0.22 1 I $6,352 $206,165 $212,516 

45 0.25 12 $7.1 18 $231.033 $238.1 51 

50 0.27 13 $7,827 $254.053 $261,880 

54 0.30 14 $8,462 $274.657 $283,119 

57 0.31 15 $9,007 $292,344 $301,351 

60 0.33 I6 $9,449 $306,699 $31 6,147 

62 0.34 17 $9,778 $31 7,393 $327,171 

64 0.35 18 $9.988 $324.1 97 $334,185 

64 0.35 19 $10.074 $326.981 $337.055 

I 

SUMMARY OF ECONOMIC ANALYSIS 

Benefits Costs I Fixed: na $500.000 

I Variable: na na 

Capacity: $919,267 na 

Energy: $28.321 na 

TOTAL: $947,588 $500,000 

Net Present Value: $447,588 

(Benefits -Costs) 

Benefit to Cost Ratio: 1.90 

(Benefits I Costs) 

Estimated Savings (All Trees): 

Average Peak Capacity: 1,948 kW-yr 

Average Energy: 356,084 kwh 1 yr 

Estimated Savings (Per Tree Planted): 

Average Peak Capacity: 0.19 W-yr 

35.61 kWh 1 yr 

ssumptions: 

1 20 vear analvsis from 1993 - 201 2 ,- , 

) 10,000 trees planted in 1993, 1 per residence, at $Soltree, which includes costs of the tree, stakes and other planting materials, 

program administration, overhead, and 3 year follow-up for tree care and public education (assumes residents plant trees). 

Costs of Shade Tree Program to SMUD have dropped from $491 tree in 1990-91 to $35ltree in 1993-94 (Rich Sequest) 

) Assume typical tree planted to shade the west wall is 3-ft wide and tall when planted and reaches 25-ft wide and tall by year 20. 

) Assume annual savings of 170 kwh and 0.93 kW for the 20-year old tree based on previously cited energy simulations. 

) Assume annual energy savings pattern is linked to tree growth, for years 1-20 follows an "S" shaped growth curve. 

) Assume the ratio of savings due to direct shade and indirect effects remains constant over time (as modeled for year 20). 

) Assume adjustment to both energy and capacity savings based on tree mortality at 5% per year during the first 2 years of establishment 

and 1 % per year for the remaining 18 years (25% mortality over 20 years). 

) Assume adjustment to both energy and capacity savings for air conditioning saturation of 50% (half of the homes where tree is planted 

do not have space cooling device). 

) Assume nominal discount rate of 11 %, avoided energy and capacity costs of $.015lWh and $89lkW-yr, and a 4.5% inflation rate 

(from Gary Rehof, Least-Cost Planning Dept.. Commonwealth Edison). 

200 Appendix E USDA Forest Service Gen. Tech. Rep. NE-186. 1994 
CONOMIC ANALYSIS OF SHADE TREE PROGRAM IN CHICAGO, ILLINOIS 

Story Brick Building (South-facing) Avoided kWh: $0.01 5 

households, 6 occupants Avoided kW: $89.00 

,562 sq ft floor area Cost I tree: $50.00 

iooling: 3,682 kWh/yr ($442), Peak: 10.6 kW Trees Planted: 10,000 

leating: 385 MBtuIyr ($1,925) Discount Rate: 11% 

Inflation Rate: 4.5% 

Adiusted Savings Adjusted Nominal Savings (All Trees) I 

per Planted ~ree kWh ~avino W ~avinos kWh+kW 1 

(Whitree kwltree Yr Total$ iota:$ Total $ 

1 0.00 1 1 $122 $1,740 $1,862 

2 $460 $6,573 $7,034 

Adjustments: 

Tree Mortality per Year 

Years 1-2 : 

Years 3-20: 

AC Present: 50% 

I 

SUMMARY OF ECONOMIC ANALYSIS 

PV of PV of 

Benefits Costs 

Fixed: na $500.000 

I Variable: na na 

Capacity: $632.61 4 na 

Energy: $44,314 na 

TOTAL: $676,928 $500,000 

Net Present Value: $1 76,928 

(Benefts -Costs) 

Benefit to Cost Ratio: 1.35 

(Benefts I Costs) 

Estimated Savings (All Trees): 

Average Peak Capacity: 1,341 kW-yr 

Average Energy: 557,166 Wh /yr 

Estimated Savings (Per Tree Planted): 

Average Peak Capacity: 0.13 kW-yr 

Average Energy: 55.72 kwh I yr 

ssumptions: 

) 20 year analysis from 1993 - 201 2 

) 10,000 trees planted in 1993, 1 per residence, at SSOitree, which includes costs of the tree, stakes and other planting materials, 

program administration, overhead, and 3 year follow-up for tree care and public education (assumes residents plant trees). 

Costs of Shade Tree Program to SMUD have dropped from $491 tree in 1990-91 to $351tree in 1993-94 (Rich Seqoest). 

) Assume typical tree planted to shade the west wall is 3-ft wide and tall when planted and reaches 244 wide and 364 la11 by year 20. 

) Assume annual savings of 266 kWh and 0.64 kW for the 20-year old tree based on previously cited energy simulations. 

) Assume annual energy savings pattern is linked to tree growth, for years 1-20 follows an "S" shaped growth curve. 

)Assume the ratio of savings due to direct shade and indirect effects remains constant over time (as modeled for year 20). 

) Assume adjustment to both energy and capacity savings based on tree mortality at 5% per year during the first 2 years of establishment 

and 1 % per year for the remaining 18 years (25% mortality over 20 years). 

) Assume adjustment to both energy and capacity savings for air conditioning saturation of 50% (half of the homes where tree is planted 

do not have space cooling device). 

) Assume nominal discount rate of 11 %, avoided energy and capacity costs of $.Ol5lkWh and $89/kW-yr, and a 4.5% inflation rate 

(from Gary Rehof, Least-Cost Planning Dept., Commonwealth Edison). 

USDA Forest Service Gen. Tech. Rep. NE-186. 1994 Appendix E 
Headquarters of the Northeastern Forest Experiment Station is in Radnor, Pennsylvania. 

Field laboratories are maintained at: 

Amherst, Massachusetts, in cooperation with the University of Massachusetts 

Burlington, Vermont, in cooperation with the University of Vermont 

Delaware, Ohio 

Durham, New Hampshire, in cooperation with the University of New Hampshire 

Hamden, Connecticut, in cooperation with the Yale University 

Morgantown, West Virginia, in cooperation with West Virginia University 

Orono, Maine, in cooperation with the University of Maine 

Parsons, West Virginia 

Princetown, West Virginia 

Syracuse, New York, in cooperation with the State University of New York, College of 

Environmental Sciences and Forestry at Syracuse University 

University Park, Pennsylvania, in cooperation with The Pennsylvania State University 

Warren, Pennsylvania 

The United States Department of Agriculture (USDA) Forest Service is a diverse 

organization committed to equal opportunity in employment and program delivery. 

USDA prohibits discrimination on the basis of race, color, national origin, sex, religion, 

age, disability, political affiliation and familial status. Persons believing they have been 

discriminated against should contact the Secretary, US Department of Agriculture, 

Washington, DC 20250, or call 202-720-7327 (voice), or 202-720-1 127 (TTY). 

"Caring for the Land and Serving People through Research"