MIT-DUSP Urban Sustainability Evaluation
Green Infrastructure: Urban Tree Planting
INTRODUCTION ................................................................................................................................................. 2
THEORETICAL AND APPLIED BENEFITS ................................................................................................... 2
Summary .......................................................................................................................................................................... 2
Urban Heat Island ......................................................................................................................................................... 2
Energy ............................................................................................................................................................................... 5
Air Pollution .................................................................................................................................................................... 5
Carbon Dioxide ............................................................................................................................................................... 6
Water ................................................................................................................................................................................. 7
%Runoff ............................................................................................................................................................................. 7
QUANTIFYING IMPACTS .................................................................................................................................. 9
Summary ........................................................................................................................................................................... 9
Models and Calculations .................................................................................................................................................. 9
PROGRAMS ....................................................................................................................................................... 11
Program Framing ............................................................................................................................................. 13
Goals .................................................................................................................................................................... 13
Funding ............................................................................................................................................................... 13
Agencies and Partners ..................................................................................................................................... 13
Supporting Laws ............................................................................................................................................... 13
Program Outputs ............................................................................................................................................ 13
Primary Output ................................................................................................................................................. 13
Trees Planted ..................................................................................................................................................... 13
Maintenance ...................................................................................................................................................... 13
Secondary Ouputs ............................................................................................................................................. 13
Policies, Regulations and Incentives Created .............................................................................................. 13
REFERENCES .................................................................................................................................................... 14
APPENDIX A: CITYGREEN ARCGIS AND I-TREE METHODOLGY ....................................................... 16
CITYgreen ..................................................................................................................................................................... 16
i-Tree .............................................................................................................................................................................. 25
1
INTRODUCTION
Public, green space has been an integral part of the early design of many U.S. cities. Taking their cue
from European examples, planners lined streets and boulevards with trees and shrubs to enhance the
aesthetic appeal of the urban landscape. The park movement of the 19th century sought to introduce a
piece of the natural world into an urban setting. The result was the creation of renowned spaces, such as
New York City’s Central Park, a scenic refuge from the chaos of city life. Such trends continued into
the mid-1900s, when tree planting was used in many cities to revitalize and “beautify” declining,
downtown neighborhoods and commercial districts (Lawrence 1995). However, for the greater part of
the 20th century, urban parks and planted trees were generally viewed (with some exception) as places
distinct from the city and as decorative elements. Their benefits were seen mainly in the context of their
social contributions to city life.
Perspectives changed dramatically in the early seventies, when organizations, such as the
International Union of Forestry Research Organizations (IUFRO), and academic institutions initiated
scientific investigations into the role of urban vegetation (Bradley 1995). Since then, a large body of
literature has grown up around the topic of urban trees, lining streets, in gardens and parks, downtown,
and in city fringes, which are collectively termed the urban forest. These studies have found that the
urban forest is not only a provider of social benefits, aesthetic, emotional, and recreational, but also
many environmental ones. The urban forest is now recognized as being inextricably linked to the
ecology of the city— the interactions between urban wildlife, water, energy, climate, and humans
(Rowntree 1995). It has become an important player in the movement to create more livable,
sustainable cities.
THEORETICAL AND APPLIED BENEFITS
Summary
[TBE]
Urban Heat Island
Issue
The urban heat island (UHI) is the phenomenon whereby urbanized areas exhibit significantly higher
temperatures than their outlying, rural areas (Figure 1). These temperature differentials vary among
urbanized regions, and are related to city characteristics, such as population size and topography.
2
However, differences have been found to range, on average, from 1C to even 10C (Bridgman et al.
1995).
Figure 1 Schematic of air temperature along gradient of urbanization
The causes of UHI phenomenon are diverse. The shape, orientation, and density of urban
structures, which tend to trap solar radiation, is one major culprit. The physical properties of urban
surfaces, which tend to have low reflectivity and the capacity to store considerable amounts of heat, are
another. Cities also have less vegetation and moisture than rural areas, leading to less transpiration (the
release water vapor from vegetation) and evaporation of water—processes that reduce temperatures.
The UHI effect has the potential to pervade many aspects of urban life. Although it has been
suggested that cities with severe winter conditions and cold climates might actually benefit from the
UHI during certain months, it is generally accepted that, overall, the harmful aspects of the UHI tend to
outweigh its benefits. In many cities, particularly ones in hot climates, the UHI negatively impacts
thermal comfort, especially in the summer. But at even higher temperatures, issues of comfort may be
overshadowed by increases in illness and mortality, which has been linked to extreme heat conditions.
Increased temperatures are also known to enhance the pathways by which ground level ozone (to be
distinguished from the stratospheric ozone) is formed (Cardelino and Chameides 1990, Taha et al
3
1997). Member of the DOE’s Lawrence Berkeley Laboratory Heat Island Group found that the ozone
levels in Los Angeles related linearly to the daily maximum temperatures, and at temperatures above
32C, ozone levels were very likely to exceed national standards (120 ppb) (Heat Island Group 2004).
Ground level ozone is known to cause a number of human health problems ranging from eye and lung
irritation to asthma aggravation and lung damage.
The UHI also increases demand for energy via air-conditioning use. According to Stone and
Rodgers (2001) a 1996 study by Rosenfield et al. estimated that the U.S. spends in excess of $10 billion
per year on energy to deal with UHI related heat costs. However, tempering the effects of the UHI with
air conditioning is not only fiscally costly, but also environmentally. Air-conditioning, by expelling
exhaust into the city, adds heat to the urban environment, potentially exacerbating the heat island. In
fact, air conditioning is one of the most significant contributors to anthropogenic heat emissions in
cities (Landsberg, 1987).
Tree Planting Impacts
Augmenting greenspace in cities is currently recognized as a key urban cooling strategy. Trees lower
temperatures by directly shading surfaces and by absorbing radiation for transpiration, thus reducing
the amount of radiation that can become sensible heat. The so-called ‘oasis’ effect produced by
vegetated urban sites is well documented. A review by Taha (1997) found that vegetated spaces could
be 2-8°C cooler than their surroundings. The same review found that studies in Montreal, Tokyo, and
Davis, CA reported that vegetated regions and parks were 1.6°C, 2.5°C, and 2°C respectively cooler
than neighboring, non-green regions. The cooling effect that vegetation has on site microclimate is largely undisputed. However,
there is much interest in the larger scale impacts of these green sites. A number of studies and models
have found that greenspaces can also lower the temperatures of tangential, downwind areas, and, when
the percent cover of such spaces across a city is large enough, mesoscale cooling is possible. A study in
Tel Aviv found that small wooded green areas had a cooling effect up to 100 meters from the site’s
boundary (Shashua-Bar and Hoffman 2000). For optimal urban cooling, the same authors
recommended “small, wooded gardens”, approximately .25 acres, situated 200 meters away from one
another. Another study that used a numerical model suggested a similar urban layout (Honjo and
Takakura 1991). Using a two dimensional model of downwind cooling, the authors found that small
green spaces spaced several hundred meters apart were preferable for cooling surrounding areas. Taha
4
et al (1997) modeled the impact of various mitigation strategies in California’s South Coast Air Basin
and calculated that increasing tree canopy cover by 10 million trees would have a regional influence,
cooling the central valley and surrounding regions by 2C and 1C respectively.
Energy
Issue
[TBE]
Tree Planting Impacts
The lowered temperatures achieved by greenspace (see Urban Heat Island) may not only positively
impacts urban health and comfort, but can also lower energy use and related costs in commercial
buildings and residences. Carefully planted deciduous trees, which lose their leaves in the winter and
do not significantly block solar gain, can control building temperatures and lower energy demand
(Meier 1990). In the winter, strategic landscaping can establish natural wind-blocks that reduce heating
requirements and associated costs. A modeling effort examining residential energy use found that trees
placed to shade a home’s roof, as well as its east and west side, reduced heating and cooling energy
costs by 20-25% when compared with the same house in the open (Heisler 1986). Akbari et al (1997)
planted 16 trees at two residential sites in Sacramento, CA and reported that summer cooling savings
averaged 30%. While most studies have looked specifically at residential units, which, given their size,
are easier to influence with plantings, there is evidence that larger structures can benefit from properly
placed vegetation as well (Papadakis 2001).
Air Pollution
Issue
[TBE]
Tree Planting Impacts
REWRITE (FROM NOWAK)
Urban trees are able to remove air pollutants in two ways. First, they
5
Trees remove gaseous air pollution primarily by uptake via leaf stomata, though some gases are
removed by the plant surface. Once inside the leaf, gases diffuse into intercellular spaces and may be
absorbed by water films to form acids or react with inner-leaf surfaces. Trees also remove pollution by
intercepting airborne particles. Some particles can be absorbed into the tree, though most particles that
are intercepted are retained on the plant surface. The intercepted particle often is resuspended to the
atmosphere, washed off by rain, or dropped to the ground with leaf and twig fall. Consequently,
vegetation is only a temporary retention site for many atmospheric particles. Standardized pollution
removal rates differ among cities according to the amount of air pollution, length of in-leaf season,
precipitation, and other meteorological variables. Large healthy trees greater than 77 cm in diameter
remove approximately 70 times more air pollution annually (1.4 kg/yr) than small healthy trees less
than 8 cm in diameter (0.02 kg/yr). Air quality improvement in New York City due to pollution removal
by trees during daytime of the in-leaf season averaged 0.47% for particulate matter, 0.45% for ozone,
0.43% for sulfur dioxide, 0.30% for nitrogen dioxide, and 0.002% for carbon monoxide. Air quality
improves with increased percent tree cover and decreased mixing-layer heights. In urban areas with
100% tree cover (i.e., contiguous forest stands), short-term improvements in air quality (one hour) from
pollution removal by trees were as high as 15% for ozone, 14% for sulfur dioxide, 13% for particulate
matter, 8% for nitrogen dioxide, and 0.05% for carbon monoxide (Nowak)
Carbon Dioxide
Issue
[TBE]
Tree Planting Impacts
As part of the process of photosynthesis, trees remove carbon dioxide from the air, expelling oxygen as
a byproduct. The carbon is integrated within the tree as biomass— its roots, branches, leaves, trunk.
(Rewrite)Trees remove carbon dioxide from the air through their leaves. Carbon storage is the total
amount of carbon held in a tree’s wood (biomass). Carbon sequestration is the rate at which trees
store carbon. Older trees have more carbon storage; younger trees have a higher sequestration rate.
Approximately half of a tree’s dry weight is carbon. For this reason, large-scale tree planting projects
are recognized as a legitimate tool in many national carbon- reduction programs.
6
Water
Issue
Urbanization transforms the landscape from one dominated by forests, wetlands, and vegetation to one
covered by buildings, asphalt, pavement, and compacted soils, all impervious to water. Indeed, city
centers can be over 90% impervious. Storm water pathways are thus limited and the majority of
rainwater becomes runoff (Table 1).
Table 1 Water flows with varying levels of impervious surface
10-20%
35-50%
Cover
Forested
Impervious
Impervious
%Flow type
75-100%
Impervious
%Evapotranspiration
40%
38%
35%
30%
%Shallow Infiltration
25%
21%
20%
10%
%Deep Infiltration
25%
21%
15%
5%
10%
20%
30%
55%
%Runoff
(After Paul and Meyer 2001)
Runoff moves more quickly through an urban environment than a natural one, as surfaces are smoother
(lowered coefficient of roughness) and flatter (lowered storage capacity). This is also a result, an
intended outcome in fact, of conventional storm water systems, which have been engineered to provide
efficient and rapid removal of rainwater from urban surfaces. Landscapes have been graded, piped, and
paved in an effort to quickly move water off urban surfaces to storm water sewers and into treatment
plants, or, more commonly, into receiving waters (Coffman et al 1998). The overall result is a larger
quantity of runoff moving across surfaces and entering waterways more rapidly than it would have predevelopment. More and faster moving runoff causes river bank erosion, increases the magnitude and
frequency of floods, and mobilizes the myriad contaminants that lie across the urban surface, such as
salts, metals, particulates, and sediments, concentrating them in the receiving rivers and lakes.
Tree Planting Impacts
The concern over the degradation of waterways by runoff coupled with the EPA’s storm water
7
permitting system (under the National Pollutant Discharge Elimination System) have provided impetus
for planners and engineers to investigate ways to mitigate the impact of impervious surfaces. Such
methods are directed at reversing the effects of ISC by:
1. increasing the permeability of surfaces
2. increasing depression storage
3. allowing infiltration and ground water recharge
4. allowing infiltration and pollutant remediation
The suite of methods, which include techniques, measures, and structures, that manage and control the
quantity and quality of runoff cost effectively, are termed best management practices (BMPs). They
include structural and non-structural methods, such as retention basins and sand filters, as well signage
and good housekeeping. In urban areas, where there is little space to implement land intensive controls,
replacing impervious surface with trees and vegetation is recognized as excellent runoff control
options. In addition to the increasing pervious surface area, trees intercept large quantities of water with
their leaves and bark, slowing the flow of runoff and increasing opportunities for evaporation. Their
roots increase the permeability of soils, aiding infiltration and groundwater recharge. A study on one
large deciduous tree in Southern California found that it reduced runoff by over 4,000 gallons per year
(Xiao 1998). This type of mitigation is especially beneficial because it is an accessible technology and
can be applied on many scales. Furthermore, unlike methods such as detention basins, trees and
vegetation control runoff at the site.
While simply replacing impervious surfaces with trees provides benefits passively, integrating
them into more comprehensive site design that employs structural BMPs can enhance runoff control.
Structural BMPs that use vegetation are based on retention and or direct infiltration methods. Native
vegetation is often recommended for use in these BMPs, as such plants are accustomed to the climate,
and require less water and pesticide/herbicide use. Structural BMPs that can be incorporated on urban
sites include:
1. Swales: Open channel devices that are designed to detain and filter water from nearby
parking areas, playgrounds or sidewalks. They can be grassed or planted with native
vegetation. Dry swales, where a highly permeable soil is used to ensure infiltration, are most
common. Wet swales, which mimic wetland conditions, can also be used.
2. Rain Gardens: Lushly planted with vegetation and slightly depressed to store and infiltrate
8
several inches of water (not as much as swales).
3. Vegetated Filter Strip: It does not employ depression storage, but is typically used to slow
the flow of runoff and provide some infiltration. They can be covered with grass or trees.
The vegetated strip was developed for use as a riparian buffer along agricultural land. Now,
they are commonly used along sidewalks and to intercept roof runoff.
These techniques also provide opportunities for contaminant remediation, thus improving water quality.
Soils, vegetation, and trees intercept and filter sediments, prevent erosion, provide binding sites for
ions, and take up nutrients. Collaborative BMP monitoring between the EPA and American Society of
Civil Engineers’ (ASCE) Urban Water Research Council found that removal efficiencies of swales and
vegetated strips could be as high as 80% for nutrient and suspended solid removal (USEPA 2002).
QUANTIFYING IMPACTS
Summary
An important aspect of urban forestry research has been the development of generalizable models that
can be used to quantify the environmental services that urban trees provide, as well as convert these
benefits to dollar values. As one of the leading urban forestry researchers noted in the 1990s, “efforts to
preserve natural areas, acquire new greenspace, initiate plantings and manage existing greenspace are
frequently hampered by our inability to fully appraise the environmental services greenspace (i.e. the
urban forest) provides (Mcpherson 1992). Since then, a number of such models have been developed,
many of which are programmed into computer based modules that provide a tool with which to
quantify the structure and function of local or regional urban forests. Primarily building on research
undertaken by scientists at the USDA Forest, the models also convert these benefits into a dollar value.
Models and Calculations
By the late 1990s, two pieces of software had been developed that for the first time combined the body
of research on the urban forest's function and value. The first, UFORE (Urban Forest , was developed
by the USDA Forest Service’s Northeastern Research Station (Urban Forests, Human Health, and
Environmental Quality division). UFORE is now part of a larger suite of USFS tools, known
comprehensively as i-Tree. Three applications are available through i-Tree: i-Tree Eco, i-Tree Streets,
9
and (under development) i-Tree Hydro. The second application, CITYgreen, released in 1996 by the
non-profit, American Forests, represented the first user friendly and widely accessible software for
calculations of urban forest services and value.
Both UFORE and CITYgreen 5.0 offer users a way to capture three qualities of the urban forest:
structure, function (or ecological services), and value. Each application is really a grouping of smaller
mathematical models based on urban forestry research. I-Tree Eco and Streets software inputs uses user
collected tree sample data as well as local data and returns the following:
Eco

Urban forest structure (e.g., species composition, number of trees, tree density, tree health, etc.),
analyzed by land-use type.

Hourly amount of pollution removed by the urban forest, and associated percent air quality
improvement throughout a year. Pollution removal is calculated for ozone, sulfur dioxide,
nitrogen dioxide, carbon monoxide and particulate matter (<10 microns).

Hourly urban forest volatile organic compound emissions and the relative impact of tree species
on net ozone and carbon monoxide formation throughout the year.

Total carbon stored and net carbon annually sequestered by the urban forest.

Effects of trees on building energy use and consequent effects on carbon dioxide emissions
from power plants.

Compensatory value of the forest, as well as the value of air pollution removal and carbon
storage and sequestration.
 Tree pollen allergenicity index.
 Potential impact of pests such as Gypsy moth, emerald ash borer, or Asian long-horned beetle.
Streets

Structure (species composition, extent and diversity)

Function (the environmental & aesthetic benefits trees afford the community)
 Value (the annual monetary value of the benefits provided and costs accrued)
 Management needs (evaluations of diversity, canopy cover, planting, pruning, and removal
needs). Reports consist of graphs, charts, and tables that managers can use to justify funding,
create program enthusiasm and investment, and promote sound decision-making. With Streets,
users can answer the most important question related to their tree program: Do the accrued
benefits of street trees outweigh their management costs?
10
(i-Tree 2009)
When complete, i-Tree hydro will “imulate hourly changes in stream flow due to changes in urban tree
and impervious cover characteristics and [use] outputs to simulate changes in water quality.”
CITYgreen offers two versions of its GIS-based application, both of which use USFS models in
several of its modules. Both versions output:
 Runoff volume and dollar value associated with removing any excess stormwater resulting from
changes in landcover, such as constructing a retention or detention pond.
 Pollutant removal capacity of tree canopy
 Carbon storage and sequestration capacity of the tree canopy
 A landcover, with each landcover feature reported both as the actual number of acres and as a
percentage of the total area
 Alternate scenario models that models the effects of future landcover changes
(American Forests 2009)
Model assumptions and methods are listed in Appendix A.
PROGRAMS
Issue
Ironically, as the scientific data on urban tree benefits has grown, urban tree coverage has, for the past
several decades, steeply declined. Metropolitan surveys by the non-profit American Forests suggest that
urban tree cover has declined by up to 30% in the past 20 years. As the cities have become more aware
of this dramatic loss of tree cover and its relationship to urban environmental health (particularly
climate, energy, and water), a number of mayors have spearheaded ambitious programs to augment
their urban forest. From Los Angeles to New York City, programs aiming to plant up to one million
trees in the next decade are underway.
Program Characteristics
General Recommendations
Although urban tree planting programs are expected to vary in terms of their structure and function,
11
based on local conditions, a number of tree planting advocates (including government agencies and
non-profits) have developed general guidelines for creating “successful” tree planting programs.
American Forests has done the most work in this area. They have established canopy benchmarks for
U.S. cities, recommending cities in the Pacific Northwest and those east of the Mississippi achieve an
average of 40% canopy cover. Recommendations have been broken down based on land use type:
For metropolitan areas east of the Mississippi and in the Pacific Northwest:
Average tree cover counting all zones
Suburban residential zones
Urban residential zones
Central business districts
40%
50%
25%
15%
For metropolitan areas in the Southwest and dry West:
Average tree cover counting all zones
Suburban residential zones
Urban residential zones
Central business districts
(American Forests 2009)
25%
35%
18%
9%
In addition to providing canopy recommendations, American Forests outline four actions that cities
should undertake if they plan to increase their urban tree cover. These recommendations also provide a
framework for official tree planting programs:
1. Think of trees as a public utility during the budget process;
2. Establish a tree canopy goal or target (25 to 40 percent tree cover) that is considered as part of
every growth, development, and maintenance project;
3. Create a formal process for measuring tree cover and a data layer in the city’s geographic
information system devoted to trees; and
4. Adopt public policies, regulations, and incentives to increase and protect the green
infrastructure.
(American Forests
Regarding the fourth recommendation (policies), a number of
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Program Framing
Goals
Funding
Agencies and Partners
Supporting Laws
Pre-existing Supporting Programs
Public Education
Program Outputs
Primary Output
Trees Planted
Number
Location
Health (over time?)
Maintenance
Monitoring
Secondary Ouputs
Policies, Regulations and Incentives Created
13
REFERENCES
Brabec E., Schulte S., Richards P.L. 2002. Impervious surfaces and water quality: a
review of the current literature and its implications for planning. Journal of Planning Literature.
16 (4):499-514.
Bradley G. 1995
Bridgman H., Warner R., Dodson J. 1995. Urban Biosphysical Environments. Oxford
University Press, Melbourne, Australia.
Burberry P. 1978. Building for Energy Conservation. Halstead Press, New York.
Cardelino and Chameides 1990
Lawrence H.W. 1995. Changing forms and persistent value: Historical perspectives on
the urban forest. In Urban forest landscapes: Integrating multidisciplinary landscapes. ed.
Bradley G.A. pp. 17-40. University of Washington Press, Seattle, WA.
Papadakis G., Tsamis P., Kyritsis S. 2001. An experimental investigation of the effect of
shading with plants for solar control of buildings. Energy and Buldings. 33: 831-836.
Paul J.M., Meyer J.L. 2001. Streams in the Urban Landscape. Annual Review of
Ecological Systems. 32: 333-65.
Rowntree 1995
Stone B., Rodgers M.O. 2001. Urban form and thermal efficiency: How the design of
cities influences the urban heat island effect. Journal of the American Planning Association 67
(2):186-198.
Taha H. 1997. Urban climates and heat islands: albedo, evapotransipration, and
anthropogenic heat. Energy and Buildings 25: 99-103.
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Taha H., Douglas S., Haney J. 1997. Mesoscale meteorological and air quality impacts of increased
urban albedo and vegetation. Energy and Buildings. 25: 169-177
Xaio Q.F. 1998. Rainfall interception by Sacremento’s urban forest. Journal of
Arboricuture 24 (4):
235-244.
U.S. Environmental Protection Agency (USEPA). 2002. Urban Stormwater BMP Performance
Monitoring. EPA-821-B-02-001.
15
APPENDIX A: CITYGREEN ARCGIS AND I-TREE METHODOLGY
CITYgreen
Air Pollution Removal
Summary
The Air Pollution Removal program is based on research conducted by David
Nowak, Ph.D., of the USDA Forest Service. Dr. Nowak developed a methodology
to assess the air pollution removal capacity of urban forests with respect to pollutants
such as nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide
(CO), and particulate matter less than 10 microns (PM10). Pollution removal is
reported on an annual basis in pounds and U.S. dollars.
Dr. Nowak estimated removal rates for 10 cities: Atlanta, Georgia; Austin, Texas;
Baltimore, Maryland; Boston, Massachusetts; Denver, Colorado; Milwaukee,
Wisconsin; New York, New York; Philadelphia, Pennsylvania; St. Louis, Missouri;
and Seattle, Washington. CITYgreen can determine which of those cities is nearest
the site, or the user can manually identify the city nearest to the area being analyzed
and use its results.. Or, the user can average results from all 10 cities.
The program estimates the amount of pollution being deposited within a certain
given study site based on pollution data from the nearest city then estimates the
removal rate based on the area of tree and/or forest canopy coverage on the site.
Technical Methodology
The methodology determines a pollutant removal rate, or flux (F), by multiplying
the deposition velocity (Vd) by the pollution concentration (C).
F (g/cm2/sec) = Vd(cm/sec) x C (g/cm3)
The pollutant flux is then multiplied by the size of the area during periods in which
the pollutant is known to exist there. This makes it possible to estimate the total pollutant
flux for that surface by the hour. Hourly fluxes can be summed to estimate
daily, monthly, or yearly fluxes.
Air pollution estimates generated from CITYgreen currently are designed for urban
and suburban forests. Therefore, CITYgreen analyses run on rural sites that are far
removed from cities may overestimate tree benefits.
References:
Atlanta, GA: Nowak, D.J. and Crane, D.E. 2000. The Urban Forest Effects
(UFORE) Model: quantifying urban forest structure and functions.
In M. Hansen and T. Burk, eds. Proceedings: Integrated
tools for natural resources inventories in the 21st century. IUFRO
Conference, 16-20 August 1998, Boise, ID; General Technical
Report NC-212, U.S. Department of Agriculture, Forest Service,
North Central Research Station, St. Paul, MN. pp. 714-720.
Austin, TX: Methodology and models from “Nowak and Crane.” City-specific
data produced for AMERICAN FORESTS.
Baltimore, MD: Nowak, D.J. and Dwyer, J.F. 2000. Understanding the benefits
and costs of urban forest ecosystems. In J. Kuser, ed. Urban and
16
Community Forestry in the Northeast. New York: Plenum
Publishing pp 11-25; Nowak and Crane.
Boston, MA: Nowak and Dwyer.
Denver, CO: Unpublished USDA Forest Service data, Northeastern Research
Station, Syracuse, NY.
Milwaukee, WI: Methodology and models from “Nowak and Crane.” City-specific
data produced for AMERICAN FORESTS.
New York, NY: Nowak and Dwyer; Nowak and Crane.
Philadelphia, PA: Nowak and Dwyer.
St. Louis, MO: Unpublished USDA Forest Service data, Northeastern Research
Station, Syracuse, NY.
Seattle, WA: Methodology and models from “Nowak and Crane.” City-specific
data produced for AMERICAN FORESTS.
Notes: Austin SO2 and NO2 data were taken from Houston and may not represent actual
conditions in Austin. Austin was missing O3 concentration data for January, February, and
December. Concentration data for these months were estimated based on average national O3
concentration trend data.
Carbon Storage and Sequestration
Summary
CITYgreen’s carbon module quantifies the role of urban forests in removing atmospheric
carbon dioxide and storing the carbon. Based on tree attribute data on trunk
diameter, CITYgreen estimates the age distribution of trees within a given site and
assigns one of three Age Distribution Types. Type I represents a distribution of comparatively
young trees. Type 2 represents a distribution of older trees. Type 3
describes a site with a balanced distribution of ages. Sites with older trees (with more
biomass) are assumed to remove more carbon than those with younger trees (less
biomass) and other species. For forest patches, CITYgreen relies on attribute data on
the dominant diameter class to calculate carbon benefits.
Each distribution type is associated with a multiplier, which is combined with the
overall size of the site and the site’s canopy coverage to estimate how much carbon is
removed from a given site. The program estimates annual sequestration, or the rate at
which carbon is removed, and the current storage in existing trees. Both are reported
in tons. Economic benefits can also be associated with carbon sequestration rates
using whatever valuation method the user feels appropriate. Some studies have used
the cost of preventing the emission of a unit of carbon—through emission control
systems or “scrubbers,” for instance—as the value associated with trees’ carbon
removal services.
Technical Methodology
Estimating urban carbon storage and sequestration requires the study area (in acres),
the percentage of crown cover, and the tree diameter distribution. Multipliers are
assigned to three predominant street tree diameter distribution types:
Distribution Types Carbon Storage Multipliers
Type 1 (Young population) 0.3226
Type 2 (Moderate age population, 10-20 years old) 0.4423
Type 3 (Even distribution of all classes) 0.5393
Average (Average distribution) 0.4303
Distribution Types Carbon Sequestration Multipliers
17
Type 1 (Young population) 0.00727
Type 2 (Moderate age population, 10-20 years old) 0.00077
Type 3 (Even distribution of all classes) 0.00153
Average (Average distribution) 0.00335
CITYgreen uses these multipliers to estimate carbon storage capacity and carbon
sequestration rates. For example, to estimate carbon storage in a study area:
Study area (acres) x Percent tree cover x Carbon Storage Multiplier = Carbon Storage
Capacity
To estimate carbon sequestration:
Study area (acres) x Percent tree cover x Carbon Sequestration Multiplier = Carbon
Sequestration Annual Rate
In recent studies conducted by Dr. David Nowak and Dr. Greg McPherson of the
USDA Forest Service, it has been suggested that if urban trees are properly maintained
over their lifespan, the carbon costs outweigh the benefits. Tree maintenance
equipment such as chain saws, chippers, and backhoes emit carbon into the atmosphere.
Carbon released from maintenance equipment and from decaying or dying
trees could conceivably cause a carbon benefit deficit if it exceeds in volume the
amount sequestered by trees.
To maximize the carbon storage/sequestration benefits of urban trees, the USFS suggests
planting larger and longer-lived species in urban areas so that more carbon can
be stored, mortality rates can be decreased, and maintenance methods can be revised
over time as technology improves. For more information on how to estimate urban
carbon storage and sequestration, please contact the USDA Forest Service
(Northeastern Forest Experiment Station, Syracuse, New York).
References
1. Nowak, David and Rowan A. Rowntree. “Quantifying the Role of Urban
Forests in Removing Atmospheric Carbon Dioxide.” Journal of Arboriculture,
17 (10): 269 (October 1, 1991).
2. McPherson, E. Gregory, Nowak, David J. and Rowan A. Rowntree, 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.
Stormwater Runoff Reduction
Summary
The CITY green stormwater runoff analysis estimates the amount of stormwater that
runs off a land area during a major storm, as well as the time of concentration and
peak flow. The program determines runoff volume based on the percentage of tree
canopy, and other landcover features as digitized by the user in the CITYgreen view
or as reported in a raster data set.
The analysis also considers a variety of localized information identified automatically
by CITYgreen or entered by the user, such as local rainfall patterns, soil type, and
other site characteristics.
The Stormwater Runoff program incorporates procedures and formulas developed by
the USDA Natural Resources Conservation Service (NRCS), formerly the Soil
Conservation Service (SCS), to estimate runoff volume as well as percent changes in
time of concentration and peak flow. The Urban Hydrology for Small Watersheds
model, commonly referred to as Technical Release 55 or TR-55, was incorporated
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into CITYgreen. The program uses NRCS curve numbers that represent the relative
amount of imperviousness and water infiltration properties of soil and land cover.
Curve numbers range from 30-98; the smaller the number the less the runoff.
TR-55 was customized with the help of Don Woodward, PE, a hydraulic engineer
with NRCS, to determine the benefits of trees and other urban vegetation with
respect to stormwater management.
Technical Methodology
CITYgreen’s stormwater runoff analysis enables a user to map urban land cover features
(grassland/shrub, trees, buildings, and impervious surfaces) and determine percentages
of each landcover feature.
Landcover percentages are then combined with average precipitation data, rainfall
distribution information, percent slope, and hydrologic soil group, to estimate how
trees affect runoff volume, time of concentration, and peak flow. In addition, the
program estimates, in cubic feet, the additional volume of water that would have to
be managed if trees were removed. This volume estimate can be associated with an
economic value since planners generally know the cost per cubic foot to build a
retention pond in their municipality. CITYgreen also enables the user to model different
landcover and precipitation scenarios to determine acceptable development or
conservation practices.
The TR-55 model was designed to analyze runoff patterns during a 24-hour single
storm event. Engineers and non-engineers typically design stormwater management
facilities for average storm events, usually 24 hours in duration, according to NRCS.
CITYgreen allows the user to input values for the amount of rain that would fall
during a typical 24-hour event observed within a 2-year span. This value is based on
NRCS estimates of rainfall distributions for different regions of the country.
Slope information is taken from georeferenced data. Alternatively,the user can input
a slope, which can be best thought of as the estimated average slope of the site.
The following formulas are used to estimate curve numbers, stormwater runoff, time
of concentration, and peak flows.
Formulas Used in Calculations
Curve Numbers:
CN (weighted) = Total Product of (CN x Percent landcover area)/Total Percent
Area or 100
Potential Maximum Retention after Runoff begins:
S = ((1000/CN) - 10)
Runoff Equation:
Q = [ P - 0.2 ((1000/CN) - 10) ]2/P + 0.8 ((1000/CN) - 10)
Flow Length:
F = (total study area acres0.6) * 209.0
Lag Time:
L = ((F0.8) *((S + 1.0) 0.7) / (1900 * ((slope)0.5)))
Time of Concentration:
Tc = 1.67 * L
Unit Peak Discharge:
log(qu) = C0 + C1log(Tc) + C2[log(Tc)]2
Peak Flow:
Peak = (qu * Am * Q * Fp)
Storage Volume:
Vs = Vr *(C0 + (C1(qo/qi)) + (C2 ((qo/qi) (qo/qi))) + (C3 (qo/qi) * (qo/qi) *
(qo/qi))) * study area acres * 43560.17/12
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Variable Definitions
P = Average rainfall for a 24-hour period (inches)
Am = Study area acres/640 to determine square miles
Fp = Swamp pond percentage adjustment factor
qo = Existing peak flow condition with trees
qi = Peak flow without trees
C0 = TR-55 coefficents in accordance with raintype
Output Values
Peak = Peak Flow (cfs)
Vs = Storage volume (cubic feet)
Vr = Runoff volume (inches)
CN = Runoff curve number (weighted)
Q = Runoff (inches)
F = Flow length (feet)
S = Potential maximum retention after runoff begins (inches)
L = Lag time (hours)
Tc = Time of concentration (hours)
qu = Unit peak discharge (csm/inches)
TR-55 formulas are used in most engineering firms, soil conservation districts, and
municipalities around the country. As of 1994, more than 300,000 copies of the TR55 manual have been sold by the U.S. National Technical Information Service. The
NRCS methods used in TR-55 are very effective in evaluating the effects of landcover/
land use changes and conservation practices on direct runoff. For more information
about TR-55, see the following website:
www.wcc.nrcs.usda.gov/water/quality/common/tr55/tr55.html
The CITYgreen stormwater runoff analysis is not intended to be used to design
stormwater management facilities, culverts, or ditches. The program is used to estimate
the effects of vegetation, especially trees, on runoff volume and peak flow.
Percent changes in runoff volume and peak flow are determined automatically by
comparing two different scenarios for the same site.
References
1. Cronshey, Roger G. 1982. “Synthetic Regional Rainfall Time Distributions,
Statistical Analysis of Rainfall and Runoff.” Proceedings of the International
Symposium on Rainfall-Runoff Modeling. Littleton, CO: Water Resources
Publications.
2. Engineering Field Handbook, Chapter 2. 1990. Washington, DC: USDA Soil
Conservation Service,.
References
References
147
3. National Engineering Handbook, Chapter 15, Section 4, “Hydrology,” 1985.
Washington, DC: USDA Soil Conservation Service.
4. Kibler, David F., Small, Aaron B. and R. Fernando Pasquel. “Evaluating
Hydrologic Models and Methods in Northern Virginia,” Virginia Tech Research
Paper Evaluating Runoff Models.Blacksburg, VA: Virginia Polytechnic Institute
and State University.
5. Rallison, Robert E. and Norman Miller. 1981. “Past, Present, and Future SCS
Runoff Procedure” Rainfall-Runoff Relationship.” Proceedings of the
International Symposium on Rainfall-Runoff Modeling. Littleton, CO: Water
Resources Publications.
20
6. Technical Release 55, Urban Hydrology for Small Watersheds. June 1986.
Washington, DC: USDA Soil Conservation Service.
7. Water Environment Federation-American Society of Civil Engineers. 1992.
Design and Construction of Urban Stormwater Management Systems. New
York: American Society of Civil Engineers.
8. Woodward, Donald M. and Helen Fox Moody. 1987. “Evaluation of Stormwater
Management Structures Proportioned by SCS TR-55.” Engineering Hydrology:
Proceedings of the Symposium. New York: American Society of Civil Engineers.
9. Sanders, Ralph A. 1986. “Urban Vegetation Impacts on the Hydrology of
Dayton, Ohio,” Urban Ecology, vol. 9. Amsterdam: Elsevier Science Publishers
B.V.
Trees and Energy Conservation
Summary
CITYgreen’s energy conservation analysis utilizes methods developed by Jill Mahon
of AMERICAN FORESTS, interpolated from research by Dr. Greg McPherson of the
USDA Forest Service. The program estimates the energy conservation benefits of
trees resulting from direct shading of one- and two-story residential buildings.
Trees are most effective when located to shade air conditioners, windows, or walls
and when located on the side of the home receiving the most solar exposure (in
addition to other criteria). In many parts of the country the west side is most valuable,
followed by the east and south, although this ranking can change based on geographical
considerations.
CITYgreen assigns each tree an energy rating, 1 through 5, based on location characteristics
listed above and information about tree size and shape. For many parts of the
country, for instance, a large tree located near the west side of a building and shading
an air conditioner or window would be assigned a near-maximum energy rating.
Each tree then is assumed to reduce a home’s annual energy bill by a percentage
associated with each energy rank, which varies based on the climate being studied.
For instance a tree with an energy ranking of 3 in one city might be assumed to
reduce an air conditioning bill by 1.2%, but in a more northern city a tree with an
energy ranking might be assumed to reduce the bill by only 1%. The percentage savings
produced by each tree around a home are multiplied by a home’s average annual
energy use for air conditioning (input by the user). CITYgreen adds the results
together to produce the savings per home, which are in turn summed to estimate
savings per site.
Technical Methodology
The program assigns an energy rating (0 = No Savings.....5 = Maximum Savings) to
each tree that has been field-verified and inventoried based on the following criteria:
_ Distance from residential building structure
_ Orientation relative to the building
_ Ability to shade a window and/or air conditioner
CITYgreen incorporates research from 11 cities distributed across the United States.
Users are asked to identify their region of the U. S.; the program uses data from the
nearest of those cities. If data is available from more than one city within that region,
the user is asked to identify which is closest to the project location.
Research from the following cities was used: Washington, DC; Tucson, Arizona;
Atlanta, Georgia; Denver, Colorado; Boston, Massachusetts; Portland, Oregon; Los
Angeles, California; Minneapolis, Minnesota; Dallas, Texas; Chicago, Illinois and
Miami, Florida.
21
The user is prompted to enter the cooling cost associated with running an air conditioner
during the summer. This information can be obtained from a local utility
company or from the U.S. Department of Energy. Multipliers associated with each
energy rating (representing % energy use-reduction) are assigned to each tree. Each
home’s annual energy use is multiplied by each associated tree’s multiplier to produce
an estimate of dollar and kilowatt hour savings per household.
Multipliers used in CITYgreen were interpolated from “Modeling Benefits and Costs
of Community Tree-Planting in 12 U.S. Cities” and “Chicago’s Urban Forest
Ecosystem: Results of the Chicago Urban Forest Climate Project.” Dr. McPherson’s
research includes savings associated with one- and two-story homes assumed to be
roughly 1,500 square feet in size. The program uses an average of the two values for
both one- and two story homes, and hence applies to both.
Estimated savings from a 20-year-old, 25-foot-high tree in each region were used as
the maximum multiplier. The program disregards any trees located more then 35
feet from a home, under the assumption that they are too far from the home to provide
significant shade. Dr. McPherson’s research has found that a second tree located
in an optimal location provides about 2/3 as much savings as the first. Therefore,
when more than one tree is assigned a rating of 5 for a given home, only one tree is
assumed to provide the full benefits; the rest are assumed to provide 2/3 of the
equivalent of a number 5 energy rating.
References
1. McPherson, E. Gregory, Nowak, David J and Rowan A. Rowntree, eds. 1994.
“Chicago’s Urban Forest Ecosystem: Results of the Chicago Urban Forest
Climate Project.” Gen. Tech. Rep. NE-186. Radnor, PA: USDA Forest Service,
Northeastern Forest Experiment Station.
2. McPherson, Greg, Sacamano, Paul and Steve Wensman. 1993. “Modeling
Benefits and Costs of Community Tree-Planting in 12 U.S. Cities.” USDA
Forest Service.
Avoided Carbon Emissions and Energy Conservation
Summary
Trees remove carbon dioxide from the air through leaves and store carbon in their
biomass. Approximately half of a tree’s dry weight, in fact, is carbon. For this reason,
large-scale tree planting projects are recognized as a legitimate tool in many national
carbon-reduction programs.
However, trees provide a secondary carbon-related benefit that can be much more
valuable, particularly in urban areas. Research by the USDA Forest Service and others
has shown that trees strategically planted to shade homes can reduce air conditioning
bills significantly. As a result, local power plants are not required to produce
as much electricity and thus emit less pollution, including carbon. In certain areas
(urban and suburban areas with high cooling costs) these indirect carbon benefits can
be significantly higher than the direct effects of sequestration.
Technical Methodology
The avoided carbon module is based in part on fuel-mix profiles for each state’s
electricity production. Different states and utility regions produce electricity using
very different sources. As a result, production of a kWh of electricity in one state
may cause the emission of far more carbon than in a neighboring state because different
fuels produce different levels of carbon per kWh.
The module also requires estimates of the amount of carbon produced per fuel
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source per kWh. Coal is said to produce about a pound of carbon while producing a
kWh of electricity. Natural gas produces about .35 of a pound. Nuclear power and
renewable sources produce essentially none.
CITYgreen estimates the energy-use reduction (in terms of kilowatt hours) produced
by direct tree shade. CITYgreen then uses the information learned in steps one and
two to convert the number of kilowatt hours reduced on a given site to the amount
of carbon avoided as a result.
For instance, on a given site, assuming:
_ CITYgreen estimates 1000 kWh are reduced in a state that uses 50% coal and 50%
natural gas to produce electricity
_ Carbon avoided would be calculated by:
For the Coal-produced portion:
1,000 x 0.5 x .575 (the coal emission factor)= 287.5
For the Gas-produced portion:
1,000 x 0.5 x .3478 (the gas emission factor)= 173.90
Total: 461.40
The third possible source is petroleum. A complete list of emission factors follows:
Coal: .575 lbs carbon /kWh
Petroleum: .5058 lbs carbon /kWh
Gas: .3478 lbs carbon/ kWh
(from Carbon Dioxide Emissions from the Generation of Electric Power in the United
States, October 15, 1999 Department of Energy, Environmental Protection
Agency.http://www.eia.doe.gov/cneaf/electricity/page/other/co2report.html#electric)
References
1. Department of Energy, Energy Information Administration. 1998, “Electricity at a
Glance: State Profiles.” (http://www.eia.doe.gov/cneaf/electricity/st_profiles/
toc.html)
2. Department of Energy, Energy Information Administration. October 15, 1999.
“Carbon Dioxide Emissions from the Generation of Electric Power in the United
States.” (http://www.eia.doe.gov/cneaf/electricity/page/other/co2report.html#electric)
References
References
151
Cool Roofs and Energy Conservation
Summary
CITYgreen’s energy conservation analysis includes estimates of the impacts of different
colored asphalt shingles on energy use. Research has shown that roof products
that reflect the sun’s heat back into the atmosphere impose lower cooling costs on
buildings than roof products that absorb the sun’s heat slowly and release it.
Reflectance, or albedo, is often higher in lighter-colored products, although the use
of certain materials can make a dark-colored roof more reflective. Scientists from the
Department of Energy have completed a considerable amount of research in this
area, particularly by the Lawrence Berkeley Laboratories (LBL), the Florida Solar
Energy Center, and others.
CITYgreen estimates the energy savings in the homes on a given site compared to a
scenario under which all the homes are roofed with black shingles. The difference is
reported in terms of dollars and kilowatt hours. As is the case with trees and energy
conservation module, the user is asked to input average annual expenditure on air
conditioning. Color of the existing shingle roof is gathered during site surveying,
which is then associated with an albedo value. If the true albedo value is known, it
23
can be used instead. The energy-related impacts of different roof products vary
according to a number of factors, including insulation levels, heat system used, geographical
location, and climate. Lawrence Berkeley Laboratories has estimated associated
savings in 17 U.S. cities. The user is asked to identify the nearest city and results
from that city are used.
Technical Methodology
CITYgreen assumes albedo values for Black, Dark Gray, Light Gray and White
asphalt shingles on the basis of research conducted by the Urban Heat Island Project
from the Environmental Energy Technologies Division of the Department of
Energy’s Lawrence Berkeley Laboratories. These values were obtained from the following
web page: http://eetd.lbl.gov/HeatIsland/
LBL research on the impacts of different roof reflectance in 17 cities was used to
compare the impacts of dark gray, light gray and white asphalt roofs to a base case of
black. The user is asked to identify their region of the country. If data is available
from more than one city within a region, the user is asked to identify the nearest
city.
For each city, a multiplier (percent energy-use reduction) is associated with each
color. Each multiplier also varies according to the home’s estimated R-value (insulation
levels) and according to the heating system (heat pump or gas furnace).
Research from the following cities was used: Albuquerque, New Mexico; Atlanta,
Georgia; Austin, Texas; Dallas/Ft Worth, Texas; Houston, Texas; Las Vegas, Nevada;
Lexington, Kentucky; Burbank, California; Long Beach, California; Nashville,
Tennessee; Tampa, Florida; Phoenix, Arizona; Raleigh, North Carolina; Sacramento,
California; Salt Lake City, Utah; Tucson, Arizona; and Sterling, Virginia.
To calculate savings per home, the multiplier is multiplied by the average annual
cooling cost per home. The results for each home can be summed to produce savings
per site.
The Cool Roof module applies only to single-family residences one and two stories
tall, with asphalt shingle roofs. It is meant to provide and estimate only, based on a
limited amount of information gathered about each home. For information and
research results about the impacts of different roofing products on energy use, and
the use of shade trees for energy conservation, see the website of LBL’s
Environmental Energy Technologies Division at http://eetd.lbl.gov/
References
For Albedo Values: The Cool Roofing Materials Database web page of the
Environmental Energy Technologies Division of the Department of Energy’s
Lawrence Berkeley Laboratories: http://eetd.lbl.gov/CoolRoof/
For % savings associated with more reflective (non-black asphalt shingles):
Research results from 17 cities provided to AMERICAN FORESTS by Dr.
Hashed Akbari, Group Leader, Heat Island Group, Lawrence Berkeley Laboratories,
September, 1998.
Tree Growth Model
CITYgreen’s tree growth model was developed by AMERICAN FORESTS. The program
“grows” the tree diameter-at-breast height (D.B.H.), the tree height, and the
tree canopy according to species and year of growth selected. CITYgreen also considers
the area of the country your project is in, since trees grow at different rates.
24
The user will choose from Northeast, Mid-Atlantic, Southeast, Midwest, Southwest,
Mountain and Pacific Northwest, or the default Mainland US. Currently, 264 trees
are supported by the growth model program. The program uses the following
method, derived from Nowak, Susinni, Stevens, and Luley, to estimate growth:
Tree Growth Rate Trunk Diameter (Inches/Year) Height (Inches/Year)
Slow-Growing Trees 0.1 1.0
Medium-Growing Trees 0.25 1.5
Fast-Growing Trees 0.5 3.0
The height change is determined by multiplying the number of growth years by the
height growth rate. The diameter (dbh) change is projected by adding the existing
diameter (inches) to the number of growth years multiplied by the diameter growth
rate.
A growth factor was derived for individual tree species based on diameter and
canopy area trends taken from AMERICAN FORESTS’ composite tree species database
of more than 13,000 trees. This growth factor is multiplied by the calculated
diameter growth for each species to estimate canopy radius and canopy area in
square feet. By looking at the largest inventoried specimen from each species, a maximum
potential growth has been determined for 264 (nearly all) tree species in the
CITYgreen species database. The Canopy Growth Factor is based on a linear regression
of canopy radius divided by diameter.
References
1. Nowak, David J., Stevens, Jack C., Luley, Christopher J. and Susan M. Susinni.
1996. “Effects of Urban Tree Management on Atmospheric Carbon Dioxide,”
Syracuse, NY: Unpublished manuscript, (To be submitted to the Journal of
Arboriculture)
2. Energy Information Administration. “Method for Calculating Carbon
Sequestration by Trees in Urban and Suburban Settings,” Voluntary Reporting of
Greenhouse Gases, September 1996. Washington, DC: U.S. Department of
Energy,.
Wenger, Karl F., ed. 1984. Forestry Handbook. New York: John Wiley & Sons.
Special acknowledgment to Nina Bassuk, Cornell University; Edward Macie,
USDA Forest Service; Mickey Merrit, Texas Forest Service; Phillip Hoefer,
Colorado State Forest Service; Gary
i-Tree
i-Tree Eco [hyperlink]
i-Street [hyperlink]
i-Hydro [hyperlink]
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