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Urban Forestry & Urban Greening 4 (2006) 11 5-123

Air pollution removal by urban trees and shrubs in the United States David J. ~owak*,Daniel E. Crane, Jack C. Stevens

USDA Forest Service, Nortlzeastem Research Station, 5 ~WoorzLibrary, S LIIV Y-ESF, Sj~rczmse,N Y 13210 USA

Abstract

A modeling study using hourly meteorological and pollution concentration data from across the coterminous United States demonstrates that urban trees remove large amounts of air pollution that consequently irnprove urban air quality. Pollution removal (03, PMio,NO2, SO2, CO) varied among cities with total annual air pollution removal by US urban trees estimated at 71 1,000 metric tons ($3.8 billion value). Pollution removal is only one of various ways that urban trees affect air quality. Integrated studies of tree effects on air pollution reveal that management of urban tree canopy cover could be a viable strategy to improve air quality and help meet clean air standards. Published by Elsevier GmbH.

Keywords: Air quality; Urban forests; Urban forestry; Environmental quality

1. Introduction coast, the physical effects of urban trees were more important than the chemical effects in terms of affecting Air pollution is a major environmental concern in ozone concentrations. most major cities across the world. An important focus Nationally, urban trees and shrubs (hereafter referred of research has been on the role of urban vegetation in to collectively as "trees") offer the ability to remove the formation and degradation of air pollutants in cities. significant amounts of air pollutants and consequently Through the emission of volatile organic compounds improve environmental quality and human health. Trees (VOC), urban trees can contribute to the formation of remove gaseous air pollution primarily by uptake via ozone (03) (Chameides et al., 1988). However, more leaf stomata, though some gases are removed by the integrative studies are revealing that urban trees, plant surface. Once inside the leaf, gases diffuse into particularly low VOC emitting species, can be a viable intercellular spaces and may be absorbed by water films strategy to help reduce urban ozone levels (Cardelino to form acids or react with inner-leaf surfaces (Smith, and Chameides, 1990; Taha, 1996; Nowak et a]., 2000), 1990). Trees also remove pollution by intercepting particularly through tree functions that reduce air airborne particles. Some particles can be absorbed into temperatures (transpiration), remove air pollutants the tree, though most particles that are intercepted are (dry deposition to plant surfaces), and reduce building retained on the plant surface. The intercepted particle energy and consequent power plant emissions (e.g., often is resuspended to the atmosphere, washed off by temperature reductions; tree shade). One study (Nowak rain, or dropped to the ground with leaf and twig fall. et al., 2000) has concluded that for the US northeast Consequently, vegetation is only a ternporary retention site for many atmospheric particles. To investigate the magnitude of air pollution removal *~orresponditlgauthor. Tel.: + 1 3 15 448 321 2: fax: +- 13154483216. by urban trees throughout the lower 48 United States, E-mtli/ ucicilress: dn0wakgfs.fed.u~(D.J. Nowak). computer modeling of air pollution removal of carbon

1618-8667/$ - see front matter Published by Elsevier GmbH. doi: 10.1016/j.ufug.2006.01.007 116 D.J. Nowak et al. / Urban Forestry & Urban Greening 4 (2006) 115-123 monoxide (CO), nitrogen dioxide (NO2), ozone, parti- 10% coniferous (Nowak, 1994). Leaf area index value is culate matter less than 10 pm (PMlo) and sulfur dioxide total leaf area (m2: trees and large shrubs [minimum 1 in (SOz) was performed for 55 US cities and for the entire stem diameter]) divided by total canopy cover in city nation based on meteorological, pollution concentra- (m2) and includes layering of canopies. Regional leaf-on tion, and urban tree cover data. Due to the need for and leaf-off dates were used to account for seasonal leaf various assumptions within the model, the model area variation. Total tree canopy cover in each city was provides a first-order estimate of the magnitude of based on aerial photograph sampling (Nowak et al., pollution removal by urban trees. 1996) or advanced very high resolution radiometer data (Dwyer et al., 2000; Nowak et al., 2001). Hourly pollution concentration data (1994) from each city were obtained from the US Environmental Protec- Methods tion Agency (EPA). Missing hourly meteorological or pollution-concentration data were estimated using the For each city, the downward pollutant flux (I;; in monthly average for the specific hour. In some locations, gm-2s-') was calculated as the product of the deposi- an entire month of pollution-concentration data may be tion velocity (Vd; in m s-') and the pollutant concentra- missing and are estimated based on interpolations from tion (C; in gm-3) (F= VdO. Deposition velocity was existing data. For example, O3 concentrations may not calculated as the inverse of the sum of the aerodynamic be measured during winter months and existing 03 (R,), quasi-laminar boundary layer (Rb) and canopy concentration data are extrapolated to rnissing months (&) resistances (Baldocchi et al., 1987). Hourly esti- based on the average national O3concentration monthly mates of R, and Rb were calculated using standard pattern. Data from 1994 were used due to available data resistance formulas (Killus et al., 1984; Pederson et al., sets with cloud cover information. To estimate percent 1995; Nowak et al., 1998) and hourly weather data from air quality improvement due to dry deposition (Nowak nearby airports for 1994. R, and Rb effects were et al., 2000), hourly boundary heights were used in relatively small compared to R, effects. conjunction with local deposition velocities for select Hourly canopy resistance values for 03, SO2, and cities with boundary layer height data. Daily morning NO2 were calculated based on a modified hybrid of big- and afternoon mixing heights from nearby stations were leaf and multilayer canopy deposition models (Baldoc- interpolated to produce hourly values using the EPA's chi et al., 1987; Baldocchi, 1988). Canopy resistance (&) PCRAMMIT program (US EPA, 1995). Minimum has three components: stomata1 resistance (r,), meso- boundary-layer heights were set to 150 m during the phyll resistance (r,), and cuticular resistance (rt), such night and 250m during the day based on estimated that: 1/& = l/(r, + r,) -t1 /r,. Mesophyll resistance was minimum boundary-layer heights in cities. Hourly set to zero s m-' for SO2 (Wesely, 1989) and 10 s m'for mixing heights (m) were used in conjunction with O3 (Hosker and Lindberg, 1982). Mesophyll resistance pollution concentrations (pg m-3) to calculate the was set to 100sm-' for NO2 to account for the amount of pollution within the mixing layer (pg mA2). difference between transport of water and NO2 in the This extrapolation from ground-layer concentration to leaf interior, and to bring the computed deposition total pollution within the boundary layer assumes a velocities in the range typically exhibited for NO2 well-mixed boundary layer, which is common in the (Lovett, 1994). Base cuticular resistances were set at daytime (unstable conditions) (Colbeck and Harrison, 8000sm-' for SOz, 10,000 sm-' for 03, and 1985). Hourly percent air quality improvement was 20,000 s m-' for NO2 to account for the typical variation calculated as grams removed/(grams removed -tgrams in r, exhibited among the pollutants (Lovett, 1994). in atmosphere), where grams in atmosphere = measured As removal of CO and particulate matter by concentration (g mm3) x boundary layer height (m) x vegetation are not directly related to photosynthesis/ city area (m2). transpiration, & for CO was set to a constant for To estimate pollution removal by all urban trees in in-leaf season (50,000 s m-') and leaf-off season the United States, national pollution concentration data (1,000,000 s rn-l) (Bidwell and Fraser, 1972). For (all EPA monitors) were combined with standardized particles, the median deposition velocity (Lovett, 1994) local or regional pollution removal rates. Pollution was set to 0.064 rn s-I based on 50-percent resuspension removal rates (gm-2 of tree cover) standardized to the rate (Zinke, 1967). The base Vd was adjusted according average pollutant concentration in the city (gm-2 per to in-leaf vs. leaf-off season parameters. To limit ppm or per pgmm3).As flux rates are directly propor- deposition estimates to periods of dry deposition, tional to pollutant concentrations, standardized removal deposition velocities were set to zero during periods of rates are used to account for concentration differences precipitation. among urban areas. Each city was assumed to have a single-sided leaf area For all urban areas in the United States outside of the index within the canopy covered area of 6 and to be 55 analyzed cities, local pollution monitoring data were D.J. Nowak et al. , Urban Forestry & Urban Greenlng 4 (2006) 115-123 117

used to calculate the average pollution concentration in tion (increased precipitation leading to reduced total the urban area for each pollutant. Urban area bound- removal via dry deposition), and other meteorological aries are based on 1990 census definitions of urbanized variables that affect tree transpiration and deposition areas (areas with population density z 1000 people velocities (factors leading to increased deposition mi-') and urban places (incorporated or unincorporated velocities would lead to greater downward flux and (census-defined) places with a population > 2500) total removal). All of these factors combine to affect outside of urbanized areas. If pollutant monitors did total pollution removal and the standard pollution not exist within the urban area, minimum state pollution removal rate per unit tree cover, concentration data were assigned to the urban area. Jacksonville's urban forest had the largest total Likewise, standardized pollution removal rates were removal, but had below median value of pollution assigned to each urban area based on data from the removal per unit tree cover. Jacksonville's high total closest analyzed city within the same climate zone. All pollution removal value was due to its large city size urban areas within a state were assigned to the dominant (1 965 km') and relatively high estimated percent tree climate zone (coo1 temperate, Desert, Mediterranean, cover within the city (53%). Los Angeles had the highest steppe, tropical, tundra, warm temperate) in the state, pollution removal values per unit tree cover due to its except for California and Texas where urban areas were relatively long in-leaf season, relatively low precipita- individually assigned to one of multiple state climate tion, and relatively high pollutant concentrations and zones. deposition velocities. Minneapolis had the lowest pollu- For each urban area exclusive of the 55 analyzed tion removal values per unit tree cover due, in part, to its cities, standardized pollution removal rates were multi- relatively short in-leaf season. plied by average pollutant concentration and total Average leaf-on daytime dry deposition velocities amount of tree cover to calculate total pollution varied among the cities ranging from 0.44 to 0.29 cm s-' removal for each pollutant in every urban area. Urban for NO', 0.40 to 0.71 cms-' for 03, and 0.38 to area pollution removal totals were combined to estimate 0.69 cm s-' for SO2. Deposition velocities did not vary the national total. Pollution removal value was for CO and PMloas deposition rates for these pollutants estimated using national median externality values were not related to transpiration rates, but rates did vary (Murray et al.. 1994). Values were based on the based on leaf-off ahd leaf-on seasons. The deposition median monetized dollar per ton externality values velocities for CO and PMlo were based on literature used in energy-decision-making from various studies. averages and assumed to be constant. The highest These values, in dollars per metric ton (t) are: deposition velocities occurred in San Jose, CA; the NO2 = $6752 t-*, PMlo= $4508 t-l, SO2 = $1653 t-l, lowest in Phoenix, AZ. and CO = $959 t'. Externality values for O3 were set to Though urban trees remove tons of air pollutants equal the value for NO2. Externality values can be annually, average percent air quality improvement in considered the estimated cost of pollution to society that cities during the daytime of the vegetation in-leaf season is not accounted for in the market price of the goods or were typically less than 1 percent (Table 2) and varied services that produced the pollution. among pollutants based on local meteorological and pollution concentration conditions. Percent air quality improvement was typically greatest for particulate matter, ozone, and sulfur dioxide. Air quality improve- Results and discussion ment increases with increased percent tree cover and decreased mixing-layer heights. In urban areas with Total pollution removal and value varied among the 100% tree cover (i.e., contiguous forest stands), average cities from 11,100 t a-' ($60.7 million a-') in Jackson- air quality improvements during the daytime of the in- ville, FL to 22 ta-I ($1 16,000a-') in Bridgeport, CT leaf season were around two percent for particulate (Table 1). Pollution removal values per unit canopy matter, ozone, and sulfur dioxide. In some cities, short- cover varied from 23.1 g m-' a-' in Los Angeles, CA to term air quality improvements (one hour) in areas with 6.2gmW2a-' in Minneapolis, MN. The median pollu- 100% tree cover are estimated to be as high as 16% for tion removal value per unit canopy cover was ozone and sulfur dioxide, 9% for nitrogen dioxide, 8% 10.8gm-~a-'. for particulate matter, and 0.03% for carbon monoxide Pollution removal values for each pollutant will vary (Table 2). among cities based on the amount of tree cover These estimates of air quality improvement due to (increased tree cover leading to greater total removal), pollution removal likely underestimate the total effect of pollution concentration (increased concentration lead- the forest on reducing ground-level pollutants because ing to greater downward flux and total removal), length they do not account for the effect of the forest canopy in of in-leaf season (increased growing season length preventing concentrations of upper air pollution from leading to greater total removal), amount of precipita- reaching ground-level air space. Measured differences in Table 1. Annual pollution removal (1994) by trees and associated value in 55 US cities

City Total

Albany, NY"

Albuquerque, NM~

Atlanta, GA

Austin, TX

Baltimore, MD

Baton Rouge, LA

Boston, MA

Bridgeport, CT

Buffalo, NY

Charleston, WV

Cincinnati, OH

Cleveland, OH

Columbia, SC

Columbus, OHC

Dallas, TX

Denver, CO

Detroit, MI

El Paso, TX

Fresno, CA

Honolulu, HI

Houston, TX

Indianapolis, IN Table 1. (co~ttii~~~?cf)

City 01 PM lo NO2 SO2 CO Total

(t) (gm-2) (t) (gm 2, 0) (gm-2) (1) (gm -') (t) (gin '1 (t) @m-") (($ x 1000) Jacksonville, FL

Jersey City, NJ~

Kansas City, MC)

Los Angeles, CA

Louisville, KY

Me~nphts,TN

Miami, FL

Milwaukee, WI

Minneapolis. MN

Nashville, TN

New Orleans, LA

New York, NY

Newark, NJ

Oklahoma OK

Omaha, NE"

Philadelphia, PA

Phoenix, AZ

Pittsburgh, PA

Portland, OR

Providence, RI

Roanoke, VA

Sacramento. CA Table 1. (con tinuecl)

City 03 I'M lo No2 so2 CO Total

(t) (sm-2) (t) (gm-2) 0) (sm-2> (t) (gm-" (0 (gm- 2, (t) ($ x 1000) Salt Lake City, UT T R San Diego, CA T R San Francisco, CA T R San Jose, CA' T R Seattle, WAg T R St. Louis, MO T R Tampa, FL T R Tucson, AZ T R Tulsa, OK T R Virginia Beach-Norfolk, VA T R Washington, DC T R

Total (T) and range (R) are given in metric tons (t) and in grams per square meter of canopy cover (g m-2) for ozone (03), r less than 10 pm (PM,~),nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The monetary value of pollution removal by trees is estimated using the median externality values for the United States for each pollutant (Murray et al., 1994). Bounds of total tree removal of 03, NO2, SO2, and PMIowere estimated using the typical range of published in-leaf dry deposition velocities (Lovett, 1994). "NO2 data from Buffalo, NY 'so2 data from El Paso, TX h 'NO2 data from Cincinnati, OH ti,

d~eatherdata from Newark, NJ wW "NO2 data from Kansas City, MO f~eatherdata froin San Francisco, CA gN02data from Portland, OR D.J. Nowak et al. / Urban Forestry & Urban Greening 4 (2006) 115-123 121

Table 2. Estimated percent air quality improvement in selected US cities due to air pollution removal by urban trees

City %tree cover % air quality improvement

Atlanta, GA

Boston, MA

Dallas, TX

Denver, CO

Milwaukee, WI

New York, NY

Portland, OR

San Diego, CA

Tampa, FL

Tucson, AZ

Washington, DC

Estimates are given for actual tree cover conditions in city for ozone (a3), particulate matter less than 10 pm (PMlo), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) based on local boundary layer height and pollution removal estimates. Bounds of total tree removal of 03, NO2, SO2, and PMlo were estimated using the typical range of published in-leaf dry deposition velocities (Lovett, 1994)

O3 concentration between above- and below-forest cities developed in forest areas averaging 34.4% tree canopies in California's San Bernardino Mountains cover, cities in grassland areas: 17.8%, and cities in have exceeded 50 ppb (40-percent improvement) (By- deserts: 9.3% (Dwyer et al., 2000; Nowak et al., 2001). tnerowicz et al., 1999). Under normal daytime condi- Total pollution air removal (5 pollutants) by urban trees tions, atmospheric turbulence mixes the atmosphere in coterminous United States is estimated at 71 1,000 t, such that pollutant concentrations are relatively con- with an annual value of $3.8 billion (Table 3). sistent with height (Colbeck and Harrison, 1985). Forest Though the estimates given in this paper are only for a canopies can limit the mixing of upper air with ground- 1-year period (1994), analysis of changes in meteorology level air, leading to significant below-canopy air quality and pollution concentration on pollution removal by improvements. However, where there are numerous urban trees over a 5-year period in Chicago (1 99 1-1995) pollutant sources below the canopy (e.g., automobiles), reveals that annual removal estimates were within 10% the forest canopy could have the inverse effect by of the 5-year average removal rate. Estimates of minimizing the dispersion of the pollutants away at pollution removal may be conservative as some of the ground level. deposition-modeling algorithms are based on homo- The greatest effect of urban trees on ozone, sulfur genous canopies. As part of the urban tree canopy is dioxide, and nitrogen dioxide is during the daytime of heterogeneous with small patches or individual trees, the in-leaf season when trees are transpiring water. this mixed canopy effect would tend to increase Particulate matter removal occurs both day and night pollutant deposition. Also, aerodynamic resistance and throughout the year as particles are intercepted by estimates may be conservative and lead to a slight leaf and bark surfaces. Carbon monoxide removal also underestimate of pollution deposition. occurs both day and night of the in-leaf season, but at Though the average percent air quality improvement much lower rates than for the other pollutants. due to trees is relatively low (< 1 %), the improvement is Urban areas are estimated to occupy 3.5% of lower 48 for multiple pollutants and the actual magnitude of states with an average canopy cover of 27%. Urban tree pollution removal can be significant (typically hundreds cover varies by region within the United States with to thousands of metric tons of pollutants per city per 122 D.J. Nowak et al. / Urban Forestry & Urban Greening 4 (2006) 115-123

Table 3. Air pollution removal and value for all urban trees provide a viable means to improve air quality and help in the coterminous United States meet clean air standards in the United States.

Pollutant Removal (t) Value ($ x lo6) Acknowledgments

This work was supported by funds through the USDA Forest Service's RPA Assessment Staff, and State and Private Forestry's, Urban and Community Forestry Program. We thank D. Baldocchi, M. Ibarra, E.L. Maxwell, and M.H. Noble for assistance with model development and data processing.

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