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httpslidepdfcomreaderfullnrs2014nowak001 111

Tree and forest effects on air quality and human health in the United

States

David J Nowak a Satoshi Hirabayashi b Allison Bodine b Eric Green1047297eld a

a USDA Forest Service 5 Moon Library SUNY-ESF Syracuse NY 13210 USAb The Davey Institute 5 Moon Library SUNY-ESF Syracuse NY 13210 USA

a r t i c l e i n f o

Article history

Received 13 December 2013

Received in revised form

18 May 2014

Accepted 26 May 2014

Available online xxx

Keywords

Air pollution removal

Air quality

Ecosystem services

Human mortality

Urban forests

a b s t r a c t

Trees remove air pollution by the interception of particulate matter on plant surfaces and the absorption

of gaseous pollutants through the leaf stomata However the magnitude and value of the effects of trees

and forests on air quality and human health across the United States remains unknown Computer

simulations with local environmental data reveal that trees and forests in the conterminous United

States removed 174 million tonnes (t) of air pollution in 2010 (range 90e232 million t) with human

health effects valued at 68 billion US dollars (range $15e130 billion) This pollution removal equated

to an average air quality improvement of less than one percent Most of the pollution removal occurred in

rural areas while most of the health impacts and values were within urban areas Health impacts

included the avoidance of more than 850 incidences of human mortality and 670000 incidences of acute

respiratory symptoms

Published by Elsevier Ltd

1 Introduction

Air pollution is a signi1047297cant problem in the United States that

affects human health and well-being ecosystem health crops

climate visibility and man-made materials The Clean Air Act re-

quires the US Environmental Protection Agency (EPA) to set Na-

tional Ambient Air Quality Standards for six ldquocriteria pollutantsrdquo e

that are both common throughout the United States and detri-

mental to human welfare (US EPA 2013a) These pollutants are

carbon monoxide (CO) nitrogen dioxide (NO2) ozone (O3) lead

(Pb) sulfur dioxide (SO2) and particulate matter (PM) which in-

cludes particulate matter less than 10 microns (PM10) and partic-

ulate matterless than 25 microns (PM25) in aerodynamic diameter

Health effects related to air pollution include impacts on pulmo-

nary cardiac vascular and neurological systems (eg Pope et al

2002) In the United States approximately 130000 PM25-related

deaths and 4700 O3-related deaths in 2005 were attributed to air

pollution (Fann et al 2012)

Trees and forests like air pollution vary throughout the United

States (eg percent tree cover species composition) Trees affect air

quality through the direct removal of air pollutants altering local

microclimates and building energy use and through the emission

of volatile organic compounds (VOCs) which can contribute to O3

and PM25 formation (eg Chameides et al 1988) However inte-grative studies have revealed that trees particularly low VOC

emitting species can be a viable strategy to help reduce urban O3

levels (eg Taha 1996 Nowak et al 2000)

Trees remove gaseous air pollution primarily by uptake via leaf

stomata though some gases are removed by the plant surface For

O3 SO2 and NO2 most of the pollution is removed via leaf stomata

Once inside the leaf gases diffuse into intercellular spaces and may

be absorbed by water 1047297lms to form acids or react with inner-leaf

surfaces Trees directly affect particulate matter in the atmo-

sphere by intercepting particles emitting particles (eg pollen) and

resuspension of particles captured on the plant surface Some

particles can be absorbed into the tree though most intercepted

particles are retained on the plant surface The intercepted particles

often are resuspended to the atmosphere washed off by rain or

dropped to the ground with leaf and twig fall During dry periods

particles are constantly intercepted and resuspended in part

dependent upon wind speed The accumulation of particles on the

leaves can affect photosynthesis (eg Darley 1971) and therefore

potentially affect pollution removal by trees During precipitation

particles can be washed off and either dissolved or transferred to

the soil Consequently vegetation is only a temporary retention site

for many atmospheric particles where particles are eventually

moved back to the atmosphere or moved to the soil Pollution

removal by urban trees in the United States has been estimated at

711000 tonnes (t) per year (Nowak et al 2006a) Corresponding author

E-mail address dnowakfsfedus (DJ Nowak)

Contents lists available at ScienceDirect

Environmental Pollution

j o u r n a l h o m e p a g e w w w e l s e v i e r c om l o c a t e e n v p o l

httpdxdoiorg101016jenvpol201405028

0269-7491Published by Elsevier Ltd

Environmental Pollution 193 (2014) 119e129

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While various studies have estimated pollution removal by trees

(eg Nowak et al 2006a McDonald et al 2007 Tallis et al 2011 )

most studies on pollution removal do not directly link the removal

with improved human health effects and associated health values

A few studies that have linked removal and health effects include

one in London where a 10 10 km grid with 25 tree cover was

estimated to remove 904 t of PM10 annually which equated to the

avoidance of 2 deaths and 2 hospital admissions per year (Tiwary

et al 2009) In addition Nowak et al (2013) reported that the to-

tal amount of PM25 removed annually by trees in 10 US cities in

2010 varied from 47 t in Syracuse to 645 t in Atlanta Estimates of

the annual monetary value of human health effects associated with

PM25 removal in these same cities (eg changes in mortality

hospital admissions respiratory symptoms) ranged from $11

million in Syracuse to $601 million in New York City Mortality

avoided wastypically around 1 personyr1 per city but was as high

as 76 people yr1 in New York City

Tree cover in the United States is estimated at 342 percent and

varies from 26 percent in North Dakota to 889 percent in New

Hampshire (Nowak and Green1047297eld 2012) As people and trees exist

throughout a landscape in varying densities not only will pollution

removal and its effects on local pollution concentrations vary but

so will the associated human health impacts and values The ob- jectives of this paper are to estimate the amount of air pollution

(NO2 O3 PM25 SO2) permanently removed by trees and forests

within urban and rural areas of the conterminous United States in

2010 and its associated monetary value and impact on human

health

2 Methods

To estimate avoided health impacts and associated dollar ben-

e1047297ts of air pollution removal by trees and forests in the contermi-

nous United States in 2010 four types of analyses were conducted

These analyses were conducted at the county-level for all urban

and rural areas to estimate 1) the total tree cover and leaf area

index on a daily basis 2) the hourly 1047298ux of pollutants to and fromthe leaves 3) the effects of hourly pollution removal on pollutant

concentration in the atmosphere and 4) the health impacts and

monetary value of the change in NO2 O3 PM25 and SO2 concen-

tration using information from the US EPA Environmental Bene1047297ts

Mapping and Analysis Program (BenMAP) model (US EPA 2012a)

Urban and rural areas were delimited using 2010 Census data with

rural land de1047297ned as land not classi1047297ed as urban (US Census

Bureau 2013)

21 Tree cover and Leaf Area Index

Tree cover within each county was derived from 2001 National

Land Cover Database (NLCD) 30-m resolution tree cover maps

(USGS 2008) These maps were used to determine tree coverwithin speci1047297c geographic locations However these maps gener-

ally underestimate tree cover (Nowak and Green1047297eld 2010) To

adjust for potential underestimates NLCD percent tree cover

within each countys NLCD land-cover class was modi1047297ed according

to the Nowak and Green1047297eld (2010) photo-interpreted values

within individual mapping zones (ie tree cover estimates were

adjusted to match the photo-interpreted estimates for each land

cover class within each mapping zone) Adjusted NLCD tree cover

estimates were within 01 percent of estimates derived from photo-

interpretation (PI) of the conterminous United States (PI frac14 342

percent adjusted NLCDfrac14 341 percent) but this difference could be

greater at the local scale

Maximum (mid-summer) leaf area index (LAI m2 leaf area per

m

2

projected ground area of canopy) values were derived from the

level-4 MODISTerra global Leaf Area Index product for the 2007

growing season across the conterminous United States (USGS

2013) In some areas LAI values per unit of tree cover were

missing or abnormally low and were estimated as 49 (Nowak et al

2008) for urban areas (65 percent of urban areas had missing

values) and 32 (Schlerf et al 2005) for rural areas (145 percent of

rural areas had missing values) Many urban areas had missing LAI

estimates due to the coarseness of the MODIS data and relatively

low amounts of forest cover in urban areas

Percent tree cover classi1047297ed as evergreen was determined for

each county based on evergreen deciduous and mixed forest land

covers as classi1047297ed by the NLCD The proportion of mixed forest

cover that was evergreen was estimated as the proportion of

evergreen to evergreen plus deciduous forest cover in each county

LAI values were combined with percent evergreen information and

local leaf-on and leaf-off (frost) dates (NCDC 2005) to estimate

total daily leaf surface area in each county assuming a four-week

transition period centered on leaf-on and leaf-off dates for spring

and autumn respectively

22 Pollution removal by trees

Hourly pollution removal or 1047298ux (F in mg m2 h1) was esti-

mated as

F frac14 V d C

Where V d is the deposition velocity of the pollutant to the leaf

surface (m h1) and C is pollutant concentration (mg m3) (eg

Hicks et al 1989) Hourly concentrations for each pollutant were

obtained from the US EPAs Air Quality System national database

for the year 2010 (US EPA 2013b) For PM data if hourly data did

not exist then daily and 6-day measurements were used to

represent the hourly concentration values throughout the day (eg

the average daily value was applied to each hour of the day) The

number of monitors ranged from 399 for NO2 to1232 for O3 (Fig1)

If no pollutant monitors existed within the rural or urban area of aparticular county the closest data monitor was assigned to repre-

sent that area As there are substantially more counties than

monitors most monitor data were derived from the nearest

monitor that existed outside of the county (between 75 percent for

O3 and 92 percent for NO2) If more than one monitor existed

hourly pollution removal was estimated for each monitor and

averaged for the annual results

To calculate the hourly deposition velocity local hourly weather

data for 2010 from the National Climatic Data Center (NCDC 2013)

were used to obtain hourly meteorological data (910 weather sta-

tions) (Fig 1) If no weather data existed within a rural or urban

area of a particular county the closest monitor data was assigned to

represent that area (72 percent of counties used data from outside

the county) If more than one monitor existed the weather dataclosest to the geographic center of the area was used Deposition

velocities for all pollutants and resuspension rates for particulate

matter were calculated based on methods detailed in Nowak et al

(2006a 2013) and Hirabayashi et al (2011 2012) Total removal of a

pollutant in a county was calculated as the annual 1047298ux value

(mg m2 yr1) times total tree cover (m2) Minimum and maximum

estimates of removal were based on the typical range of published

in-leaf dry deposition velocities (Lovett 1994)

23 Change in pollutant concentration

To estimate percent air quality improvement due to dry depo-

sition hourly mixing heights from the nearest radiosonde station

(74 stations NOAA 2013 Fig1) were used in conjunction with local

DJ Nowak et al Environmental Pollution 193 (2014) 119e129120

7272019 nrs_2014_nowak_001


hourly 1047298uxes based on methods detailed in Nowak et al (2013) As

pollution removal by trees affects local measured pollution con-

centrations this removal effect is accounted for in the calculation of

percent air quality improvement (Nowak et al 2006a)

24 Health incidence effects and monetary value of NO 2 O 3 PM 25

and SO 2 removal

The US EPAs BenMAP program was used to estimate the inci-

dence of adverse health effects (ie mortality and morbidity) and

associated monetary value that result from changes in NO2 O3

PM25 and SO2 concentrations due to pollution removal by trees

BenMAP is a Windows-based computer program that uses

Geographic Information System (GIS)-based data to estimate the

health impacts and monetary value when populations experience

changes in air quality (Davidson et al 2007 Abt Associates 2010

US EPA 2012a) To calculate the health and monetary effects at the

county level the following six steps were conducted The 1047297rst four

steps were processed using BenMAP (income and currency year of

2010) the last two steps were processed using BenMAP census andair pollution model outputs for each county

1) Air quality grid creation Air quality grids were created

for a baseline and control year for each pollutant Years for

baseline and control were selected to yield the greatest change in

pollution concentration based on national pollution trends

(wwwepagovairtrendsindexhtml) Baseline and control years

were 2002 and 2004 for O3 2000 and 2007 for NO2 and SO2 and

2000 and 2006 for PM25 respectively The pollution concentra-

tion for the grids was interpolated from existing pollution data

sets from EPA pollutant monitors using Voronoi neighborhood

averaging

2) Incidence estimation Incidence estimates were calculated

using several concentration-response functions (Table 1) that es-

timate the change in adverse health effects due to change in

pollutant concentrations Health impact functions relate a change

in pollutant concentration to a change in the incidence of a health

endpoint (ie premature mortality) These functions are typically

derived from the estimated relationship between the concentration

of a pollutant and the adverse health effects suffered by a given

population (US EPA 2012a) The model was run using populationstatistics from the US Census 2010 county dataset using an eco-

nomic forecasting model described in the BenMAP user manual

(Abt Associates 2010) BenMAP con1047297gures Census block pop-

ulations into grid cell level data and the calculation is at grid cell

level BenMap data were aggregated to the county level

3) Aggregation and pooling Incidence estimates were

aggregated and pooled The health effects categories potentially

have multiple estimates corresponding to different air quality

metrics and age groups Different age groups are represented

because the concentration-response functions are age speci1047297c

and incidence rate can vary across different age groups Multiple

estimates were pooled by either averaging the estimates using

the random1047297xed effects method or summing the estimates

depending on which process was appropriate In the end a 1047297nalestimate was produced to cover all possible metrics and age

groups within a health category For example equations for 0e17

18e64 and 65e99 age groups were summed to produce an es-

timate for 0e99 age group More details on the BenMAP model

are found in the literature (Davidson et al 2007 Abt Associates

2010 US EPA 2012a)

4) Valuation estimation Valuation estimates were calculated

using functions that estimate the health-care expenses (ie cost of

illness and willingness to pay to avoid illness) and productivity

losses associated with speci1047297c adverse health events and on the

value of a statistical life in the case of mortality After running the

model BenMAP reports incidence monetary value change in

pollution concentration and population results for each county

within the conterminous United States

Fig 1 Location of pollutant weather and radiosonde stations

DJ Nowak et al Environmental Pollution 193 (2014) 119e129 121

7272019 nrs_2014_nowak_001


Table 1

Concentration-response functions used in BenMAP

Pollutant Health effect Metric Start age End age Reference

PM25 Acute Bronchitis D24MeanQ 8 12 Dockery et al 1996

Acute Myocardial Infarction

Acute Myocardial Infarction nonfatal D24Mean 18 99 Peters et al 2001

D24Mean 0 99 Pope et al 2006

D24Mean 0 99 Sullivan et al 2005

D24Mean 0 99 Zanobetti and Schwartz 2006D24Mean 0 99 Zanobetti et al 2009

Acute Respiratory Symptoms

Minor Restricted Activity Days D24Mean 18 64 Ostro and Rothschild 1989

Asthma Exacerbation

Asthma Exacerbation Cough D24Mean 6 18 Mar et al 2004

Asthma Exacerbation Shortness of Breath D24Mean 6 18 Mar et al 2004

Asthma Exacerbation Wheeze D24Mean 6 18 Ostro et al 2001

Chronic Bronchitis D24MeanQ 27 99 Abbey et al 1995

Emergency Room Visits Respiratory

Emergency Room Visits Asthma D24Mean 0 99 Mar et al 2010

D24Mean 0 17 Norris et al 1999

D24Mean 0 99 Slaughter et al 2005

Hospital Admissions Cardiovascular

All Cardiovascular (less Myocardial Infarctions) D24Mean 65 99 Bell et al 2008

D24Mean 18 64 Moolgavkar 2000


D24Mean 65 99 Peng et al 2008


D24Mean 65 99 Zanobetti et al 2009

Hospital Admissions Respiratory

All Respiratory D24Mean 65 99 Zanobetti et al 2009

Lower Respiratory Symptoms D24Mean 7 14 Schwartz and Neas 2000

Mortality

Mortality All Cause D24MeanQ 25 99 Laden et al 2006

D24MeanQ 0 1 Woodruff et al 1997


Upper Respiratory Symptoms D24MeanQ 9 11 Pope et al 1991

Work Loss Days D24Mean 18 64 Ostro 1987

NO2 Hospital Admissions Respiratory

All Respiratory D1Max 0 14 Luginaah et al 2005

D1Max 15 64 Luginaah et al 2005

D24Mean 65 99 Fung et al 2006

D24Mean 65 99 Yang et al 2003


Emergency Room Visits Asthma D1Max 0 99 Peel et al 2005D24Mean 0 99 NYDOH 2006

D24Mean 0 99 Ito et al 2007

Asthma Exacerbation

Asthma Exacerbation Missed school days D24Mean 4 12 OConnor et al 2008

Asthma Exacerbation Slow play D24Mean 4 12 OConnor et al 2008

Asthma Exacerbation One or More Symptoms D24Mean 4 12 OConnor et al 2008

D24Mean 4 12 Schildcrout et al 2006

D4Mean 4 12 Mortimer et al 2002

D8Max 9 17 Del1047297no et al 2002

D8Max 18 18 Del1047297no et al 2002


Cough D24Mean 7 14 Schwartz et al 1994

O3 Acute Respiratory Symptoms

Minor Restricted Activity Days D1Max 18 64 Ostro and Rothschild 1989

D8Max 18 64 Ostro and Rothschild 1989


Emergency Room Visits Asthma D8Max 0 99 Peel et al 2005D8Max 0 99 Wilson et al 2005


All Respiratory D8Max 0 1 Burnett et al 2001

D1Max 0 1 Burnett et al 2001

D24Mean 65 99 Schwartz 1995

D8Max 65 99 Schwartz 1995

Mortality

Mortality All Cause D1Max 0 99 Levy et al 2005

D24Mean 0 99 Bell et al 2005

D8Max 0 99 Bell et al 2005

D8Max 0 99 Levy et al 2005

School Loss Days

School Loss Days All Cause D1Max 5 17 Chen et al 2000

D8Max 5 17 Chen et al 2000

D8Max 5 17 Gilliland et al 2001

D8Mean 5 17 Gilliland et al 2001


7272019 nrs_2014_nowak_001


5) County multiplier creation Multipliers were created for

each county in the conterminous United States using the results

reported in BenMAP Incidence and value results for each

pollutant were divided by the county population within age

group classes and change in pollution concentration to produce

an estimate of number of incidences and monetary value per

person per age group per unit concentration (ppb or mg m3)(US EPA 2012b)

6) Tree effect estimates To estimate the tree effects on inci-

dence and value for each health category each county multiplier

was multiplied by the 2010 Census county urban and rural popu-

lation per age group and 2010 estimated change in pollutant con-

centration due to trees in the urban and rural county areas The

monetary values for all health categories were summed to deter-

mine the total value of all pollutant effects from trees in each

county

Dollar value results derived from the health impact of trees in

every county were used to determine the relationship between

dollar values per tonne of pollution removed and population den-

sity using linear robust regression Errors occurred in BenMAP runs

in 06 percent of the counties For these counties the regression

equations and county population data were used to estimate the

health values and impacts

3 Results

The total amount of pollution removal in 2010 by trees and

forests in the conterminous United States was 174 million t (range90 million t to 232 million t) with a human health value of $68

billion (range $15 billion to $130 billion) (Table 2) The range in

values is based on the typical range of deposition velocities but

other uncertainties based on input data (eg tree cover pollution

concentration) and modeling of health bene1047297ts would increase the

range but the value of these uncertainties is unknown Removal

was substantially greater in rural areas (167 million t) than urban

areas (651000 t) but the pollution removal monetary value (2010)

was substantially greater in urban areas ($47 billion) compared

with rural areas ($22 billion) (Table 2 Fig 2) The greatest amount

of pollution removal was for O3 and NO2 while the greatest value

associated with removal was for PM25 and O3 (Table 2) States with

the greatest pollution removal amounts were California Texas and

Georgia while states with greatest pollution removal values were

Table 1 (continued )


SO2 Acute Respiratory Symptoms


Asthma Exacerbation




D24Mean 4 12 Schildcrout et al 2006D3Mean 4 12 Mortimer et al 2002


Emergency Room Visits Asthma D1Max 0 99 Peel et al 2005

D24Mean 0 99 Michaud et al 2004


D24Mean 0 99 Wilson et al 2005




D24Mean 0 99 NYDOH 2006






D24Mean 65 99 Schwartz et al 1996



D24Mean e average of the 365 days of daily means

D24MeanQ e average of the 4 quarterly means of daily means The 4 quarters are de 1047297ned as JaneMar Aprile June JuleSep OcteDec

D4Mean e daily mean of hours 6am-10am

D1Max e maximum 1 h value in a day

D8Max e greatest mean for any 8 h window in a day

Table 2

Estimated removal of pollution (tonnes 1000) and associated value ($ 1000) due to trees in the conterminous United States Values in parentheses indicate minimum and

maximum range of estimate

Pollutant Conterminous US Urban areas Rural areas

Removal (t 1000) Value ($ 1000) Removal (t 1000) Value ($ 1000) Removal (t 1000) Value ($ 1000)

NO2 1439 (999e1750) 38470 (23390e48830) 68 ( 41e85) 29500 (17650e37930) 1371 (958e1661) 8939 (5736e10900)

O3 14330 (7330e18520) 2219000

(864400e2917000)

523 (201e691) 1497000

(550000e1988000)

13810 (7130e17830) 721600

(314400e929800)

PM25 696 (95e1560) 4579000

(607600e10070000)

27 (4e58) 3127000

(414700e6928000)

669 (91e1503) 1452000

(193000e3141000)

SO2 907 (583e1390) 7457 (4391e11680) 33 (20e52) 4923 (2864e7793) 873 (564e1339) 2534 (1527e3891)

Total 17370 (9010e23220) 6844000

(1500000e13050000)

651 (266e887) 4659000

(985000e8960000)

16720 (8740e22330) 2185000

(515000e4090000)


7272019 nrs_2014_nowak_001


Florida Pennsylvania and California (Table 3) Most of these ben-

e1047297ts were dominated by the effects of reducing human mortality

with a national reduction of more than 850 incidences of human

mortality (range 184e1634) (Table 4) Other substantial health

bene1047297ts include the reduction of more than 670000 incidences of

acute respiratory symptoms (range 221000e1035000) 430000

incidences of asthma exacerbation (range 198000e688000) and

200000 school loss days (range 78000e266000)

The monetary values associated with reduced adverse healtheffects increased with county population density Dollar values per

tonne removed were highest in New York County New York

(Manhattan) NO2 frac14 $7200 t1 O3 frac14 $63800 t1

PM25frac14 $3852400 t1 SO2frac14 $2600 t1 Average pollution removal

values per t in urban areas were NO2 frac14 $436 t1 O3 frac14 $2864 t1

PM25 frac14 $117106 t1 SO2 frac14 $148 t1 (Table 5) These values were

substantially higher than in rural areas

The regression equations estimating dollars per tonne ( y) based

on population density (people per km2 x) were

NO2 y frac14 07298 thorn 06264 x (r 2 frac14 091)

O3 y frac14 94667 thorn 35089 x (r 2 frac14 086)

PM25 y frac14 4280011 thorn1217864 x (r 2 frac14 083)

SO2 y frac14 01442 thorn 01493 x (r 2 frac14 086)

These equations will produce average values based on popula-

tion density not speci1047297c population parameters (eg age class

distribution) and can give rough estimates of values in areas where

BenMAP cannot be applied

Average removal per square meter of canopy cover for all pol-

lutants varied from 665 g m2 yr1 in rural areas to673 g m2 yr1

in urban areas with a national average of 666 g m2 yr1 (Table 5)

The national average value per hectare of tree cover was about $26

but varied from$9 in rural areas to$481 in urban areasThe average

annual percent air quality improvement due to trees varied among

pollutants and ranged from a low of 013 in urban areas for PM25

to a high of 051 in rural areas for O3 (Table 5)

4 Discussion

Pollution removal by trees and forests in the United States is

substantial at more than 17 million t removed in 2010 As 964

percent of the conterminous United States is rural land and percent

tree cover is comparable between urban and rural land (Nowak andGreen1047297eld 2012) 963 percent of pollution removal from trees

occurred on rural land However as human populations are

concentrated in urban areas the health effects and values derived

from pollution removal are concentrated in urban areas with 681

percent of the $68 billion value occurring with urban lands Thus

in terms of impacts on human health trees in urban areas are

substantially more important than rural trees due to their prox-

imity to people The greatest monetary values are derived in areas

with the greatest population density (eg Manhattan)

The reason urban areas have substantially greater values than

rural areas is that the BenMAP values and effects analyzed are

based upon human health which is related to US EPA air primary

quality standards Primary standards are designed to provide public

health protection while secondary standards provide public wel-fare protection including protection against decreased visibility

and damage to animals crops vegetation and buildings (US EPA

2013a) If the analysis shifted more toward secondary standard is-

sues particularly protection from damage to animals crops and

vegetation the valuation in urban and rural areas would change

The valuation provided in this study is conservative as it predom-

inantly addresses only human health values It also only addresses

four of the six criteria pollutants

BenMAP values are relatively low compared to other valuation

approaches Using median air pollution cost factors from Europe

that include health costs building and material damage and crop

losses (Van Essen et al 2011) the value of pollution removal by US

trees would jump to $86 billion a 13 fold increase in value Exter-

nality values and pollution costs are constant values per tonne that

Fig 2 Estimated removal per square kilometer of land (tonnes km2) of all pollutants (NO2 O3 PM25 SO2) by trees per county in 2010


7272019 nrs_2014_nowak_001


estimate more than human health impacts while BenMAPs health

valuation is dependent on human population density Health values

vary with human populations as humans are the recipients of the

health bene1047297ts

The greatest impact of trees on air pollution in terms of both

magnitude and value were for O3 and particulate matter Pollution

removal amounts were highest for O3 due to the combination of

relatively high concentrations and removal rates by trees for these

pollutants (eg Lovett 1994) Pollution removal monetary values

were greatest for O3 and PM25 due to the estimated impact of

changes in these pollutant concentrations on human mortality

BenMAP assigns the greatest value per incidence for human mor-

tality averaging $78 million per incidence

The amount and pattern of pollution removal in this study is

comparable to those found for US urban areas circa 1994 (Nowak

et al 2006a) which used 1990 census data and 1994 pollution

data to estimate pollution removal in US urban areas at 711000 t

($38 billion) This amount compares to the current studys 2010

estimate for US urban areas of 651000 t ($47 billion) These

numbers are not directly comparable as the 1990 values included

estimates for CO and PM10 removal but did not directly include

PM25 removal In addition the valuation process has changed

shifting from externality-based estimates to human-health (Ben-

MAP) estimatesof dollar values The total amountof urban landand

thus urban tree cover has also increased between 1990 and 2010

Percent urban land in the conterminous United States increased

Table 3

Estimated removal of pollution and associated value (total and per hectare of land area) due to trees in the conterminous United States by state and District of Columbia

State All land Urban land Rural land

Removal Value Removal Value Removal Value

t 1000 kg ha1 $ Ma $ ha1 Urbanb Treec t 1000 kg ha1 $ Ma $ ha1 t 1000 kg ha1 $ Ma $ ha1

Alabama 6398 488 2271 173 44 700 188 329 1042 1820 6210 496 1229 98

Arizona 4466 152 249 08 19 192 60 106 209 369 4405 153 40 01

Arkansas 5486 408 958 71 21 572 70 248 377 1327 5416 412 582 44California 10353 256 4462 110 53 361 364 170 4043 1894 9990 261 419 11

Colorado 5343 199 157 06 15 236 20 50 50 128 5324 201 106 04

Connecticut 490 390 1203 957 377 726 156 329 1023 2160 334 426 180 230

Delaware 157 310 211 417 209 333 27 254 158 1500 130 325 53 132

District of Columbia 03 185 77 4833 1000 286d 03 185 77 4833 na na na na

Florida 6389 446 5692 398 137 549 615 314 4655 2378 5775 467 1037 84

Georgia 7317 487 3523 235 83 664 500 401 2262 1817 6817 495 1261 92

Idaho 5657 264 428 20 06 379 14 105 187 1440 5644 265 241 11

Illinois 1403 98 1494 104 71 156 112 109 1330 1304 1292 97 164 12

Indiana 1640 177 962 104 70 257 84 129 631 969 1555 181 330 38

Iowa 865 60 282 20 17 104 21 84 185 753 844 59 97 07

Kansas 858 41 167 08 12 80 21 82 117 465 838 40 50 02

Kentucky 3349 326 999 97 36 580 69 188 421 1150 3280 331 577 58

Louisiana 4477 403 1426 128 46 515 157 310 855 1684 4319 407 570 54

Maine 4010 499 783 97 12 831 36 383 232 2481 3974 500 551 69

Maryland 952 376 1349 533 206 428 168 321 1118 2144 785 390 231 115

Massachusetts 897 438 2501 1222 380 708 302 389 2228 2863 594 468 273 215Michigan 4963 337 1774 120 64 595 218 232 1071 1139 4745 344 703 51

Minnesota 3355 163 469 23 22 348 46 105 267 603 3309 164 201 10

Mississippi 5642 468 1568 130 24 640 105 366 604 2109 5537 470 964 82

Missouri 5027 282 1277 72 30 403 104 195 702 1321 4924 285 575 33

Montana 7277 193 281 07 02 275 05 66 56 724 7272 193 225 06

Nebraska 440 22 54 03 07 36 05 39 39 286 435 22 15 01

Nevada 2101 74 90 03 07 116 17 86 81 410 2084 74 09 00

New Hampshire 1155 496 441 189 72 889 59 352 173 1037 1096 507 267 124

New Jersey 691 360 1813 943 397 570 219 287 1655 2169 472 408 157 136

New Mexico 4527 144 85 03 07 191 21 99 43 200 4506 144 42 01

New York 4225 346 4334 355 87 650 319 300 3459 3255 3906 351 875 79

North Carolina 5647 448 3154 250 95 626 420 351 1765 1475 5227 458 1389 122

North Dakota 212 12 14 01 03 26 01 21 08 161 211 12 06 00

Ohio 2333 221 2680 253 108 399 245 215 2053 1797 2088 221 626 66

Oklahoma 3029 171 586 33 19 259 39 115 269 796 2990 172 316 18

Oregon 6761 271 1599 64 12 408 50 175 1028 3584 6711 273 571 23

Pennsylvania 4370 377 5435 469 105 658 308 252 3688 3023 4062 392 1747 168

Rhode Island 105 387 336 1233 387 703 29 278 279 2646 76 456 57 343South Carolina 3712 476 2043 262 79 646 236 384 1185 1923 3476 484 858 119

South Dakota 457 23 37 02 03 57 02 42 19 332 454 23 18 01

Tennessee 4025 377 1832 172 70 571 199 265 1031 1372 3826 386 801 81

Texas 10119 149 3172 47 33 234 365 161 2220 978 9754 148 952 14

Utah 3314 156 150 07 11 178 24 100 115 486 3290 156 35 02

Vermont 964 403 222 93 17 815 10 255 61 1509 954 405 161 68

Virginia 4461 435 1716 167 68 667 214 308 1039 1497 4247 444 677 71

Washington 5355 310 2411 139 36 472 139 225 1686 2726 5215 313 725 43

West Virginia 2628 422 777 125 27 814 45 275 289 1745 2582 426 488 81

Wisconsin 3331 237 848 60 35 477 70 144 477 980 3261 241 371 27

Wyoming 2966 118 43 02 02 145 04 70 19 368 2962 118 24 01

Conterminous US 173703 227 68432 89 36 342 6505 237 46584 1696 167198 226 21849 30

a Millions of dollarsb Percent of state land classi1047297ed as urban (2010)c Percent tree cover in state (from Nowak and Green1047297eld 2012)d From Nowak et al (2006b)


7272019 nrs_2014_nowak_001


from 25 percent in 1990 to 31 in 2000 (Nowak et al 2005) and to

36 in 2010 The amount of urban tree cover has increased from

around 67 million hectares in 1990 to 96 million hectares in 2010

Thus as urban land and population continue to expand the amount

and value of pollution removal by urban trees will continue toincrease

Typical annual air quality improvement due to pollution

removal by trees was less than one percent which is comparable to

values in Nowak et al (2006a) Maximum annual air quality

improvement in some areas reached between 2 and 45 percent

depending upon meteorological conditions In heavily forested

areas peak one hour improvements could reach as high as 16

percent (Nowak et al 2006a)

In general the greater the tree cover the greater the pollution

removal and the greater the removal and population density the

greater the value However trees also affect air quality in ways not

analyzed in this paper Trees reduce air temperatures which can

lead to reduced emissions fromvarious anthropogenic sources (eg

Cardelino and Chameides 1990) Trees around buildings alter

building energy use (eg Heisler 1986) and consequent emissions

from power plants Trees reduce wind speeds lowering mixing

heights and can therefore increase pollution concentrations (eg

Nowak et al 2006a) Trees also emit varying levels of volatile

organic compounds (VOCs) that are precursor chemicals to O3 andPM25 formation (eg Chameides et al 1988 Hodan and Barnard

2004) More research is needed on how these factors combine to

affect air pollution concentrations

The issue of 1047297ne-scale effects on pollution concentrations also

needs to be addressed e how do tree con1047297gurations alter local

pollutant concentrations Local-scale effects will differ depending

upon vegetation designs This county-wide modeling focused on

broad-scale estimates of pollution removal by trees on air quality

At the local scale pollution concentrations can be increased if trees

a) trap the pollutants beneath tree canopies near emission sources

(eg along road ways Gromke and Ruck 2009 Wania et al 2012

Salmond et al 2013 Vos et al 2013) b) limit dispersion by

reducing wind speeds andor c) lower mixing heights by reducing

wind speeds (Nowak et al 2006a) Under stable atmospheric

Table 4

Reduction in number of incidences and associated monetary value ($) for various health effects due to pollutant reduction from trees

Conterminous US Urban areas Rural areas

Pollutant Adverse health Effect No Inca Value No Inca Value No Inca Value

NO2 Asthma Exacerbation 271402 21772000 214236 17178000 57166 4594000

Hospital Admissions 640 16037000 470 11823000 170 4214000

Acute Respiratory Symptoms 18179 565000 14666 455000 3513 110000

Emergency Room Visits 238 100000 185 78000 53 22000

Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000

Chronic Bronchitis 149 41706000 106 29720000 43 11987000


Acute Myocardial Infarction 125 11219000 85 7629000 40 3590000

Asthma Exacerbation 137298 11161000 98467 8005000 38831 3157000

Work Loss Days 28815 4758000 20836 3602000 7979 1157000

Hospital Admissions Cardiovascular 71 2705000 49 1876000 22 829000

Hospital Admissions Respiratory 58 1850000 39 1246000 19 604000

Lower Respiratory Symptoms 3900 202000 2809 146000 1091 57000

Upper Respiratory Symptoms 3168 142000 2284 103000 883 40000

Emergency Room Visits 203 84000 150 62000 53 22000Acute Bronchitis 320 28000 231 20000 89 8000

Total 4578503000 3126703000 1451800000

SO2 Acute Respiratory Symptoms 2865 90000 2042 64000 823 26000




Total 7457000 4923000 2534000

a reduction in number of incidences

Table 5

Average annual values per tonne ($ t1) of removal andper hectareof tree cover($ ha1)average gramsof removal persquaremeter of treecover (gm2) and average absolute

and percent reduction in pollutant concentration in the conterminous United States (2010)


Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665

a Average annual reduction in hourly concentration in ppb except for PM 25 (mg m3)b Average percent annual reduction in hourly concentration


7272019 nrs_2014_nowak_001


conditions (limited mixing) tree removal could lead to greater re-

ductions in pollution concentrations at the ground level by limiting

mixing with air pollutants above the canopy Large stands of trees

can also reduce pollutant concentrations in the interior of the stand

due to increased distance from emission sources and increased dry

deposition (eg Dasch 1987 Cavanagh et al 2009) Thus local-

scale design of trees and forests can affect local-scale pollutant

concentrations More research is needed that accounts for vegeta-

tion con1047297guration and source-sink relationships in order to maxi-

mize bene1047297cial tree effects on pollutant concentrations and human

exposure to air pollution

Removal rates by trees will vary locally based on several addi-

tional factors including a) amount of tree cover e increased cover

increases removal b) pollution concentration e increased con-

centration generally increases removal c) length of growing season

e longer growing seasons increase removal d) percent evergreen

leaf area e increased evergreen leaf area increases pollution

removal during leaf-off seasons and e) meteorological conditions

e these affect dry deposition pollution removal rates In addition

various factors that affect tree health and transpiration (eg

drought or other environmental stressors) can affect the removal of

gaseous pollutants by trees by limiting gas exchange at the leaf

surfaceThis study does not address the issue of advection where

pollution removal in rural areas surrounding urban areas could

lower the pollution concentrations arriving into urban areas (or

vice versa) As many pollutants are generated locally this may not

be a major factor but for some pollutants particularly secondary

pollutants such as O3 that are formed from chemical reactions the

reduction of pollutants in rural areas could have an impacton urban

pollutant concentrations The magnitude of this potential impact is

unknown

Though there are various limitations to these estimates the

results give a 1047297rst-order approximation of the magnitude of

pollution removal by trees and their effect on human health Lim-

itations of the analysis include a) limitations associated with

modeling particulate matter removal and resuspension (see Nowaket al 2013) b) limited number of weather and pollutant monitors

nationally ie use of closest weather and pollution data might not

represent the true average for the county and rural concentrations

may be overestimated if using urban monitors to represent rural

areas c) uncertainties associated with estimating tree cover and

leaf area indices in each county d) the boundary layer is assumed to

be well-mixed (unstable) which will likely lead to conservative

estimates of concentration reductions during stable conditions e)

limitations associated with estimating human health effects and

values using BenMAP and f) results focus only on pollution removal

and do not include other generally positive (ie air temperature

reduction building energy use conservation) and negative (VOC

emissions reduced wind speeds) effects of trees on air quality

Despite the limitations there are several advantages to themodeling estimates which include a) use of best available

measured tree weather population and pollution data for each

county b) incorporating hourly interactions between deposition

velocities and pollution concentrations (F frac14 V d C) c) hourly

resuspension of PM25 based on wind speeds d) estimates of

pollution removal effects on pollution concentration changes and

e) linking pollution effects with human health effects through

BenMAP The methodological approach used in this paper can also

be applied in other countries to help assess the broad-scale impacts

of pollution removal by trees on air quality If BenMAP analyses are

not run to determine health impacts the generalized regression

equations could give a broad indication of health values provided

by improved air quality based on population density Though future

research and modeling are needed to help overcome current

limitations these estimates provide the best available and most

comprehensive estimates of pollution removal effects by US trees

on human health

5 Conclusion

Modeling broad-scale effects of pollution removal by trees on air

pollution concentrations and human health reveals that while thepercent reduction in pollution concentration averages less than one

percent trees remove substantial amounts of pollution and can

produce substantial health bene1047297ts and monetary values across the

nation with most of the health values derived from urban trees

Acknowledgments

Funding for this project was provided in part by the US Forest

Services RPA Assessment Staff and State amp Private Forestrys Urban

and Community Forestry Program We thank Laura Jackson and

Shawn Landry for comments on previous versions of this manu-

script and John Stanovick for statistical assistance and review

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7272019 nrs_2014_nowak_001


While various studies have estimated pollution removal by trees

(eg Nowak et al 2006a McDonald et al 2007 Tallis et al 2011 )

most studies on pollution removal do not directly link the removal

with improved human health effects and associated health values

A few studies that have linked removal and health effects include

one in London where a 10 10 km grid with 25 tree cover was

estimated to remove 904 t of PM10 annually which equated to the

avoidance of 2 deaths and 2 hospital admissions per year (Tiwary

et al 2009) In addition Nowak et al (2013) reported that the to-

tal amount of PM25 removed annually by trees in 10 US cities in

2010 varied from 47 t in Syracuse to 645 t in Atlanta Estimates of

the annual monetary value of human health effects associated with

PM25 removal in these same cities (eg changes in mortality

hospital admissions respiratory symptoms) ranged from $11

million in Syracuse to $601 million in New York City Mortality

avoided wastypically around 1 personyr1 per city but was as high

as 76 people yr1 in New York City

Tree cover in the United States is estimated at 342 percent and

varies from 26 percent in North Dakota to 889 percent in New

Hampshire (Nowak and Green1047297eld 2012) As people and trees exist

throughout a landscape in varying densities not only will pollution

removal and its effects on local pollution concentrations vary but

so will the associated human health impacts and values The ob- jectives of this paper are to estimate the amount of air pollution

(NO2 O3 PM25 SO2) permanently removed by trees and forests

within urban and rural areas of the conterminous United States in

2010 and its associated monetary value and impact on human

health

2 Methods

To estimate avoided health impacts and associated dollar ben-

e1047297ts of air pollution removal by trees and forests in the contermi-

nous United States in 2010 four types of analyses were conducted

These analyses were conducted at the county-level for all urban

and rural areas to estimate 1) the total tree cover and leaf area

index on a daily basis 2) the hourly 1047298ux of pollutants to and fromthe leaves 3) the effects of hourly pollution removal on pollutant

concentration in the atmosphere and 4) the health impacts and

monetary value of the change in NO2 O3 PM25 and SO2 concen-

tration using information from the US EPA Environmental Bene1047297ts

Mapping and Analysis Program (BenMAP) model (US EPA 2012a)

Urban and rural areas were delimited using 2010 Census data with

rural land de1047297ned as land not classi1047297ed as urban (US Census

Bureau 2013)

21 Tree cover and Leaf Area Index

Tree cover within each county was derived from 2001 National

Land Cover Database (NLCD) 30-m resolution tree cover maps

(USGS 2008) These maps were used to determine tree coverwithin speci1047297c geographic locations However these maps gener-

ally underestimate tree cover (Nowak and Green1047297eld 2010) To

adjust for potential underestimates NLCD percent tree cover

within each countys NLCD land-cover class was modi1047297ed according

to the Nowak and Green1047297eld (2010) photo-interpreted values

within individual mapping zones (ie tree cover estimates were

adjusted to match the photo-interpreted estimates for each land

cover class within each mapping zone) Adjusted NLCD tree cover

estimates were within 01 percent of estimates derived from photo-

interpretation (PI) of the conterminous United States (PI frac14 342

percent adjusted NLCDfrac14 341 percent) but this difference could be

greater at the local scale

Maximum (mid-summer) leaf area index (LAI m2 leaf area per

m

2

projected ground area of canopy) values were derived from the

level-4 MODISTerra global Leaf Area Index product for the 2007

growing season across the conterminous United States (USGS

2013) In some areas LAI values per unit of tree cover were

missing or abnormally low and were estimated as 49 (Nowak et al

2008) for urban areas (65 percent of urban areas had missing

values) and 32 (Schlerf et al 2005) for rural areas (145 percent of

rural areas had missing values) Many urban areas had missing LAI

estimates due to the coarseness of the MODIS data and relatively

low amounts of forest cover in urban areas

Percent tree cover classi1047297ed as evergreen was determined for

each county based on evergreen deciduous and mixed forest land

covers as classi1047297ed by the NLCD The proportion of mixed forest

cover that was evergreen was estimated as the proportion of

evergreen to evergreen plus deciduous forest cover in each county

LAI values were combined with percent evergreen information and

local leaf-on and leaf-off (frost) dates (NCDC 2005) to estimate

total daily leaf surface area in each county assuming a four-week

transition period centered on leaf-on and leaf-off dates for spring

and autumn respectively

22 Pollution removal by trees

Hourly pollution removal or 1047298ux (F in mg m2 h1) was esti-

mated as

F frac14 V d C

Where V d is the deposition velocity of the pollutant to the leaf

surface (m h1) and C is pollutant concentration (mg m3) (eg

Hicks et al 1989) Hourly concentrations for each pollutant were

obtained from the US EPAs Air Quality System national database

for the year 2010 (US EPA 2013b) For PM data if hourly data did

not exist then daily and 6-day measurements were used to

represent the hourly concentration values throughout the day (eg

the average daily value was applied to each hour of the day) The

number of monitors ranged from 399 for NO2 to1232 for O3 (Fig1)

If no pollutant monitors existed within the rural or urban area of aparticular county the closest data monitor was assigned to repre-

sent that area As there are substantially more counties than

monitors most monitor data were derived from the nearest

monitor that existed outside of the county (between 75 percent for

O3 and 92 percent for NO2) If more than one monitor existed

hourly pollution removal was estimated for each monitor and

averaged for the annual results

To calculate the hourly deposition velocity local hourly weather

data for 2010 from the National Climatic Data Center (NCDC 2013)

were used to obtain hourly meteorological data (910 weather sta-

tions) (Fig 1) If no weather data existed within a rural or urban

area of a particular county the closest monitor data was assigned to

represent that area (72 percent of counties used data from outside

the county) If more than one monitor existed the weather dataclosest to the geographic center of the area was used Deposition

velocities for all pollutants and resuspension rates for particulate

matter were calculated based on methods detailed in Nowak et al

(2006a 2013) and Hirabayashi et al (2011 2012) Total removal of a

pollutant in a county was calculated as the annual 1047298ux value

(mg m2 yr1) times total tree cover (m2) Minimum and maximum

estimates of removal were based on the typical range of published

in-leaf dry deposition velocities (Lovett 1994)

23 Change in pollutant concentration

To estimate percent air quality improvement due to dry depo-

sition hourly mixing heights from the nearest radiosonde station

(74 stations NOAA 2013 Fig1) were used in conjunction with local


7272019 nrs_2014_nowak_001







and SO 2 removal






















averaging



























2010 US EPA 2012a)











7272019 nrs_2014_nowak_001


Table 1











Asthma Exacerbation



















Mortality














Asthma Exacerbation






D8Max 9 17 Del1047297no et al 2002

D8Max 18 18 Del1047297no et al 2002













Mortality





School Loss Days






7272019 nrs_2014_nowak_001
















county








3 Results




















Asthma Exacerbation



























Table 2





NO2 1439 (999e1750) 38470 (23390e48830) 68 ( 41e85) 29500 (17650e37930) 1371 (958e1661) 8939 (5736e10900)

O3 14330 (7330e18520) 2219000

(864400e2917000)

523 (201e691) 1497000

(550000e1988000)

13810 (7130e17830) 721600

(314400e929800)

PM25 696 (95e1560) 4579000

(607600e10070000)

27 (4e58) 3127000

(414700e6928000)

669 (91e1503) 1452000

(193000e3141000)

SO2 907 (583e1390) 7457 (4391e11680) 33 (20e52) 4923 (2864e7793) 873 (564e1339) 2534 (1527e3891)

Total 17370 (9010e23220) 6844000

(1500000e13050000)

651 (266e887) 4659000

(985000e8960000)

16720 (8740e22330) 2185000

(515000e4090000)


7272019 nrs_2014_nowak_001



































4 Discussion

































7272019 nrs_2014_nowak_001




























Table 3





Alabama 6398 488 2271 173 44 700 188 329 1042 1820 6210 496 1229 98

Arizona 4466 152 249 08 19 192 60 106 209 369 4405 153 40 01


Colorado 5343 199 157 06 15 236 20 50 50 128 5324 201 106 04

Connecticut 490 390 1203 957 377 726 156 329 1023 2160 334 426 180 230

Delaware 157 310 211 417 209 333 27 254 158 1500 130 325 53 132


Florida 6389 446 5692 398 137 549 615 314 4655 2378 5775 467 1037 84

Georgia 7317 487 3523 235 83 664 500 401 2262 1817 6817 495 1261 92

Idaho 5657 264 428 20 06 379 14 105 187 1440 5644 265 241 11

Illinois 1403 98 1494 104 71 156 112 109 1330 1304 1292 97 164 12

Indiana 1640 177 962 104 70 257 84 129 631 969 1555 181 330 38

Iowa 865 60 282 20 17 104 21 84 185 753 844 59 97 07

Kansas 858 41 167 08 12 80 21 82 117 465 838 40 50 02

Kentucky 3349 326 999 97 36 580 69 188 421 1150 3280 331 577 58

Louisiana 4477 403 1426 128 46 515 157 310 855 1684 4319 407 570 54

Maine 4010 499 783 97 12 831 36 383 232 2481 3974 500 551 69

Maryland 952 376 1349 533 206 428 168 321 1118 2144 785 390 231 115


Minnesota 3355 163 469 23 22 348 46 105 267 603 3309 164 201 10

Mississippi 5642 468 1568 130 24 640 105 366 604 2109 5537 470 964 82

Missouri 5027 282 1277 72 30 403 104 195 702 1321 4924 285 575 33

Montana 7277 193 281 07 02 275 05 66 56 724 7272 193 225 06

Nebraska 440 22 54 03 07 36 05 39 39 286 435 22 15 01

Nevada 2101 74 90 03 07 116 17 86 81 410 2084 74 09 00

New Hampshire 1155 496 441 189 72 889 59 352 173 1037 1096 507 267 124

New Jersey 691 360 1813 943 397 570 219 287 1655 2169 472 408 157 136

New Mexico 4527 144 85 03 07 191 21 99 43 200 4506 144 42 01

New York 4225 346 4334 355 87 650 319 300 3459 3255 3906 351 875 79

North Carolina 5647 448 3154 250 95 626 420 351 1765 1475 5227 458 1389 122

North Dakota 212 12 14 01 03 26 01 21 08 161 211 12 06 00

Ohio 2333 221 2680 253 108 399 245 215 2053 1797 2088 221 626 66

Oklahoma 3029 171 586 33 19 259 39 115 269 796 2990 172 316 18

Oregon 6761 271 1599 64 12 408 50 175 1028 3584 6711 273 571 23

Pennsylvania 4370 377 5435 469 105 658 308 252 3688 3023 4062 392 1747 168


South Dakota 457 23 37 02 03 57 02 42 19 332 454 23 18 01

Tennessee 4025 377 1832 172 70 571 199 265 1031 1372 3826 386 801 81

Texas 10119 149 3172 47 33 234 365 161 2220 978 9754 148 952 14

Utah 3314 156 150 07 11 178 24 100 115 486 3290 156 35 02

Vermont 964 403 222 93 17 815 10 255 61 1509 954 405 161 68

Virginia 4461 435 1716 167 68 667 214 308 1039 1497 4247 444 677 71

Washington 5355 310 2411 139 36 472 139 225 1686 2726 5215 313 725 43

West Virginia 2628 422 777 125 27 814 45 275 289 1745 2582 426 488 81

Wisconsin 3331 237 848 60 35 477 70 144 477 980 3261 241 371 27

Wyoming 2966 118 43 02 02 145 04 70 19 368 2962 118 24 01

Conterminous US 173703 227 68432 89 36 342 6505 237 46584 1696 167198 226 21849 30



7272019 nrs_2014_nowak_001






































Table 4








Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000





Work Loss Days 28815 4758000 20836 3602000 7979 1157000






Total 4578503000 3126703000 1451800000





Total 7457000 4923000 2534000


Table 5




Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665



7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001







and SO 2 removal






















averaging



























2010 US EPA 2012a)











7272019 nrs_2014_nowak_001


Table 1











Asthma Exacerbation



















Mortality














Asthma Exacerbation






D8Max 9 17 Del1047297no et al 2002

D8Max 18 18 Del1047297no et al 2002













Mortality





School Loss Days






7272019 nrs_2014_nowak_001
















county








3 Results




















Asthma Exacerbation



























Table 2





NO2 1439 (999e1750) 38470 (23390e48830) 68 ( 41e85) 29500 (17650e37930) 1371 (958e1661) 8939 (5736e10900)

O3 14330 (7330e18520) 2219000

(864400e2917000)

523 (201e691) 1497000

(550000e1988000)

13810 (7130e17830) 721600

(314400e929800)

PM25 696 (95e1560) 4579000

(607600e10070000)

27 (4e58) 3127000

(414700e6928000)

669 (91e1503) 1452000

(193000e3141000)

SO2 907 (583e1390) 7457 (4391e11680) 33 (20e52) 4923 (2864e7793) 873 (564e1339) 2534 (1527e3891)

Total 17370 (9010e23220) 6844000

(1500000e13050000)

651 (266e887) 4659000

(985000e8960000)

16720 (8740e22330) 2185000

(515000e4090000)


7272019 nrs_2014_nowak_001



































4 Discussion

































7272019 nrs_2014_nowak_001




























Table 3





Alabama 6398 488 2271 173 44 700 188 329 1042 1820 6210 496 1229 98

Arizona 4466 152 249 08 19 192 60 106 209 369 4405 153 40 01


Colorado 5343 199 157 06 15 236 20 50 50 128 5324 201 106 04

Connecticut 490 390 1203 957 377 726 156 329 1023 2160 334 426 180 230

Delaware 157 310 211 417 209 333 27 254 158 1500 130 325 53 132


Florida 6389 446 5692 398 137 549 615 314 4655 2378 5775 467 1037 84

Georgia 7317 487 3523 235 83 664 500 401 2262 1817 6817 495 1261 92

Idaho 5657 264 428 20 06 379 14 105 187 1440 5644 265 241 11

Illinois 1403 98 1494 104 71 156 112 109 1330 1304 1292 97 164 12

Indiana 1640 177 962 104 70 257 84 129 631 969 1555 181 330 38

Iowa 865 60 282 20 17 104 21 84 185 753 844 59 97 07

Kansas 858 41 167 08 12 80 21 82 117 465 838 40 50 02

Kentucky 3349 326 999 97 36 580 69 188 421 1150 3280 331 577 58

Louisiana 4477 403 1426 128 46 515 157 310 855 1684 4319 407 570 54

Maine 4010 499 783 97 12 831 36 383 232 2481 3974 500 551 69

Maryland 952 376 1349 533 206 428 168 321 1118 2144 785 390 231 115


Minnesota 3355 163 469 23 22 348 46 105 267 603 3309 164 201 10

Mississippi 5642 468 1568 130 24 640 105 366 604 2109 5537 470 964 82

Missouri 5027 282 1277 72 30 403 104 195 702 1321 4924 285 575 33

Montana 7277 193 281 07 02 275 05 66 56 724 7272 193 225 06

Nebraska 440 22 54 03 07 36 05 39 39 286 435 22 15 01

Nevada 2101 74 90 03 07 116 17 86 81 410 2084 74 09 00

New Hampshire 1155 496 441 189 72 889 59 352 173 1037 1096 507 267 124

New Jersey 691 360 1813 943 397 570 219 287 1655 2169 472 408 157 136

New Mexico 4527 144 85 03 07 191 21 99 43 200 4506 144 42 01

New York 4225 346 4334 355 87 650 319 300 3459 3255 3906 351 875 79

North Carolina 5647 448 3154 250 95 626 420 351 1765 1475 5227 458 1389 122

North Dakota 212 12 14 01 03 26 01 21 08 161 211 12 06 00

Ohio 2333 221 2680 253 108 399 245 215 2053 1797 2088 221 626 66

Oklahoma 3029 171 586 33 19 259 39 115 269 796 2990 172 316 18

Oregon 6761 271 1599 64 12 408 50 175 1028 3584 6711 273 571 23

Pennsylvania 4370 377 5435 469 105 658 308 252 3688 3023 4062 392 1747 168


South Dakota 457 23 37 02 03 57 02 42 19 332 454 23 18 01

Tennessee 4025 377 1832 172 70 571 199 265 1031 1372 3826 386 801 81

Texas 10119 149 3172 47 33 234 365 161 2220 978 9754 148 952 14

Utah 3314 156 150 07 11 178 24 100 115 486 3290 156 35 02

Vermont 964 403 222 93 17 815 10 255 61 1509 954 405 161 68

Virginia 4461 435 1716 167 68 667 214 308 1039 1497 4247 444 677 71

Washington 5355 310 2411 139 36 472 139 225 1686 2726 5215 313 725 43

West Virginia 2628 422 777 125 27 814 45 275 289 1745 2582 426 488 81

Wisconsin 3331 237 848 60 35 477 70 144 477 980 3261 241 371 27

Wyoming 2966 118 43 02 02 145 04 70 19 368 2962 118 24 01

Conterminous US 173703 227 68432 89 36 342 6505 237 46584 1696 167198 226 21849 30



7272019 nrs_2014_nowak_001






































Table 4








Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000





Work Loss Days 28815 4758000 20836 3602000 7979 1157000






Total 4578503000 3126703000 1451800000





Total 7457000 4923000 2534000


Table 5




Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665



7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001


Table 1











Asthma Exacerbation



















Mortality














Asthma Exacerbation






D8Max 9 17 Del1047297no et al 2002

D8Max 18 18 Del1047297no et al 2002













Mortality





School Loss Days






7272019 nrs_2014_nowak_001
















county








3 Results




















Asthma Exacerbation



























Table 2





NO2 1439 (999e1750) 38470 (23390e48830) 68 ( 41e85) 29500 (17650e37930) 1371 (958e1661) 8939 (5736e10900)

O3 14330 (7330e18520) 2219000

(864400e2917000)

523 (201e691) 1497000

(550000e1988000)

13810 (7130e17830) 721600

(314400e929800)

PM25 696 (95e1560) 4579000

(607600e10070000)

27 (4e58) 3127000

(414700e6928000)

669 (91e1503) 1452000

(193000e3141000)

SO2 907 (583e1390) 7457 (4391e11680) 33 (20e52) 4923 (2864e7793) 873 (564e1339) 2534 (1527e3891)

Total 17370 (9010e23220) 6844000

(1500000e13050000)

651 (266e887) 4659000

(985000e8960000)

16720 (8740e22330) 2185000

(515000e4090000)


7272019 nrs_2014_nowak_001



































4 Discussion

































7272019 nrs_2014_nowak_001




























Table 3





Alabama 6398 488 2271 173 44 700 188 329 1042 1820 6210 496 1229 98

Arizona 4466 152 249 08 19 192 60 106 209 369 4405 153 40 01


Colorado 5343 199 157 06 15 236 20 50 50 128 5324 201 106 04

Connecticut 490 390 1203 957 377 726 156 329 1023 2160 334 426 180 230

Delaware 157 310 211 417 209 333 27 254 158 1500 130 325 53 132


Florida 6389 446 5692 398 137 549 615 314 4655 2378 5775 467 1037 84

Georgia 7317 487 3523 235 83 664 500 401 2262 1817 6817 495 1261 92

Idaho 5657 264 428 20 06 379 14 105 187 1440 5644 265 241 11

Illinois 1403 98 1494 104 71 156 112 109 1330 1304 1292 97 164 12

Indiana 1640 177 962 104 70 257 84 129 631 969 1555 181 330 38

Iowa 865 60 282 20 17 104 21 84 185 753 844 59 97 07

Kansas 858 41 167 08 12 80 21 82 117 465 838 40 50 02

Kentucky 3349 326 999 97 36 580 69 188 421 1150 3280 331 577 58

Louisiana 4477 403 1426 128 46 515 157 310 855 1684 4319 407 570 54

Maine 4010 499 783 97 12 831 36 383 232 2481 3974 500 551 69

Maryland 952 376 1349 533 206 428 168 321 1118 2144 785 390 231 115


Minnesota 3355 163 469 23 22 348 46 105 267 603 3309 164 201 10

Mississippi 5642 468 1568 130 24 640 105 366 604 2109 5537 470 964 82

Missouri 5027 282 1277 72 30 403 104 195 702 1321 4924 285 575 33

Montana 7277 193 281 07 02 275 05 66 56 724 7272 193 225 06

Nebraska 440 22 54 03 07 36 05 39 39 286 435 22 15 01

Nevada 2101 74 90 03 07 116 17 86 81 410 2084 74 09 00

New Hampshire 1155 496 441 189 72 889 59 352 173 1037 1096 507 267 124

New Jersey 691 360 1813 943 397 570 219 287 1655 2169 472 408 157 136

New Mexico 4527 144 85 03 07 191 21 99 43 200 4506 144 42 01

New York 4225 346 4334 355 87 650 319 300 3459 3255 3906 351 875 79

North Carolina 5647 448 3154 250 95 626 420 351 1765 1475 5227 458 1389 122

North Dakota 212 12 14 01 03 26 01 21 08 161 211 12 06 00

Ohio 2333 221 2680 253 108 399 245 215 2053 1797 2088 221 626 66

Oklahoma 3029 171 586 33 19 259 39 115 269 796 2990 172 316 18

Oregon 6761 271 1599 64 12 408 50 175 1028 3584 6711 273 571 23

Pennsylvania 4370 377 5435 469 105 658 308 252 3688 3023 4062 392 1747 168


South Dakota 457 23 37 02 03 57 02 42 19 332 454 23 18 01

Tennessee 4025 377 1832 172 70 571 199 265 1031 1372 3826 386 801 81

Texas 10119 149 3172 47 33 234 365 161 2220 978 9754 148 952 14

Utah 3314 156 150 07 11 178 24 100 115 486 3290 156 35 02

Vermont 964 403 222 93 17 815 10 255 61 1509 954 405 161 68

Virginia 4461 435 1716 167 68 667 214 308 1039 1497 4247 444 677 71

Washington 5355 310 2411 139 36 472 139 225 1686 2726 5215 313 725 43

West Virginia 2628 422 777 125 27 814 45 275 289 1745 2582 426 488 81

Wisconsin 3331 237 848 60 35 477 70 144 477 980 3261 241 371 27

Wyoming 2966 118 43 02 02 145 04 70 19 368 2962 118 24 01

Conterminous US 173703 227 68432 89 36 342 6505 237 46584 1696 167198 226 21849 30



7272019 nrs_2014_nowak_001






































Table 4








Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000





Work Loss Days 28815 4758000 20836 3602000 7979 1157000






Total 4578503000 3126703000 1451800000





Total 7457000 4923000 2534000


Table 5




Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665



7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001
















county








3 Results




















Asthma Exacerbation



























Table 2





NO2 1439 (999e1750) 38470 (23390e48830) 68 ( 41e85) 29500 (17650e37930) 1371 (958e1661) 8939 (5736e10900)

O3 14330 (7330e18520) 2219000

(864400e2917000)

523 (201e691) 1497000

(550000e1988000)

13810 (7130e17830) 721600

(314400e929800)

PM25 696 (95e1560) 4579000

(607600e10070000)

27 (4e58) 3127000

(414700e6928000)

669 (91e1503) 1452000

(193000e3141000)

SO2 907 (583e1390) 7457 (4391e11680) 33 (20e52) 4923 (2864e7793) 873 (564e1339) 2534 (1527e3891)

Total 17370 (9010e23220) 6844000

(1500000e13050000)

651 (266e887) 4659000

(985000e8960000)

16720 (8740e22330) 2185000

(515000e4090000)


7272019 nrs_2014_nowak_001



































4 Discussion

































7272019 nrs_2014_nowak_001




























Table 3





Alabama 6398 488 2271 173 44 700 188 329 1042 1820 6210 496 1229 98

Arizona 4466 152 249 08 19 192 60 106 209 369 4405 153 40 01


Colorado 5343 199 157 06 15 236 20 50 50 128 5324 201 106 04

Connecticut 490 390 1203 957 377 726 156 329 1023 2160 334 426 180 230

Delaware 157 310 211 417 209 333 27 254 158 1500 130 325 53 132


Florida 6389 446 5692 398 137 549 615 314 4655 2378 5775 467 1037 84

Georgia 7317 487 3523 235 83 664 500 401 2262 1817 6817 495 1261 92

Idaho 5657 264 428 20 06 379 14 105 187 1440 5644 265 241 11

Illinois 1403 98 1494 104 71 156 112 109 1330 1304 1292 97 164 12

Indiana 1640 177 962 104 70 257 84 129 631 969 1555 181 330 38

Iowa 865 60 282 20 17 104 21 84 185 753 844 59 97 07

Kansas 858 41 167 08 12 80 21 82 117 465 838 40 50 02

Kentucky 3349 326 999 97 36 580 69 188 421 1150 3280 331 577 58

Louisiana 4477 403 1426 128 46 515 157 310 855 1684 4319 407 570 54

Maine 4010 499 783 97 12 831 36 383 232 2481 3974 500 551 69

Maryland 952 376 1349 533 206 428 168 321 1118 2144 785 390 231 115


Minnesota 3355 163 469 23 22 348 46 105 267 603 3309 164 201 10

Mississippi 5642 468 1568 130 24 640 105 366 604 2109 5537 470 964 82

Missouri 5027 282 1277 72 30 403 104 195 702 1321 4924 285 575 33

Montana 7277 193 281 07 02 275 05 66 56 724 7272 193 225 06

Nebraska 440 22 54 03 07 36 05 39 39 286 435 22 15 01

Nevada 2101 74 90 03 07 116 17 86 81 410 2084 74 09 00

New Hampshire 1155 496 441 189 72 889 59 352 173 1037 1096 507 267 124

New Jersey 691 360 1813 943 397 570 219 287 1655 2169 472 408 157 136

New Mexico 4527 144 85 03 07 191 21 99 43 200 4506 144 42 01

New York 4225 346 4334 355 87 650 319 300 3459 3255 3906 351 875 79

North Carolina 5647 448 3154 250 95 626 420 351 1765 1475 5227 458 1389 122

North Dakota 212 12 14 01 03 26 01 21 08 161 211 12 06 00

Ohio 2333 221 2680 253 108 399 245 215 2053 1797 2088 221 626 66

Oklahoma 3029 171 586 33 19 259 39 115 269 796 2990 172 316 18

Oregon 6761 271 1599 64 12 408 50 175 1028 3584 6711 273 571 23

Pennsylvania 4370 377 5435 469 105 658 308 252 3688 3023 4062 392 1747 168


South Dakota 457 23 37 02 03 57 02 42 19 332 454 23 18 01

Tennessee 4025 377 1832 172 70 571 199 265 1031 1372 3826 386 801 81

Texas 10119 149 3172 47 33 234 365 161 2220 978 9754 148 952 14

Utah 3314 156 150 07 11 178 24 100 115 486 3290 156 35 02

Vermont 964 403 222 93 17 815 10 255 61 1509 954 405 161 68

Virginia 4461 435 1716 167 68 667 214 308 1039 1497 4247 444 677 71

Washington 5355 310 2411 139 36 472 139 225 1686 2726 5215 313 725 43

West Virginia 2628 422 777 125 27 814 45 275 289 1745 2582 426 488 81

Wisconsin 3331 237 848 60 35 477 70 144 477 980 3261 241 371 27

Wyoming 2966 118 43 02 02 145 04 70 19 368 2962 118 24 01

Conterminous US 173703 227 68432 89 36 342 6505 237 46584 1696 167198 226 21849 30



7272019 nrs_2014_nowak_001






































Table 4








Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000





Work Loss Days 28815 4758000 20836 3602000 7979 1157000






Total 4578503000 3126703000 1451800000





Total 7457000 4923000 2534000


Table 5




Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665



7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001



































4 Discussion

































7272019 nrs_2014_nowak_001




























Table 3





Alabama 6398 488 2271 173 44 700 188 329 1042 1820 6210 496 1229 98

Arizona 4466 152 249 08 19 192 60 106 209 369 4405 153 40 01


Colorado 5343 199 157 06 15 236 20 50 50 128 5324 201 106 04

Connecticut 490 390 1203 957 377 726 156 329 1023 2160 334 426 180 230

Delaware 157 310 211 417 209 333 27 254 158 1500 130 325 53 132


Florida 6389 446 5692 398 137 549 615 314 4655 2378 5775 467 1037 84

Georgia 7317 487 3523 235 83 664 500 401 2262 1817 6817 495 1261 92

Idaho 5657 264 428 20 06 379 14 105 187 1440 5644 265 241 11

Illinois 1403 98 1494 104 71 156 112 109 1330 1304 1292 97 164 12

Indiana 1640 177 962 104 70 257 84 129 631 969 1555 181 330 38

Iowa 865 60 282 20 17 104 21 84 185 753 844 59 97 07

Kansas 858 41 167 08 12 80 21 82 117 465 838 40 50 02

Kentucky 3349 326 999 97 36 580 69 188 421 1150 3280 331 577 58

Louisiana 4477 403 1426 128 46 515 157 310 855 1684 4319 407 570 54

Maine 4010 499 783 97 12 831 36 383 232 2481 3974 500 551 69

Maryland 952 376 1349 533 206 428 168 321 1118 2144 785 390 231 115


Minnesota 3355 163 469 23 22 348 46 105 267 603 3309 164 201 10

Mississippi 5642 468 1568 130 24 640 105 366 604 2109 5537 470 964 82

Missouri 5027 282 1277 72 30 403 104 195 702 1321 4924 285 575 33

Montana 7277 193 281 07 02 275 05 66 56 724 7272 193 225 06

Nebraska 440 22 54 03 07 36 05 39 39 286 435 22 15 01

Nevada 2101 74 90 03 07 116 17 86 81 410 2084 74 09 00

New Hampshire 1155 496 441 189 72 889 59 352 173 1037 1096 507 267 124

New Jersey 691 360 1813 943 397 570 219 287 1655 2169 472 408 157 136

New Mexico 4527 144 85 03 07 191 21 99 43 200 4506 144 42 01

New York 4225 346 4334 355 87 650 319 300 3459 3255 3906 351 875 79

North Carolina 5647 448 3154 250 95 626 420 351 1765 1475 5227 458 1389 122

North Dakota 212 12 14 01 03 26 01 21 08 161 211 12 06 00

Ohio 2333 221 2680 253 108 399 245 215 2053 1797 2088 221 626 66

Oklahoma 3029 171 586 33 19 259 39 115 269 796 2990 172 316 18

Oregon 6761 271 1599 64 12 408 50 175 1028 3584 6711 273 571 23

Pennsylvania 4370 377 5435 469 105 658 308 252 3688 3023 4062 392 1747 168


South Dakota 457 23 37 02 03 57 02 42 19 332 454 23 18 01

Tennessee 4025 377 1832 172 70 571 199 265 1031 1372 3826 386 801 81

Texas 10119 149 3172 47 33 234 365 161 2220 978 9754 148 952 14

Utah 3314 156 150 07 11 178 24 100 115 486 3290 156 35 02

Vermont 964 403 222 93 17 815 10 255 61 1509 954 405 161 68

Virginia 4461 435 1716 167 68 667 214 308 1039 1497 4247 444 677 71

Washington 5355 310 2411 139 36 472 139 225 1686 2726 5215 313 725 43

West Virginia 2628 422 777 125 27 814 45 275 289 1745 2582 426 488 81

Wisconsin 3331 237 848 60 35 477 70 144 477 980 3261 241 371 27

Wyoming 2966 118 43 02 02 145 04 70 19 368 2962 118 24 01

Conterminous US 173703 227 68432 89 36 342 6505 237 46584 1696 167198 226 21849 30



7272019 nrs_2014_nowak_001






































Table 4








Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000





Work Loss Days 28815 4758000 20836 3602000 7979 1157000






Total 4578503000 3126703000 1451800000





Total 7457000 4923000 2534000


Table 5




Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665



7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001




























Table 3





Alabama 6398 488 2271 173 44 700 188 329 1042 1820 6210 496 1229 98

Arizona 4466 152 249 08 19 192 60 106 209 369 4405 153 40 01


Colorado 5343 199 157 06 15 236 20 50 50 128 5324 201 106 04

Connecticut 490 390 1203 957 377 726 156 329 1023 2160 334 426 180 230

Delaware 157 310 211 417 209 333 27 254 158 1500 130 325 53 132


Florida 6389 446 5692 398 137 549 615 314 4655 2378 5775 467 1037 84

Georgia 7317 487 3523 235 83 664 500 401 2262 1817 6817 495 1261 92

Idaho 5657 264 428 20 06 379 14 105 187 1440 5644 265 241 11

Illinois 1403 98 1494 104 71 156 112 109 1330 1304 1292 97 164 12

Indiana 1640 177 962 104 70 257 84 129 631 969 1555 181 330 38

Iowa 865 60 282 20 17 104 21 84 185 753 844 59 97 07

Kansas 858 41 167 08 12 80 21 82 117 465 838 40 50 02

Kentucky 3349 326 999 97 36 580 69 188 421 1150 3280 331 577 58

Louisiana 4477 403 1426 128 46 515 157 310 855 1684 4319 407 570 54

Maine 4010 499 783 97 12 831 36 383 232 2481 3974 500 551 69

Maryland 952 376 1349 533 206 428 168 321 1118 2144 785 390 231 115


Minnesota 3355 163 469 23 22 348 46 105 267 603 3309 164 201 10

Mississippi 5642 468 1568 130 24 640 105 366 604 2109 5537 470 964 82

Missouri 5027 282 1277 72 30 403 104 195 702 1321 4924 285 575 33

Montana 7277 193 281 07 02 275 05 66 56 724 7272 193 225 06

Nebraska 440 22 54 03 07 36 05 39 39 286 435 22 15 01

Nevada 2101 74 90 03 07 116 17 86 81 410 2084 74 09 00

New Hampshire 1155 496 441 189 72 889 59 352 173 1037 1096 507 267 124

New Jersey 691 360 1813 943 397 570 219 287 1655 2169 472 408 157 136

New Mexico 4527 144 85 03 07 191 21 99 43 200 4506 144 42 01

New York 4225 346 4334 355 87 650 319 300 3459 3255 3906 351 875 79

North Carolina 5647 448 3154 250 95 626 420 351 1765 1475 5227 458 1389 122

North Dakota 212 12 14 01 03 26 01 21 08 161 211 12 06 00

Ohio 2333 221 2680 253 108 399 245 215 2053 1797 2088 221 626 66

Oklahoma 3029 171 586 33 19 259 39 115 269 796 2990 172 316 18

Oregon 6761 271 1599 64 12 408 50 175 1028 3584 6711 273 571 23

Pennsylvania 4370 377 5435 469 105 658 308 252 3688 3023 4062 392 1747 168


South Dakota 457 23 37 02 03 57 02 42 19 332 454 23 18 01

Tennessee 4025 377 1832 172 70 571 199 265 1031 1372 3826 386 801 81

Texas 10119 149 3172 47 33 234 365 161 2220 978 9754 148 952 14

Utah 3314 156 150 07 11 178 24 100 115 486 3290 156 35 02

Vermont 964 403 222 93 17 815 10 255 61 1509 954 405 161 68

Virginia 4461 435 1716 167 68 667 214 308 1039 1497 4247 444 677 71

Washington 5355 310 2411 139 36 472 139 225 1686 2726 5215 313 725 43

West Virginia 2628 422 777 125 27 814 45 275 289 1745 2582 426 488 81

Wisconsin 3331 237 848 60 35 477 70 144 477 980 3261 241 371 27

Wyoming 2966 118 43 02 02 145 04 70 19 368 2962 118 24 01

Conterminous US 173703 227 68432 89 36 342 6505 237 46584 1696 167198 226 21849 30



7272019 nrs_2014_nowak_001






































Table 4








Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000





Work Loss Days 28815 4758000 20836 3602000 7979 1157000






Total 4578503000 3126703000 1451800000





Total 7457000 4923000 2534000


Table 5




Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665



7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001






































Table 4








Total 38473000 29534000 8939000

O3 Mortality 275 2137630000 185 1439586000 90 698044000



School Loss Days 202399 19874000 146939 14428000 55460 5446000


Total 2219069000 1497479000 721590000

PM25 Mortality 577 4488013000 394 3062289000 183 1425724000





Work Loss Days 28815 4758000 20836 3602000 7979 1157000






Total 4578503000 3126703000 1451800000





Total 7457000 4923000 2534000


Table 5




Pollutant $ t1

$ ha1

g m2

$ t1

$ ha1

g m2

DCa

DCb

$ t1

$ ha1

g m2

DCa

DCb

NO2 27 015 055 436 305 070 0018 0229 7 004 055 0021 0296

O3 155 850 549 2864 15476 540 0107 0359 52 287 549 0156 0514

PM25 6587 1754 027 117106 32314 028 0013 0127 2169 578 027 0019 0199

SO2 8 003 035 148 051 034 0006 0340 3 001 035 0009 0483

Total 2622 666 48147 673 869 665



7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001
































unknown


































on human health

5 Conclusion






Acknowledgments






References






















54


7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001








































miology 12 (2) 200e









e1141






















7272019 nrs_2014_nowak_001

















7272019 nrs_2014_nowak_001

















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nrs_2014_nowak_001