Air Quality Gradients in Western
Oregon and Washington Indicated by
Lichen Communities and Chemical
Analysis of Lichen Tissue
Linda Geiser, USDA-Forest Service Pacific Northwest
Region Air Program
Peter Neitlich, USDA-Forest Inventory Assessment/Forest
Health Monitoring, Lichen Indicator
4 March 2003
Introduction
Lichens are used by the USFS to:
Monitor air quality
Help air managers make decisions regarding
management of air resources
Support recommendations regarding PSD permits
that affect forest resources, especially Wilderness
AQ Regulation
ORDEQ and WADOE:
Measure air pollutants
Plan and implement air pollution reduction strategies
Issue and enforce air pollution control permits for
industry
Enforce other regulations
Inform, educate, and involve the public
AQ Regulation
Criteria Pollutants
Ground-level
ozone (smog)
Carbon
monoxide
Fine particulate
matter (PM10,
PM2.5)
Nitrogen oxides
Sulfur dioxide
Lead
National Ambient Air
Quality Standards
(NAAQS)
•1 hr
•8 hr
•24 hr
•Annual average
CAA Class 1 Areas
AQ Regulation
New Source Review includes the Prevention of
Significant Deterioration (PSD) permitting process.
New sources cannot exceed allowable increments for
the criteria pollutants
Air Quality Related Values: flora and fauna, soil,
water, visibility, biological diversity, cultural and
archeological resources, odor
**Because the NAAQs are not sufficient to protect the
most sensitive AQRVs, documenting concerns
regarding effects on AQRVs is the primary way FLMs
can protect air quality in Wilderness**
AQ Regulation
To monitor air pollution in the PNW, FLMs use:
Criteria Pollutant or AQRV
Visibility
PM10, PM2.5
N, S deposition and effects on
biodiversity and on terrestrial and
aquatic resources
Ozone
Pb
CO
Federally Sponsored Information
Source
Nephalometers, IMPROVE, Camera
Nephalometers
NADP, CastNet, lichens, water and snow
Active and passive monitors
IMPROVE, lichens
Not measured
Emissions
Most pollution is from individual actions:
 Driving cars,using wood stoves,gas-powered lawn
mowers, motor boats, paints, aerosol products,
outdoor burning
EPA National Emissions Inventory:
http://www.epa.gov/ttn/chief/
Emissions
Pollutant Tons N/yr Source
% of total N Emissions
126,788 Mobile
43
Utility and industrial boilers
10
Outdoor burning
4
Solid waste incineration
1
Forest fires, volcanoes
?
Transpacific
? 5-15?
95,647 Agriculture (3:1 animals:crops)
36
NH3
Mobile (catalytic converters)
3
Industry
2
Waster disposal
1
Total
222,435
100
Source: EPA 1999 Criteria Pollutants and Ammonia Emissions Inventory
NOx
Deposition
Trends
State
1980 population 2000 population % Increase
Oregon
2.6 million
3.4 million
30
Washington
4.1 million
5.9 million
43
Trends
Median
Increase
Analyte
(%)
NH4+
24.9
NO321.4
Inorganic N
23.4
SO4-2
-80
Lower Upper
95% 95%
Year
CI
CI
Value
12.3
38.8 0.0111
13.4
30
0.0097
14.6
45.4 0.0105
-60.1 -100 -0.0294
SE
0.0053
0.0034
0.0037
0.003
T
2.081
2.818
2.828
-9.72
p (>[t])
0.039
0.005
0.005
0.0001
r2
0.73
0.86
0.83
0.95
Why lichens?
Lichen Communities Are
Good AQ Indicators

Lichens are highly sensitive to
SO2, NOx, F, acid rain, NH3.
Provide an early warning
signal of adverse ecosystem
effects.
Lichens are important
AQRVs



Contribute to biodiversity
Play important ecological roles
Are an important AQRV
Why lichens?
Lichen Tissue Analyses also Indicate Air Quality


Lichens are good accumulators of N, S, metals
Lichens have consistent ranges in clean sites, different from
polluted sites
Why Lichens?
Lichen Tissue analysis (cont)
Different species show similar
responses to the same changes in air
pollution


Across seasons, and
Across geographic space
0.17
0.16
0.15
PLAGLA
0.14
0.13
0.12
0.11
0.10
0.09
0.08
0.07
0.06
0.05
0.04
0.03
.1
.2
HYP I NA
Federal Lichen
Monitoring
FIA/FHM:
28 km2 sampling grid over US forests
 Provides early detection and quantification
of potential air pollution effects
 Monitors spatial and temporal changes

Lichens and Air Quality Workgroup

http://ocid.nacse.org/research/airlichen/wor
kgroup
Plot
Locations
Methods and Data
Plot area = 1 ac (0.38
ha)
Epiphytic “Macrolichens” collected from
all woody substrates
above 0.5 m
Rated in abundance on
coarse 4 pt scale
Plot-level variables
Diversity Analysis
255 macrolichens on 1500 plots in western OR/WA
Group
>50% frequency
25-50% frequency
Most common
cyanolichens
Species
Platismatia glauca, Parmelia sulcata, Hypogymnia
enteromorpha, Hypogymnia physodes, Hypogymnia
inactiva, Tuckermannopsis chlorophylla, Usnea filipendula
group
Evernia prunastri, Tuckermannopsis orbata, Platismatia
herrei, Sphaerophorus globosus, Ramalina farinacea,
Hypogymnia imshaugii, Hypogymnia tubulosa, Alectoria
sarmentosa, Parmelia hygrophila, Parmeliopsis hyperopta,
Bryoria capillaris, Cladonia transcend
Lobaria pulmonaria, Peltigera collina, Pseudocyphellaria
anthraspis, Pseudocyphellaria anomala, Lobaria oregana,
Nephroma helveticum, Nephroma resupinatum, Sticta
fuliginosa, and Lobaria scrobiculata
Diversity Analysis
Alpha diversity: range 0-50 species, highest and
lowest values in the OR Coast Range and Cascades,
lower in WA than OR
Beta diversity: highest in WA Coast Range and Puget
Trough
Gamma diversity: Highest in OR Western Cascades
and Klamath Mtns
ON-FRAME
FIA PLOTS
α diversity
α SD
β diversity
γ diversity
N
Coast Coast Puget Willamette Klamath Western Western Southern Eastern Eastern
Ranges Ranges Trough
Valley
Mtns Cascades Cascades Cascades Cascades Cascades
WA
OR
WA
OR
OR
WA
OR
OR
WA
OR
13.7
18.8
14.2
24.5
20.8
16.2
24.3
21.6
22.1
22.0
7.0
8.5
7.7
9.9
10.2
5.4
8.6
9.9
6.7
6.1
5.8
5.0
5.6
3.1
5.2
5.4
4.1
2.7
2.7
3.0
79
94
79
75
108
87
101
59
59
66
24
29
26
11
32
38
29
5
7
10
Choosing the area to be
Modeled
Mean Ann
Precip
Mean July Max
Temp
Mean Annual
Extreme Min
Temp
Multi-variate Analysis
Initial ordination
Problem: pollution
signal is not
separate from
elevation,
precipitation, or %
hardwood– cannot
tell how pollution
alone affects lichen
communities
Multivariate Analysis
Solution: Balance the data set
Assigned each plot to one of
12 gps. Began with 1500+
plots.
Pollution (0/1) using threshold
for clean sites %0.59 N, or
urban
Elevation (1,2,3): 0-800, 8012200, >2200 ft)
Hardwoods (0/1): <20, >20%
BA
Sorted plots within each group
by precipitation and random #
Selected 30 plots from each
group to represent the
precipitation range in that
group
90% of plots had tissue data
Group Poll? HWD? Elev Precip # Plots
1
0
0
1 18-160
24
2
0
0
2 28-158
27
3
0
0
3 38-189
28
4
0
1
1 34-158
28
5
0
1
2 29-179
31
6
0
1
3 31-150
21
7
1
0
1 23-90
31
8
1
0
2 20-95
30
9
1
0
3 23-112
12
10
1
1
1 20-102
28
11
1
1
2 54-131
20
12
1
1
3 33-126
5
NMS Ordination
Polluted?
0
1
Continentality
CoastDis Lon
Elev
Axis 2
S Plagla
N Plagla
% Rel Hum
PNVTemp
Min Dec Temp
Axis 1
AQ Gradient Model: Calibration Data Set
1.5
Area
BLM
CRGNSA
GIP
MBS and ONP
MTH
SIU
UMP
Urban
Will Valley
WIL
Immediate Coast
FIA-FHM Grid
Elev
CoastDis Longitude
Continentality
Axis 2
0.5
Air Pollution (tissue %N and %S)
-0.5
Relative Humidity
Mean Temp
Min Dec Temp
-1.5
-1.5
-0.5
0.5
Axis 1
1.5
Environmental Variable Max r2 Axis1 r2 Axis2 r2 Axis3 r2
CS Min DecTemp
0.629
0.004
0.629
0
Continentality
0.54
0.107
0.54
0.026
% N lichen tissue
0.529
0.529
0
0.004
Elevation
0.466
0.099
0.466
0
% S lichen tissue
0.457
0.457
0.002
0.002
Longitude
0.456
0.111
0.456
0.009
CoastDis
0.447
0.051
0.447
0.01
PNV MeanTemp
0.344
0.106
0.344
0.029
CS Relative Humidity%
0.328
0.087
0.328
0.014
CS Mean Ann Precip
0.24
0.24
0.012
0.078
Fog Effect
0.208
0.011
0.208
0.037
CS Max Aug Temp
0.208
0.208
0.037
0.125
CS No of Wet Days
0.175
0.175
0.005
0.138
CS Mean Dew Pt Temp
0.168
0.168
0.017
0.031
Latitude
0.149
0.038
0.009
0.149
Basal Area Live Trees
0.125
0.125
0.01
0.005
% BA Hardwoods
0.118
0.118
0.044
0.042
Pb ppm lichen tissue
0.084
0.084
0.01
0.028
Year
0.074
0.014
0.016
0.074
Age of oldest trees
0.073
0.001
0.073
0.005
Wet S dep kg/ha/yr
0.067
0.023
0.001
0.067
Wet N dep kg/ha/yr
0.054
0.024
0.008
0.054
Quadratic Mean Diameter
0.008
0.008
0.001
0.002
Multivariate
Analysis
Lichens of Polluted Areas
Evernia prunastri
Xanthoria polycarpa
Physcia adscendens
Ramalina farinacea
Parmelia sulcata
Physcia aipolia
Melanelia exasperatula
Candelaria concolor
Physconia perisidiosa
Physcia tenella
Melanelia fuliginosa
Physconia enteroxantha
Melanelia subaurifera
Xanthoria fallax
Physconia isidigera
Hypogymnia tubulosa
Ramalina subleptocarpha
Melanelia subelegantula
Xanthoria candelaria
r
0.77
0.71
0.68
0.61
0.51
0.50
0.48
0.44
0.43
0.42
0.41
0.38
0.37
0.36
0.36
0.36
0.28
0.27
0.26
Lichens of Clean Areas
Sphaerophorus globosus
Hypogymnia enteromorpha
Hypogymnia appinata
Lobaria oregana
Alectoria sarmentosa
Platismatia herrei
Parmeliopsis hyperopta
Pseudocypellaria anthraspis
Nodobryoria oregana
Usnea cornuta
Cavernularia lophyrea
Menegazzia terrebrata
Pseudocypellaria crocata
Platismatia norvegica
Bryoria capillaris
Usnea filipendula
Nephroma bellum
Cavernularia hultenii
Bryoria fuscescens
r
-0.67
-0.56
-0.48
-0.47
-0.40
-0.37
-0.34
-0.30
-0.30
-0.29
-0.29
-0.28
-0.27
-0.26
-0.25
-0.25
-0.25
-0.24
-0.21
AQ and Climate Response Maps
Bellingham
Bellingham
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Cities (pop.)
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25,000 - 50,000
50,000 - 100,000
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100,000 - 200,000
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200,000 - 1,000,000
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Urban Areas
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Best AQ--All Sensitive
Species Present
(-1.4 - -0.19)
Good AQ --90% Lobaria
oregana quantile
(-0.19 - -0.07)
Moderate AQ--90% quantile
for Usnea filipendula and
Bryoria capillaris
(-0.07 - 0.13)
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Fair AQ--Some of the
Most Sensitive Species
Absent (0.13 - 0.24)
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Degraded AQ--Most of
the Sensitive Species
Absent (0.24 - 0.35)
Poor AQ--Weedy
Nitrophilous Species
Enhanced (0.35 - 0.49)
Worst AQ--All Sensitive
Species Absent (0.49 - 2)
Roseburg
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Centralia
Longview
Portland
Climate Score
Maritime (-1.249 - -0.384)
Valley (-0.384 - 0.051)
Foothills (0.051 - 0.377)
Montane (0.377 - 0.76)
High Elevation (0.76 - 1.743)
State (W of Cascades)
Urban Areas
Salem
Eugene
Roseburg
Medford
#
#
Air
Pollution
Response
Maps
Pollution indicators:
X. polycarpa
C. concolor
Sensitive species:
L. oregana
S. globosus
Statistic
Sensitivity
Max Score
Quartile
Median
Quartile
Min Score
N
Sphaero
Alectoria
Bryoria Lobaria phorus
Usnea
Usnea
sarmentosa capillaris oregana globosus filipendula scabrata MeanSens Mean All Sens
Quantile
Sensitive
Sensitive Sensitive Sensitive Sensitive Sensitive Sensitive
Sensitive
100
0.30
0.32
0.21
0.55
0.88
0.58
0.47
0.40
97.5
0.15
0.21
0.05
0.13
0.49
0.44
0.24
0.21
90
-0.01
0.05
-0.07
0.03
0.14
0.11
0.04
0.02
75
-0.14
-0.08
-0.19
-0.09
0.03
-0.02
-0.08
-0.11
50
-0.29
-0.24
-0.32
-0.24
-0.12
-0.15
-0.23
-0.25
25
-0.43
-0.39
-0.46
-0.41
-0.27
-0.27
-0.37
-0.39
10
-0.57
-0.50
-0.66
-0.56
-0.41
-0.45
-0.52
-0.55
2.5
-0.75
-0.69
-0.84
-0.76
-0.55
-0.62
-0.70
-0.72
0
-1.00
-1.37
-1.19
-1.19
-1.37
-0.76
-1.14
-1.11
725
360
202
577
517
192
429
802
Candelaria Physcia
Xanthoria
concolor
adscendens polycarpa MeanNitro
Nitrophytic Nitrophytic
Nitrophytic Nitrophytic
1.37
1.74
1.35
1.49
1.35
1.35
1.30
1.33
1.23
1.11
1.10
1.14
0.92
0.87
0.80
0.86
0.69
0.59
0.57
0.62
0.38
0.35
0.33
0.35
0.14
0.15
0.14
0.14
-0.21
-0.09
-0.09
-0.13
-0.42
-0.38
-0.19
-0.33
64
147
148
120
Standard Error
Air Scores
Bellingham
[
Bellingham
[
[
[
[
[
[
Port Angeles
[
[
[
Port Angeles
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
Seattle
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
Olympia
Tacoma
Centralia
[
Longview
[
[
Tacoma
[
[
Centralia
[
[
Cities (pop.)
10,000 - 25,000
25,000 - 50,000
50,000 - 100,000
[
100,000 - 200,000
[
200,000 - 1,000,000
State (W of Cascades)
Urban Areas
Longview
[
[
[
[
[
[
[
[
Olympia
Seattle
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
Cities (pop.)
10,000 - 25,000
25,000 - 50,000
50,000 - 100,000
[
100,000 - 200,000
200,000 - 1,000,000
[
State (W of Cascades)
Urban Areas
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
Standard Error Kriged
Air Scores
0.227 - 0.247
0.247 - 0.267
0.267 - 0.287
0.287 - 0.307
0.307 - 0.327
0.327 - 0.347
0.347 - 0.368
[
[
[
[
[
[
[
[
[
[
[
Portland
[
[
[
[
[
[
Salem
[
[
[
Eugene
[
[
[
[
Roseburg
[
[
Kriged Air Quality Scores
[
[
[
[
[
[
[
[
[
[
[
[
Medford
[
[
[
[
[
[
[
[
[
[
Portland
[
[
[
[
[
Salem
[
[
[
[
[
[
[
Eugene
[
Roseburg
[
[
Medford
[
[
Best--All Sensitive Species
Present; 75% Quantile for
All Sensitive Species
(-1.4 - -0.11)
Good--All Sensitive Species
Present; 90% Quantile for
All Sensitive Species
(-0.11 - 0.02)
Fair--Some of the Sensitive
Species Absent; 97.5%
Quantile for All Sensitive
Species (0.02 - 0.21)
[
[
Percentage of Total Land Area in
Each Air Quality Class
Fair
Polluted
Degraded--Most of
the Sensitive Species
Absent (0.21 - 0.35)
Poor--Weedy
Nitrophilous Species
Enhanced (0.35 - 0.49)
Worst--All Sensitive
Species Absent (0.49 - 2)
Block kriged air quality scores based on 30 neighboring data points and 3 km grid.
9%
14%
7%
15%
8%
47%
Good
Conclusions
Lichen communities show location and relative
severity of air pollution impacts
Low levels of anthropogenic nitrogen and sulfur
(primarily as SO2, acid rain, and fertilizing N)
detrimentally affect lichen communities.
Combined with trends and instrument monitoring
lichens provide a broad picture of relatively clean
region with impacts primarily to:

Corridors with densest population, high traffic volume,
industrial development, multiple small point sources, and
intensive agriculture
In the 1990s the total area with some AQ
deterioration was 24-38%
Conclusions
Future Information Needs
Continued monitoring to detect trends
Higher density of plots in some areas, lower in others
Establish acceptable thresholds for tissue data and
lichen community scores to aid decision-making
processes.
Better differentiation of effects of individual pollutants
(NO3 and SO4 vs NH3, oxidants, F) on lichen
communities
Multi-methodologies approach: combine biomonitors,
water and snow chemisry data with active/passive
monitoring of ambient air and deposition
Conclusions
Air quality and our future
Bottom Line: growing population equals growing
transportation, food, and energy needs, therefore to
maintain the same AQ requires stabilizing population,
and/or reducing emissions.
Acknowledgements
Abbey Rosso Adam Blackwood Aimee Lundee Alexander Mikulin
Anne Ingersoll Bruce McCune Carolina Hooper Cheryl Coon Chiska
Derr Christine Lindquist Christine Ott-Hopkins Colleen Rash Cort
Skolout Dan Powell Daphne Stone Deigh Bates Delphine Miguet
Dottie Riley Doug Glavich Eric Peterson Eric Phenix Eric Youngstrom
Heath Kierstead Heather Laub Jason Unrine Jen Kalt Jenifer
Hutchinson Jim Belsher-Howe Jim Riley Jim Russell John Coulston
John Kelley John Wade Jon Martin Julie Evans Ken Snell Ken Stolte
Kim Gossen Kristin Myers Linda Chesnut Linda Hasselbach Mark
Boyll Mark Pistrang Mike Kania Nancy Diaz Natalia Bonilla Pekka
Halonen Riban Ulrich Richard Helliwell Rick Shorey Roger Eliason
Roger Rosentreter Sally Campbell Sally Claggett Samuel Solano
Sarah Butler Sarah Jovan Scott Rash Shanti Berryman Star
Hormann Suzy Will-Wolf Tom High Trevor Goward Walter Foss
Walter Grabowiecki William Bechtold Yarrow Wolfe