United States
Environmental Protection
Agency
Atmospheric Sciences Research
Laboratory
Research Triangle Park NC 27711
Research and Development
EPA/600/M-85/019 Jan. 1986
ENVIRONMENTAL
RESEARCH BRIEF
Atmospheric Acid Deposition Damage to Paints
Fred H. Haynie
Abstract
Available data from laboratory and field studies of damage
to paints by erosion have been analyzed to develop an
atmospheric acid deposition damage function for exterior
house paints containing calcium carbonate or silicate
extenders. Regression analysis coefficients associated with
sulfur dioxide levels are consistent with the reaction
between the SC>2 and calcium carbonate to form soluble
calcium sulfate. The effect of sulfuric acid in rain on paint is
expected to be similar. Observed actual household painting
frequencies prior to 1970 are consistent with the damage
functions calculated from the experimental erosion data
obtained in the 1950's, 1960's and early 1970's. Changes
in both environmental conditions and types of paints that
are marketed make it necessary to make assumptions when
using the damage functions to estimate costs associated
with repainting. The magnitude of the error of these
estimates is the same as the estimate. Research is needed
to reduce this error and to determine the effects of acid
deposition on other mechanisms of paint failure such as
peeling from wood and rusting of painted steel.
Introduction
Reducing the rate of atmospheric acid deposition (both wet
and dry) will reduce the amount of "weathering" experi-
enced by certain materials. In the case of paints, an
economic benefit results from fewer paintings during the
life of a structure. Paint life varies considerably with
environmental changes. The most significant factors
related to paint life are (a) time-of-wetness, (b) temperature,
and (c) sunlight (1). Sulfur dioxide, however, contributes to
The author is with the Emissions Measurement and Characterization
Division, Special Techniques Group, Atmospheric Sciences Research
Laboratory, USEPA, Research Triangle Park, NC 27711
. AGENCi
. , . LltJRAKY, REGIONS
the erosion of at least those paints containing carbonate or
silicate extenders (2,3,4). These effects were observed
under several conditions of time-of-wetness, temperature,
simulated sunlight, and pollution levels in laboratory
controlled environments. All of these studies used erosion
of paints on inert substrates as a measure of damage. Field
studies conducted in the past have not been adequate to
show whether or not there exists a relationship between
paint damage and atmospheric acid deposition. Nor has
there been an attempt to translate laboratory observations
into atmospheric damage functions. This study uses
available field and laboratory data to develop functions for
exterior house paints damaged by acid deposition at
ambient conditions observed in the United States. These
relationships can be used to assess the economic benefit
that results from fewer paintings associated with reduced
acid deposition.
Several controversies have divided the research community
on the paint damage function. The controversies are on
issues such as:
1. Is weight change due to paint erosion an acceptably
precise measure of damage?
2. Is wearing away the surface or is blistering and
cracking the more significant mode of damage for
painted wood?
3. Is a blistered surface on painted steel sufficient to
define the material as damaged and in need of a repair
action?
4. What are the essential components of a paint damage
function?
This Research Brief is intended to bring information to the
scientific community and not to advocate a conclusion on
these types of issues.
-------
Field Data
The results of a comprehensive study in St Louis, MO,
provide the best evidence of the functional shape of the
relationship between paint erosion rate and weather
variables (1). There was no evidence of SC>2 damage
because, (a) the two studied house paints contained no
carbonate and relatively low levels of silicates, and (b) the
ambient levels and variations between levels of S02 were
relatively low. The function is:
ER = Vf + ERS + ZB.P.f
(D
where ER = Paint erosion rate - /jm/year
ERS = Sunlight contribution -/L/m/year
IB,P,f = Sum of pollutant contributions-jum/year
(any set of pollutants)
V = Temperature factor
f = Fraction of time-when-wet
The temperature factor (V) is an Arrhenius relationship-
V = EXP(ff-/5/T) (2)
where a and fl are regression coefficients (constants)
T = Average absolute temperature - °K
The fraction of time-when-wet (f) was defined as:
f = 1 -EXP(- 0.694(1 00/RHS
- 0.975)7(100/RHA-1)
(3)
where RHA = average relative humidity
RHS = relative humidity above which a surface
is expected to be wet
A best fit of the St. Louis paint damage data with respect to
average relative humidity produced a coefficient corre-
sponding to an RHs of approximately 87%. Thus:
f = 1 -EXP(-0.121 RHA/(100-RHA)) (4)
In St. Louis, the sunlight contributions ERS were 0.66 ±
0.10/urn/year and 0.26 ± .10/ym/year for a latex and an oil
base house paint, respectively. In each case the contribu-
tions amounted to 35.1% and 10.7% of the south facing
latex and oil erosion rates, respectively. The sum of the
pollutant effects (IB,P,f) was relatively insignificant, leaving
ff the primary effect.
In a contracted study for the U.S. Environmental Protection
Agency (EPA) by Sherwin-Williams Co. (2,3) erosion rates
of five paints were measured at sites in four different cities.
They also reported data they had previously obtained in
eight other cities for the same paints. Table 1 lists the cities
and their estimated average environmental conditions.
Four of the five exposed paints are of interest in the
Sherwin-Williams study: (a) a latex house paint; (b) an oil
base paint; (c) a maintenance paint; and (d) a coil coating.
The significant compositional differences for these and the
EPA St. Louis study paints are given in Table 2.
The previously obtained data in this study were based on
calibrated visual ratings. Visual ratings were made on
Table 1. Estimated Environmental Conditions at
Paint Exposure Sites
Estimated Long-Term Average
Relative
Humidity8 Temperature' SO2C
City (%) f (°Q (//g/m3)
Concord/Oakland, CA
Los Angeles, CA
Wilmington, DE
Miami, FL
Valparaiso, IN
Chicago, IL
St Louis, MO"
Atlantic City, NJ
Leeds, ND
Newton, PA
Palmerton, PA
Garland, TX
77
63
66
73
69
67
71
70
68
66
68
64
0.33
019
0.21
0.28
0.24
0.22
026
0.24
022
021
0.23
019
13
18
13
24
10
10
9
12
5
13
12
16
8
37
50
7
20
97
47
21
5
61
42
7
'Calculated from long-term normal values from weather stations
near each site. Estimated standard deviations on true values are
±3% for relative humidity and ±1 °C for temperature.
"Calculated using equation (4) and average relative humidity (RHA).
Calculated from multi-year annual average data (late 1960's, early
1970's) reported for pollutant measuring stations nearest each
site Estimated standard deviations on the true means are ±20% of
the means
"Averages for nine sites in St. Louis during the time of the study
cited. Included for comparison.
Table 2. Compositional Differences of Paints
Paint Use Base Extender
1 House Paint"
House paint*
Acrylic
latex/water
Oil
3 Industrial
maintenance* Alkyd
4 Coil coating*
House paint"
6 House paint"
Alkyd
Acrylic
latex/water
Oil/alkyd
Silicates
Calcium
carbonate/silicates
None
Calcium
carbonate/
magnesium silica
Silicates (11.6%)
Silicates (0.5%)
*Sherwin-Williams study.
"St. Louis study.
panels having different measured film thickness. The
number of months to reach a visual rating of 7 (sufficient
show through to require repainting) was taken as paint life.
With these paints, a visual rating of 7 corresponded to a
calibration thickness of 18 /urn (0.7 mils). The desired initial
dry film thickness varies with the type of paint. The lowest
value for a field applied coating is around 38 Aim (1.5 mi I) for
a coverage of about 46.5 m2 (500 ft2)/gal. Thus, erosion rate
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Table 3. Paint Erosion Rates and Estimated Standard Deviations at Four Sites (/u/year)*
Latex House(1) Oil House(2) Maintenance^)
Site
North
South
North South
North South
Coil Coating(4)
North South
Los Angeles, CA 0.91 ±0.14 0.91 ±035 3.96±0.95 4.88±0.79 1.82±0.21 2.44±0.32 3.05±O.32 3.66±0.3
Valparaiso, IN 0.61 ±0.09 0.61 ±0.04 2.44±0.36 3.05±0.23 1.52±0.70 1.83±0.18 2.49±O.O9 2.74±0.2
Chicago, IL 0.91±0.08 0.91±0.04 3.96±0.33 4.88±0.60 1.52±0.70 1.83±0.34 3.O5±0.12 3.35±0.2
Leeds, NO 0.30±0.08 0.30±0.08 0.30±0.14 0.61 ±0.22 0.91 ±0.20 1.22±0.37 0.61±O.O9 0.61 ±0.17
Based on weight loss.
Source: Campbell et al. (3).
can be calculated by dividing paint life into 20 /urn.
Conversely, paint life can be calculated by dividing 20 /urn by
erosion rate. All the paints were exposed facing south.
During the study, erosion rates were determined by weight
loss as a function of time. Dry film paint density was used to
calculate thickness loss. Paints were exposed vertically
facing both north and south. The results of this field test are
given in Table 3.
The differences in the erosion rates of the paints facing
south and north average 16.3% of the total. For the
individual paints, the values are 0, 20.6, 21.2, and 11.7%
for latex, oil, maintenance, and coil coating, respectively.
The visual rating results converted to erosion rates are
given in Table 4. These panels faced south, thus, the effect
of sunlight could be as much as 20% of the total.
Taking 80 percent of each value in Table 4 and dividing by
the fraction of time-when-wet from Table 1 gives the erosion
rate when wet and not exposed to the sun. Similar rates can
be obtained by dividing the north-facing values of Table 3 by
the fraction of time-when-wet. The resulting calculated
values using these data are presented in Table 5.
Both weight loss and visual ratings were obtained at
Valparaiso, however, they were not the same exposure
periods and environmental conditions could have differed
from the long term averages. The visual rating results are
consistently higher than the weight loss results. The weight
loss data are more accurate; however, the visual rating data
are more consistent with normal paint life. If the initial
thickness of paints on the visual rating samples was
actually less than the desired 38 /jm, the values from the
two methods would be more nearly the same and still be
consistent with normal paint life.
The weight loss and visual data were normalized by
multiplying the visual data by the ratios of the weight loss to
visual values for Valparaiso. The resulting pooled data were
least squares fitted to the environmental data in Table 1
using the following functional relationship:
Table 4. Paint Erosion Rates and Estimated Standard
Deviations Based on Visual Rating Data
(///year)'
ERW =
where ERW = erosion rate when wet
a and/8 are regression coefficients
(constants)
(5)
Site
Concord/Oakland, CA
Wilmington, DE
Miami, FL
Valparaiso, IN
Atlantic City, NJ
Newton, PA
Palmerton, PA
Garland, TX
Latex
House) 1)
3.95±0.85
3 60±0.67
2.80±0 55
4.00"
3.10±075
Oil
House(2)
400±0.75
4.90±0 55
6.10±1 55
3.72±0.77
Mainte-
nance(3)
641b
4.80±0.78
4.43"
6.25"
353"
'Exposed vertically facing south.
"Single value
Source: Campbell et al. (2).
T = average absolute temperature°K
B! = coefficient for S02 damage
(/um/year)/(//g/m3)
S02 = sulfur dioxide (/jg/m3)
The results are given in Table 6.
The only coefficients that are not significant are for the
shaded maintenance paint. With the exception of latex, the
unshaded S02 coefficients are larger than the shaded SC>2
coefficients, indicating a photochemical interaction with
S02 damage.
Laboratory Studies
Two controlled environment laboratory studies have
demonstrated cause-effect relationships between pollu-
tants and paint damage (3,4). The formulations of the paints
evaluated by Sherwin-Williams Co. were the same as those
used in their field study. The compositional differences of
the paints in the EPA study are given in Table 7.
Both studies used dew-light cycles with xenon arcs as a
simulated sunlight source for a total of 1000 h of exposure.
In the Sherwin-Williams study, half of the specimens were
shaded from the light source. None were shaded in the EPA
study. The EPA study used two different input relative
humidities. The Sherwin-Williams study had only one. The
-------
Table 5. Paint Erosion Rates and Standard Deviations When Wet and Shaded (/urn/year)
Latex House( 1)
Site
Concord/Oakland, CA
Los Angeles, CA
Wilmington, DE
Miami, FL
Valparaiso, IN
Chicago, IL
St. Louis, MO
Atlantic City, NJ
Leeds, ND
Newton, PA
Palmerton, PA
Garland, TX
Weight Loss
4 84 ±0 74
2.57±038
4.16±0.37
4.79±0.10b
1 .35±0 36
Visual
1512±3.25
10.18±1.89
9.95±1.86
.._
15.31'
10.92±264
Oil House(2)
Weight Loss
21.06±5.05
10.30±1.52
18.08±1.51
2.71±0.07b
1.35±0.63
Visual
9.70±1.87
1876±2.11
17.29±4.38
12.56±2.60
Mamtenance(3)
Weight Loss
9.68±1.11
6.41 ±1.01
6.94 ±0.91
4.08 ±0.90
Visual
1554*
1357±220
19.95"
20.75'
19.76'
Coil Coatmg(4)
Weight Loss
16.22±1.8
13.93±0.55
2 74±0 40
'Single value
''Different formulations included for comparison.
No data.
1,2,3,4 Number of paint from Table 2.
Table 6. Coefficients and Estimated Standard Deviations for Field-Obtained Paint Damage Functions
Paint
Latex
Oil
Maintenance
Coil Coating
Latex1"
Oil111
No.
1
2
3
4
5
6
a
12.92±3.83*
24.64±5.77*
6.46±3.07*
3243±4 17*
1230±0.02*
27.29±0.03*
Shaded (North)
(3
-3470±1100*
-6490±1 660*
-1310±880
-8600±1180*
-3040±90*
-7490±120*
Unshaded (South)
B,
(//m/year)/0ug/m3)
0 027±0.004*
0 154 ±001 7*
0.020±0.11
0.067 ±0007*
a
9 19 ±583*
23.77 ±4 77*
9 600±3 1 1 *
35.86 ±453*
1243 ±0.02*
2740 ±0.03*
0
-2400±1090*
-6150±1360*
-2150± 900*
-9606±1 290*
-3040±90*
-7490±1 20*
B,
(//m/year)/(«j/m3)
0.023±0.003*
0.195±0.025*
0095±0.018*
0.093±0.012*
From St. Louis study (1), for comparison
'Statistically significant at the 95% confidence level.
environmental conditions are summarized in Table 8.
These conditions do not fully simulate actual long term
exposures which have rain and freezing but do indicate the
effects of the parameters that were controlled.
The erosion rates for the acrylic coil coating (paint 9 from
Table 7) were extremely low and not statistically related to
any of the environmental factors. All of the others with the
exception of the industrial maintenance paint were affected
by SOz. All the erosion rates were divided by the fraction of
time-when-wet to get the erosion rate-when-wet. The
Sherwin-Williams data were fitted to the equation:
ERW = A + B1-S02+ B2-03
(6)
where A, 81, and B2 are regression coefficients, SO2 and Oa
are expressed in micrograms per cubic meter.
Table 9 gives the resulting coefficients.
The differences between xenon arc exposed and shaded
erosion rates are most significant in this experiment. All of
the coefficients appear to be affected, indicating not only a
Table 7. Compositional Differences of Paints in EPA
Study
3amt Use
7 House paint
8 Coil coating'
9 Coil coating'
Base
Oil/alkyd
Vinyl
Acrylic
Extender
Magnesium silicate
not known
not known
'Factory applied to aluminum siding.
Table 8. Laboratory Controlled Environment
Conditions
Cycle Fraction of
Expen- Time Time-When-
ment (mm) Wet Shade
Sherwm- 120 05 1/2
Williams
EPA low 40 05175 None
RH
EPA high 90 0.6125 None
RH
Dark (Dew)
Temperature Pollutants
°k
-------
Table 9. Laboratory-Obtained Damage Function Coefficients and Standard Deviations from Sherwin-Williams
Data
Paint
Latex
Oil
Maintenance
Coil Coating
No"
(D
(2)
(3)
(4)
Shaded
Yes
No
Yes
No
Yes
No
Yes
No
^m/year
A
1043± 4.05*
23.25± 1 25*
34.13± 948*
5367± 834*
43 10±1008*
6056±11 10*
1249± 700
3980± 487*
(/j/yearX/L/g/m3)
B,
0 0096±0.0039*
0.0098±0.0018*
0.0643±0 01 84*
0 1952±00336*
0 0056±0 0080
00125±00132
00266±00179
0.0396±0 0084*
(/u/yearX/^g/m3)
B2
-00042 ±0.0044
-0.0719±00118*
00014±0.0098
00327±00247
00175±00341
00278±00320
00012±0.0103
0 0076±0 0068
'Statistically significant at the 95% confidence level
"Number from Table 2
direct effect of radiation but also a photochemical inter-
action with the pollutants. The mean percent of the light
effect on coefficients, A, B,and B2, is 47 + 18%, 39 ± 29%,
and 78 ± 28%. The overall average effect on corrosion rate
is around 43% The amount of simulated sun radiation
absorbed by the paints in these laboratory experiments is
much greater than that absorbed on vertically mounted
southfacmg paints in the field studies The average effect in
the field was only around 20%.
The EPA laboratory data were multiplied by 0.57 (1 -.43) to
eliminate the radiation effect and fitted to equation (5) for
comparison with the shaded field data and the Sherwin-
Williams laboratory data The results are given in Table 10.
Regressions were done including ozone as a variable with
the result that B2 coefficients were not statistically signif-
icant. Thus, ozone was excluded in the analysis reported in
Table 10.
Although the experimental conditions and paints that were
evaluated were different in the two laboratory tests, some
comparisons can be made. There was only one ozone
coefficient in either study that was statistically significant.
The sign for that coefficient was not in the expected
direction and probably has no physical significance. Six of
the ten SO2 coefficients are statistically significant. The
EPA oil house paint contains silicate extenders while the
Sherwin-Williams oil paint contains calcium carbonate. In
the shaded condition, the SO2 coefficient for the latter is
about 2.5 times that of the former. Unshaded, the ratio of
the two is more than four times.
The Sherwin-Williams experiments were done at only one
dew temperature while the EPA study was done at two. The
A coefficients from the Sherwin-Williams study are con-
sistent with the a and ft coefficients from the EPA study at
comparable temperatures. More important, the a and ft
coefficients from the laboratory study are consistent with
those in Table 6 obtained from field studies. This field-
laboratory consistency is also true for the S02 coefficients.
The magnitudes are comparabale for the same types of
paints.
Please refer to the cited literature for details on experi-
mental designs and procedures for both the field and
laboratory studies.
Table 10. Damage Function Coefficients and Esti-
mated Standard Deviation from EPA
Laboratory Study
Paint
No'"
0
B,
Oil
(71 25976 + 5041' -6736i1500' 00261±00044'
Vinyl Coil
Coating (8) 10630+4676' 2929±1399' 00007±00004
'Significance at the 95% confidence level
'"'Number from Table 7
Actual House Painting Frequency
Individuals paint their houses for several reasons including
substrate show through caused by paint erosion Cracking
and peeling from moisture damage, desiring a color change,
and soiling are equally as important reasons for painting.
Thus, if the acid deposition damage functions developed in
this study are to be useful in an economic analysis, erosion
must be the mechanism limiting paint life
Two surveys on soiling effects included house painting
frequency as a possible economic effect (5,6) The most
detailed survey was done in Philadelphia in 1 970 by Booz,
Allen and Hamilton, Inc (BAH) (5). Less rigorous surveys
had previously been done by Michelson and Tourm (6) in
two Ohio cities and in three suburbs of Washington, D C
In their analysis, BAH never looked at more than two
possible causative factors at a time. In some cases, they
observed no statistical significance because possible
causative factors were negatively covanant, canceling
effects Outside wall painting is an example. The reported
results are in Table 11.
The meansforall householdsare negatively correlated with
pollution levels, not a realistic expectation For example,
painting outside walls only every 33 years is not typical.
That mean frequency (.03) includes a lot of people that do
not need to paint. Realistic frequencies are noted for those
households that actually performed the task. There is a
negative correlation between pollution level and percent of
households performing the task. The reason for this
relationship becomes apparent when it is noted that the
-------
percent of houses having more than 10% painted wood
outside wall area for zones 1 through 4, respectively, was
28.1, 23.8, 8.7, and 3.5 (BAH values adjusted with non-
responses prorated). Thus, approximately nine houses in
the most polluted zone required painting of outside wooden
walls. Apparently, about 17 households in that zone painted
masonry. This represents a fairly small sample upon which
to make a statistical analysis; however, the standard error
indicates it can be done.
One would expect that the ability to do something about a
high cost pollution effect would be related to household
income. The percent of households with incomes of
$10,000 or more for zones 1 through 4, respectively, was
45, 37, 16, and 14 (BAH values adjusted with non-
responses prorated). Thus, there are fewer households in
the most polluted zones that have the ability to do anything
about it. BAH analyzed all responses by income level but did
not further restrict the analysis to only those performing the
task. If one makes the assumption that the given per-
centages of households doing the task apply equally to all
income levels, this adjustment can be made. The results are
given in Table 12.
The maintenance frequencies for those performing the
tasks are reasonable and the effect of pollution is in the
expected direction. The small sample sizes in the two most
polluted zones, however, makethe probability of statistically
significant differences very low. There is only a probability
of about 0.5 that zones 2 and 4 are different or the same.
In Table 13 these frequencies compare favorably with
values reported by Michelson and Tourm (6).
Paint life is the reciprocal of painting frequency and varies
from about one to four years. There appears to be a strong
dependence on paniculate matter level. Generally, how-
ever, in the 1960's paniculate matter levels were covariant
with S02 levels. For example, from modelled isopleths for
Philadelphia (7), comparable SO2 levels for zones 1 through
4, were estimated to be 40, 60, 170, and 240 jug/m3,
respectively. Using the field-obtained coefficients for the
Sherwin-Williams unshaded oil paint, a critical thickness
loss of 20 //m, an average temperature of 12.5°C, a fraction
of time-when-wet of 0.21, and the above SO2 levels, the
respective painting frequencies for zones 1 through 4 are
calculated to be 0.18, 0.22, 0.44, and 0.58 times per year,
respectively. These values are consistent with the survey
values. Thus, it is possible that either the aesthetic soiling
effect, the physical acid damage, or both could have caused
households to repaint.
Significant Market Changes Since 1970
House paints have been improved significantly since the
late 1960's and early 1970's; however, cheaper, less
durable paints are often used by house builders and
painting contractors. Less oil base paints are now being
used. In 1980, the ratio of exterior latex to exterior oil sold
was 6.5 (8) as compared to 1.375 in 1968 (9). An informal
survey of four local paint dealers yielded the data presented
in Table 14.
The least durable paints contain significant amounts of
calcium carbonate. One dealer referred to them as con-
tractor paints. Significant amounts of silicates are used in
all grades of flat latex paints; however, concentration tends
to decrease with increasing durability. Silicates have bene-
ficial effects other than as an extender. They contribute to a
flat sheen and improve resistance to chipping, checking,
and cracking (8).
Table 11. BAH Results of Painting Outside Walls
All Households
Households Performing Task
Annual Mean
Zone TSP (fjg/m3)
1 50-74
2 75-99
3 100-124
4 125-151
Number
in Sample
471
421
299
251
Mean Annual
Frequency
1 /years
0 11
010
0.04
0.03
Standard
Error of
Mean
0.009
0.017
0.008
0.008
% of all
Households
38.6
28.5
11.4
10.4
Mean Annual
Frequency
1 /years
0.28
0.35
0.35
029
Standard
Error of
Mean
0.016
0.053
0.041
0055
TSPTotal Suspended Paniculate Matter.
Table 12. Outside Wall Painting Frequency for Households With Income of $10,000 or More
All Households
Households Performing Task
Zone
1
2
3
4
Annual Mean
TSP (/jg/m3)
50-74
75-99
100-124
125-151
Number
212
156
48
35
Mean
Annual
Frequency
0 14
0 10
005
005
Standard
Error of
Mean
0017
0.016
0023
0023
% of All
Households
82
44
5
4
Mean
Annual
Frequency
0.36
0.35
044
048
Standard
Error of
Mean
0044
0056
0202
0.221
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Table 13. Annual Average TSP and Painting Fre-
quency in Different Cities
TSP
City (/jg/m3) Painting Frequency/Year
Fairfax
Philadelphia
Rockville
Suitland
Philadelphia
Philadelphia
Uniontown
Philadelphia
Steubenville
- Zone 1
- Zone 2
- Zone 3
- Zone 4
60
61
75
86
86
111
115
137
235
0.26
0.36
0.28
034
0.35
0.44
0.53
0.48
1 14
Table 14. Paint Data from Informal Dealer Survey
Warranty
or
Price Estimated
Brand Base/Sheen Extender per Gallon Life
A
A
A
A
B
B
B
B
B
B
B
C
C
C
D
D
Latex/Flat
Latex/Flat
Latex/Satin
Latex/Flat
Latex/Flat
Latex/Flat
Latex/Flat
Latex/Satin
Latex/Flat
Latex/Gloss
Alkyd/Gloss
Latex/Flat
Latex/Flat
Latex/Flat
Latex/Flat
Latex/Flat
1 5 6% CaCO3
22% Silicates
2. 5% Silicates
15% Silicates
17% Silicates
18% Silicates
21% Silicates
20% Silicates
19% Silicates
2% Silicates
2% Silicates
12.9% Silicates
11. 5% Silicates
13% Silicates
22% CaCOa
5.9% Silicates
$ 9.53
$11.97
$14.97
$16.97
$11.97
$15.99
$18.99
$16.97
$21.99
$22.99
$23.99
$1699
$16.99
$19.99
$11.99
$16.99
3
5
8
9
3
6
8
9
10
10
10
5
10
15
3
9
One dealer confirmed the national figures; in that only
about 15% of his sales were oil base paints. His most
popular paints were the 8-year warranty latex that outsold
the 10-year warranty latex by about 50% Another dealer
sold most of his paints to contractors. His cheapest or
contractor grade outsold his best grade by about 2 to 1. One
might expect that the more durable paints are bought by
homeowners to replace the original lower grade paints
purchased by builders and contractors.
Selected Coefficients for a
Composite Damage Function
More than 85% of house paints are water base latex. Thus,
damage coefficients for latex should be representative. The
a and /? coefficients for the latex paint exposed in St. Louis
have the least variation and are not significantly different
from the Sherwin-Williams latex study. The S02 coefficients
(81) are generally a function of the type of extender. If half of
the paint sold contains calcium carbonate and lasts four
years and the other half contains silicates and lasts eight
years, then there would be a 3 to 1 ratio of silicate to
carbonate containing paints on walls at any point in time.
Other ratios could be used. Thus, any analysis should be
weighted accordingly For general application to unknown
paint compositions, it may be assumed that the S02
coefficients apply equally well to all types of paints. They
are summarized in Table 1 5.
The SO2 coefficient for the calcium carbonate-containing
paints is about what can be calculated for a stoichiometric
leaching out of product calcium sulfate, with an S02
deposition velocity of 0.7 cm/s. Assuming a deposition
velocity of 0.7 cm/s, the coefficient can be expressed in
terms of rate of accumulation of SO2 or acidity and, thus,
can be applied to acidity in ram. If SO2 flux is expressed in
micrograms per cubic centimeter per year, the CaCOs
coefficient is 0.00540 ± 0.00425 /am///g/cm2. If rain is
expressed in centimeters per year and acidity is due to SO2,
the coefficient is
[174 + 136] x10~pH ^urn/cm
Similar coefficients for the silicate-containing paints are
0.00088 ± 0.00045 fjm/fjg/cm2 and
[27±15]x10"pH/um/cm.
It should be noted that the estimated standard deviations on
all of these coefficients are the same order of magnitude as
the mean values with which they are associated. None of
the calculated coefficients are statistically significant from
zero at the 95% confidence level We have shown, however,
that coefficients for individual paints are statistically
significant and the mean SO2 coefficient for carbonate-
containing paints is consistent with theory.
Translating Physical Damage to Economic Loss
The basic assumption in using these data in a cost analysis
is that erosion of paint film to substrate show through is the
life limiting factor that causes households to repaint. There
are other major causes for repainting Two of the most
important are the aesthetic effects of soiling (dirtiness) and
peeling of the paint from the substrate. Neither of these
damage functions is considered in this study. Both could
possibly be life limiting factors.
Undiscounted annual costs are directly proportional to
annual painting frequency. The marginal costs associated
Table 15.
Summary of Unshaded SO2 (81) Coeffi-
cients (A/m/year)/(//g/m3)
Extender
Calcium Carbonate
Silicates
Individual values
Weighted geometric
mean*
0.1950±0.0280
0.0928±0.0119
01952±0.0336
0.0396±0.0084
0 1191 ±00939
0.0098±0.0018
00261 ±0.0044**
0.0229±0.0029
0.0194 ±0.0099
'Coefficients and standard deviations converted to log forms,
coefficients weighted by the reciprocal of the estimated variation.
**Shaded.
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with pollution is a function of the annual additional painting
frequency.
In order to estimate annual additional cost as a function of
wet and dry deposition, the damage functions should be
expressed as annual additional painting frequency:
PF = (ER-ER0)/tc
(8)
where PF = additional painting frequency (per year)
ER0 = erosion rate for clean conditions (/urn/year)
tc = critical thickness loss (about 20/y for
normal application)
The additional annual cost is'
CA = CpAER/tc
where Cp = cost per painting
AER = (ER-ERo)
(9)
Because erosion rate difference is used, only the pollutant
coefficients and fraction of time-when-wet are needed to
calculate additional costs. For CaC03-contaming paints
assuming a pH of 5.2 and an SO2 level of zero as clean:
AER/tc = r[8.7 ±6.8][10~pH- 10~52] +
[0.0060 ± 0.0045]SOzf (1 Oa)
and for silicate-containing paints.
AER/tc = r[1.35 ± 0.75][1 0~pH - 10~5 2] +
[0.00097 ± 0 00050]S02f (1 Ob)
where r = annual rainfall (cm)
SOi = average sulfur dioxide (fjg/m3)
f = fraction of time-when-wet
The annual additional cost per house for the two paints can
be compared. Assume that painting with the cheaper
calcium carbonate-containing paint costs $800 and that
painting with the better silicate-containing paint costs
$900. Assume the annual rainfall is 100 cm and the
fraction of time-when-wet is 0.2. The calculated additional
costs as a function of pollutant level are given in Table 16.
Without pollutants, at an average temperature of 12.5°C,
the annual painting costs for the carbonate and silicate
paints are estimated to be $49 and $55, respectively. From
Table 16 it is obvious that it does not take much pollution to
make the silicate paint the better buy
Expenditures in the future such as repainting are perceived
to not be as important as present expenditures. Thus, they
are usually discounted. The amount of the discount depends
on how far in the future the expenditure must be made and
the expected change in the value of money along with other
subjective factors. Discount rates are not necessarily the
same as interest rates or inflation rates although these can
be used as a guide. Pensioners on low fixed income
probably discount future repainting much greater than
middle income families that intend to live in their houses for
more than ten years. In any case, discounting does reduce
the annual additional painting costs associated with wet
and dry acid deposition
Table 16. Undiscounted Annual Additional Painting
Cost Per House
Dollars*
Carbonate
Pollutant
Type
S02
U/g/m3)
Wet
deposition
(pH)
Level
20
40
60
80
100
3
3.5
4
45
5
Best
Estimate
19
38
58
77
96
692
216
65
18
3
(Upper 95%
Confidence
Limit)1"
48
96
144
192
240
1773
553
167
45
7
Silicate
Best
Estimate
3
7
10
14
17
121
38
11
3
0
(Upper 95%
Confidence
Limit)'8'
7
14
21
28
35
255
79
24
6
1
*To nearest dollar
"'(Lower 95% confidence limits for both paints are zero for all levels
of pollutants)
Discussion and Conclusions
Erosion of paint films to the point at which the substrate
begins to show is one of several mechanisms of paint
failure result ing in repainting. There are data available from
field and laboratory studies that show this type of failure to
be dependent on SC>2 levels Paints containing calcium
carbonate extenders are susceptible to attack by SOz. The
magnitude of the attack is consistent with a mechanism of
the formation of soluble CaSO.*. Sulfuric acid deposition in
rain is expected to have the same effect. Thus, coefficients
for wet and dry acid deposition effects can be calculated for
calcium carbonate-containing paints.
The effects of temperature and other additive factors ca ncel
out when the damage function is expressed as additional
annual painting frequency This greatly simplifies its
application to areas having different climatic conditions.
The annual marginal costs can then be directly related to
acid deposition levels.
Just based on the variability of the available data, the
magnitude of the error in any economic cost estimate will
be the same as the estimate. This means the cost could be
zero or twice as much as the estimate. Assumptions in
economic analysis increase this error. This information,
however, does provide a first-cut basis for estimating the
relative magnitude of the problem of acid deposition effects
on paints.
Recommendations
This study also shows that much more information is
needed to accurately assess the damaging effects of acid
deposition on paints.
Research is needed to determine the effects on painted hot
rolled steel (bridges, etc.).
Research is needed to determine the mechanism of paint
peeling from wood associated with acid hydrolysis.
-------
Surveys are needed to determine the relative magnitude of
costs associated with different types of paint failures on
different substrates, and to see if there is any association
with known environmental differences.
More work is needed on the relatively simple mechanism of
erosion to reduce the magnitude of the observed error to an
acceptable level.
Literature Cited
1. Haynie, F. H. and Spence, J. W. (1984) Air Pollution
Damage to Exterior Household Paints. J. Air Pollut.
Control Assoc., 34:941 -944.
2. Campbell, G. G., Schurr, G. G., and Slawikoski, D. E.
(1972) A Study to Evaluate Techniques of Assessing
Air Pollution Damage to Paints. Final Report EPA
Contract 68-02-0030. Sherwin-Williams Co., Chicago,
Illinois. 85 pp.
3. Campbell, G. G., Schurr, G. G., Slawikoski, D. E., and
Spence, J. W. (1974) Assessing Air Pollution Damage
to Coatings. J. Paint Tfichnol., 46(593):59-71.
4. Spence, J. W., Haynie, F. H., and Upham, J. B. (1975)
Effects of Gaseous Pollutants on Paints: A Chamber
Study. J. Paint Technol., 4(609):57-63.
5. Booz, Allen and Hamilton, Inc. (1970) Study to Deter-
mine Residential Soiling Costs of Paniculate Air Final
Report National Air Pollution Control Administration
Contract No. CPA 22-69-103. Washington, DC.
6. Spence, J.W. and Haynie, F.H.C\972)Paint Technology
and Air Pollution: A Survey and Economic A ssessment.
Office of Air Porgrams Publication No. AP-103. U.S.
Environmental Protection Agency, Research Triangle
Park, NC. 44 pp.
7. Public Health Service. (1968) Report for Consultation
on the Metropolitan Philadelphia Interstate Air Quality
Control Region. Department of Health, Education and
Welfare, National Air Pollution Control Administration.
74pp.
8. Rich, S. (1981) The Kline Guide to the Paint Industry;
Sixth Edition. Charles H. Kline & Co., Fairfield, NJ. 199
PP-
9. Noble, P. (1969) Marketing Guide to the Paint Industry.
Charles H. Kline & Co., Fairfield, NJ.
» US GOVERNMENT PRINTING OFFICE 1986 - 646-116/20744
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