U.S. Department of the Interior

U.S. Geological Survey

U.S. Geological Survey Professional Paper 1635

The Effect of Selected Cleaning Techniques on 

Berkshire Lee Marble: A Scientific Study at 

Philadelphia City Hall
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall I

The Effect of Selected Cleaning 

Techniques on Berkshire Lee Marble: 

A Scientifi c Study at Philadelphia City Hall

By

Victor G. Mossotti, A. Raouf Eldeeb, Terry L. Fries, and Mary Jane Coombs,

U.S. Geological Survey;

Virginia N. Naudé,

Norton Art Conservation, Inc.;

Lisa Soderberg,

Vitetta Group; and

George S. Wheeler,

Metropolitan Museum of Art, New York

Professional Paper 1635

U.S. Department of the Interior

U.S. Geological Survey
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall II

U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

Any use of trade, product, or fi rm names in this publication is for 

descriptive purposes only and does not imply endorsement by the 

U.S. Government.

U.S. Geological Survey, Reston, Virginia: 2002

For additional copies please contact:

USGS Information Services

Box 25286

Denver, CO 80225

This report and any updates to it are available on line at

http://geopubs.wr.usgs.gov/prof-paper/pp1635/

Additional USGS publications can be found on line at

http://geology.usgs.gov/products.html

For more information about the USGS and its products:

Telephone: 1–888–ASK–USGS

World Wide Web: http://www.usgs.gov/

Published in the Western Region, Menlo Park, California

Manuscript approved for publication, April 26, 2000

Edited by George Havach

Layout and design by Sara Boore and Michael Diggles

COVER

East façade of Philadelphia City Hall.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall III

Abstract............................................................................................................................................

Introduction.....................................................................................................................................

Background............................................................................................................................

Goals of the masonry-cleaning process.....................................................................................

Preliminary testing program....................................................................................................

 Water-based cleaning techniques...................................................................................

 Dry cleaning techniques.................................................................................................

 Conclusions..................................................................................................................

Expansion of the masonry-cleaning program.........................................................................

Condition of the building stone........................................................................................................

Petrology of unweathered building stone...............................................................................

Condition of weathered building stone...................................................................................

 Lightly soiled, smooth...................................................................................................

 Lightly soiled, rough.....................................................................................................

 Gypsum crusted, smooth...............................................................................................

 Gypsum crusted, rough.................................................................................................

Methods and procedures..................................................................................................................

Framework for study..............................................................................................................

Measurement tools.................................................................................................................

Experimental design...............................................................................................................

Cleaning effi cacy..............................................................................................................................

Particulate material.................................................................................................................

Salts........................................................................................................................................

Soiling....................................................................................................................................

 LSS and LSR surfaces...................................................................................................

 GCS and GCR surfaces.................................................................................................

Surface structure before and after cleaning.......................................................................................

Friability of the stone before cleaning.....................................................................................

Surface fracturing and roughness after cleaning.....................................................................

Accelerated weathering of cleaned surfaces....................................................................................

Surface recession and friability of the stone............................................................................

Surface reactivity to water......................................................................................................

 Theoretical summary.....................................................................................................

 Results..........................................................................................................................

 Effect of surface reactivity to water on global variables..................................................

Surface sensitivity to SO2 and particulate matter....................................................................

Synthesis and interpretation of results..............................................................................................

Stone stability.........................................................................................................................

Data-synthesis model.............................................................................................................

 Soiling load...................................................................................................................

 Surface microstructure..................................................................................................

 Surface recession..........................................................................................................

 Surface reactivity..........................................................................................................

Cleaning criteria and selection of cleaning techniques...........................................................

 Capacity, accuracy, and fi nesse....................................................................................

 Criteria for removal of soiling load...............................................................................

 Removal of salts............................................................................................................

 Removal of soiling........................................................................................................

 Criteria for alteration of microstructure........................................................................

 Surface roughness.........................................................................................................

 Friability index..............................................................................................................

 Statistical link between fracture density and surface roughness...................................

Contents

1

2

2

2

3

3

3

3

3

3

3

4

4

4

4

4

4

4

5

5

6

6

6

7

7

7

8

8

9

9

10

11

11

12

13

13

13

13

14

14

14

15

15

15

15

16

16

16

17

18

18

18
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall IV

Conclusions and preservation strategy.............................................................................................

Selection of cleaning techniques............................................................................................

 Summary of testing and analysis...................................................................................

 Scientific cleaning criteria.............................................................................................

 Cleaning techniques not differentiated by surface recession.........................................

 The role of soluble salts in marble deterioration.............................................................

 Surface reactivity..........................................................................................................

 Selection criteria: Cleanliness and friability..................................................................

Cleaning techniques that satisfy the selection criteria.............................................................

 Alteration of microstructure: Hard versus soft cleaning techniques..............................

 Cleaning criteria established by preservation architects and conservators..................

 Gommage technique...........................................................................................

 Combination technique.......................................................................................

 Armax technique.................................................................................................

 JOS technique.....................................................................................................

Recommendations................................................................................................................

Specification and monitoring of cleaning techniques.............................................................

Impact of cleaning techniques on the long-term condition of the stone...................................

Significance of this study as a model.......................................................................................

Significance of this study to preservation strategies................................................................

Acknowledgments..........................................................................................................................

References cited...............................................................................................................................

Appendix 1. Cleaning techniques and core inventory.......................................................................

Surface-soiling conditions......................................................................................................

 Lightly soiled, smooth..................................................................................................

 Lightly soiled, rough....................................................................................................

 Gypsum crusted, smooth..............................................................................................

 Gypsum crusted, rough.................................................................................................

Choice and description of cleaning techniques.......................................................................

 Cleaning techniques selected for the first phase of this study........................................

 Power wash.........................................................................................................

 Misting...............................................................................................................

 Gommage............................................................................................................

 Cleaning techniques selected for the second phase of this study....................................

 Combination.....................................................................................................

 Armax................................................................................................................

 JOS......................................................................................................................

 Laser...................................................................................................................

 Dry ice.................................................................................................................

Coring methods......................................................................................................................

 Site selection and equipment........................................................................................

 Use of epoxy to preserve surface soiling........................................................................

Core inventory........................................................................................................................

Appendix 2. Sample preparation, image indexing, and SEM-image analysis...................................

Sample preparation.................................................................................................................

Image generation....................................................................................................................

Image indexing.......................................................................................................................

SEM-image analysis...............................................................................................................

 Hardware requirements.................................................................................................

 Program SEM2BIN.......................................................................................................

 Program SHOWPIX.....................................................................................................

 Program PROFILE........................................................................................................

Contents

20

20

20

20

20

20

20

20

20

21

21

21

21

21

22

22

22

22

22

22

23

23

23

23

24

24

24

24

24

24

24

24

24

25

25

25

25

25

25

26

26

26

26

26

26

27

27

27

27

27

28

28
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall V

Contents

 Calcite and epoxy................................................................................................

 Program EDGE.............................................................................................................

Appendix 3. Optical microscopy and gray-scale measurements......................................................

 Visual assessment of surface soiling.......................................................................................

Electronic gray-scale measurements......................................................................................

Appendix 4. Salt-penetration measurements...................................................................................

Sulfate analysis.......................................................................................................................

 Methods........................................................................................................................

 Applications.................................................................................................................

 Sulfate content of the applied resins.....................................................................

 Sulfate content of soiling on sample surfaces.......................................................

 Sulfate content of cracks, fissures, and matrix.....................................................

Appendix 5. Surface-recession test..................................................................................................

Surface recession based on change in surface level....................................................................

Surface recession based on surface smoothing.......................................................................

Appendix 6. Fracture-density measurements and SEM-image-analysis data...................................

Appendix 7. Surface-roughness test................................................................................................

Appendix 8. Surface-reactivity test.................................................................................................

Theoretical overview..............................................................................................................

Laboratory implementation....................................................................................................

 Apparatus....................................................................................................................

 Test procedure...............................................................................................................

 Results......................................................................................................................

Footnotes........................................................................................................................................

28

29

29

29

31

31

31

31

31

31

31

32

32

32

32

32

33

33

33

34

34

34

34

35

 Figures

1. Typical examples of surface-soiling conditions on Philadelphia City Hall.................................

2. State diagram of stone surface, in which each surface-soiling condition is viewed as a

 unique thermodynamic state...........................................................................................................

3. Generalized cleaning process....................................................................................................

4. Basic components of stone-weathering model..........................................................................

5. Primary deposition and secondary mobilization processes in stone-weathering model...............

6. Experimental design for controlling extraneous variables......................................................

7. Photomicrographs of typical soiled surfaces in cores series 3 through 6 

 before and after application of cleaning techniques................................................................... 

8. Typical cross-sectional scanning-electron-microscope images of uncleaned cores.................

9. Scanning-electron-microscope images of core samples with gypsum-crusted, rough

 surface-soiling condition........................................................................................................... 

10. Surface reflectivity of soiled stone after cleaning.......................................................................

 11. Friability of stone, showing near-surface fracture density versus shape factor for all

 cores before and after cleaning................................................................................................... 

12. Model of soiling-pattern development.......................................................................................

13. Fracture density versus shape factor before and after cleaning for various

 surface-soiling conditions..........................................................................................................

14. R2 value versus surface-soiling condition of stone for data plotted in figure 13........................

15. Model of surface alteration by soiling load................................................................................

16. Output of program EDGE for cleaned side of sample GCRC–1, showing ~278 μm of

 surface recession........................................................................................................................

17. Surface recession versus friability index for two groups of smooth and rough 

 uncleaned cores............................................................................................................................

36

37

37

37

38

38

39

41

43

45

47

48

49

53

53

54

54
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall VI

Contents

55

57

58

58

59

61

63

64

65

66

66

69

71

73

74

75

82

83

85

85

85

85

86

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

18. Average friability index after application of each cleaning technique for all cores tested.......

19. Model of laboratory configuration used for implementation of surface-reactivity tests...........

20. Absolute rate of release of Ca2+ ions for all cores tested......................................................... 

21. Absolute rate of release of Ca2+ ions versus shape factor for all cleaned cores....................... 

22. Average absolute rate of release of Ca2+ ions for each cleaning technique for various

 surface-soiling conditions............................................................................................................

23. Change in absolute rate of release of Ca2+ ions versus surface color for all cores and for

 various surface-soiling conditions...............................................................................................

24. Model of physical alteration of stone surface by water.............................................................. 

25. Model of factors controlling deposition of salts and particulate matter on stone surface.........

26. Integrated model of dominant soiling-agent-delivery and stone-alteration processes.............. 

27. Utility versus surface cleanliness, showing general shape of esthetic-utility curve for 

 the building..................................................................................................................................

28. Finesse-classification diagrams for various surface-soiling conditions.................................

29. Finesse-classification diagrams for gypsum-crusted, rough stone, based on a range of

 target-cleanliness values..............................................................................................................

30. Finesse-classification diagrams for various surface-soiling conditions.....................................

31. Approximate shape of projections of general utility curve onto utility/roughness plane for

 gypsum crusted, rough stone......................................................................................................

32. Utility versus surface roughness for various target-cleanliness values for gypsum

 crusted, rough stone after cleaning.............................................................................................. 

33. Fracture density versus surface roughness for uncleaned and cleaned sides of core

 samples cleaned by different techniques..................................................................................... 

34. Summary of statistical data for various cleaning techniques plotted in figure 33......................

35. Summary diagram showing criteria used to filter cleaning techniques applied to lightly

 soiled, smooth and rough stone and gypsum-crusted, smooth and rough stone.......................

36. East façade of building, south of east portal, showing locations of masonry-testing sites.........

37. Ledge outside Room 428, upper and lower scaffold levels, showing lightly soiled marble

 on ledge and gypsum-crusted areas on projecting cornice and frieze below ledge...................

38. South wall, upper scaffold level, showing area of lightly soiled, rough stone.......................... 

39. West wall, upper scaffold level, showing area of lightly soiled, smooth stone........................ 

40. North wall, upper scaffold level................................................................................................. 

41. Lower scaffold level.................................................................................................................... 

42. South side of building.................................................................................................................. 

43. Ashlar on ledge outside Room 622, showing area of lightly soiled, rough stone cleaned

 by laser cleaning technique.......................................................................................................... 

44. Ledge outside Room 622, showing back of capital on column..................................................

45. Marble fragment from unknown location on building, showing gypsum-crusted, 

 smooth stone................................................................................................................................

46. Marble fragment from unknown location on building, showing lightly soiled, 

 smooth stone after dry-ice cleaning.............................................................................................. 

47. North wall, upper scaffold level.................................................................................................

48. South wall, upper scaffold level.................................................................................................

49. West wall, upper scaffold level..................................................................................................

50. West wall, upper scaffold level..................................................................................................

51. North wall, upper scaffold level.................................................................................................

52. Entablature, lower scaffold level................................................................................................

53. Entablature, lower scaffold level................................................................................................

54. Entablature, lower scaffold level................................................................................................

55. Entablature, lower scaffold level................................................................................................

56. Entablature, lower scaffold level................................................................................................

57. Entablature, lower scaffold level................................................................................................
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall VII

Contents

103

104

105

105

106

106

107

107

108

108

108

108

109

109

109

110

110

111

112

112

113

114

115

115

115

116

118

120

124

125

126

127

128

129

58. Entablature on south side, lower scaffold level.........................................................................

59. Entablature, lower scaffold level..............................................................................................

60. Column base at 6th-floor level...................................................................................................

61. Detail of column base at 6th-floor level.....................................................................................

62. Marble fragment from unknown location on building...............................................................

63. Marble fragments from unknown locations on building............................................................

64. Sample-preparation protocol for core samples selected for SEM analysis...............................

65. Summary of SEM image-analysis programs............................................................................

66. Syntax for program SEM2BIN as run from DOS command line............................................

67. Syntax for program SHOWPIX as run from DOS command line..........................................

68. Syntax for program PROFILE as run from DOS command line............................................

69. Typical output image from program PROFILE.......................................................................

70. Syntax for program EDGE as run from DOS command line..................................................

71. Brown stain on sample LSRP–6.................................................................................................

72. Brown stain on sample GCRM–3...............................................................................................

73. Zoning on sample GCRG–3.......................................................................................................

74. Typical stained area on sample GCRC–5...................................................................................

75. Measured color values of core samples........................................................................................

76. Electronic exposure time versus F-M color value for grayness measurements on

 core series.................................................................................................................................

77. X-ray-fluorescence spectra of Devcon 5 epoxy resin and L.R. white acrylic resin....................

78. Typical X-ray-fluorescence spectra of uncleaned samples in core series GCR......................... 

79. Typical X-ray-fluorescence spectra of silicate and iron minerals in uncleaned

 samples in core series GCS and GCR........................................................................................

80. Schematic diagram of system used for surface-reactivity test..................................................

81. Configuration of alternative apparatus used for surface-reactivity test.....................................

82. Relative surface activity of samples in core series 3..................................................................

 Tables

1. Summary of technical details on four cleaning techniques selected during fi rst

 phase of this study....................................................................................................................

2. Summary of important operational features of four of the eight cleaning techniques

 evaluated in this study..............................................................................................................

3. Inventory of core samples from the building.........................................................................

4. Inventory of SEM images for computer analysis...................................................................

5. Summary of visual grayness measurements.........................................................................

6. Chemical analyses of rainwater in the Philadelphia area........................................................

7. Semiquantitative analyses of silicate inclusions on soiled stone surfaces...............................

8. Estimated surface recession caused by the various cleaning techniques, as measured

 by two methods........................................................................................................................

9. Results of fracture-density measurements based on SEM-image analysis of core

 samples obtained in this study.................................................................................................
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 1

Abstract

This report describes a scientifi c investigation of the 

effects of eight different cleaning techniques on the Berkshire 

Lee marble component of the façade of the East Center Pavilion 

at Philadelphia City Hall; the study was commissioned by the 

city of Philadelphia. The eight cleaning techniques evaluated 

in this study were power wash (proprietary gel detergent followed by water rinse under pressure), misting (treatment with 

potable, nebulized water for 24–36 hours), gommage (proprietary Thomann-Hanry low-pressure, air-driven, small-particle, 

dry abrasion), combination (gommage followed by misting), 

Armax (sodium bicarbonate delivered under pressure in a 

water wash), JOS (dolomite powder delivered in a low-pressure 

rotary-vortex water wash), laser (thermal ablation), and dry ice 

(powdered-dry-ice abrasion delivered under pressure).

In the quasi-experimental design, a control sample, taken 

from an area of stone protected by epoxy adjoining an unprotected test area, was simultaneously cored with the test sample. 

In our study, approximately 160 cores were removed from the 

building for laboratory analysis. As a basis for differentiating 

the various cleaning techniques, we measured the changes in 

the structural properties of the stone on the microscale (0.1–100 

μm), and in the esthetic features of the stone on the mesoscale 

(0.1–5 mm) and macroscale (>∼ 0.5 cm), caused by each of the 

cleaning techniques.

The spatially averaged distribution of particulate matter 

and salts on the control and test surfaces side by side was 

measured by electron microscopy on the microscale and by 

optical reflectance on the mesoscale to macroscale (0.1–20 

mm). We developed a computer program to analyze scanningelectron-micrograph (SEM) images for the fractal dimension 

and other morphologic parameters of the stone surface on the 

microscale; the fractal dimension is a factor in the surfaceroughness test. The program also computed the near-surface 

fracture density of the stone.

An analysis of more than 1,100 samples cut from the 

cores provided a statistical basis for crafting the essential elements of a reduced-form, mixed-kinetics conceptual model that 

represents the deterioration of calcareous stone in terms of selforganized soiling and erosion patterns. This model, in turn, provided a basis for identifying the variables that are affected by 

the cleaning techniques and for evaluating the extent to which 

such variables influence the stability of the stone. The model 

integrates the mutual dependence of processes controlling the 

primary deposition of soiling agents, the secondary mobilization of transportable materials, and the permanent alteration of 

the stone through critical feedback paths in the model system.

The model recognizes three classes of variables that may 

influence the soiling load on the stone, including such exogenous environmental variables as airborne moisture, pollutant 

concentrations, local aerodynamics, and so on, and such endogenous stone variables as surface chemistry and microstructure 

(fracturing, roughness, and so on). Our study showed that morphologic variables on the mesoscale to macroscale are not 

generally affected by the choice of a cleaning technique. For 

example, surface-recession tests indicate that differences in 

mass loss from technique to technique are virtually inconsequential over the long term. The model also explains the spatial 

distribution of particulate matter and salts over the building 

by the action of water on the stone surface. According to this 

mechanism, the soiling pattern on the building is controlled 

mainly by the macromorphology and orientation of the stone. 

Thus, the long-term soiling pattern on the building is independent of the cleaning technique applied.

This study also showed that micromorphologic variables 

are differentially affected by the various cleaning techniques. 

Although the extent to which surface microstructure and microchemistry influence the soiling load requires further study, we 

assumed in our evaluation that the long-term esthetic and structural properties of the stone are closely related to the lateral and 

vertical distribution of particulate matter and salts and to the 

mechanical bonding between calcite, phlogopite, and dolomite 

grains in the matrix. Contrary to our original conjecture, we 

found no evidence that soluble salts play a signifi cant role in 

the deterioration of Berkshire Lee marble. Although salts were 

evident in cracks and fissures of the heavily soiled stone, such 

salts did not penetrate the surface to a depth of more than a few 

hundred micrometers.

Interestingly, we noted that the strength of the relation 

between the fracture density and the surface roughness was 

distinctly sensitive to the cleaning technique used, and so we 

used this relation as a measure of the marginal ability of each 

technique to clean the stone without causing collateral damage 

to the surface. Thus, the criteria used to differentiate the cleaning techniques were ultimately based on the ability of each 

technique to remove soiling without altering the texture of the 

stone surface. This study identified both the gommage and JOS 

techniques as appropriate for cleaning ashlar surfaces and the 

combination technique as appropriate for cleaning highly carved 

surfaces at the entablatures, cornices, and column capitals.

The Effect of Selected Cleaning Techniques on Berkshire 

Lee Marble: A Scientific Study at Philadelphia City Hall

By Victor G. Mossotti, A. Raouf Eldeeb, Terry L. Fries, Mary Jane Coombs, Virginia N. Naudé,1 Lisa Soderberg,2

and George S. Wheeler3

1Norton Art Conservation, Inc.

2Vitetta Group. 3Metropolitan Museum of Art, New York.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 2

Introduction

Background

One of the finest American examples of the Second 

Empire Style of picturesque eclectic architecture, Philadelphia City Hall, dedicated in 1901, is a bold and massive 

marble and granite edifice that occupies the city’s central 

square. Each of the building’s exterior walls consists of a 

granite base topped with marble that extends almost 500 ft 

in length, together representing one of the largest load-bearing masonry structures in the United States. The masonry 

structure, designed by John McArthur, Jr., is distinguished 

by a rich and vast sculptural program: all the major architectural elements are adorned with marble carvings by Alexander 

Milne Calder. The unusual historic and architectural signifi -

cance of the building, as well as the magnitude and importance of the sculptural program, provided both the impetus 

and rationale for initiating an extensive testing program for 

masonry cleaning and preservation techniques.

In 1991, the city of Philadelphia contracted the Vitetta 

Group, in association with Kelly/Maiello, to complete the 

building’s restoration, modernization, and rehabilitation and 

to provide a framework for the restoration of the entire 

exterior of the building. A multidisciplinary project team 

of preservation architects, conservators, and scientists helped 

formulate a conservative and comprehensive masonry-cleaning program as a critical part of the exterior restoration and 

long-term preservation of the building. The project team for 

the work—completed during winter-spring 1995—included 

preservation architects from the Vitetta Group; the conservator Virginia Naudé of Norton Art Conservation, Inc.; and the 

chemist George S. Wheeler. In addition, a group of scientists 

at the U.S. Geological Survey (USGS) in Menlo Park, Calif., 

who developed a process to analyze and measure the characteristics and alteration patterns of the stone, were selected to 

collaborate with the project team. This group of scientists, 

consisting of Victor Mossotti, Raouf Eldeeb, and Terry L. 

Fries, had recently completed an investigation of cleaning 

techniques on the limestone façade of the Chicago Tribune

tower that compared the effects on the underlying substrate 

of the Thomann-Hanry dry process with those of water-based 

techniques. As participants in the U.S. National Acid Precipitation Assessment Program since its inception in 1983, these 

scientists developed software to investigate the fractal structure of stone surfaces as viewed in SEM images, as well as 

a wet-deposition model. This software and model facilitated 

the quantitative study of the complex stone structure at a level 

well beyond the reach of classical models. The scientists proposed to apply their recent research on the Chicago Tribune

tower in order to evaluate the characteristics and alteration 

patterns of the marble at Philadelphia City Hall after cleaning 

and artificial weathering of the masonry. Toward this goal, 

they conducted preliminary tests as part of the design phase 

of the building’s restoration. These tests were planned as a 

demonstration project to serve as a model for the restoration 

of the entire exterior of the building.

Goals of the Masonry-Cleaning Process

The institution of a testing program was essential to 

identify the type of stone deterioration at Philadelphia City 

Hall and to determine how the stone would be affected by 

selected cleaning techniques. Unlike metals, which can be 

preserved by maintenance programs and by the application 

of surface coatings, no coatings have been developed to 

ensure the preservation of stone. In fact, on many buildings, 

the application of coatings can contribute to stone deterioration. The most effective strategy for preservation of 

the dolomitic marble and granite at Philadelphia City Hall 

includes the selection of cleaning techniques to provide 

a stone surface that is minimally reactive and maximally 

durable against the inevitable attack of weather and airborne pollutants within an urban environment. This strategy 

necessitates the selection of a cleaning technique that will 

achieve an appropriate balance between what is removed 

from and what is allowed to remain on the surface. The 

efficacy and impact of a given cleaning technique depend 

on the petrologic and surficial features of the dolomite and 

granite, properties determined by the source of the stone, 

the method of quarrying, and surface dressing; the exposure 

to the environment; the soiling patterns; and any previous 

cleanings.

The project team identified these goals of the 

masonry-cleaning program: to replicate as closely as possible the building’s original appearance, with a minimal 

impact on the integrity of the underlying substrate; and 

to apply a cleaning technique that promotes the health 

and well-being of people, extends the longevity of the 

building, and protects the surrounding environment. The 

masonry-cleaning program, which removes destructive pollutant crusts and arrests established cycles of stone deterioration, is integral to the long-term preservation of the 

building. All the cleaning treatments tested on the centuryold stone remove grime and gypsum with a minute loss 

of the original substrate. The project team acknowledged 

that some people would advocate no cleaning at all as the 

safest option because of potential damage to the underlying 

substrate. However, the city did not consider “no cleaning” 

as a viable option. If the city was to commit funds for the 

restoration of the exterior masonry, an appropriate public 

image had to be maintained, and the building’s original 

clean appearance had to be replicated as closely as possible. 

Also, cleaning was essential to gain a complete understanding of the structural condition of the building and to ensure 

that adequate masonry-stabilization measures were instituted. Layers of soiling and guano on large sections of 

the building, particularly at the locations of carvings and 

former repairs, prevented any determination of the stability 

of the underlying marble before cleaning. The project team 

was committed to cleaning with the assurance that any 

alteration of the stone surface would be minimal, predictable, and acceptable. An overriding consideration was to 

determine the degree of loss that is acceptable in order to 

achieve an appropriate esthetic solution, while ensuring the 

long-term preservation of the stone.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 3

Preliminary Testing Program

The first tests of cleaning techniques at Philadelphia City 

Hall were conducted during March and April 1994 as part of 

the design phase for restoration of the East Center Pavilion. 

These tests, which addressed the preservation of the exterior 

marble walls, were designed to determine:

• The least destructive cleaning techniques for the building

• The locations on the building where each cleaning technique, 

within the selected set, would best be used

• The development of a restoration program and contract 

documents for preservation of the building

• The cost implications of these decisions

Water-Based Cleaning Techniques

Initially, the project team tested several water-based cleaning 

techniques. In the United States, such techniques, both with and 

without detergent, have traditionally been the least expensive and 

safest available—safer than both chemical and abrasive techniques. 

However, the harmful effects of water infiltration on the building, as 

well as the volume of water required to clean large stone structures, 

have led preservationists to seek alternative cleaning techniques.

Dry Cleaning Techniques

The impact of water-based cleaning techniques have been 

recognized in Europe, where the dry cleaning of stone with very 

small particles delivered at low air pressure has played an important 

role in conservative stone cleaning for decades. Because open 

joints are typical throughout large areas of Philadelphia City Hall, 

the preliminary testing program addressed alternatives to the traditional water-based cleaning techniques. A low-pressure, smallparticle, air-abrasive cleaning technique—one that uses no water, 

chemicals, or detergents and eliminates the potential for water 

infiltration—was identified for testing during this preliminary testing program. Because the city determined that specifications would 

be developed for all cleaning techniques tested, the project team 

requested demonstrations from vendors with long performance 

records of cleaning buildings with low-pressure, small-particle, 

air-abrasive techniques. One vendor, Thomann-Hanry, Inc., had 

developed a proprietary cleaning technique in France in 1965. The 

project team themselves tested all the chemical and water-based 

cleaning techniques. The various cleaning techniques tested were 

described and briefly analyzed by Naudé (1994).

Conclusions

On the basis of a qualitative assessment, both visual and 

under low-power, hand-held magnification, the project team 

rejected chemical cleaning as too harsh but concluded that the stone 

could be cleaned by either power-wash and detergent techniques or 

a low-pressure, small-particle, air-abrasive technique. The effects 

of the various cleaning techniques on different conditions of the 

marble surfaces indicated the desirability of cleaning the stone with 

a combination of wet and dry techniques. However, the optimal 

combination of such techniques was unclear after the preliminary 

testing program, particularly in regard to the removal of salts, 

which, if present, may contribute to continued deterioration of the 

stone. Onsite observations during the preliminary testing program 

raised additional questions about surface alterations that the project 

team concluded could be answered only with quantitative scientific 

data. Among the questions to be addressed by these data were (1) 

the quantity and location of soluble salts; (2) the effectiveness of 

the removal of soluble salts, if present, by water-based and abrasive 

techniques; and (3) the degree of damage to the stone surface by 

abrasive techniques.

Expansion of the Masonry-Cleaning Program

After the preliminary testing program was completed, the 

project team recommended that testing be continued in a second 

phase to provide additional scientific data to measure the impact 

of any masonry-cleaning program on the long-term preservation of 

the building. The primary goal of the second phase, which took 

place during winter-spring 1995, was to characterize and quantify 

alterations to the surface after cleaning and, in some areas, artificial 

weathering of the stone. Eight cleaning techniques were evaluated 

in this phase of our study:

• Power wash: proprietary gel detergent application, followed by 

a water rinse under pressure

• Misting: potable, nebulized water applied at low pressure for 

24 to 36 hours

• Gommage: proprietary Thomann-Hanry, Inc., low-pressure, airdriven, small-particle, dry abrasion

• Combination: gommage followed by misting

• Armax: sodium bicarbonate delivered under pressure in a water 

wash

• JOS: dolomite powder delivered in a low-pressure rotary vortex 

water wash

• Laser: thermal ablation

• Dry ice: powdered-dry-ice abrasion delivered under pressure

A description of each cleaning technique, including the rationale of its selection for testing and information on how each technique is currently used in architectural conservation, is presented in 

appendix 1. The specific purpose of this study was to establish a 

set of criteria by which the various cleaning techniques could be 

evaluated. This evaluation involved scoring each technique against 

a reference level of cleanliness for the various masonry components 

of the building and against specifications that define an acceptable 

level of physical and chemical stability of the cleaned stone.

Condition of the Building Stone

Petrology of Unweathered Building Stone

The composition of the building stone was determined by 

petrographic analysis of thin sections prepared from the center 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 4

of two core samples taken from representative areas of the 

building. The core samples were also studied by qualitative 

energy-dispersive X-ray-fluorescence and backscattered-electron imaging in an electron microprobe. The two core samples 

studied for baseline petrology showed no significant differences.

The marble is composed of more than 95 volume percent 

dolomite. Grain size ranges from 0.13 to 0.66 mm in a granoblastic texture, with the essential mineral constituents approximately equal in size. The grain boundaries are generally 

smooth and curved. Some calcite occurs as small (less 

than 0.1 mm) grains at grain boundaries between larger 

dolomite crystals. Somewhat more abundant than calcite is 

phlogopite, a magnesium-rich mineral of the mica group: 

K(Mg,Fe)3

AlSi3O10(OH,F)2. Phlogopite, which is yellowish 

brown to brownish red or copper colored, commonly occurs 

in limestone as a result of dedolomitization. Phlogopite is 

near biotite in composition but contains little iron. A few scattered thick, platelike, honey-colored crystals of this mineral are 

visible in the core samples under low magnifi cation. In thin 

sections, phlogopite is colorless and occurs in thin, elongate 

prismatic grains, as much as 1 mm long, that appear to have 

some preferred orientation. Also observed in the marble were 

a few grains of apatite: Ca5(PO4,CO3)3(F,OH,Cl). Altogether, 

these minor minerals do not appear to make up more than 2 to 3 

volume percent of the marble.

Condition of Weathered Building Stone

The richly articulated sculptural program of the building 

provides a wide variety of surface-soiling conditions from 

which to sample. However, visual inspection of the façade from 

the street and at close range suggests that most of the surface 

can be characterized as one of four conditions: lightly soiled, 

smooth (LSS, fi g. 1A); lightly soiled, rough (LSR, fi g. 1B); 

gypsum crusted, smooth (GCS, fi g. 1C); or gypsum crusted, 

rough (GCR, fi g. 1D).

Lightly Soiled, Smooth

LSS-condition stone, which comprises approximately 8 

percent of the building’s façade, is observed mainly on ashlars. 

These areas are lightly washed during periods of rain and are 

protected from extreme weather by cornices and columns. A 

few pits or ridges are visible in the surface. The colors of LSS 

areas are various shades of light gray.

Lightly Soiled, Rough

LSR-condition stone, which comprises approximately 

85 percent of the building’s façade, includes both ashlars and 

sculptural ornaments, which generally are directly exposed 

to weather. To the naked eye, the surface is pitted and commonly exhibits ridges that reflect subtle patterns of rain runoff. 

Surface erosion is clearly apparent where cracks or natural fissures are present. The colors of LSR areas are various shades 

of dark gray.

Gypsum Crusted, Smooth

GCS-condition stone, which comprises approximately 4 

percent of the building’s façade, is recognizable by smooth 

black soiling that cannot be brushed away. Such soiling is conspicuous in protected areas and in pockets on exposed sculptural ornament. Gypsum crusted, smooth stone was later found 

to be consistently carbon-soiled.

Gypsum Crusted, Rough

GCR-condition stone, which comprises approximately 1 

percent of the building’s façade, is easily distinguishable by 

blotches of thick black crust with a granular texture. Virtually 

all of these areas were identifi ed in deeply carved stone that 

is protected from rain runoff. Laboratory tests show that the 

dark crust which forms the exterior veneer on both GCS and 

GCR surfaces is composed largely of gypsum impregnated with 

partially soluble particulate matter.

In addition to these four surface conditions, about 2 

percent of the building’s façade is severely deteriorated. Such 

stone, which generally has a sugary texture, is commonly 

found in sculptural projections with maximum exposure and 

on stone located in the path of rain runoff. Such severely 

deteriorated stone, which has vastly different requirements 

for cleaning and preservation, was considered atypical of the 

marble that requires cleaning and so was omitted from our 

study.

Methods and Procedures

Framework for Study

The central focus of this study was on the capability 

of a given cleaning technique to remove soiling from the 

exterior stone on the building and on the effect, if any, of each 

technique on the stone itself. The many issues related to this 

problem can be viewed in terms of a model in which the thermodynamic state of the stone is represented by a set of state 

variables.1 For example, several variables that reflect the state 

of the stone include the chemical composition of the surface, 

surface roughness, and surface fracturing. Consider the four 

surface-soiling conditions (LSS, LSR, GCS, GCR), as represented in the state diagram in figure 2. We can view stone 

erosion and surface soiling in terms of the set of processes 

that act to transform the surface from one state to another. In 

this view, we regard cleaning as an effort to reverse some of 

the effects of erosion and soiling, as represented in figure 3, 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 5

although, in general, a complete reversibility of weathering is 

impossible.

To define the specifications against which the various 

cleaning techniques can be scored, we first need to identify 

the variables that are affected by the cleaning techniques and 

to evaluate the extent to which such variables influence the 

stability of the stone. The basic elements of a model that 

expands on the notion introduced in figure 2 are illustrated in 

fi gure 4. These elements include the processes that determine 

the future state of the stone (center panel), and the variables 

that influence such processes and that reflect the state of the 

stone at a given point in time (boxes A–C). Three general 

classes of variables are illustrated in figure 4: those that characterize the ambient conditions to which the building is subjected (box A), those that quantify the physical condition of 

the stone (morphology, orientation of surface, subsurface fracturing, degree of grain consolidation, and so on; box B), and 

those that determine the chemical state of the stone (elemental 

composition, crystalline structure, and so on; box C). In general, such variables are both spatially and temporally dependent. The schematic segregation of these three classes of 

variables in figure 4 acknowledges their mutual dependence 

with regard to a set of complex linked processes that themselves are controlled by the ambient environment and by 

the state of the stone. Two main classes of linked processes 

are diagrammed in figure 4: those driving the delivery and 

removal of weathering agents to and from the stone (transport), and those affecting the chemistry and structure of the 

stone (alteration).

We found it useful to consider material-transport processes exclusively in terms of primary or secondary mechanisms that deliver materials to or remove materials from a 

particular location on the surface, as illustrated in figure 5. 

Primary deposition is the mode by which weathering agents 

that soil or deteriorate the stone are introduced from outside 

the surface area, including the gravity-independent attack of 

such gases as SO2

 and the gravity-dependent deposition of 

particulate matter and water. The critical distinction between 

primary and secondary deposition is that in primary deposition, the weathering agent of interest first interacts with the 

stone at the point of delivery.2 This interaction governs the 

local flux of the weathering agent to the stone. In contrast, 

secondary transport takes place when the weathering agents, 

or their reaction products, are mobilized after primary deposition and redeposited elsewhere within the catchment.3

 Such 

mobilization is commonly driven by the action of water. Both 

primary and secondary deposition commonly occur on rainwashed buildings and monuments.

Measurement Tools

Many physicochemical factors influence actual stone 

erosion and its measurement. We assumed that the physical microstructure of the stone is a key factor governing the 

stone’s reactivity in both primary and secondary deposition 

processes. In particular, we hypothesized that the fractal 

morphology of the stone over a range of scale from perhaps a 

few nanometers to tens of micrometers4 critically influences 

the reactivity of the surface in primary deposition processes; 

controlled atmospheric-chamber experiments to evaluate 

this hypothesis were conducted at the U.S. National Center 

for Preservation Technology and Training in Natchitoches, 

La., and are discussed in Bede (2001). We also suspect that 

the stone porosity over the nanoscale and microscale regulates the transport of salts below the stone surface and that 

microscale grain consolidation and pore-space morphology 

govern the friability of the stone surface.

For the purposes of this study, we used microscale 

fracturing and surface-roughness measurements and nanoscale 

surface-reactivity measurements to infer the relative stability 

of test surfaces for each cleaning technique. The surfaceroughness measurements, which are based on a mathematical 

analysis of digitized electron micrographs, utilize the fractal 

properties of the stone surface to characterize morphologic 

features of the stone on the microscale. The near-surface 

fracture density was also assessed by computer analysis of 

microscale electron micrographs. The surface-reactivity measurements, which are based on the release rate of Ca2+ from 

the stone surface into water, reflect the fractal structure of 

the stone surface on the nanoscale. The degree of cleanliness 

is determined on the microscale by scanning-electron microscopy (SEM) and X-ray microanalysis,5

 and on the macroscale 

by systematic optical examination of both uncleaned and 

cleaned surfaces. Most of these measurements provide ratiotype data; the details of laboratory operations are discussed in 

appendixes 2 through 8.

Experimental Design

Many extraneous variables could influence the score 

assigned to a given cleaning technique. In addition to the 

degree of surface soiling before the stone is cleaned, such 

variables might include the surface morphology, friability, and 

porosity over a wide range of scale, as well as the mechanical 

stress between mineral grains at the stone surface (surface free 

energy introduced by carving, erosion, and so on). Although 

the influence of such variables can be minimized by testing 

the cleaning technique on stone surfaces of only one type (for 

example, ashlar), such optimization of internal validity would 

sacrifi ce the generalizability of the results and otherwise limit 

their value to preservation architects and conservators. As an 

alternative approach, we undertook an experimental design that 

partly controlled for extraneous variables by including a control group of test surfaces in the study. However, in view of the 

surface transformation caused by the cleaning technique, the 

exact same surface could not be examined before and after a 

given cleaning technique was applied. We developed a strategy 

by which a control surface adjacent to a given test surface was 

sampled simultaneously with the test surface. The actual coring 

procedure required protection of the soiled control surface such 

that both the control and cleaned surfaces were represented in 

each core, as illustrated in fi gure 6. As part of the setup pro-
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 6

cedure for each core, the soiled control surface was protected 

from cleaning with a layer of epoxy resin in advance of application of the cleaning technique. Specifically, about 150 cm2

 of 

the soiled stone surface was coated with epoxy resin to provide 

an approximately 15-cm-long boundary between the adjacent 

control and test surfaces. The cleaning technique was then 

applied to the test surface over an area that included the line of 

demarcation between the control and test surfaces. After application of the cleaning technique, the cleaned area was treated 

with an extremely low viscosity acrylic resin (L.R. white), 

followed by a layer of epoxy resin, to stabilize the stone before 

coring. The core was then taken from the cleaned area in such a 

way to include the line of demarcation between the control and 

test surfaces. Once in the laboratory, the cores were studied in 

cross section such that both the control and test surfaces could 

be examined in the same SEM image.

To represent as many extraneous variables as possible, 

a total of 160 1-in.-diameter cores were taken from pseudorandomly selected sites representing each surface condition. The 

choice of core location was generally biased toward extreme 

examples of a given surface-soiling condition. An overview 

of the sample-coring methods and information on the sample 

inventory selected for this study are presented in appendix 1; 

the procedures used for measuring these properties are detailed 

in appendixes 2 through 9. The exposed surfaces of the cores 

were tested for cleanliness and surface reactivity.

Cleaning Efficacy

The central question in this study concerns the ability of 

a given cleaning technique to remove a given type of surface 

soiling from the façade of the building with minimal impact 

on the integrity of the substrate and without compromising the 

surrounding environment. Three measurements on the cleaned 

stone were used to determine the efficacy of each cleaning 

technique (see fig. 35): (1) the surface distribution of particulate 

matter, (2) the structural penetration of salts, and (3) the surface 

refl ectivity (grayness).

Particulate Matter

The ability of the cleaning techniques to remove soiling 

from the stone was first evaluated with optical techniques. 

To study the surface distribution of particulate matter on 

the microscale, we prepared optical photomicrographs, mostly 

at the relatively low magnification of 67×, for most of the 

uncleaned and cleaned surfaces used in this study.6 The closeup 

surface views provided by the optical microscope supplemented 

the SEM and X-ray images7

 used to investigate the penetration 

of salts into the stone.

The photomicrographs in figure 7 show a qualitative 

difference between the soiling on LSS- and LSR-condition 

stone and on GCS- and GCR-condition stone. Although the 

soiling increases on uncleaned surfaces along the series LSSLSR-GCS-GCR, in LSS- and LSR-condition stone particulate 

matter appeared to be widely disseminated among dolomite 

crystals that are relatively pristine, whereas in GCS- and 

GCR-condition stone, aggregations of dark particles and white 

deposits appear to virtually cover the dolomite substrate and 

to be deeply encrusted in nooks and crannies of the surface. It 

is precisely the distribution of such particulate matter on the 

microscale that determines general surface reflectivity on the 

macroscale.

Salts

At the outset of this study, one of our chief concerns was 

the distribution of various salts in pores and cracks of the stone. 

In addition to causing unsightly blemishes on the stone, such 

salts as CaSO4⋅2H2O (gypsum) may participate in processes 

that eventually can result in catastrophic mechanical failure of 

the stone.

Primary X-ray analysis with an electron microscope was 

used to depth-profile the core cross sections. The spatial resolution of the analytical method was ≈5 μm, and the chemical 

detection limit and sensitivity were ≈0.25 and 0.50 atomic percent, respectively. The lateral distribution of salts was measured 

in uncleaned and cleaned areas of cores with all four surface 

conditions (LSS, LSR, GCS, GCR).

Typical cross-sectional SEM images of uncleaned core 

samples in the LSS, LSR, GCS, and GCR series are shown in 

fi gure 8. Among LSS, LSR, and GCS cores, the stone substrate 

supporting the layer of soiling seems to be relatively undamaged by the overlayer of soiling, whereas GCR cores presented 

a broad and continuous distribution in the extent of surface 

fracturing and the degree of surface roughness. Six GCR cores 

with extensive fracturing and surface roughness were particularly conspicuous by their apparent mechanical instability. 

These cores appeared in two forms—either sugary textured or 

highly fractured. In both forms, the surfaces below the crusts 

were noticeably more friable when examined with a mechanical 

probe than were the other GCR cores with smoother surfaces. 

The sugary-textured stone was distinguished by large, uniform, 

unconsolidated grains throughout the matrix. SEM images of 

samples GCRD–1A and GCRC–1, the only two examples of 

sugary-textured stone identified in the data set, are shown in 

fi gures 9A and 9B, respectively.

Except in cores of the sugary-textured stone, we discovered no relation between near-surface and subsurface fracturing. Apart from these cores, the extent of subsurface fracturing 

consistently ranged from 0 to 4 percent. The main connection 

between the sugary-textured and highly fractured GCR cores is 

that cores representing both surface-soiling conditions invariably were retrieved from carved stone (see figs. 50–52 for 

locations). Carved stone is generally positioned on the building 

in locations directly exposed to weathering and is commonly 

in the path of water streaming from adjacent carved elements. 

Because we are observing this material in its uncleaned weathered state, we have no information on the state of the freshly 

carved virgin stone. However, we speculate that the virgin 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 7

stone may have been inordinately stressed during carving and, 

accordingly, was predisposed to extreme weathering.

We assume that salt deposits, which may be present at 

concentrations of less than ≈1 weight percent of the stone matrix, 

play no significant role in the degradation of the stone by mechanisms involving salt crystallization. Evidently, the Berkshire Lee 

marble on the building is not deteriorating in a manner that 

enables salts to penetrate and collect in significant concentrations.

As expected, various mineral inclusions were discovered 

within several hundred micrometers of the exposed surfaces in 

all of the cores. Such mineral inclusions are easily differentiated from salt deposits and from particulate matter by their 

distinctive chemical profiles and morphology. Bremsstrahlung 

X-radiation was used to qualitatively identify organic particulate matter. Signifi cant observations relating to the distribution 

and penetration of salts include the following:

• No salts were detected on either uncleaned or cleaned LSS, 

LSR, and GCS surfaces.

• Gypsum generally was found to be irregularly distributed 

throughout the crust on the uncleaned surfaces of GCR cores. 

The gypsum did not appear to penetrate cracks and fissures of 

the surface to a depth more than a few micrometers.

• Only three of the eight cleaning techniques tested were successful in completely removing gypsum from GCR surfaces: 

combination, Armax, and JOS.

For future research, we suggest an investigation of the 

dissemination of salts into pores with more sensitive techniques, 

such as electron microprobe or secondary-ion mass spectroscopy. 

Although the results of such proposed work probably will not 

affect the conclusions of the current study, such information 

could significantly further our understanding of the fundamental 

mechanisms of salt transport into and out of porous stone.

Soiling

The procedures that we developed for measuring the surface reflectivity of core samples were based on the method of 

Reimann (1994). Surface reflectivity governs the general appearance of the stone as viewed from a distance of several meters or 

more. Two methods were used to estimate the surface reflectivity 

of the core surfaces: a quantity ß in gray-scale (gs) units, and 

a quantity complementary to the surface reflectivity. For both 

methods, ß was calibrated on a scale of 0 to 10 gs units, where 

0 indicates the shade of a freshly fractured white marble surface 

and 10 indicates 100-percent soiling of the surface (for example, 

sample GCRV–4). In the first method, the automatic shutter on 

the optical-microscope camera provided a means for electronically measuring the surface reflectivity of core samples in a 

highly localized area (≈3.4 mm2). In this method, the exposure 

time was used as a measure of the surface reflectivity of the core 

surface: The longer the shutter time required for a preset exposure, the darker the surface. We used the electronic reflectance 

measurements to explore the lateral distribution of soiling, on 

the microscale, across core surfaces. Not surprisingly, the lateral 

cluster density of particulate matter strongly correlates with the 

appearance of the surfaces, as shown in figure 7.

The second method was based on a systematic ordinal 

ranking of core surfaces by visual inspection. The reproducibility 

of this method, at ±0.2 gs units, was about 10 times better than 

that of the first method. Because this second method measures 

the average surface reflectivity of the entire core surface, we 

decided to rely on it for making ratio-data grayness assignments 

for evaluating the cleaning techniques. Details relating to the 

production of the optical photomicrographs and to the measurement of surface reflectivity are given in appendix 3. Grayness 

values for the various soiling-surface conditions and cleaning 

techniques are summarized in table 5 (see app. 3) for core series 

3 through 6 (see app. 1), and are graphed in figure 10.

LSS and LSR Surfaces

Most LSS and LSR cores studied were distinguished by 

relatively light surface fracturing and well-consolidated grains. 

The surface reflectivity of LSS and LSR cores is due to superfi -

cially distributed particulate matter, as shown in figure 7. This 

interpretation was verifi ed by SEM energy-dispersive X-ray 

analysis, which failed to detect any salts on or below the 

protected (uncleaned) surfaces of LSS or LSR cores. Because 

the surface reflectivity of these cores was about 2.0 and 4.0 gs 

units, respectively, the demands on the cleaning techniques are 

relatively low when applied to LSS- and LSR-condition stone. 

Although figure 10 shows that the gommage and combination 

techniques produce slightly cleaner surfaces than do the Armax 

and JOS techniques, the various cleaning techniques are nonetheless relatively undifferentiated when applied to LSS- and 

LSR-condition stone.

GCS and GCR Surfaces

The cleaning of GCS and GCR surfaces without concomitant damage to the stone is especially important because 

such surfaces are generally found on the carved stone that gives 

the building much of its esthetic appeal and historic character. 

Many GCS and GCR cores had well-consolidated grains near 

the surface and throughout the matrix. Five of the eight cleaning 

techniques successfully reduced the surface reflectivity of GCS 

cores to 2 gs units or less (fig. 10C: gommage, combination, 

Armax, JOS, and dry ice; the laser technique was not tested on 

GCR cores for surface reflectivity due to off-schedule completion 

of laser field work). The gommage and combination techniques 

produced slightly cleaner surfaces than those produced by the 

Armax, JOS, and dry-ice techniques, especially on GCR cores.

The blotches of black crusts that distinguish uncleaned 

GCS and GCR surfaces provide a suitable challenge for differentiation of the cleaning techniques. The gommage, combination, Armax, JOS, and laser techniques are superior in 

removing surface soiling that imparts a dark color to GCR-condition stone (fig. 10D). In this test, these fi ve cleaning techniques produced a consistently uniform stone color. Although 

the laser technique removed most dark material, the resulting 

stone had splotches of bleached, yellow deposits that appeared 

to be fused into the surface. Also, similar to the dry-ice tech-
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 8

nique, the laser technique was less reproducible from point to 

point on the surface.

The dry-ice technique was virtually eliminated from contention by the test on GCR-condition stone. The test area was 

originally dark and somewhat yellow. The core provided for 

testing was sugary-textured and friable, and no protected control area was available against which to reference the results. 

We recommend that the dry-ice technique not be used without 

additional testing.

The gommage, combination, Armax, and JOS techniques 

produce cleaned stone with a surface reflectivity in the range 

1.5–3 gray-scale units; the combination technique produced the 

cleanest surfaces of these four cleaning techniques. The photomicrographs shown in figure 7 are qualitatively consistent with 

the grayness values of cleaned GSR surfaces. They show that 

the Armax technique removes slightly more particulate material 

than does the JOS technique. With the possible exception of the 

gommage technique,8

 SEM images show that virtually all of the 

gypsum and particulate matter was removed from cracks and 

crevices in the stone by all four cleaning techniques. To further 

differentiate them, a quantitative definition of the reference level 

of cleanliness, C *, and an established limit on the acceptable 

level of damage to the stone would be needed (see section below 

entitled “Synthesis and Interpretation of Results”).

Surface Structure Before and After Cleaning

In this section, we focus on those properties of the stone 

which influence its susceptibility to processes which alter its structural integrity. Our operating assumption here is that the soiling 

load on the surface and the actual friability of the stone are related 

to the surface roughness and the near-surface fracture density. 

Possibly the most critical property controlling the microstructure 

of the stone is the degree and type of the contact among calcite, 

phlogopite, and dolomite grains in the matrix. As these grains are 

eroded, the stone develops a discernible sugary texture, and the 

near-surface fracture density increases. As a means of monitoring 

the influence of the various cleaning techniques on stone microstructure, we measured the near-surface and subsurface fracture 

density and the surface roughness before and after cleaning. These 

measurements were made by computer analysis of 100× SEM 

images of core cross sections; both binary-image files and photomicrographs were generated to document the profiles. Details 

of the surface-roughness and fracture-density tests are given in 

appendixes 6 and 7, respectively, and the software for SEM image 

analysis is outlined in appendix 2. The fracture density is reported 

as a volume percentage of the matrix occupied by fractures, crevices, and pore space. Although the measurement, which is limited 

by the operator’s ability to duplicate the calibration of the SEM 

image, is reproducible to within about ±0.25 percent, natural variations in the near-surface and subsurface fracture density are generally about ±4 and ±0.4 percent, respectively.9

The mathematical tool (Mossotti and others, 2001) used 

to monitor surface roughness (see app. 7) takes into account 

the fractal properties of the surface morphology over the range 

of observation. The surface roughness is reported in terms of 

a shape factor, Γ, whose units, in μmD–2, depend on the surface 

fractal dimension D. For the core samples tested in this study, 

the shape factor ranges from 0 to about 10, where 0 indicates 

a perfectly smooth plane and 10 corresponds to an irregular 

surface over the range 10–104 μm (0.0001–1 cm). The absolute 

reproducibility of the measured shape factor, as estimated by an 

analysis of replicate samples and of replicate views of a given 

surface-soiling condition, was calculated to be 0.41 cmD–2.

Friability of the Stone Before Cleaning

Before we consider the effect of various cleaning techniques on the structure of the stone, we must understand the 

structural state of the various surface conditions before any 

cleaning. If the stone has substantially deteriorated, our ability 

to replicate the building’s original appearance would be limited, 

and it would be unrealistic to establish specifi cations that are 

impossible to achieve.

In this analysis, we used the near-surface fracture density 

and the surface roughness to infer the friability of the stone 

on the microscale. In figure 11A, the near-surface fracture density is plotted against the shape factor for uncleaned stone 

(protected side of core below soiling) and for all surface-soiling 

conditions. As we might have expected, from the general distribution of data points, the fracture density and surface roughness 

appear to be directly related. Although all but one of the data 

points in fi gure 11A are widely scattered, the distribution of 

data points along the surface-roughness axis does not extend 

below a shape factor of 2.0. As discussed below in “Synthesis 

and Interpretation of Results,” this observation provides a basis 

for establishing a target surface roughness against which we 

can evaluate the various cleaning techniques.

Under our operating assumption, the farther a data point 

falls from the origin in the plot, the more friable will be the 

stone. To facilitate a statistical analysis of these plots, we used 

the fracture density (FD) and the shape factor (Γ) to define 

a parameter that we call the friability index (FI), which is a 

measure of the distance from the origin to a given data point in 

the plot. To uniformly weigh the fracture density and the shape 

factor in the definition of the friability index, the shape factor is 

scaled, ΓR, as a percentage of an arbitrary full-scale value of 10 

cmD–2; thus, ΓR is unitless. The friability index is given by

 FI = (FD)2 + (ΓR)

2 . (1)

The mean FI values and standard deviations for each 

surface-soiling condition before and after cleaning are plotted 

in fi gure 11C. The friability index was found to be gammadistributed for all of these plots. Among the uncleaned cores, 

the statistical patterns in fi gure 11C reveal two distinct data 

sets with a signifi cant statistical disparity, one containing 

cores visually classifi ed as smooth (LSS, GCS), and the other 

containing cores visually classifi ed as rough (LSR, GCR).10

In particular, most cores in the fi rst data set exhibit FI values 

in the range 31–51 percent, with an average of 40 percent; the 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 9

cores in this data set show no discernible relation between the 

fracture density and the shape factor. In contrast, most cores in 

the second data set have FI values in the range 20–80 percent, 

with an average of 51 percent.

The data plotted in figure 11C provide information on the 

relevance of the friability index in the formation of the soiling 

pattern on the building. Because both lightly and heavily soiled 

surfaces share the same friability index, the local soiling must 

be independent of the local microstructure of the stone. The 

data plotted in figure 11C also indicate that the formation 

of the soiling pattern on the building must be dominated by 

variables other than fracture density or surface roughness. We 

speculate that the soiling pattern is predominately controlled 

by secondary mobilization processes acting over a wide range 

of scale. This model, which precludes a strong influence of 

microstructure of the substrate on the local surface chemistry of 

the stone, is modeled in figure 12. Note that the model does not 

preclude the possibility of a significant influence of the fracture 

density and the surface roughness on the primary deposition of 

SO2

 and particulate matter.

The data points in figures 11A and 11B are resolved in 

plots of fracture density versus shape factor for all four surfacesoiling conditions before and after cleaning (fi g. 13). As the 

stone becomes more heavily soiled in the series LSS→LSR→

GCS→GCR, the relation between the fracture density and the 

shape factor becomes closer. The fraction of the variation in 

the fracture density that is associated with the variation in the 

shape factor is measured by the R2 value (square of the correlation coefficient) indicated on each plot. In the LSS and 

LSR group shown in figures 13A through 13D, the distribution 

of data points is nearly random; in the GCS group shown in 

fi gures 13E and 13F, a very weak correlation is evident; and 

in the GCR group shown in figures 13G and 13H, a defi nitive 

correlation is apparent.

Surface Fracturing and Roughness After Cleaning

A plot of fracture density versus shape factor for cleaned 

cores is shown in figure 11B, similar to that for uncleaned cores 

in fi gure 11A. As in figure 11A, nearly all data points in fi gure 

11B fall above a surface-roughness value of 2.0. Also similar 

to fi gure 11A, a conspicuous, though somewhat veiled, correlation between fracture density and the shape factor is evident 

in fi gure 11B; this correlation augments our understanding of 

the causality between the soiling load on the stone and the 

microstructure of the substrate. On resolution of figure 11B into 

component sets for each surface-soiling condition in figures 

13B, 13D, 13F, and 13H, the correlation evident in figure 11B

is blurred by the random distribution of data points. The relation between the fracture density and the surface roughness is 

shown in sharper relief in fi gures 13F and 13H.

The R2

 values of the plots for uncleaned and cleaned 

stone in figure 13 are summarized in figure 14, where the 

surface-soiling conditions can be partitioned into the LSS 

and LSR group, characterized by random plots, and the GCS 

and GCR group, which features a strong relation between the 

fracture density and the shape factor.11 From this defi nitive 

partitioning, we conclude that the marked dependence of the 

friability index on the shape factor emerges as a result of applying the cleaning techniques to the heavily soiled stone. However, the fact that the correlation between the fracture density 

and the shape factor does not appear in the microstructure of 

either uncleaned or cleaned surfaces of LSS or LSR stone is 

strong evidence that the cleaning techniques do not generally 

cause such damage to the stone surfaces but simply expose the 

close relation between fracture density and the surface roughness (see section below entitled “Synthesis and Interpretation 

of Results”). The data in fi gure 14 also support the conclusion 

that GCS and GCR surfaces have been marginally damaged by 

surface alteration mechanisms concurrently affecting the fracture density and the surface roughness, and that such mechanisms are driven by the soiling load on the stone, as modeled in 

fi gure 15.

The ensemble mean friability indices and standard deviations for all of the cleaned cores are summarized in fi gure 

11B. Although we could not statistically differentiate the various surface-soiling conditions on the basis of friability index 

after cleaning, we could sharply partition the cores into two 

groups on the basis of the statistical disparity between cleaning 

techniques. The cleaning techniques producing the lowest friability indices and, presumably, the most stable stone surfaces 

are gommage, combination, Armax, and JOS; with few exceptions, these techniques result in surfaces with FI values of 

30±5 percent. In contrast, the cleaning techniques with the most 

erratic friability indices and the least stable stone surfaces are 

dry ice, laser, misting, and power wash; these techniques result 

in surfaces with FI values of 48±8 percent. The interpretation of 

these results is clarifi ed by the surface-recession tests discussed 

in the next section, and is examined in detail in the section 

below entitled “Synthesis and Interpretation of Results.”

How well do the above observations support our operating assumption relating the fracture density and the roughness to the actual friability of the stone? Under our operating 

assumption, we expect that surfaces with different friability 

indices would exhibit corresponding differences in mass loss 

when cleaned by a particular technique, provided the technique 

is suffi ciently destructive to breach the threshold for disintegration of the stone. Even disregarding statistical considerations, 

we believe that the relation between the friability index and the 

actual friability of the stone is radically nonlinear, as well as 

dependent on the specifi c process acting to alter the stone. In 

the next section, we use surface-recession measurements (actually, an estimate of the friability of the stone during cleaning) to 

statistically analyze the relation between the friability index and 

the actual friability of the stone. Once we have demonstrated 

this relation, we use the friability index of cleaned surfaces to 

differentiate the various cleaning techniques.

Accelerated Weathering of Cleaned Surfaces

A critical factor in the design of strategies for the maintenance of stone buildings is an understanding of how the envi-
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 10

ronment at a given site affects the utility12 of the building stone. 

Because the marginal effects of weathering on stone appear 

only after an extended period of time, researchers in preservation technology have been motivated to design techniques that 

allow such research to be conducted over a relatively brief 

period, and that accelerate our understanding of the effects of 

the environment on stone. In the approach most commonly 

used to accelerate the study of weathering effects, the test 

surface is exposed to controlled chemical and (or) physical 

stress by systematic manipulation of the chemical and (or) 

physical environmental variables. To expedite the appearance 

of weathering effects, one or more of these environmental 

variables are raised to levels far exceeding those that occur 

naturally. For example, the test surface might be subjected 

to extreme concentrations of SO2 at an elevated temperature, 

with the intention of causing discernible chemical alteration 

in the shortest possible time. In contrast to this approach, the 

project team designed a program to study weathering effects 

without resorting to artificial manipulation of the environmental variables to their extreme limits. Instead, we developed techniques that closely replicate the effects of the actual 

ambient environment on the building stone. Accordingly, we 

designed our physicochemical measurement and data-analysis 

techniques to emphasize the differential sensitivity of cleaned 

surfaces to specific processes that are known to degrade the 

utility of the stone. Requisite to this strategy is the use of the 

most exacting, state-of-the-art analytical-chemical methodology. To the extent that the tests conducted by the project team 

produced results within a relatively brief period, these techniques also accelerated the study of weathering effects. We 

believe that this strategy provides a more effective evaluation 

of the impact of environmental conditions on the building 

stone than would previously published accelerated-weathering tests.

Stone deterioration is driven by complexly interacting 

physical and chemical mechanisms. The three most important 

chemical agents acting to diminish the utility of calcareous 

stone are water, SO2 gas, and particulate matter. In both primary 

and secondary deposition, water weakens the stone and alters 

its morphology over a wide range of scale by dissolving dolomite grains. The rate of mass loss by dissolution in water 

is directly proportional to the fractal area exposed to flowing 

water, a complex quantity related to the microscale morphology 

of the stone surface. SO2

 gas can be an important factor in 

stone deterioration within an urban environment because of its 

alteration effect on calcareous stone. A complex set of mechanisms involving oxidation of SO2

, neutralization of H2SO4, and 

secondary deposition of gypsum into poorly washed areas of 

the building surface can set the stage for discoloration of the 

stone and for surface spallation. Similarly, particulate material 

degrades the stone over a wide range of scale by serving as 

a nutrient base for biological organisms (mold, lichen, moss) 

and through mechanisms involving the entrapment of fly ash in 

gypsum deposits.

Physical mechanisms generally dominate the transport of 

chemical reactants and reaction products. The physical aspects 

of primary and secondary deposition, mainly hydrodynamicfl ow patterns, are governed by the macroscale properties of 

the building itself (morphology and orientation of the stone 

structures) and do not fall under the purview of acceleratedweathering testing. The deterioration processes controlled by 

the microscale physical properties of the stone that critically 

influence building-maintenance decisions include (1) the 

loss of structural integrity of the stone on the macroscale due 

to fractures in the stone, (2) the loss of surface mass on the 

microscale due to wind- and water-driven abrasion, and (3) the 

affi nity of the surface for particulate matter. All three of these 

processes are governed to some degree by the near-surface 

fracture density and the surface roughness.

Our strategy for accelerating the study of weathering 

effects makes use of physical and chemical stressing of core 

samples to differentiate the relative stabilities of cleaned 

stone. Three tests were designed for this purpose; the results 

from two of these tests are included in this report. (1) The 

surface-recession test, described below, measures the mass 

loss from the surface due to the various cleaning techniques. 

The results from this test clarify the interpretation of the friability index and provide insight into the affinity of cleaned 

surfaces for primary deposition of particulate matter. (2) The 

controlled-atmosphere test (Bede, 2000, 2001), which permits 

close control over such experimental conditions as SO2

 concentration, windspeed, temperature, and relative humidity, 

measured the deposition velocity of SO2 onto cleaned core 

surfaces. (3) The surface-reactivity test, also described below, 

which exploits the clean-rain effect to measure the fractal 

area of the stone over the nanoscale to microscale range, was 

used here to assess the susceptibility of the cleaned stone to 

dissolution by water.

Surface Recession and Friability of the Stone

The surface-recession test (actually, an indirect measurement of mass loss from the stone during cleaning) was designed 

to determine the mass loss associated with each cleaning 

technique, and to explore the relation between the friability 

index and the actual friability of the stone. By exploiting the 

destructiveness of the various cleaning techniques, this test 

provides information with which we can infer the friability of 

cleaned stone without having to subject the cleaned stone to 

any mechanical stress.

The mass loss from the surface during cleaning depends 

on at least four factors. We hypothesize that the fi rst factor, 

the actual friability of stone, depends on the near-surface 

fracture density and the surface roughness. As mentioned in 

the preceding section, we believe that the relation between the 

friability index and the actual friability of the stone is radically 

nonlinear; this relation is determined by the surface-recession 

test. The second factor is the specific cleaning technique used. 

Although experience teaches us that the more destructive the 

cleaning technique, the greater will be the mass loss, we also 

suspect a power threshold below which the mass loss from 

the surface will be minimal. The third factor is the thickness 

and composition of the overlayer of soiling on the stone. A 

heavy soiling overlayer generally will require the extended 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 11

application of a given cleaning technique or the use of a more 

destructive cleaning technique on the stone. The fourth factor 

is the marginal friability of the cleaned stone. Once the soiling 

has been removed from the substrate, the cleaned surface is 

exposed to the treatment for a fi nite period, during which the 

mass loss from the surface may marginally increase.

Given the complexity of the issues relating to mass loss, 

how can we obtain useful weathering information with the surface-recession test? Because our purpose is to rank the cleaning 

techniques in terms of the expected friabilities of the cleaned 

stone, the issue of nonlinearity is inconsequential. Therefore, 

we simply need to establish the relation between the friability 

index and the actual friability of the stone. Then, we can use 

the FI values of cleaned surfaces to rank the various cleaning 

techniques.

The surface-recession test is based on a measurement of 

the change in microscale relief of the stone substrate during 

cleaning. These measurements were made by computer-assisted 

analysis of 10× SEM images of polished cross sections of core 

samples in which the lateral field of view included both the 

protected and cleaned area of the core. Two methods were 

used to measure the change in relief, one based on direct 

observation of the distance between the average elevation of the 

cleaned surface and the average elevation of the substrate on 

the protected side of the core, and the other based on the 

amount of surface smoothing resulting from the cleaning technique. Because the second method contains a systematic offset 

error, this method estimates only the minimal surface recession 

(fi g. 16). The first method is fundamentally limited by natural 

irregularities in the stone surface over the lateral range of 

observation. The standard deviation of the natural morphology 

of the surface over 1 cm ranged from 35 to 150 μm, depending 

on the particular location on the building from which the core 

was taken. These measurements are discussed in more detail 

in appendix 5.

The observed surface-recession values for all of the 

cores for which such tests were possible are listed in table 

8 (see app. 5). The most favorable values, with uncertainties 

of ±33 μm, apply when the measured surface recession was 

largely due to changes in morphologic relief from the protected side of the core to the cleaned side rather than from 

laterally uniform mass loss across the sample surface. The 

uncertainties in such values were estimated from the statistical reproducibility of the morphologic variations across the 

lateral range of observation.

Under our operating assumption, we expect that surfaces cleaned by a given technique would undergo a mass loss 

in direct relation to the magnitude of the friability index. A 

plot of surface recession versus friability index for uncleaned 

cores (fig. 17) generally validates this operating assumption 

by differentiating the cores that were visually classified as 

rough from those classified as smooth. For FI values greater 

than ≈35 percent and surface-recession values greater than 

≈75 μm, a distinct correlation exists between the friability 

index and the measured surface recession: the R2

 value for 

the regression line through the points falling above (30,75) 

is 0.60. For FI values and surface-recession values excluding the point (30,75), the correlation, if any, is much less 

definitive. This result is understandable if we recall that the 

uncertainty in the measured surface recession ranges from 33 

to 90 μm, an interval that virtually guarantees poor analytical precision for the points below an FI value of 30 percent 

and a surface-recession value of 75 μm. This interpretation is 

supported by an analysis of the residuals from the regression 

line which indicates that the conditional probability distributions of the measured surface recession for a given friability 

index do not have the same variance, and that the relation 

between friability index and surface recession is not signifi -

cantly nonlinear over the range of observation. Although the 

cleaning techniques are not statistically differentiated by the 

surface-recession test, the results plotted in figure 17 validate the use of the friability index as an appropriate tool for 

ranking the various cleaning techniques on the basis of the 

inferred friability of the cleaned stone. The fact that the cleaning techniques are statistically differentiated by the friability 

indices of the uncleaned stone but not by the surface-recession tests, as shown in figure 11C, indicates that the mass loss 

due to cleaning depends more on the microstructure of the 

soiled stone than on the cleaning technique used.

The results plotted in fi gure 18E show that the dry-ice, 

misting, and laser techniques are less reproducible and result 

in potentially more friable surfaces than do the JOS, gommage, 

combination, and Armax techniques. Interestingly, this grouping is consistent with the statistical partitioning of cleaning 

techniques shown in fi gure 11B that was based on the friability 

index of uncleaned stone.

For future work, we recommend that all cleaning techniques be tested on the same soiling condition of ashlar so that 

they can be compared under similar limitations. Because of 

statistical problems related to undersampling, the relative contributions of the various factors contributing to surface recession could not be determined by multivariate analysis.

Surface Reactivity to Water

The surface-reactivity test was designed as a simple indirect 

method for measuring the reactivity of the stone over the nanoscale 

to microscale. Central to this test is a reduced-form model which 

fi nds that the rate of release of Ca2+ ions into flowing water is 

directly proportional to the effective reactive area of the stone. 

The surface-reactivity test, which is based on the clean-rain effect 

(Mossotti and others, 2001), measures the susceptibility of the 

cleaned stone to dissolution by flowing water.

Theoretical Summary

The dissolution of calcite is described by the reaction 

(Plummer and others, 1978)

CaCO3 + H2

O↔Ca2+ + HCO3

− + OH− (k3

=10−6.92 cm/s), (2)

where k3 is the rate constant for the reaction. In this formulation, we explicitly ignore the reverse reaction, Ca2++CO3

2–→
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 12

CaCO3

, and regard the test solution as undersaturated in Ca2+

with respect to calcite. Given the sensitivity of analytical methods for calcium, this condition is easy to satisfy. If the stone 

surface is Euclidean, the rate of mass loss due to reaction (2) 

(in millimoles per second), Mt, is given by

 Mt = k3A, (3)

where A (in square centimeters) represents the Euclidean area. 

If the stone surface exhibits fractal properties, the mass of Ca2+

lost over time t (in seconds), Mt (in millimoles), is given by an 

integral of the form

 Mt = k3 ∫ ∫ kH dHdt, 0 surface

t

 (4)

where dH represents the two-dimensional Hausdorf measure13

of a cleaned stone surface of fractal dimension D and kH, a 

constant equal to 1 cm2−D, reconciles noninteger units of the 

Hausdorf measure. For the conditions under which the rate of 

stone dissolution is controlled by reaction 2, we define the 

surface reactivity, Â (in square centimeters), by the surface 

integral embedded in equation 4 as follows:

 

A=k ˆ H ∫ dH. surface 

(5)

Thus, the mass loss of Ca2+ (in millimoles) is given by

 Mt = k3Ât. (6)

Equation 6, which encapsulates the central principle of the 

surface-reactivity test, shows that the total amount of Ca2+

lost depends only on the product of the surface reactivity and the time of wetness. This result is subject to two 

caveats: (1) the flow rate of water over the stone must be 

sufficiently fast to sweep the surface of reaction products 

that may accumulate and participate in the reverse reaction, 

and (2) steady-state conditions must be maintained at the 

molecular scale. Phenomenologically, the surface-reactivity 

test, which is outlined in detail in appendix 8, is designed 

to measure the area covered by H2

O molecules on the stone 

surface. The surface reactivity,14 which is a property of the 

stone, is an intricate function of the stone morphology at 

the nanoscale.

In principle, the laboratory implementation of the 

surface-reactivity test is quite simple because the rate of 

release of Ca2+ ions is independent of the local hydrodynamics of the test solution once a critical threshold surface 

fl ow rate has been exceeded.15 As modeled in figure 19, any 

apparatus that provides for continuous recycling of a fixed 

volume of water over a selected area of stone surface will 

meet the basic conditions required for the surface-reactivity test. Continuous recycling can be implemented with an 

infusion pump with a feedback loop or, possibly, by swirling 

the test solution over the sample surface in a beaker. After a 

measured period, the Ca2+ concentration in the test solution 

is determined, and the release rate is computed. Depending 

on the sensitivity of the analytical method of choice for Ca2+ 

determination, the test can be completed in less than 3 or as 

long as 45 minutes.

Results

The absolute rate of release of Ca2+ ions, sorted in 

descending order for all cores tested, is plotted in fi gure 20. 

The reproducibility of the surface-reactivity test on a given 

sample on a given day is generally better than ±1 percent; 

the reproducibility on the same sample from month to month 

is approximately ±7 percent. Because the variation in relative 

surface activity of different cores in a given series (for example, 

samples GCS–3, GCS–4) ranged from about ±8 to 11 percent, 

the natural variation in relative surface activity from point to 

point on the building appears to dominate sample-to-sample 

excursions in the test results. The absolute rate of release of 

Ca2+ ions versus shape factor for all cleaned (unprotected side) 

cores for all surface-soiling conditions (fi g. 21) shows that at 

least 17 percent of the relative surface activity can be accounted 

for by the surface roughness of the stone on the microscale.

The average relative surface activities for each surfacesoiling condition and each cleaning technique in core series 3 

and 4 are plotted in fi gure 22. Without the use of a sulfate 

correction on the Ca2+ concentration in the test solution, the 

possible interference from gypsum in the surface-reactivity 

test precluded any measurement of the surface reactivity on 

uncleaned surfaces. This interference, however, was not a problem for any of the cleaned surfaces, for two reasons. First, all of 

the cores were extensively washed with deionized water before 

being tested. Second, gypsum was detected by X-ray analysis 

only on core series GCRM, which, interestingly, exhibited the 

lowest surface reactivity in the entire data set.

The relative surface activities plotted in figures 22A and 

22B show similar patterns for the four cleaning techniques 

(power wash, gommage, JOS, dry ice) that were used on both 

LSS and LSR surfaces. For these surfaces, those cleaned by the 

gommage technique generally exhibit the highest rate of stone 

dissolution, whereas those cleaned by the JOS technique exhibit 

the lowest. Note that the relative surface activity of surfaces 

cleaned by the gommage technique is statistically the same as 

that expected from a Euclidean calcite surface. Similarly, the 

range in relative surface activity observed for GCR surfaces 

(fig. 22A) is consistent with those observed for LSS and LSR 

surfaces. On the basis of the natural variation in relative surface 

activity between samples, the probability of a chance occurrence 

of the patterns observed in figures 22A and 22B is vanishingly 

low. Thus, the relative surface activity is a surface property that 

is remarkably sensitive to the state of the stone.

The relative surface activities associated with GCS 

surface provide insight into the mechanisms of stone erosion 

by water. Most notable are the high relative surface activities for cores cleaned by the Armax and dry-ice techniques. 

Apparently, the surface-reactivity test is hypersensitive to 

small differences in the state variables associated with LSS, 

LSR, GCS, and GCR surfaces; conceivably, such differences 

may be amplified when the surfaces are cleaned by either the 

gommage, Armax, or dry-ice technique. Because the same 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 13

cleaning technique applied to various surface-soiling conditions gives drastically different relative surface activities, 

the surface reactivity appears to depend on a complex set 

of variables that are unique to the state of the surface over a 

highly localized area. The data plotted in figure 22C suggest 

that cleaned areas of the stone are statistically undersampled 

in the surface-reactivity test when a projected surface area of 

only ≈3 cm2

 is probed. Such erratic relative surface activities 

could account for the highly localized pockets of acute erosion that are commonly observed on carved stone.

The results for the gommage and combination cleaning 

techniques plotted in figure 22C are particularly puzzling. 

Because these two techniques are virtually identical once 

the cores have been subjected to the surface-reactivity test, 

we would expect to observe the same relative surface activities for both techniques. The measured difference in relative 

surface activities indicates that the misting applied immediately after gommage deactivates the surface before the cores 

cleaned by the gommage technique are taken for laboratory 

testing. In particular, such deactivation can occur by dissolution of mechanically stressed microstructures on the surface. 

Presumably, such deactivation would occur naturally when 

the stone is washed during the first rain event after the building has been cleaned. Thus, from figure 22, we infer that the 

absolute surface reactivity depends more on the petrologic 

structure and composition of the stone than on the cleaning 

technique used. The relation between a marginal change in 

the rate of release of Ca2+ ions and marginal increases in the 

level of stone cleanliness for all surface conditions is plotted 

in figure 23A; the surface reflectivity, given by 1−ß (grayness value, in gray-scale units), was used as a measure of the 

cleanliness of the stone. Plots for cleaned LSS, LSR, GCS, 

and GCR cores (fi gs. 22B through 23E, respectively) suggest 

that marginal application of the various cleaning techniques 

to surfaces carrying different soiling loads does not systematically change the surface reactivity.

Effect of Surface Reactivity to Water on Global Variables

In general, water erosion can alter the morphology of 

the stone over a wide range of scale. On the nanoscale to 

microscale, water can differentially alter the microstructure 

of the stone by preferential dissolution of the more reactive 

surface areas. On the building, for example, such hyperreactive stone might include dolomite crystals with a high 

surface free energy in areas of highly carved stone, or, possibly, the less consolidated calcite and phlogopite grains in 

the stone. Given enough time, the action of water erosion can 

alter the morphology of the stone in highly carved areas over 

an intermediate range of scale (millimeters to centimeters) 

and can even change the local orientation of the surface. A 

general model of the relation between water-driven physicochemical-alteration processes and the physical surface structure of the stone is shown in figure 24. As discussed earlier, 

the surface chemistry, with regard to the local soiling load, 

can also accelerate changes in the surface microstructure (see 

fi g. 15).

Surface Sensitivity to SO2 and Particulate Matter

It is well known that SO2 can be an important factor 

in stone deterioration because of its alteration effect on calcareous stone. Equally damaging, primarily to the esthetic utility 

of a building in an urban environment, are such agents as 

airborne particulate matter. The first stage in the sequence of 

processes leading to SO2-driven damage to the stone is gravityindependent primary deposition of SO2

 onto the stone surface. 

Similarly, the total load of particulate matter on the building 

surface is governed by the efficiency of primary deposition 

processes. The influence of surface microstructure on the effi -

ciency of primary deposition processes is still under active 

investigation. The U.S. National Park Service and the University of Delaware conducted collaborative research on primary-deposition effects at the National Center for Preservation 

Technology and Training in Natchitoches, La. (Bede, 2001). 

The primary-deposition experiments included measurement of 

the deposition velocity of SO2 onto model surfaces; materials 

studied in the controlled-atmosphere chamber included the 

Salem limestone, quarried in Bloomington, Indiana; the Cordova Cream limestone, quarried in Cedar Park, Texas; the Cottonwood Top Ledge limestone, quarried in Council Grove, 

Kansas; and the Monks Park limestone, quarried in Wiltshire, 

England. This collaborative research extends the earlier drydeposition studies by Spiker and others (1992) and Wu and 

others (1990). The twofold purpose of this research was to 

increase our understanding of the role and importance of surface microstructure on (1) the deposition velocity of SO2

 under 

meteorologic conditions similar to those on Philadelphia City 

Hall, and (2) the deposition effi ciency of particulate matter, as 

illustrated in fi gure 25.

Synthesis and Interpretation of Results

Stone Stability

A central issue in the evaluation of cleaning techniques is 

the long-term utility of the cleaned stone. Implicit in the operational concept of long-term utility are considerations of esthetic 

value and stone stability. In the parlance of the conservation 

community, the esthetic value is refl ected by the “presentation 

appearance” of the stone; and in the idiom of mathematics, 

stone stability has meaning only when considered in terms of a 

set of specifi c degradation processes on a well-defined planning 

horizon.

The concept of stone stability is simple in principle but 

somewhat complex in practice. All deterioration processes 

manifest as changes in the physicochemical properties of the 

stone that affect its general utility. Changes in such properties, 

including color, structural integrity, and morphology, are driven 

by both physical and chemical mechanisms. During erosion, 

the state of the stone is slightly altered immediately on the fi rst 

contact with soiling agents. The time required for the stone 

surface to change from its initial state to some reference state 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 14

depends on the states traced by the stone. Because deterioration processes are iterative, the traditional use of a statistical 

half-life to represent the stability of a section of a building is 

inappropriate. However, models of the relation between the 

variables defining the present state of a stone surface and its 

state at some point in the future are of only limited external 

validity and are generally restricted to application over a 

range of scale of 0.1 to 10 cm and within a relatively short 

period.

For the purposes of this study, we developed a semiquantitative framework within which we can infer the relative 

stone stability of test surfaces without explicitly specifying 

a planning horizon. Our intention in this analysis is to rank 

the expected stabilities of the cleaned surfaces produced by 

the various cleaning techniques. This framework rests on the 

assumption that the state trajectories followed by dissimilar 

surfaces may converge but may never cross if the surfaces 

weather within the same environment. Consider the following 

example. If the stone morphology on the microscale is regarded 

as a state variable, we assume that a given surface which is 

originally rougher than a reference surface will never spontaneously become smoother than the reference surface as long as 

both surfaces weather within the same environment. Under this 

assumption, it follows that if the stone stability depends on the 

surface state, then the ranking of a set of stone surfaces on the 

basis of stability will never change if the test surfaces weather 

under identical conditions. Our goal in this evaluation then 

reduces to that of identifying a suitable set of parameters from 

which we can rank the stabilities of stone surfaces. Central to 

this model are a set of assumptions about how each particular 

parameter relates to the stone stability.

Data-Synthesis Model

An important goal of the experimental work described in 

the preceding paragraphs, and a task prerequisite to defining 

the specifications against which the various cleaning techniques 

can be evaluated, was to (1) identify the variables that are 

affected by the cleaning techniques, and (2) evaluate the extent 

to which such variables influence the stability and utility of 

the stone. Toward this goal, we found it useful to consolidate 

the relation represented in figures 5 (primary deposition and 

secondary mobilization), 12 (soiling-pattern development), 15 

(alteration by soiling load), 24 (alteration by water), and 25 

(factors controlling primary deposition) into a composite model 

of the soiling-agent-delivery and stone-alteration processes, as 

illustrated in fi gure 26.

A critical factor controlling the operation of the overall 

system represented in figure 26 is the multiplicity of feedback 

loops. The importance of this feature to the operation of the 

system cannot be overestimated. If the feedback loops are 

negative, the state of the system tends to adapt to changes 

in the input variables and to maintain a stable output. If the 

feedback loops are positive, the state of the system tends to 

move toward a particular extreme limit imposed by a set of 

physical constants; an extreme example of such a limit would 

be mechanical failure of the stone. In figure 26, only the main 

feedback paths are shown.

Two additional properties of the overall system are not 

apparent in figure 26: (1) the system dynamics are iterative, and 

(2) the system has a long memory. In iterative systems with 

long-term memory, the inputs at any given time are infl uenced 

by the cumulative outputs over an extended period in the past. 

Such systems, which run continuously, are exceedingly sensitive to initial conditions and tend to be attracted to extreme 

limit states. The four surface-soiling conditions identifi ed on 

the building in this study, LSS, LSR, GCS, and GCR, represent 

possible examples of such limit states.16 From the perspective 

of fi gure 26, we now consider the variables that are affected by 

the cleaning techniques, and evaluate the extent to which such 

variables influence the utility and stability of the stone.

Soiling Load

The most obvious effect of the soiling load is on the 

esthetic utility of the stone (box E, fig. 26). Clearly, the variable 

over which the cleaning techniques have the greatest control is 

the grayness value of the stone.

In accordance with the model shown in figure 26, we 

believe that processes on the macroscale are regulated by the 

waterflow, which itself is controlled by the gross morphology 

and orientation of the stone. Thus, the model incorporates the 

expectation that secondary mobilization processes play a predominant role in determining the soiling pattern. Because the 

cleaning techniques being evaluated can affect the physicochemical properties of the stone only at the nanoscale and microscale, 

the influence of the cleaning techniques on the stone stability 

will be evident only in the total soiling load on the building 

surface. Although the soiling load has been shown to influence 

the microstructure of the stone, we did not consider the effect of 

residual soiling on the stone stability as a means to differentiate 

the cleaning techniques, because all the techniques will have to 

fi rst pass the esthetic specification in order to be considered. 

To pass this specification, the cleaning technique will have to 

remove virtually all the soiling load from the surface. However, 

because this removal is a primary function of the buildingcleaning program, the level of residual surface soiling will constitute a key element in the specifications for cleaning techniques.

Surface Microstructure

The three variables related to surface microstructure that 

are significantly influenced by the choice of cleaning techniques are the fracture density, the surface roughness, and the 

correlation coefficient reflecting the relation between the fracture density and the surface roughness. How significant are 

these variables with regard to the long-term stability of the 

stone, and should we use them to establish cleaning criteria for 

the various techniques?

Three links connect the stone morphology to the dominant processes in the model shown in fi gure 26, all three of 

which are significantly controlled by the microstructure of the 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 15

stone. The fi rst infl uence of the stone microstructure on the 

stone stability is shown by the connection between panel B and 

box G in figure 26, which is part of a positive feedback loop 

with panels B, C, and D, and box G. If the fracture density or 

surface roughness increases, the number of active sites for dry 

deposition of SO2

 and particulate matter likewise increases. In 

addition, the ability of the surface to attract and retain moisture 

increases; the additional moisture on the surface accelerates 

the rate of dry deposition on the stone surface and enhances 

its affi nity for airborne particulate matter. Thus, the greater the 

fracture density and the surface roughness, the more efficient 

will be the primary deposition processes that feed the mechanisms controlling the evolution of the soiling load. Over the 

long term, the increased soiling load drives irreversible damage 

to the stone substrate, as suggested by the connections between 

panels D and E and box G in figure 26. Such damage is commonly evident as an increase in the fracture density and the 

surface roughness.

The second influence of the stone microstructure on the 

stone stability is suggested by the connection between panel 

C and box G in figure 26. This connection acknowledges the 

effect of the stone microstructure on the waterflow pattern over 

the surface. Because water is the dominant environmental agent 

mobilizing salts and particulate matter in secondary deposition 

processes, the stone microstructure is clearly a critical determinant of the long-term stability of the stone.

The third influence of the stone microstructure on the 

stone stability is through its regulation of physicochemical 

alteration processes, as shown by the connection between panel 

D and box G in figure 26. The monomolecular dissolution of 

dolomite by water is one of many examples of physicochemical 

alteration processes that are surface controlled on the nanoscale 

and microscale. In view of the significant influence of the stone 

microstructure on the long-term stability of the stone, we used 

the microstructure-variables (fracture density, shape factor, R2

value) to evaluate the cleaning techniques.

Surface Recession

Two aspects of the surface-recession test are of interest 

in this discussion, both relating to the significance of the mass 

loss to the state variables represented in box F in figure 26. The 

fi rst aspect concerns the influence of a single episodic mass 

loss on secondary mobilization processes (panel C, fig. 26), 

and the second aspect pertains to the effect of a single episodic 

mass loss on the esthetic value of highly carved stone. From a 

strictly mechanistic viewpoint, the pattern of waterflow on the 

surface that drives secondary deposition processes is mainly 

governed by the morphologic relief and orientation of the surface 

on the macroscale. Because the worst-case surface recession 

is measured on the microscale, the effect of surface recession 

is relatively insignificant. From an esthetic point of view, it is 

inconceivable that the utility of the stone could be affected by 

a single episodic recession event measured in microns. Finally, 

because we can account for 60 percent of the variation in the 

measured surface recession greater than 75 μm by the friability 

index of the uncleaned surface (see subsection above entitled 

“Surface Recession and Friability of the Stone”), we believe that 

the observed surface recession primarily reflects the initial state 

of the surface rather than the various cleaning techniques or the 

initial state of soiling. Accordingly, we disregarded the surface 

recession as a factor for evaluating the cleaning techniques.

Surface Reactivity

Water is probably the most important environmental 

agent in the degradation of building stone. Water is included in 

box A in figure 26 as an environmental agent that is delivered 

to the building surface by primary deposition, and water constitutes the carrier agent in box C. In figure 22, the rate of stone 

dissolution by pure water is drastically affected by the choice 

of cleaning technique. Nonetheless, because our understanding 

of the absolute rate of release of Ca2+ ions is incomplete, 

we used the surface-reactivity test only as a supplementary 

factor for evaluating the cleaning techniques. Because figure 

21 shows that the surface reactivity is linearly related to the 

surface roughness, we used the surface-reactivity test only in 

conjunction with the surface-roughness test for evaluating the 

cleaning techniques.

Cleaning Criteria and Selection of Cleaning 

Techniques

In the above discussion, we argued that measurements 

which reflect the soiling load and the surface microstructure 

could be used to evaluate the cleaning techniques. In this section, we consider how to define the reference (target) values for 

the measurements to be used for evaluation, and we develop 

a scoring technique for discovering which of the cleaning techniques come closest to concurrently matching more than a 

single reference value. This scoring technique, which assumes 

the format of the common dartboard, provides a basis for 

estimating the expected relative reproducibility of the various 

cleaning techniques.

Capacity, Accuracy, and Finesse

The capacity, accuracy, and finesse of a cleaning technique are all closely related concepts. The capacity of the technique measures its ability to alter a particular state variable; 

the difference between the final value and the initial value of 

the variable is a measure of the capacity. The accuracy of the 

technique measures its ability to produce the target value of 

a given state variable; the systematic deviation between the 

fi nal value and the target value of the state variable is a 

measure of the accuracy.17 Finally, the fi nesse of the technique 

measures its marginal effect on the variable of interest relative 

to the reference state. Depending on how the reference state is 

defined, the finesse can measure the ability of the technique to 

produce the desired result without causing collateral damage 

to the stone surface.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 16

In fi gure 3, we defined a general measurement variable 

X that provides a signature of an underlying state variable. In 

fi gure 3, the initial surface is schematically represented by the 

variable X°, the fi nal surface by the variable X, and the reference or target surface by the variable X*. For the purposes of 

this study, we can uniquely specify the reference surface for 

any particular cleaning problem. For example, if we specify a 

target value of the variable, X*, for the cleaned stone, we can 

compute a score for each of the cleaning techniques that gauges 

how closely the actual cleaned surface matches the reference 

surface. For a given variable X, we define the capacity and 

accuracy of the cleaning technique in terms of the difference 

variables ∆X and ∆X°, respectively, where

 ∆X = X − X° (fi nal − initial) (7)

and

 ∆X° = X − X* (fi nal − target). (8)

That is, the difference variable ∆X measures the ability of the 

technique to change the variable X, and the difference variable ∆X*

measures the ability of the technique to restore the variable X to 

its target value. If the final condition of the cleaned surface as 

measured by the variable X exactly matches the reference condition, 

then ∆X*=0. In such a situation, we would regard the particular 

cleaning technique as ideal as measured by the variable X.

We can further classify the cleaning techniques on the 

basis of the fi nesse of each technique. If a given technique 

undershoots the target value as measured by the variable X, 

we classify the technique as soft (S). If the cleaning technique 

overshoots the target value as measured by the variable X, we 

classify the technique as hard (H). Although the difference variables ∆X and ∆X* are analog-ratio variables, we can downgrade 

them to nominal Boolean variables for the purpose of defining 

the attributes H and S as Boolean classifications. Thus

 S = ∆X ∆X* + ∆X ∆X*

= ∆X (+) ∆X*,

 (9)

and

 

 

H = ∆X ∆X* + ∆X ∆X*

= S, 

(10)

where ∆X* and ∆X are logically true if the measurement variables are equal to or greater than zero, and logically false if 

the measurement variables are less than zero. In equations 9 

and 10, the symbols “+” and “(+)” denote the Boolean OR 

and exclusive-OR functions, respectively. Equations 9 and 10 

are consistent with the definitions of the attributes H and S. 

To illustrate, because class H techniques overshoot the target 

value, both the capacity and the accuracy will have the same 

sign under any circumstances. In contrast, because class S 

techniques undershoot the target value, the signs of the capacity 

and accuracy will always be opposite.

The definitions provided by equations 9 and 10 enable us 

to exclusively categorize the cleaning techniques as either class 

S or class H. Incremental application of class S techniques drives 

the measurement variable toward the target value, X*, regardless 

of the initial state of the stone. In contrast, incremental application of class H techniques drives the measurement variable away 

from the target value. Equations 9 and 10 show that the actual 

classification of a given cleaning technique depends on the variable selected to measure the state of the system, as well as on the 

definition of the reference, or target, state.

Criteria for Removal of Soiling Load

Although the reflectivity of the stone surface is generally 

controlled by microscale variables, a sidewalk view of the building may not necessarily reveal the presence of isolated pockets 

of particulate matter and salts, which may play an active role 

in deterioration of the stone. Therefore, we used three variables 

to score the cleaning efficacy of each technique, two on the 

microscale and one on the macroscale. On the microscale, we 

determined the amount and distribution of particulate matter over 

the surface, and we estimated the amount and composition of 

salts in the cracks and crevices before and after cleaning. On the 

macroscale, we measured the general surface reflectivity.

Removal of Salts

Energy-dispersive X-ray-fl uorescence microanalysis was 

used to vertically explore the test cores for all common salts, 

including gypsum. The spatial resolution was less than ≈5 μm, 

and the chemical detection limit and sensitivity were ≈0.25 

and 0.50 atomic percent, respectively. As implied in our earlier 

discussion in the subsection above entitled “Particulate Matter,” 

our objective was to remove all adventitious salts from the 

building. The ability of the various cleaning techniques to 

remove salts from the building, along with the results of other 

tests applied to GCS- and GCR-condition stone, is summarized 

in the section below entitled “Conclusions and Preservation 

Strategy” (see fig. 35). In particular, only three of the eight 

techniques successfully reduced the gypsum concentration to 

a level below the detection limit of the analysis technique: 

combination, Armax, and JOS.

Removal of Soiling

The removal of soiling, which is largely in the form of 

particulate matter, from the building was the chief motivation 

for this work. If the stone does not appear to be clean on the 

basis of esthetic criteria, any question relating to the distribution 

and chemistry of salts and particulate matter is inconsequential. 

Our observations on the building show that the lateral cluster density of particulate matter on the microscale (see fig. 7) 

strongly correlates with the visual appearance of the stone on 

the macroscale. The cluster density of particulate matter and the 

surface reflectivity of the stone are integrated into a single filter 

for evaluating the ability of the various cleaning techniques to 

remove soiling from the building. This filter, which we call the 

esthetic utility, is discussed in the following paragraphs.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 17

We will now consider the ability of the cleaning techniques to affect the appearance of the stone from a sidewalk 

point of view. We need to address three questions as we 

consider the cleanliness of the stone: (1) When is the stone 

suffi ciently clean? (2) When is the stone overcleaned? and (3) 

When is the cleaning method sufficiently reproducible?

The answer to the first question depends on the scale 

at which we probe the surface. On the microscale, the issue 

could be fundamentally framed in terms of the number of dirt 

particles per unit area. Ultimately, however, the criteria have 

to be anchored in subjective considerations of how the stone 

appears on the macroscale—that is, the esthetic utility of the 

stone. An approximate esthetic-utility curve for the building is 

plotted in figure 27. The profile of this curve was determined 

subjectively by a visual evaluation of the cleaned stone against 

reference color standards in the building’s setting. The most 

important feature of the curve is its shape. If the stone initially 

is heavily soiled but is not structurally deteriorated, marginal 

changes in the esthetic utility will be conspicuous when the 

stone is cleaned initially. As the cleanliness of the stone 

improves, it is reasonable to assume that incremental application 

of the cleaning technique will diminish the esthetic utility of the 

stone. On the basis of our subjective interpretations of the data 

in figures 7 and 10 and on our visual inspection of the stone in 

the field, we suggest that the maximally acceptable cleanliness 

of the cleaned stone should be 3.0 gray-scale (gs) units.

How, then, can we can we determine when the stone is 

overcleaned, and how can we discover the optimal target cleanliness? If we were to base our reply on figure 27 alone, we would 

conclude that the stone is never overcleaned because the slope of 

the esthetic-utility curve is positive everywhere. In principle, the 

stone is overcleaned when one or more of the critical state variables have been adjusted beyond the range of acceptable values 

corresponding to the optimal general utility. Because our goal 

is to restore the building’s original appearance with a minimal 

impact on the underlying substrate, the general utility will be 

optimized when each of the state variables is corrected to a value 

that defines the slightly weathered stone appearing on the building shortly after its original construction. These reference values 

for the state variables, once established, will provide a basis for 

defining the cleaning criteria for the building. Before we can 

identify the range of acceptable values for the cleanliness of the 

stone, we need to consider issues related to the reproducibility 

of the cleaning techniques, as well as how the microstructure 

variables impact the shape of the general-utility curve.

By what criteria should we judge the reproducibility of 

the cleaning techniques? There are two issues of concern here, 

the first linked to the ability of the technique to replicate a 

preset level of cleanliness, and the second associated with the 

intrinsic application intensity of the method. The importance 

of the first issue depends on the shape of the esthetic-utility 

curve (fig. 27). Because of the diminishing marginal esthetic 

utility, the importance of the reproducibility of the cleaning 

techniques decreases as the minimally acceptable cleanliness is 

set at increasingly higher values. If surface grayness were our 

only consideration, the maximum target cleanliness would have 

to be set to allow for a standard deviation of at least 1.5σ in 

the grayness value associated with a given cleaning technique. 

For example, if our maximally acceptable cleanliness is 3.0 gs 

units, the target cleanliness would have to be set at 1.5–3.0σ

to accommodate variations in the application of the technique. 

The reproducibility of a given cleaning technique would be 

inadequate if the variation in surface grayness of the cleaned 

stone exceeded the range of acceptability as determined by the 

shape of the esthetic-utility curve.

To determine the reproducibility of the cleaning techniques, we need to couple the criterion based on the estheticutility curve (fig. 27) with the criterion based on the intrinsic 

application intensity of each technique. We noticed that, for 

each cleaning technique, there is an intrinsic application intensity used by the operator to remove the soiling. We argue that 

each cleaning technique is most reproducible when it is applied 

without any constraints imposed on the operator. With the use of 

the finesse classification defined in equations 9 and 10, we can 

determine the intrinsic application intensity of the techniques in 

conjunction with the esthetic-utility criterion. Finesse-classification diagrams based on a target cleanliness (C*) within the range 

1.5–3.0 gs units for all surface-soiling and cleaning techniques 

are shown in figure 28. Our objective in using these diagrams is 

to discover the target cleanliness, within the range 0–3.0 gs units, 

that will maximize the number of cleaning techniques with an 

optimal reproducibility of C*.

Two elements of information are contained in the finesseclassification diagrams (fig. 28). First, because the radius vector 

to any point in the diagram represents the accuracy of the cleaning technique, ∆C*, the circles represent how far the techniques 

miss the target cleanliness; the central value, 0.2 gs units, is the 

reproducibility of the measurement. Second, the plotting algorithm for each finesse-classification diagram prescribes a grouping of cleaning techniques such that all points falling below the 

horizontal dashed line correspond to techniques which underclean the surface relative to the target cleanliness (soft methods), 

and all points falling above the horizontal dashed line correspond 

to techniques which overclean the surface relative to the target 

brightness (hard methods). These finesse-classification diagrams 

are best viewed in rapid sequence from figures 28A through 28E. 

Note the natural convergence of data points as C* is systematically varied from 3.0 to 1.5 gs units. Clearly, all of the techniques 

overclean LSS, LSR, and GCS surfaces when C*=3.0 gs units. 

Because most techniques come close to producing a grayness 

value in the range 1.5–2.0 gs units, we selected this range for the 

grayness criterion.

We can now determine which cleaning techniques produce the best scores on GCR surfaces relative to a range in 

target cleanliness of 1.5<∼ C<∼ 3.0 gs units. Finesse-classifi cation 

diagrams based on a target cleanliness in the range 1.5–3.0 gs 

units for GCR-condition stone are shown in figure 29. For the 

selected grayness values C*=1.5 gs units (fi g. 29C) and C*=2.0 

(fi g. 29D), the combination, Armax, and JOS techniques all 

produce excellent results.

Criteria for Alteration of Microstructure

The statistical disparity between cleaned and uncleaned 

LSR and GCR cores in figure 11B indicates that the surface 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 18

roughness is especially affected when the stone is cleaned. 

Moreover, figure 21 shows that the surface reactivity is closely 

related to the surface roughness. Therefore, the cleaning criteria 

should be based on a general-utility curve that simultaneously 

incorporates the effect of the cleaning techniques on the surface 

roughness and grayness value of the stone.

Surface Roughness

The selection of a target roughness, R*, is based on the 

idea of an optimal surface roughness that would maximize the 

stability of the stone under ambient conditions of the building. 

Experience, as well as thermodynamics, tells us that a perfectly 

smooth stone surface will eventually become rougher from 

exposure to the elements. Likewise, an exceedingly jagged 

stone surface may well lose a certain amount of morphologic 

relief from exposure to wind and rain. This experience suggests 

that every type of stone will change to its most stable condition 

in a given environment and that the intrinsic surface roughness 

for each particular type of stone, in general, will be fi nite.

Figure 21 indicates that the smoother the stone surface, the 

stabler will be the stone with respect to erosion by pure water. 

In contrast, the regression (dashed) line plotted in figure 11B suggests that the intrinsic surface roughness in the environment of the 

building ranges from 1.75 to 2.0 (in shape-factor units). Finesseclassification diagrams based on target-surface-roughness (R*) 

values over the range 1.6–3.5 shape-factor units for all surfacesoiling conditions and cleaning techniques are shown in figure 30. 

Evidently, the surface roughness converges toward a target value 

R* in the range 1.8≤R*≤2.5 shape-factor units.

In the preceding paragraphs, we established the reference 

ranges over which the surface grayness and surface roughness 

of the cleaned stone will optimize the reproducibility of most 

cleaning techniques on most surface-soiling conditions. However, we still have not determined the reproducibility criteria. 

To address this question, we need to know the shape of the 

utility curve that incorporates the effect of all critical variables 

on both the long-term stability and the esthetic utility of the 

stone. This general-utility curve has a more complex shape than 

the curve shown in figure 27.18 By considering the problem 

within a general-utility framework, we can deal directly with 

the mutual dependence between state variables. For example, 

any adjustment in the degree of cleanliness of the stone will 

simultaneously affect other state variables, including the surface roughness. Because the general-utility curve captures the 

mutual dependence of the state variables, we simultaneously 

identify the reference cleanliness and the reference surface 

roughness by finding the optimal utility from the general-utility 

curve. Thus, the general-utility curve provides a means for us 

to specify both the acceptable range of surface grayness and the 

acceptable surface roughness, at least in principle. Once these 

criteria have been established jointly, we can then specify the 

acceptable reproducibility.

On the basis of our interpretation of figures 7, 10, and 

28 through 30, we selected a range in target cleanliness of 

1.5≤C*≤2.0 gs units and a range in target surface roughness 

of 1.8≤R*≤2.5 shape-factor units for the cleaned stone. The 

approximate shape of projections of the general-utility curve 

onto the utility/roughness plane for various grayness values 

based on the above ranges is shown in figure 31. The utility 

response to both cleanliness and surface roughness represented 

in fi gure 31 delimits the reproducibility of the cleaning techniques.

We can now use the joint criteria 1.5≤C*≤2.0 gs units 

and 1.8≤R*≤2.5 shape-factor units for cleanliness and surface 

roughness, respectively, to concurrently select the optimal 

cleaning techniques for GCR-condition stone. A fi nesse-classifi cation diagram based on the grayness value and surface 

roughness of cleaned GCR-condition stone for the reference 

pair C*=1.5 gs units and R*=2.5 shape-factor units is shown 

in fi gure 32. The combination, Armax, and JOS cleaning techniques all simultaneously satisfy the joint criteria.

Friability Index

The correlation between the friability index of uncleaned 

surfaces and the surface recession (fig. 17) validates the use 

of the FI value as a surrogate variable for the friability of the 

cleaned stone. The target FI value is based on the defi nition 

of FI given in the subsection above entitled “Friability of the 

Stone Before Cleaning” and on the reference fracture density 

and surface roughness. Because it is reasonable to set the 

reference fracture density to zero, the range in target FI value 

(FI*) follows directly as a rescaled range in target surface: 

15≤FI*≤25. The summary of average FI values for cleaned 

GCR-condition stone plotted in figure 18E indicates that the 

JOS, gommage, combination, and Armax cleaning techniques 

not only are the most reproducible, as measured by the FI 

value, but also produce surfaces with FI values in the target 

range, as diagrammed in figure 35 below.

Statistical Link Between Fracture Density and 

Surface Roughness

In this section, we consider the extent to which the various cleaning techniques affect the apparent correlation between 

the fracture density and the shape factor. By way of review, 

plots of fracture density versus shape factor for both uncleaned 

and cleaned stone (fi gs. 11A and 11B, respectively) show that 

the cleaning techniques generally change the strength of the 

relation between the fracture density and the shape factor of 

the cleaned stone. The R2

 values for all surface conditions 

are summarized in figure 14. Our analysis is based on the 

implied assumption that for certain surfaces, notably those most 

heavily soiled, the fracture density and the shape factor are 

inherently associated. Figure 33 resolves figures 11A and 11B

into component sets for each cleaning technique.

As a point of clarifi cation, it would be unrealistic to 

expect a functional, much less linear, relation between the fracture density and the surface roughness. It is entirely reasonable, 

however, that the fracture density might statistically vary with 

the extent of surface roughness. Although we cannot predict the 

exact fracture density of an individual sample from the surface 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 19

roughness, we might expect to be able to estimate the average 

fracture density of all samples with the same shape factor in 

a given set. If the errors in the fracture density and the shape 

factor are small relative to the inherent variation among randomly selected samples, and if the variables are unrestricted, 

the sample correlation coefficient can be used to estimate the 

parent-population correlation coefficient (ρ, fi g. 33), which we 

assume to be greater than zero.

In this analysis, we distinguish the cleaning techniques on 

the basis of their capacity to alter the apparent microstructure 

of the stone (see eqs. 11, 12). We adopt the parent-population 

correlation coefficient (ρ, fi g. 33) as a measure of the relation 

between the fracture density and the shape factor. Accordingly, 

the capacity of the technique to alter the microstructure is 

measured by the difference variable ∆r=r−r°, and the accuracy 

of the technique to produce a target microstructure is measured 

by the difference variable ∆r*=r−r*, where r° is the initial 

value, r is the fi nal value, and r* is the target value of the 

correlation coefficient.

Because we are interested in measuring changes in the 

strength of the relation between the fracture density and the 

shape factor on cleaning, we define r*=− r°. Therefore, ∆r =− ∆r*, 

and so a high-capacity method will have a low accuracy with 

respect to changes in the microstructure of the stone. Under the 

convention that r*=r°, any cleaning technique that modifies the 

microstructure would be classified as hard because the marginal 

effect of the technique would cause the surface microstructure 

to diverge from its original state. Our interest is in the capacity 

of the technique, ∆r. If the initial value of the correlation 

coefficient is positive, a positive capacity indicates that the 

technique tends to strengthen the relation between the fracture 

density and the shape factor, and a negative capacity indicates 

that the technique tends to eradicate the original microstructure. 

If the initial value of the correlation coefficient is negative, 

these relations are reversed.

The statistics given in figure 33 are summarized in figure 

34. Interpretation of figures 33 and 34 depends on a strict 

application of the confidence intervals, which are expected 

to include the true fracture density for any shape factor 95 

percent of the time.19 For example, although the confidence 

intervals in figure 33K allow considerable latitude for the 

location of the negatively sloped least-squares regression 

line, the exclusion of zero from the confidence intervals of 

laser-cleaned cores for the parent-population correlation coeffi cient in figure 34 is convincing evidence that the slope of the 

least-squares regression line in figure 33K is truly negative. 

The capacity (horizontal bar, fig. 34) is greater than 1 because 

of the drastic change from a negative to a positive correlation 

coefficient after laser cleaning. The confidence interval for the 

parent-population correlation coefficient of laser-cleaned stone 

indicates that the true value of this correlation coefficient may 

be as low as −0.54, as high as 0.90, or anywhere in between 

these two limits. Because zero falls nearly in the center of 

this confidence interval, we conclude that the fracture density and the surface roughness are essentially unrelated for 

laser-cleaned stone. The uncertainty in the confidence interval 

for the parent-population correlation coefficient is reflected 

by the divergent confidence intervals toward the ends of the 

least-squares regression line in figure 33L. Furthermore, the 

R2 value for this regression line indicates that only 25 percent 

of the variance in the fracture density is accounted for by the 

least-squares regression line. Therefore, the slightly positive 

slope of the least-squares regression line should not be overinterpreted. Because the sign of the initial value of the correlation coefficient is negative, the positive capacity suggests that 

laser cleaning eradicates the initial microstructure rather than 

exposing it by delicately removing the soiling. Thus, we classify the laser technique as hard (H).

Likewise, for the JOS cleaning technique, we observe that 

the R2 value and the confidence interval for the parent-population 

correlation coefficient of soiled stone indicate that the measured 

variables are initially uncorrelated. However, after JOS cleaning, 

the confidence interval is sufficiently shifted in a positive direction so as to exclude zero, indicating a high probability of a 

strengthened relation between the fracture density and the surface roughness. Although the JOS technique does not appear to 

damage the stone surface, it does seem to remove soiling that 

obscures the intrinsic relation between the measured variables. 

Thus, we classify the JOS technique as soft (S).

We note that the power-wash and combination cleaning 

techniques show statistically similar patterns. Although both of 

these techniques show positive capacities of about the same magnitude, the confidence intervals for the parent-population correlation coefficients of both soiled and cleaned stone are broad 

and include zero. We find little evidence of a statistically signifi -

cant relation between the measured variables on either soiled or 

cleaned stone for these two techniques. Although the finesse score 

for these two techniques indicates that they are soft (S), the sample 

population (degrees of freedom) in the measurement is insufficient 

to validate such a classification. Similarly, although the capacity 

of the dry-ice cleaning technique is nearly zero, the statistics 

of this technique are similar to those of the power-wash and 

combination techniques. Because all three cleaning techniques are 

statistically undetermined, we do not filter them specifically from 

consideration in the summary diagram in figure 35, where they are 

indicated by a question mark where appropriate.

In contrast to the fi ve cleaning techniques already discussed, the Armax and the gommage techniques appear to 

virtually obliterate the microstructure of the soiled stone, as 

illustrated in fi gures 33C, 33D, 33G, and 33H. Thus, we classify the Armax and gommage techniques as hard (H).

From the results summarized in fi gure 34, we might 

argue that the preferred cleaning technique should remove the 

soiling without altering the microstructure of the stone. The 

problem with this approach is that the stone surface may be 

inconsistent from point to point and the operator would lose 

some control over the final condition of the stone. Alternatively, we might argue that the preferred cleaning technique 

should remove the microstructure below the soiling to a depth 

of perhaps 75 μm and impose a consistent, possibly more 

stable, microstructure over all the cleaned areas. We reason 

that any cleaning technique would be most reproducible when 

applied without any constraints imposed on the operator. In 

particular, if the operator either has to hold back in applying 

the technique or push it beyond its limitations to reach a given 

target grayness value, then the reproducibility of the technique 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 20

will be compromised. The summary diagram in figure 35 

shows our selection of the gommage, Armax, and JOS techniques for the cleaning of lightly soiled, smooth and rough 

stone, and the gommage, combination, and Armax cleaning 

techniques for the cleaning of gypsum crusted, smooth and 

rough stone on the basis of this reasoning. We retained the 

combination method for final consideration because of its superior ability to remove salts and because it extends the gommage 

technique.

Conclusions and Preservation Strategy

In the preceding sections of this report, we established 

a set of criteria for judging the effectiveness of eight cleaning 

techniques on the marble façade of the building. The data we 

collected narrowed the options regarding appropriate cleaning 

techniques for the building. The architects and conservators 

overlaid the scientific analysis with additional fi lters or criteria 

to establish their fi nal recommendations, delineated below.

Selection of Cleaning Techniques

Summary of Testing and Analysis

Our testing of eight different cleaning techniques on four 

surface conditions of soiling of the Berkshire Lee marble at 

Philadelphia City Hall was done onsite under varying field 

conditions by technicians skilled in applying the specific techniques. We assumed that each technique would be applied 

with some variation from area to area. A total of 160 cores, 

randomly selected from several possible 1-in.-round sites, represent a limited statistical sampling of the surface-soiling conditions of the stone.

Scientifi c Cleaning Criteria

Of the specifi c criteria identifi ed during this study for 

differentiating the various cleaning techniques, several proved 

to be ineffective in our evaluation.

Cleaning Techniques Not Differentiated by Surface 

Recession

The surface-recession measurements (see app. 5) 

indicate that differences in mass loss from cleaning technique 

to cleaning technique and from surface-soiling condition to 

surface-soiling condition are virtually inconsequential over 

the long term. Some differences in surface recession between 

techniques were noted, but they were insignificant. With only a 

few exceptions, the surface recession produced by all the cleaning techniques tested was less than 175 μm, which is less than 

the maximum surface-recession criterion established by this 

study. In comparison with the rate of natural weathering, the 

minute measurable surface recession associated with any of the 

cleaning techniques, applied as a one-time event every quartercentury, including the familiar methods of power washing and 

misting, is irrelevant to the long-term stability or utility of the 

stone.

The Role of Soluble Salts in Marble Deterioration

Contrary to our original conjecture, we found no evidence that soluble salts play a significant role in the deterioration of Berkshire Lee marble. Although salts were evident 

in the cracks and fissures of GCR cores, they did not penetrate the surface to a depth of more than a few micrometers.

Surface Reactivity

The project team initially considered that cleaning may 

create a more reactive surface and therefore accelerate deterioration on the basis of preliminary, though statistically compromised, surface-reactivity measurements on phase I cores (see 

app. 8). The results of a larger statistical sampling of cores 

indicated that cleaning the stone does not marginally activate its 

surface for dissolution.

Selection Criteria: Cleanliness and Friability

The project team concluded that the cleaning standards 

which would satisfy the needs of the client and fulfi ll the 

goals of this study include a target grayness value, C*, in the 

range 1.5<∼C*<∼ 2.0 gs units, a target surface roughness, R*, in 

the range 1.8 R* 2.5 shape-scale units, and a minimal level 

of surface fracturing, a criterion corresponding to a target friability index, FI*, in the range 15 FI * 25 percent. The recommended cleaning techniques produce a presentation surface that 

closely approximates the original appearance of the historic 

stone, while also leaving a surface that is stable and minimally 

reactive to future cycles of weathering.

Cleaning Techniques That Satisfy the Selection 

Criteria

As shown in figure 35, four cleaning techniques did not 

meet all the selection criteria. Cores taken from future test sites 

where these techniques might be performed, with modifications, may yield more successful scores for these techniques. 

Four techniques emerge from this study as satisfactory to 

meet the goals of esthetic presentation and long-term utility: 

gommage and Armax, which are classified as hard (H); JOS, 

which is classifi ed as soft (S); and combination, which has 

a statistically undetermined fi nesse factor. The “soft” classifi cation designates a technique that cleaned the stone to the 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 21

target level but did not alter its characteristics as described by 

the friability index, and the “hard” classification designates a 

technique that cleaned the stone to the target level but imposed 

a new microstructure on the surface.

Alteration of Microstructure: Hard Versus Soft 

Cleaning Techniques

At this time, the project team cannot provide an argument 

that supports the selection of a hard over a soft technique on 

the basis of this classifi cation alone. One remaining question is 

whether Berkshire Lee marble is more stable and less reactive 

to alteration by soiling and chemical processes if a hard or 

soft technique is used. It remains to be determined whether 

a stone substrate that retains the microcracking and surfaceroughness patterns formed over many years is more stable than 

a freshly cut substrate obtained through application of a hard 

technique. The choice between soft and hard cannot be resolved 

practically because all the techniques are operator dependent. It 

is reasonable to assume that, over the cleaned area of an entire 

building, any technique may be marginally more destructive in 

some places than in others, owing to variations in its application. Unless a given cleaning technique is applied with nearly 

perfect reproducibility, a marble façade that is cleaned by even 

a single technique may represent a mixture of both hard and 

soft fi nishes.

Cleaning Criteria Established by Preservation 

Architects and Conservators

This study recommended four cleaning techniques that 

were noninvasive, as determined by reproducible laboratory 

measurements, of the stable marble substrate. However, these 

scientifi c criteria must be overlaid with practical considerations 

to produce an effective result. The foremost variables that need 

to be evaluated to ensure that a selected cleaning technique is 

in keeping with our preservation strategy for the building are 

as follows:

• The ease of applying the technique

• The skill required by operators to ensure a safe and effective 

result

• The capacity of the technique to provide a uniformly clean 

surface

• Environmental and health factors; for example, the effective, 

controlled, and safe disposal of the cleaning media

• The risk of damage from cleaning with large volumes of 

water, particularly on a building with numerous open joints

• The cost of the technique

Apparently, it will always be cheaper to clean a building with chemicals and water than by using gentle air abrasion. Although the gommage and JOS techniques are more 

expensive than traditional water-wash techniques, the results 

of this study emphasize that the Armax, JOS, gommage, 

and combination techniques significantly increase the utility 

of the building, ensuring the long-term preservation of this 

important marble edifice.

The following brief comments summarize the criteria 

that the client’s agents, including future preservation architects 

and conservators, are encouraged to consider. The project team 

concluded that three of the four recommended cleaning techniques are equally satisfactory in meeting these criteria.

Gommage Technique

The gommage technique, tested in 1994, was chosen for 

cleaning of the building. The other three techniques were not 

tested until 1995, too late for the execution of construction 

documents for the building’s restoration. The work was carried 

out satisfactorily by Thomann-Hanry, Inc.; operators were able 

to control the cleaning by using small nozzles and by minimizing the abrasive impact on the more fragile carved areas. The 

project team had two chief concerns about the gommage technique: (1) the diffi culty of safe collection of spent cleaning 

media in a situation where a pressure cabin cannot be used, 

and (2) the skill required of operators in carrying out the 

technique. The quality assurance from future contractors who 

may compete with Thomann-Hanry may be difficult to validate 

until after injury to the stone has occurred.

Combination Technique

The combination technique—gommage followed by misting—was used on carved areas to remove grime and pollutants 

from crevices that could not be reached by the cleaning gun; this 

technique is the most thoroughly cleaning of all the techniques 

evaluated. Though more expensive initially, the combination 

technique provides the most complete preservation over the 

long term and is the most likely technique to remove potentially 

destructive salts from GCR-condition stone. The above comments about the gommage technique apply here as well.

Armax Technique

The advantage of the Armax technique is its relatively 

low cost. Quality is assured to the extent that the media manufacturer requires that the media and equipment be sold only 

to offi cial agents who employ trained personnel. The project 

team had some concern that residual sodium bicarbonate would 

convert to sodium bisulfate and that subsurface crystallization 

would lead to spalling. Although no salt residues were found 

on the core samples of Berkshire Lee marble used in this study, 

no cores were taken at sites where a visible accumulation of 

residual sodium bicarbonate became apparent after the stone 

dried. Protection of deep cracks and crevices, good control of 

overspray from powerful delivery of the cleaning media, and 

adequate rinsing of cleaned and oversprayed areas are critical; 

proper attention to these details may affect cost. We do not 

recommend the Armax technique for the building at this time 

because of the observed inability of operators to control overspray and runoff and to remove residual cleaning media from 

the masonry surfaces.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 22

JOS Technique

The JOS technique is carried out exclusively by 

trained operators; however, it is potentially more applicable 

than the gommage technique because it can be used by 

all contractors who purchase the required equipment and 

media and who are trained and certified by the equipment 

manufacturer. In this study, the JOS technique seemed to 

allow more control in delivery of the cleaning media. The 

presence of a small volume of water in the delivery system 

resulted in cleaner air in the work area than was observed 

in applying a dry technique. The slurry runoff could be controlled for collection.

Recommendations

For future cleaning of the building, the project team 

recommends the gommage, combination, and JOS techniques. 

Both the JOS and combination techniques use an extremely 

small volume of water. As a result, they do not introduce the 

problems associated with traditional water-wash methods 

such as (1) infiltration of water into the building through 

holes, fissures, and open joints in the stone; (2) slow drying of 

the stone and associated potential promotion of salt efflorescence and staining, including iron staining; and (3) control 

of a large volume of water runoff. To ensure that all salts are 

removed, the project team recommends that:

• the gommage or JOS technique be used on ashlar surfaces,

• the combination technique be used on highly carved surfaces at the entablatures, cornices, and column capitals, 

and

• the combination technique, with especially delicate application, be used on fragile GCR-surface stone.

The recommended cleaning techniques for the building are in keeping with a current trend in the United States 

toward masonry-cleaning treatments that are more environmentally friendly and have minimal disposal issues. Our 

study is a major contribution to the importance of this trend in 

that it provides scientific data which demonstrate that the risk 

for stone damage by some of these newer techniques is equal 

to or, in many places, less than that of traditional water-wash 

methods.

Specification and Monitoring of Cleaning 

Techniques

The specifications for selection of a cleaning technique 

should be written so that only qualified bidders with experienced operators undertake the cleaning of the building. A 

safe and effective cleaning technique can be monitored in the 

fi eld by a trained preservation architect or conservator. Judgments can easily be made by visual comparison of the color 

and texture of the cleaned stone against a set of reference 

standards.

Impact of Cleaning Techniques on the LongTerm Condition of the Stone

One of the important findings of this study is that most 

of the cleaning techniques evaluated did not significantly affect 

the long-term condition of the stone in regard to its projected 

utility. The study identified variables and processes that affect all 

buildings constructed of calcareous stone. According to figure 

26, the processes controlling the primary deposition of soiling 

agents, the secondary mobilization of transportable materials, 

and the permanent alteration of the stone are all mutually dependent. Although the long-term condition of the stone is governed 

by the collective action of these interdependent processes, the 

distribution of soiling agents over the building is chiefly controlled by the action of water on the stone surface. Accordingly, 

the soiling pattern on the building is mainly controlled by the 

large-scale morphology and orientation of the stone; these macroscopic variables are not affected by the choice of cleaning 

technique. Moreover, the total soiling load is largely influenced 

by environmental variables (airborne moisture, local aerodynamics, concentrations of such pollutants as SO2

 and airborne 

particulate matter, and so on) and by certain microscale variables (for example, the chemical composition of the stone) that 

also are unaffected by cleaning. To a lesser degree, certain 

microscale variables that are affected by cleaning (for example, 

surface roughness) may influence the total soiling load on the 

building. Thus, after the stone is cleaned, both the pattern and 

amount of soiling appearing on the building over a period of 

decades should be independent of cleaning technique because 

the variables controlling these phenomena were found to be 

unaffected by any of the cleaning techniques evaluated. This 

conclusion has yet to be experimentally tested.

Signifi cance of This Study as a Model

This study appears to be unique in its design of controlled 

fi eld tests for eight cleaning techniques, especially with respect 

to the use of scientific analysis for formulating a conservation 

strategy for a particular type of stone on a particular building. 

Accordingly, the study can be used as a model for other buildings constructed of Berkshire Lee marble or of marble with 

a similar petrology located in a temperate urban environment. 

Although the study was stone specific, our questions about 

the characteristics of surface alteration due to a given cleaning 

technique are universally applicable. The study should also 

serve as a starting point for preservation architects and conservators in collecting data about other building stones.

Signifi cance of This Study to Preservation 

Strategies

The construction of Philadelphia City Hall spanned 

more than 30 years (1870–1901), a period of vast technologi-
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 23

cal change. It is somehow fitting that the decade in which the 

building is being restored is witnessing technological advances 

that increase our understanding of the application of cleaning techniques and that will ensure the long-term preservation of this historic edifi ce. As the masonry-cleaning program 

progresses around the perimeter and courtyard façades, other 

cleaning techniques may be developed that prove safer to the 

environment. These new techniques will have to ensure the 

long-term preservation of the stone as did the earlier-selected 

techniques. As these techniques are developed and become economically feasible, the city of Philadelphia will need sophisticated methods to evaluate their effectiveness. This study has 

established baseline data that will enable preservation architects and conservators to evaluate new cleaning techniques in 

the future.

Ideally, cleaning techniques should be evaluated during 

the design phase of any masonry-cleaning project. Such evaluation is critical to understand the construction of a building and 

the condition of its stone, and to formulate an appropriate and 

effective preservation strategy for the building. The completion 

of this study underscores the importance of scientific testing in 

formulating such a strategy for any building.

Acknowledgments

This research was supported by the U.S. National Park 

Service (NPS)’s National Center for Preservation Technology 

and Training, the Philadelphia Historical Commission, and the 

city of Philadelphia; development of the SEM image-analysis 

software (MORPH–I and MORPH–II) was specifically supported by the NPS. Susan Sherwood assisted in organizing 

the multidisciplinary project team of preservation architects, 

conservators, and scientists. We also thank Chandra Reedy, 

ElizaBeth Bede, Susan I. Sherwood, and Richard Tosdal for 

their reviews of the manuscript.

References Cited

Bede, E.A., 2000, Characterization of surface morphology of carbonate stone and its effect on surface uptake of SO2

 in International Conference on the Deterioration and Conservation of 

Stone, 9th, Venice, Italy, 2000 [Proceedings], v. 1, p. 303–311.

Bede, E.A., 2001, The surface morphology of limestone and its 

effect on SO2 deposition: Newark, University of Delaware, Ph.D. thesis, 298 p.

Mossotti, V.G. and Eldeeb, A.R., 2000, MORPH–II, a software 

package for the analysis of scanning-electron-micrograph 

images for the assessment of the fractal dimension of exposed 

stone surfaces: U.S. Geological Survey Open-File Report 

00–013 (URL http://geopubs.wr.usgs.gov/open-file/of00-013).

Mossotti, V.G., Eldeeb, A.R., and Oscarson, Robert, 1998, 

MORPH–I (Ver. 1.0), a software package for the analysis 

of scanning electron micrograph (binary formatted) images 

for the assessment of the fractal dimension of enclosed pore 

surfaces: U.S. Geological Survey Open-File Report 98–248 

(URL http://geopubs.wr.usgs.gov/open-file/of98-248)

Mossotti, V.G., Eldeeb, A.R., Reddy, M.M., Fries, T.L., 

Coombs, Mary Jane, Schmiermund, R.L., and Sherwood, 

S.I., 2001, Statistical compilation of NAPAP chemical erosion observations on limestone and marble: U.S. 

Geological Survey Open-File Report 98–755 (URL http:

//geopubs.wr.usgs.gov/open-file/of98-755).

Murray, J.D., and vanRyper, William, 1996, Encyclopedia of 

graphics fi le formats (2d ed.): Sebastopol, Calif., O’Reilly 

& Associates, 1,154 p.

Naudé, V.N., 1994, Report on stone cleaning tests March–

April 1994—to support a masonry preservation strategy 

for the restoration of the east center pavilion of the 

Philadelphia City Hall, sec. 8 in app. 1 of Proposal for 

masonry testing; development of cleaning treatments and 

preservation strategies: Philadelphia, Vitetta Group, p. 

8.1–8.20.

Peitgen, H.O., Jürgens, Hartmut, and Saupe, Dietmar, 1992, 

Chaos and fractals—new frontiers of science: New York, 

Springer-Verlag, 953 p.

Plummer, L.N., Wigley, T.M.L., and Parkhurst, D.L., 1978, The 

kinetics of calcite dissolution in CO2

-water systems at 5 

to 60°C and 0.0 to 1.0 atm CO2: American Journal of Science, v. 278, p. 179–216.

Reimann, K.J., 1994, Chemical alteration of limestone and 

marble samples exposed to acid rain and weathering in the 

eastern United States: Argonne National Laboratory Report 

ANL–89/34, 104 p.

Spiker, F.C., Comer, V.J., Hosker, R.P., and Sherwood, S.I., 

1992, Dry deposition of SO2

 on limestone and marble—

role of humidity in International Conference on Deterioration and Conservation of Stone, 7th, Lisbon, 1992 

[Proceedings], p. 397–406.

Vitetta Group, 1994, Proposal for masonry testing; development of cleaning treatments and preservation strategies: 

Philadelphia, Vitetta Group [variously paged].

Wu, V.L., Davidson, C.I., Dolske, D.A., and Sherwood, 

S.I, 1990, Dry deposition of atmospheric contaminants 

at Gettysburg, Penn.: Air and Waste Management Association Annual Meeting, 83d, Pittsburgh, Preprints, paper 

90–110.5.

Appendix 1. Cleaning Techniques and 

Core Inventory

Surface-Soiling Conditions

The richly articulated sculptural program of Philadelphia City Hall provides a wide variety of surface-soiling conditions to sample. However, visual inspection of the façade 

both from street level and at close range suggests that most 

of the stone surface may be characterized as being in one of 

four conditions: (1) lightly soiled, smooth (LSS); (2) lightly 

soiled, rough (LSR); (3) gypsum crusted, smooth (GCS); and 

(4) gypsum crusted, rough (GCR). About 2 percent of the sur-
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 24

face is severely deteriorated, sugary stone, found in sculptural 

projections with maximum exposure and on stone located in 

the path of rain runoff. These areas were not considered typical of the masonry that requires cleaning.

Lightly Soiled, Smooth

LSS areas, which are largely ashlar, constitute about 8 

percent of the surface. Such areas are generally protected from 

extreme weather by cornices and columns, but become wet 

during periods of rain. A few pits or ridges are visible. The 

color of LSS areas is various shades of gray.

Lightly Soiled, Rough

LSR areas, which are commonly found on both ashlar 

and sculptural stone, constitute about 85 percent of the surface. 

Such areas generally are directly exposed to weather. LSR 

areas are visibly pitted and commonly exhibit surface ridges 

that subtly reflect the rain-runoff flow pattern. Where cracks or 

natural fissures are present, excessive erosion is apparent. The 

color of LSR areas is various shades of dark gray.

Gypsum Crusted, Smooth

GCS areas, which have a smooth black surface that 

cannot be brushed away, constitute about 4 percent of the 

surface. Such areas were found in places protected from rain 

runoff by projecting architectural elements and in pockets of 

carving on exposed sculptural ornament.

Gypsum Crusted, Rough

GCR areas constitute about 1 percent of the surface. 

Such areas generally are identified on carved stone—that is, 

areas that are critical in providing the building with its esthetic 

value. Thick black crusts, with a granular texture, characterize 

GCR areas protected from rain runoff.

Choice and Description of Cleaning Techniques

Cleaning Techniques Selected for the First Phase of 

This Study

The selection of water-based cleaning techniques for the 

fi rst phase of the study was based on tests carried out in March 

1994 as a part of research for the building restoration project. 

Power wash without detergent, power wash with various chemical cleaning agents, and steam cleaning with and without detergent were all tested on the building’s façade. The power-wash 

and misting techniques were judged to be the most effective 

without deleterious effects on the stone. Technical details of the 

four cleaning techniques selected in the first phase of this study 

are summarized in table 1. The Vitetta Group (1994) reported on 

these tests and presented a rationale and design for further testing with scientific support that led to the 1995 masonry-cleaning 

program which is the subject of the present report.

Power Wash

Tests were conducted using a Simpson Powerwasher 

operating at 3 gal per minute and capable of delivering 1,500 

lb/in2 measured at the unit. Operations were carried out with 

a 12-in. hose using a 25° tip and a working distance of 10 to 

12 in. from the surface of the stone. We calculated a cleaning 

force of about 900 to 1,000 lb/in2. A proprietary detergent 

was selected: ProSoCo 1026 Liquid Marble Cleaner, a highalkali cleaning compound in gel form, widely used in the 

industry. Brushed on wet stone directly from the container, 

the blue gel was allowed to sit for 10 to 15 minutes, 

after which it was rinsed with the Powerwasher. Repeated 

applications of the detergent did not appear to significantly 

change the color of the cleaned stone. Areas sampled for this 

study were all at sites that had been cleaned with only one 

treatment.

Misting

The stone was washed for 8 hours on each of two 

consecutive days with a water mist, which served to keep 

the surface wet but had no other impact. The spray was delivered from small nozzles (Spraying Systems nozzles LN 1.5) 

mounted on a plastic (polyvinyl chloride) pipe with flexible 

adapters leading to the nozzle tips. The system was designed 

to position nozzles at 10-in. intervals 10 in. from the wall, but 

the direction of the nozzles was adjusted onsite to best clean 

sculptural detail. The direction of the nozzles was readjusted, 

and loosened grime was brushed away with a bristle brush 

twice each day or about every 2 to 3 hours.

Gommage

The Thomann-Hanry, or gommage, dry cleaning technique uses what is essentially a low-pressure, small-particle 

air abrasive system familiar to museum conservators that was 

developed by a French company for large-scale use. Delivered 

at pressures of 35 to 50 lb/in2, the particles range in size 

from 15 to 90 μm. Published information states that the mix 

contains SiO2 (49.65 weight percent), Al2O3 (27.64 weight percent), Fe2

O3 (8.49 weight percent), K2O (4.11 weight percent), 

CaO (3.64 weight percent), and MgO (2.41 weight percent).

The operator held the nozzles at eye level, perpendicular to and about 10 in. from the surface. Test sampling 

was conducted to determine the number of passes needed 

to slowly diminish the soiling layer. During testing, it was 

noted that two passes produced a good cleaning and a third 

pass made the stone whiter; the level of cleaning could be 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 25

monitored visually. The nozzle and media mix are proprietary, as is the light-weight vacuum cabin that can be 

installed against a building on a boom truck or suspended 

from outriggers. Airborne particles were captured in a 

vacuum unit, moistened with a water spray, and flushed 

through a hose to a ground-level containment system. The 

nontoxic powders are safe for normal landfill disposal. The 

benefits of the vacuum cabin, a device requiring a fairly flat 

façade, are unavailable for buildings like Philadelphia City 

Hall. Work was accomplished from scaffolding, and vacuums were used to clear waste.

Cleaning Techniques Selected for the Second 

Phase of This Study

Combination

The combination cleaning technique was specified for 

testing during phase 2 because the project team was concerned 

that a dry cleaning technique, such as gommage, would fail to 

remove water-soluble salts from the marble.

Armax

The blast medium for the popular, inexpensive Armax 

cleaning technique is sodium bicarbonate. This technique was 

developed by the Church and Dwight Co., which sells Arm 

and Hammer products. The medium is delivered by proprietary 

equipment that is sold to local companies whose employees 

receive training and are then authorized to use the system. The 

equipment they proposed for the January 18, 1995, demonstration at Philadelphia City Hall was the Accustrip System model 

12X, blast nozzle Hypro No. 6. The particle size was 180 μm, 

provided as Armax Blast Media Maintenance Formula with 

SupraKleen Rinse Accelerator.

The stone surface was prewetted and then cleaned with 

a stream of medium and water mixed for delivery at 40- to 

50-lb/in2 pressure at a standoff distance of 12 in. The blast 

angle was 60°, at a water flow rate of 2 gal per minute.

JOS

Named for the developer of the technique by a German 

materials-cleaning company, the JOS cleaning technique 

operates by delivering pressurized air, water, and particles 

from the same nozzle. This nozzle is designed so that the 

cleaning media travel forward in a vortex and provide a 

circular scrubbing action on the stone surface. For demonstration on our samples, JOS Quintek Corp. used the largest 

nozzle they had at the time with No. 2 dolomite grade, particles 60 to 600 μm in size, with 75 percent of the particles 

in the size range 70 to 270 μm. They worked at 28 lb/in2

pressure approximately 12 in. from the surface. The waterfl ow rate can be adjusted within the range 1.5 to 12 gal per 

hour. Waste, in the form of a wet slurry on the ground, can 

be channeled for runoff. Operators are trained to work with 

three nozzles and with several grades of cleaning granules. 

During tests at the building, the smallest (piccolo) nozzle 

was used with the same No. 2 medium to clean the more 

intricate parts of carvings.

Laser

Tests of the laser cleaning technique were made on the 

building by Atlas Laser Systems, Inc., of Sainte-Foy, Quebec, 

Canada. Their technique uses an intense beam of light in 

the near-infrared or deep-ultraviolet range to clean the stone. 

As the energy of the photon stream is absorbed by the 

target surface, bonds are broken between the substrate and 

surface soiling. The equipment provided was an infrared 

Q-switched Nd-YAG-type laser developed by the Quantel 

Group of France.

A 23-Hz portable (25 lb) machine, 2 by 2 by 1 ft in 

dimensions, was brought to the 6th-fl oor level of City Hall 

and lifted out the window onto the wide ledge outside Room 

622, where it was plugged into a 110-V outlet just inside the 

window. Outside, light was carried to the test areas by a 25-ft 

fi ber-optic cable so that cleaning could be carried not only on 

ashlar near the machine but also on column capitals that the 

specialist conservator reached by ladder. The machine provided 

energy of 230 mJ per pulse and ran at a rate of 25 pulses per 

second, with a pulse duration of 20 ns; the wavelength was 

1.064 μm. During the test, approximately 1 in2

 was cleaned in 

half a minute.

Precautions were taken that the light beam was shielded, 

at all times, by dense black cloth because it can damage the 

eye. Operators and people near the light source wore goggles. 

Observers who could not see the cleaning process directly followed its sound.

The rapid ejection of material from the surface causes 

a shock pulse audible as a snapping sound. When the light 

does not find any more dark material to vaporize, it causes no 

cleaning action, and the shock pulses cease. Residues, in the 

form of a dust spray, are typically removed by a vacuum unit 

at the work area.

Dry Ice

The dry-ice cleaning technique uses equipment that 

converts liquid CO2 into dense dry-ice pellets that are air 

driven to the cleaning surface; the impact removes superficial contaminants, which form a powdery residue. The 

cleaning medium reverts to CO2 gas, and waste is removed 

by a vacuum unit. The project team investigated this 

technique because of its obvious health and environmental advantages and future potential. Onsite tests were not 

requested because of high costs to the vendor; the dry-ice 

technique was not expected to be selected in 1995.

Stone fragments found loose on the building’s façade 

were prepared in the same way as the test areas and sent to 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 26

Marlimar Enterprises, Inc., near Buffalo, N.Y., which was 

concurrently working on tests to clean limestone, granite, and 

bronze with the graduate program in art conservation at the 

State University of New York, Buffalo. We were told that the 

samples were cleaned with pellets driven at 30-lb/in2

 pressure 

at a velocity of 60 to 80 mi per hour. We noted that the areas 

appeared especially clean, possibly even more than we would 

have requested. The process can be controlled to vary the size 

and density of pellets, rate of delivery, air pressure, velocity, 

standoff distance, and dwell time.

Operational features of four of the eight cleaning techniques evaluated in this study, as well as chemical techniques studied in 1994 (Naudé, 1994), are summarized in 

table 2.

Coring Methods

Site Selection and Equipment

The areas on the building from which cores were selected 

were those that were considered typical of a particular surfacesoiling condition and that presented the most continuous surface 

without pitting, cracking, stains, or inclusions. Closeups of the 

areas that were cored are shown in figures 36 through 63. The 

actual sites selected for coring were marked in pencil on the 

stone and located on a diagram where they were numbered 

according to the inventory system described in the next section.20

A series of 1-in.-outside-diameter cores were taken with 

a RemGrit tungsten carbide hole saw; cores were drilled 1 

in. deep. As each core was taken, it was bagged along with 

a numbered label; the outside of the bag was also numbered. 

Later, in the conservation studio, the cores were inspected one 

at a time, and a number was written on the side of the core 

in pencil before it was returned to its bag for shipment to 

the USGS.

Use of Epoxy to Preserve Surface Soiling

Thick epoxy resin that would protect the surface from 

cleaning procedures was placed on the uncleaned stone in strips 

approximately 3/4 in. wide by 3 in. long. After cleaning, 3/4-in.-

inside-diameter core samples were taken with a presentation 

surface 50 percent cleaned and 50 percent protected by epoxy 

resin. The strips of epoxy resin were laid on the uncleaned 

stone over an area larger than required to provide several 

choices for the core locations. Devcon 5-minute epoxy resin 

was chosen because of its ready availability, ease of working 

with in the field, and rapid curing time.

A second type of protectant, an acrylic resin from Ernest 

F. Fullam, Inc., was chosen to seal the cleaned surface so as to 

prevent alteration during coring. This low-viscosity resin was 

applied adjacent to the Devcon epoxy strip in the areas that 

appeared to provide the least pitted study surface. The curing 

time for this resin is about half an hour in moderate weather 

conditions. During much of the fieldwork in January and February 1995, the resin was heated and cured in about 1 hour. The 

acrylic resin is designated L.R. white, medium grade, catalog 

No. 51002, with catalyst and L.R. white accelerator, catalog 

No. 51000.

Core Inventory

Seven series of cores were taken for each surface-soiling 

condition, and numbered 1 through 7. Core series 1 and 2 were 

prepared with the two types of epoxy resin, as described above; 

these cores were used to prepare cross sections, as described 

in appendix 2. Core series 3 and 4 were taken for acceleratedweathering tests; no epoxy resins were applied to the surfaces 

of these samples. Core series 5 and 6 were taken for depositionvelocity tests; likewise, no epoxy resins were applied to the 

surfaces of these samples. Core series 7 was taken for archival 

purposes and as backup for the laboratory tests.

A complete inventory of all cores taken from the building 

is listed in table 3. Note that certain cores are missing from 

this inventory because, during the course of the project, considerations for the cost of replacing the cores with dutchmen 

led to the intentional omission of certain cores from the set 

that was originally planned. Thus, core series 1 and 2 are 

complete, but the cores in series 3 through 6 were not always 

replicated. Several additional cores indicated in the inventory 

were taken after onsite modification of the cleaning techniques 

upon request of the USGS.

At the end of the project, marble dutchmen prepared from 

a similar (but not Berkshire Lee) marble were used to replace 

the core samples removed from the building. The dutchmen 

were set slightly proud, using Hilti C–100 epoxy resin tinted 

off-white with titanium white dry pigment. The surfaces of the 

dutchmen were dressed with chisels to resemble the texture of 

the adjacent stone.

Appendix 2. Sample Preparation, Image 

Indexing, and SEM-Image Analysis

Core samples from series 1 and 2 (see app. 1) were 

selected for SEM-image analysis of their physical and chemical 

properties. This appendix provides detailed information on 

SEM-image indexing and collection, computer analysis, and 

procedural details related to the preparation of samples for 

SEM-image analysis.

Sample Preparation

The core samples from series 1 and 2 were prepared 

for SEM-image analysis according to the protocol outlined in 

fi gure 64.

After general shaping of the potted samples with a belt 

sander, the exposed faces of the samples were polished in the 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 27

following sequence: (1) 200-grit wheel, (2) 600-grit wheel, 

(3) 5-μm Al2

O3 on Texmet polishing cloth, (4) 0.3-μm Al2O3

on Texmet polishing cloth, and (5) 0.05-μm Al2O3 on Texmet 

polishing cloth. After the samples were polished to a mirror 

fi nish, they were ultrasonically cleaned, washed with deionized 

water, dried at 105 °C for 2 hours, and gold-coated for SEMimage analysis.

Image Generation

The basic premise of SEM-image analysis is that the 

physical properties of interest can be measured by computer 

analysis of SEM images. The images were collected by rasterscanning the SEM beam over selected areas of the sample; 

the magnification was controlled by varying the size of the 

scanned area. All of the images used for computer analysis 

were electron-backscatter images made with the primary-beam 

illumination angle set at 20° from normal. The electron-beam 

current, nominally 350 μA, was focused into a spot approximately 0.05 μm in diameter, providing a depth of focus 

of about 5 μm. Under the instrumental conditions used in 

this study, the lateral extent represented by the width of a 

single pixel at the highest resolution is approximately 0.01 

μm; because of excitation-volume effects along the edge of the 

sample, the actual lateral resolution ranges from 0.01 to 0.05 

μm. Thus, to make full use of the spatial-resolution capabilities 

of the instrument, the sample must be polished to a fl atness 

of 0.05 μm. This approach to image generation eliminated 

any signal distortions associated with stereo shadows and minimized problems related to contrast interpretation.

Image Indexing

The objective of SEM-image analysis was to determine 

the salt distribution in the samples and to measure such physical properties as mass loss (surface recession), surface roughness, and fracture density.

The main method for measuring the mass loss (surface 

recession) caused by a given cleaning technique was based on 

estimating the elevation difference between the protected and 

cleaned sides of the core surface. For this purpose, a relatively 

low magnification of 10× was used to obtain a vista view of 

both the protected and cleaned sides in the same image; vista 

views show 3 to 4 mm of the protected and cleaned sides in 

the same exposure. All such low-magnification images were 

designated “A” in the indexing system used in this study. For 

several samples, a higher-magnification view of the border 

between the protected and cleaned sides was needed; such 

border views were designated “H”.

Such physical properties as surface roughness and fracture density were measured from the SEM images at a magnifi -

cation of 100×. Three images were collected from each sample 

on both the protected and cleaned sides; these images were 

designated “B” through “D” (protected) and “E” through “G” 

(cleaned), respectively. For example, the eight images collected 

from sample LSRP-2 were indexed as follows:

 Sample Description

 LSRP–2A 10×, vista view

 LSRP–2B 100×, protected or virgin surface

 LSRP–2C 100×, protected or virgin surface

 LSRP–2D 100×, protected or virgin surface

 LSRP–2E 100×, cleaned surface

 LSRP–2F 100×, cleaned surface

 LSRP–2G 100×, cleaned surface

 LSRP–2H 100×, transition region

The inventory of samples for SEM-image analysis is 

summarized in table 4. Some additional exposures were 

made below the exposed surface to assess the extent of subsurface fracturing; these exposures were indexed by adding 

an extension onto the main designation. Thus, the notation 

“LSRP–2F.1” would denote a second exposure below the 

exposed surface on the cleaned side of sample LSRP–2 in the 

region covered by the SEM image of sample LSRP–2F.

SEM images were made of the views, as denoted by 

the checkmarks in table 4. Both binary-file SEM images and 

photomicrographs were made for each view. The binary-file 

SEM images are included with the CD–ROM version of this 

report; the photomicrographs are available on request.

SEM-Image Analysis

This study exploits the ability of SEM to provide crosssectional images of samples over a wide range of scales. Details 

of the computer programs for SEM-image analysis were 

described by Mossotti and others (1998), and Mossotti and 

Eldeeb (2000). The technique, which depends on direct observation of edge geometry in cross section, is based on the notion 

that the observed variegations along the polished cross section of 

a sample represent irregularities in the stone surface. We assume 

that the marble microstructure is isotropic. The set of programs 

used for SEM-image analysis are diagrammed in figure 65. The 

function and operation of each program are summarized below.

Hardware Requirements

The programs used for SEM-image analysis are executable on personal computers (PC’s) running an MS–DOS-compatible operating system with an SVGA display. A 486DX PC 

is recommended; 640K of RAM is required.

Program SEM2BIN

Program function.—SEM2BIN, version 1.0, is a standalone program that converts the special Tracor Northern IMG 

formatting to a binary format suitable for image processing. 

The original SEM image consists of 512 rows each containing 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 28

512 pixels. The SEM encodes the image data with eight bits 

of information per pixel representing 256 shades of gray. The 

original image data can be regarded as a serial stream representing a raster scanned image made up of 262,144 pixels. 

For local storage, the original SEM data files are passed to a 

translator program called XI that is supplied by Tracor Northern for the SEM. The translator program generates and downloads the specially formatted files to a block device; in our 

SEM laboratory, a Macintosh II (40-MB hard disk) was used to 

log the image files. These files can then be downloaded to a PC 

over telephone lines, over a LAN, or with a physical diskette. 

Programs such as MAC2DOS are available for translation of 

the Macintosh fi les to a PC-compatible format. For transmission over telephone lines, any set of standard communication 

packages may be been used; the Kermit protocol is especially 

useful because of its ability to support batch processing.

Program environment.—SEM2BIN is scheduled from an 

MS–DOS-compatible command line.

Synopsis of the command line.—SEM2BIN is run from 

the DOS command line in accordance with the syntax shown 

in fi gure 66,21 where the /h switch cues SEM2BIN to write 

the header information to the monitor (stdout); version 2.0, 

Mossotti and Eldeeb (2000) uses the /t switch to prompt the 

program to write the output file in the standard TIFF format.

Program I/O.—The input file is an image file in the 

Tracor Northern IMG format; the output file is given the input 

fi le name with an SEM extension. SEM2BIN provides no 

graphics display and requires no user interaction. Details of the 

program functioning and the source code were presented by 

Mossotti and others (1998), and Mossotti and Eldeeb (2000). 

SEM2BIN generates an output file consisting of an array of 

262,144 bytes if the header information is discarded. If the /h 

command-line switch is used, the first line of the IMG fi le is 

discarded, and the next 64 lines are converted to their original ASCII characters. Thus, the actual data, which start at the 

2,049th byte, will be indexed 2,048 (800 hexidecimal). Each 

byte represents the intensity of one pixel on a scale of 256 

shades of gray. Each row in the 512- by 512-byte image is represented by serially ordered subsets of 512 bytes in the array, 

associated with the original image as follows:

{

pixel at row0, column0 ; pixel at row0, column1 ; ...

pixel at row1, column0 ; pixel at row1, column1 ; ...

... (no line feeds between rows) ...

}

The translator program XI, supplied by Tracor Northern, 

carries out its function without error checking. From time to 

time, XI fails to accurately transmit part of a line or several 

complete lines. If the image file is corrupted by an odd number 

of bytes, the resulting image may appear as a blur of random 

pixels after a certain point. If an even number of bytes are 

lost, a shift may appear at some point in the image. When 

such problems occur, it is possible to repair the image file 

with a text editor by padding the corrupted line or by inserting 

additional lines into the fi le. As a diagnostic for such problems, 

SEM2BIN reports on all lines that do not contain exactly 64 

characters, and on the total number of lines in the fi le; this 

information is useful for identifying the corrupted location in 

the image file. The reader who is interested in converting the 

image fi les to a standard format (TIFF, PCX, GIF, Postscript, 

and so on) is referred to Murray and vanRyper (1996).

Use of the program.—SEM2BIN requires no user interaction.

Program SHOWPIX

Program function.—Three DOS applications, each of 

which uses different combinations of graphics modes and 

image manipulations, were written to display the binary image 

on a PC monitor.

Program environment.—Three versions of SHOWPIX 

have been developed:

• SHOWPIX version 1.0 uses standard VGA (640 by 480 

pixels) and requires the Borland graphics library; standard 

VGA can display 16 shades of gray.

• SHOWPIX version 1.1 uses standard VGA and near pointers 

in video memory to create the illusion of SEM operation.

• SHOWPIX version 2.0 uses SVGA mode 103 (800 by 600 

pixels) and requires the proprietary Flash Graphics library; 

SVGA mode 103 can display 256 colors with 64 shades 

of gray. In addition, the Flash Graphics library produces 

faster and more efficient code than the Borland graphics 

library.

Synopsis of the command line.—SHOWPIX is run from 

the DOS command line in accordance with the syntax shown 

in fi gure 67. All versions of SHOWPIX can be invoked with 

a DOS mask to select a file series. Because DOS cannot recognize its own mask symbols, these programs can display only the 

fi rst 50 files that match the mask.

Use of the program.—All versions of SHOWPIX display 

the SEM image on the PC monitor. Version 1.1 accepts input 

from the arrow keys to translate the displayed part of the image 

on the monitor, creating the illusion that the user is sweeping 

the SEM over a magnified surface of the sample.

Program PROFILE

Program function.—Image calibration is the fi rst step 

in SEM-image analysis. A fundamental technical problem in 

automated SEM-image analysis concerns the interpretation of 

contrast. Because the SEM images are optimized for interpretation by the human eye, an intensity-modulated image will show 

intermediate gray levels monotonically allocated between the 

extreme light and dark areas of the image. The purpose of 

image calibration is to identify the threshold gray level that will 

be used to identify and partition the pixels into the logical sets.

Calcite and Epoxy

Because all of the SEM-image analysis in this study was 

conducted with epoxy-potted specimens in which the aver-
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 29

age atomic number of the epoxy was less than the average 

atomic number of calcite, and because all the samples were 

highly polished, we assume that all gray values exceeding a 

certain threshold value represent the backscatter signal from 

calcite. This approach allows us to dismiss any requirements 

for a linear relation between gray level and topologic depth 

or between gray level and average atomic number. However, 

because the surface of the sample is not perfectly flat, we can 

acknowledge any polishing imperfections by identifying a 

range of gray values to represent the uncertainty in the signal 

transition between the calcite and the epoxy.

Program environment.—PROFILE is scheduled from an 

MS–DOS-compatible command line. The program requires 

SVGA mode 103. The source code for PROFILE was presented 

by Mossotti and others (1998).

Synopsis of the command line.—PROFILE is run from 

the DOS command line in accordance with the syntax shown 

in fi gure 68. PROFILE accepts binary image fi les as input. On 

termination, the program displays the upper and lower values of 

the threshold pseudocolor.

Program I/O.—PROFILE is a stand-alone program that 

generates calibration parameters for the input image. PROFILE 

displays the input binary image on the monitor along with a 

histogram of the image gray values. The program also shows a 

menu instructing the user to use the arrow keys to move the upper 

and lower threshold levels. In response, the program refreshes the 

tristate image with the new threshold values. On termination, the 

program outputs the final threshold values to the monitor.

Use of the program.—The selection of a given pseudocolor for partitioning the calcite and epoxy pixels is the fi rst 

crucial step in SEM-image analysis. Because the selection of a 

suitable threshold gray level is somewhat subjective, the fi rst 

program provided in our image-processing package, PROFILE, 

is interactively designed to assist in this selection. Using PROFILE, the user can partition the full set of image pixels into 

three sets. The user graphically selects upper and lower threshold values from the histogram of pixel pseudocolors while 

viewing a dynamically updated tricolor thresholded image. A 

typical image displayed by PROFILE is shown in figure 69. 

The bimodal histogram in figure 69 shows a high occurrence 

frequency of both dark and light pixels. The locations of the 

bimodal peaks along the gray scale differ from image to image 

because SEM images do not have the same average brightness. 

Threshold values can be interactively selected on the histogram 

such that all pixels falling below the lower threshold value are 

displayed as black (logical value, EPOXY), all pixels falling 

above the upper threshold value are displayed as white (logical value, CALCITE), and all pixels falling in between the two 

threshold levels are displayed as a particular gray-scale pseudocolor (logical value, EDGE, shown in red on the computer 

screen and in black in fig. 69).

Program EDGE

Program function.—EDGE is used to calibrate and edit 

SEM images and to calculate surface roughness, fracture density, and surface recession.

Program environment.—EDGE is scheduled from an 

MS–DOS-compatible command line. The program requires 

SVGA mode 103. The source code for EDGE was presented by 

Mossotti and Eldeeb (2000).

Synopsis of the command line.—EDGE is run from the 

DOS command line in accordance with the syntax in figure 70.

Program I/O.—EDGE requires a binary image fi le as 

input. The program displays the image and a menu of operations on the PC monitor; the menu is accessible by the user 

with either arrow keys or hot keys, as indicated by highlighted 

colors on the menu. Calibration parameters are displayed and 

automatically used by the program for various computations, 

the results of which are also displayed on the PC monitor. At 

the user’s request, the program will write a new edited output 

image file.

Use of the program. —EDGE displays the following 

menu below the image on the PC monitor:

Edit Clip eRase Save Profile Density Loss eXit

The corresponding image operations are:

• Edit: Pixel-by-pixel edit function

• Clip: Clips large sections of image

• Erase: Erases section of image with swath control

• Save: Saves edited image

• Profile: Changes calibration parameters

• Density: Computes fracture density

• Loss: Provides image calipers for measuring recession

• Exit: Exits the program

Appendix 3. Optical Microscopy and 

Gray-Scale Measurements

To evaluate the efficacy of the cleaning techniques, we 

developed a quantitative assessment of the extent of superfi cial 

soiling on the surfaces of the cores taken from the building. 

Two methods were used for this purpose: a visual assessment ranking the cores by surface grayness, and an electronic 

method providing quantitative interval data representing the 

surface reflectivity of the cores over an area of 23 mm 2

. This 

appendix presents the procedural details and unprocessed laboratory data associated with these measurements.

Visual Assessment of Surface Soiling

A color scale, from 0 to 10, was developed such that the 

higher the number, the greater the apparent darkness of the 

core surface, with two significant figures used to represent the 

degree of soiling. The bottom of the scale, 0.0, was assigned 

to the inside surface of a fractured core, which we assumed 

to represent a pristine, unsoiled, unexposed surface. The high 

end, 10, was assigned to the surface of sample GSRV–4, on 

which the dark crust covered virtually 100 percent of the 

exposed surface. To be precise, this gray scale is without explicit 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 30

units because each value corresponds to the fractional reflectivity of the stone surface. In the following discussion, the notation 

“c/” is used with a number to designate a gray-scale value.

Core series 3 through 6 were taken from the building as 

replicates of the same material (class of soiling, cleaning, coring 

procedure). These series were compared for consistency in color 

to determine the variation of the surfaces in a given class and 

for quality-assurance purposes. Core series 1 and 2 were not 

examined for color because 50 percent of the core surfaces were 

covered with epoxy resin and because the cleaned surfaces were 

treated with L.R. white, which may have altered the surface 

color. Detailed observations on core comparisons for surface 

roughness and color are given below. The surface-grayness 

determinations for the core series 3 through 6 are summarized in 

table 5. In the following discussion, the notations “S–3”, “S–4”, 

and so on designate the core series.

LSSV (lightly soiled, smooth; uncleaned):

S–3R and S–4 match (c/3).

S–5 and S–6 match (c/2).

• S–3R and S–4 are discernibly darker than S–5 and S–6.

• S–3R and S–4 are discernibly rougher than S–5 and S–6.

LSSP (lightly soiled, smooth; power wash):

S–3 and S–4 match (c/1.4).

S–5 and S–6 match (c/1.9).

• S–3 and S–4 are discernibly cleaner than S–5 and S–6.

• S–3 and S–4 have about the same surface roughness as 

S–5 and S–6.

LSSG (lightly soiled, smooth; gommage):

S–3 and S–4 match (c/1.4).

S–5 (c/1.5).

• S–3, S–4, and S–5 have about the same surface roughness.

Uncleaned surfaces appeared to be rougher than cleaned 

surfaces. Gommage-cleaned surfaces are generally smoother 

than power-wash-cleaned surfaces.

LSRV (lightly soiled, rough; uncleaned):

S–3 and S–4 match (c/4.0).

S–5 and S–6 match (c/4.0).

• S–3 and S–4 have about the same surface roughness as 

S–5 and S–6.

LSRP (lightly soiled, rough; power wash):

S–3 and S–4 match (c/2.0).

S–5 and S–6 match (c/1.8).

• S–3 and S–4 have about the same surface roughness as 

S–5 and S–6.

• Sample LSRP–6 has a ≈21/2-mm-wide brown stain (see 

fi g. 71).

• Sample LSRP–3 has small, diffuse brown stains.

LSRG (lightly soiled, rough; gommage):

S–3 and S–4 match (c/1.4).

S–5 (c/1.4).

• S–3 and S–4 have about the same color as S–5.

• S–3 and S–4 have about the same surface roughness as S–5.

GCSV (gypsum crusted, smooth; uncleaned):

S–3 (c/8.0).

S–5 and S–6 match (c/8.0).

• S–3 has about the same color as S–5 and S–6.

• S–3 has about the same surface roughness as S–5 and S–6.

GCSM (gypsum crusted, smooth, misting):

S–3 (c/4.0).

S–5 and S–6 match (c/4.0).

• S–3 has about the same color as S–5 and S–6.

• S–3 has about the same surface roughness as S–5 and S–6.

GCSG (gypsum crusted, smooth; gommage):

S–3 and S–4 match (c/1.4).

S–5 and S–6 match (c/1.4).

• S–3 and S–4 have about the same color as S–5 and S–6.

• S–5 and S–6 have about the same surface roughness as 

S–5 and S–6.

GCSC (gypsum crusted, smooth; combination):

S–3 (c/1.4).

S–5 (c/1.3).

S–6 (c/1.4).

• S–3 has about the same surface roughness as S–5 and S–6.

GCRV (gypsum crusted, rough; uncleaned):

S–3R (c/8.0).

S–3 (c/10.0).

S–4 (c/10.0).

S–5 (c/9.0).

S–6 (c/9.0).

• S–3 has an uncertain surface roughness.

• Information on S–5 is unavailable.

• S–4 and S–6 have about the same surface roughness, but 

greater than S–5.

GCRM (gypsum crusted, rough; misting):

S–3 (c/3.2); sample GCRM–3 has a brown stain (≈21/2 by 

11/2 mm; fi g. 72).

S–5 (c/4.0).

S–6 (c/5.0).

• S–3 is rough but has less surface roughness than S–5 

and S–6.

• S–5 and S–6 have about the same surface roughness.

GCRG (gypsum crusted, rough; gommage):

S–3 (c/2.5).

S–5 (c/3.0).

S–6 (c/4.0).

• S–3 is highly irregular; its surface roughness cannot be 

determined.

• S–5 and S–6 have about the same surface roughness.

All three fractured surfaces and core sides are darker (c/3.5) 

than corresponding surfaces on any of the other cores. S–3 has a 

discernibly lighter color (c/2.0) zonation visible in cross section, 3 to 

4 mm thick, that follows the external surface morphology (fig. 73).

GCRC (gypsum crusted, rough; combination):

S–3 (c/1.5).

S–5 (c/1.5).

S–6 (c/1.5).

• S–3, S–5 and S–6 all have about the same surface roughness.

All GCRC cores have a general blotchy brown stain over 

the entire surface (fig. 74). In general, the GCRG and GCRC 

series are smoother than the GCRV and GCRM series.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 31

Electronic Gray-Scale Measurements

The automatic shutter on the optical-microscope camera 

provided a means for electronically measuring the surface 

reflectance of core samples in a highly localized area (≈23 

mm2). In this technique, the exposure time was used as a measure of the grayness of the core surface. A two-step procedure 

was used. First, an image of the cleaned surface was brought 

into focus with a 67× lens, with the surface obliquely illuminated by a fixed tungsten source. The tilt of the sample was 

adjusted to maximize the amount of reflected light into the 

microscope. The method was calibrated on a gray scale of 0 

to 10 (gs units). By setting the ƒ-stop of the camera lens such 

that an exposure of the surface of sample GCRV–4 required 

10 s, we were able to calibrate the electronic exposure measurement at the high end of the scale. In this technique, we 

implicitly assumed a linear relation between the electronically 

measured exposure time and the extent of surface soiling 

within the surface area intersecting the solid angle subtended 

by the camera lens.

The observed color values, in descending order from 

10 to 0, and as measured by the electronic technique and 

the visual method, are plotted in figures 75A and 75B, 

respectively. The electronic measurements (fig. 75A) ranged 

smoothly from the most heavily soiled to the cleanest surfaces, whereas the visual measurements relegated the cores to 

a well-defined level across the series. Because of the differences between the average surface reflectivity as perceived by 

the visual method and the local reflectivity as electronically 

measured, the samples spanned the full range of the scale.

The relation between the electronic exposure times and 

the visually assessed gray-level measurements is plotted in 

fi gure 76. The reproducibility of the electronic method was 

about a factor of 10 poorer than the visual method (visual, 

±0.2 gray-scale units; electronic, ±2 gray-scale units). Not 

only does the electronic method survey a much smaller area 

than does the visual method, but the electronic technique 

also is more sensitive to the local angle of incidence of the 

light beam on the sample surface. Also, the intensity of the 

reflected light as measured electronically is more sensitive to 

small-scale surface roughness (≈5 mm) than is the intensity 

of the reflection over the full area of the sample as visually 

perceived. Thus, the wide variation in the electronic graylevel measurements is largely due to local inhomogeneities in 

surface color and texture.

Appendix 4. Salt-Penetration Measurements

Numerous reports in the literature have speculated on the 

influence of such adventitious salts as calcium sulfate dihydrate 

(gypsum) on the appearance and stability of calcareous building 

stone. Rain-chemistry data for the Philadelphia area (table 6) 

show average Ca2+ and Mg2+ concentrations of 73 and 30 μequiv/

L, respectively. This appendix reviews representative laboratory 

data on the lateral and vertical distribution of gypsum over the 

stone surface and in pores and cracks of the stone.

Sulfate Analysis

Methods

Two methods, both based on X-ray-fluorescence spectroscopy, were used to measure the sulfur concentration in the 

stone samples. The first method, which was more convenient, 

was by energy-dispersive X-ray analysis, while the sample 

was mounted under vacuum on the SEM. The chemical sensitivity of this method for sulfur in the small area sampled by 

the primary electron beam was ≈0.25 atomic percent, and the 

spatial resolution was ≈5 μm. The second method was conducted with a Kevex energy-dispersive spectrometer with the 

specimens open to air. This method provided trace analysis 

for sulfur with a sensitivity of ≈200 ppm over a sample area 

of about 1 cm2.

Applications

Sulfate Content of the Applied Resins

To assess the contribution of the epoxy and acrylic 

resins to the sulfur signal, we analyzed the coatings on 

fractured sections of the marble cores that were previously 

determined to be sulfur free. Typical X-ray-fluorescence 

spectra of the epoxy and acrylic resins are shown in figure 

77. It is evident from the respective background levels that 

the exposures were nominally the same for both sample types 

and that a substantial amount of sulfur is present in the epoxy 

resin, whereas sulfur is absent in the acrylic resin.

Sulfate Content of Soiling on Sample Surfaces

The uncleaned samples in core series GCS and GCR 

(1 and 2) were analyzed for sulfur in the open air with the 

Kevex spectrometer. In figure 78, typical X-ray-fluorescence 

spectra of uncleaned samples (free of epoxy resin) provide 

evidence for the presence of gypsum on the stone surface in 

the GCR core series. The inorganic component of the crust on 

sample GCRV–2, the most well defined in core series GCR, 

was found to be mainly calcium and sulfur. No sulfur was 

detected at the interface between the crust and matrix, in the 

void areas where the crust had separated from the matrix, or 

in the calcite matrix.

The surfaces of uncleaned samples in core series GCR 

appear as much darker (lower average atomic number) 

areas in most SEM images, partly because of the surface 

roughness in the direction parallel to the electron beam. 

The bright spots on the surface were identified as silicate 

and iron minerals; the very dark regions in the SEM images 

are holes. Typical X-ray-fluorescence spectra of such 

silicate and iron minerals are shown in figure 79. A typical 

semiquantitative analysis of the silicate inclusions is listed 

in table 7. One grain of a mineral that showed the X-rayfluorescence profile of apatite was observed directly on the 

stone surface.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 32

Sulfate Content of Cracks, Fissures, and Matrix

A vertical path from the several hundred micrometers 

below protected or cleaned surfaces, through debris, into the 

potting material, was analyzed by X-ray-fluorescence spectroscopy. This procedure was repeated on every sample that we 

suspected might contain gypsum. No sulfur was detected at any 

point along such vertical paths in any sample, or anywhere in 

the cracks and crevices or in the debris that remained in some 

of the pockets on cleaned surfaces.

Appendix 5. Surface-Recession Test

This appendix details the procedures used for measuring 

the mass loss associated with each cleaning technique. The 

amount of surface recession caused by the cleaning was used as 

a surrogate measure for the mass loss. As detailed in appendix 

1, approximately half of each core was protected during cleaning by a thick layer of epoxy resin. This procedure permitted a 

side-by-side comparison of the uncleaned and cleaned surfaces 

of the stone by examining polished cross sections taken at a 90° 

angle across the cleaned and uncleaned zones.

Two methods were used to estimate the surface recession. 

The first method was based on the change in the level of the 

cleaning surface relative to that of the original soiled surface. 

The second method was based on changes in the deviation of 

the surface profile around the mean level between the cleaned 

and uncleaned surfaces; this method, a measure of surface 

smoothing, yields an estimate of the minimum mass loss due 

to cleaning. These minima were used in the data analysis only 

when the values obtained by the first method were highly 

uncertain or below background.

Surface Recession Based on Change in Surface 

Level

An example of the application of the SEM-image-analysis 

program EDGE (see app. 2) to estimation of the surface recession on the clean side of sample FCRC–1 is shown in figure 16. 

The recession calipers shown in figure 16 are used to estimate the 

average change in relief across uncleaned and cleaned surfaces. 

The program EDGE displays the calipers in “recession” mode and 

continuously outputs the separation of the calipers to the left of the 

image on the computer screen (not shown); precalibration of the 

calipers is based on the assumption that the image magnification is 

10×. The calipers can be rotated to match the slope of the surfaces 

in accordance with the action of the function keys described in the 

menu below the image on the computer screen.

This method of surface-recession measurement is fundamentally limited by natural irregularities in the stone surface 

over the lateral range of observation. The standard deviation of 

the natural morphology over a 1-cm scale was found to range 

from 35 to 150 μm, depending on the particular location on the 

building from which the core was taken. The estimated surface 

recession for different surface-soiling conditions as measured by 

the two methods is listed in table 8. The uncertainties listed were 

estimated from precise measurements of the standard deviations 

of the surfaces and from the estimated statistical reproducibility of 

the morphologic variations across the lateral range of observation. 

With the possible exception of GCR surfaces, the mass loss due 

to any of the cleaning techniques is insignificant. In four of the 

seven cases studied, the mass loss is less than the detection limit of 

the method, which averages about 75 μm. Therefore, we suggest 

that much of the mass loss on stone surfaces is due to smoothing. 

Furthermore, the change in the deviation of the surface profile 

from the mean level should provide an alternative measure of the 

mass loss. The extent of surface smoothing can be estimated with 

considerable precision by computer analysis of the SEM images of 

core cross sections.

Surfaces Recession Based on Surface Smoothing

Use of the program EDGE (see app. 2) in “trace” mode 

provided a measure of the standard deviation of surface excursions from the mean level. The program is calibrated to output 

the standard deviation, in micrometer units, on the computer 

monitor. Although the precision of this method based on surface 

smoothing ( 35 μm) is about double that of the first method, the 

second method is flawed by a systematic offset error that cannot 

be estimated.22 Because of this error, the second method provided an estimate of only the minimum mass loss by the cleaning 

technique. The surface-recession values obtained by the second 

method were used for data analysis only when those obtained by 

the first method were highly uncertain (table 8).

Appendix 6. Fracture-Density Measurements 

and SEM-Image-Analysis Data

The fracture density is a measure of the fraction of the 

stone volume filled by fractures. The measurement of fracture 

density is based on SEM-image analysis. The program EDGE 

(see app. 2) was developed inhouse to analyze the SEM images 

of samples prepared as described in appendix 2. The results of 

program computations are displayed on a PC monitor.

The first step in the measurement of fracture density 

is calibration of the SEM image. The user can control the 

threshold gray level that delineates a fracture from surrounding 

unfractured material in the SEM image. As the user adjusts the 

threshold gray level, the image is refreshed in real time, with 

all of the pixels to be counted as fractures and edges colored in 

red. Subjective judgment must be exercised in selection of the 

threshold gray level, which delineates pixels into three logical 

classes: fracture, edge, and mass.

The second step in the measurement of fracture density is 

selection of the size of the spatial window for the measurement. 

The program EDGE reports the percentage of pixels identified 

as components of the fractures in the window, which can be 

moved to any location on the SEM image. We noted that the 
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 33

natural variation in fracture density was about ±4 percent in 

most of the SEM images examined: The most homogeneous 

stone showed a natural variation of about ±0.5 percent, and 

the most inhomogeneous stone showed a natural variation of 

about ±15 percent. The results of fracture-density measurements on all the SEM images obtained in this study are listed 

in table 9.

Appendix 7. Surface-Roughness Test

The surface roughness of core samples was measured by 

mathematical analysis of their cross-sectional SEM profiles at 

100× magnification. The measure of surface roughness derived 

for this study, the shape factor, is not unlike a “gathering 

factor” that might be conjured when sewing ruffles in fabric. 

The shape factor measured on the cores examined in this study 

ranged from 0 to about 10, where 0 represents a perfectly 

smooth plane and 10 corresponds to a surface exhibiting an 

irregular morphology over the range 10 to 105 μm.

The shape factor is based on the fractal nature of 

the exposed surface. The challenge of computing the fractal 

dimension of a surface from the shape of its cross section is 

similar in many respects to the classical length-of-shoreline 

problem. Our method for determining the fractal dimension 

is based on a Richardson structured walk along the crosssectional lateral trace of the surface. The Richardson effect, 

in its elegant simplicity, asserts that the trace of a ragged 

pattern can be approximated with a broken line made up of N

intervals of length ε, where

 N = μDε−D. (11)

If the value of the exponent D is constant within a range of ε

values, the ragged pattern is said to be fractal, and the exponent 

D is called the fractal dimension, on which the proportionality constant μD depends. In keeping with common analytical 

practice, our algorithm uses the slope of the curve log N versus 

log ε to estimate the fractal dimension for a given contour. 

Since the measurement is made across the trace of the surface 

cross section, the fractal dimension will range from 1 to 2. If 

the marble surface is morphologically isotropic, we can calculate its fractal dimension by simply incrementing by the fractal 

dimension determined for the surface cross section. However, 

in calculating the shape factor for measuring the surface roughness, the fractional part of the Richardson dimension is used as 

the scale-independent paramenter.

In general, the length, L, along the trace of the surface 

cross section is given by

 L = Nε

 = (μDε−D)ε (12)

 = μDε1−D,

where L has the units μm1−D.

If we measure L relative to a reference state L*, we can 

define the shape factor, Γ, by the ratio μD/μD

* and use the following algorithm for its computation:

 Γ ≈ (L/L*)εD−D*, (13) 

where Γ has the units μm*.

In this study, L was taken as the number of pixels in the 

trace of the exposed surface. L values for all SEM images analyzed are listed in table 9. The reference value L*, the Euclidean length of the trace of the exposed surface, is reported in 

the rows labeled “Ecldn Length” in the same table. The ε value 

used in the SEM images made at 100× magnification is equal 

to 1.76 μm/pixel. In equation 13, the Euclidean length is converted to pixel units by use of the factor 512/22.5 pixels/cm. 

The fractal dimension of the trace of the exposed surface, given 

in the row labeled “FRghness” in table 9, is reported as the 

fractional part of the Richardson dimension multiplied by 100.

Appendix 8. Surface-Reactivity Test

Theoretical Overview

Equation 6 shows that the total Ca2+ released into a flowing solution over calcareous stone by the dissolution action of 

pure water depends only on the product of the surface reactivity and the time of wetness.23 Incorporated into equation 6 is 

the assumption that the fl ow rate of water over the stone is suffi ciently fast to sweep the surface of reaction products which 

may accumulate and participate in the backreaction. Under 

such conditions, the monomolecular kinetics (Plummer and 

others, 1978) of the forward dissolution reaction

CaCO3

+ H2O → Ca2++ HCO3

−

+ OH− (k3 = 10−6.92 cm/s) (14)

is unchecked by the backreaction in which CaCO3 is precipitated. From equation 6, we can expect that the rate of release 

of Ca2+ ions is independent of the local hydrodynamics of the 

test solution once a critical threshold surface flow rate has been 

exceeded. We have empirically established that the flow rate 

of the test solution must exceed 0.01 cm 3

/s per centimeter of 

contour for the surface-reactivity test to be relatively insensitive 

to the dynamics of the test solution.

To compare the results of the surface-reactivity test from 

sample to sample, we normalized the amount of Ca2+ released 

in a given test by the amount of Ca2+, EMt, that theoretically 

would be released from Euclidean area A in t seconds. The 

ratio Mt/

EMt

, here called the relative surface activity, is denoted 

Ω. From this defi nition, using equation 6, Ω is given by

 Ω = Mt

/

EMt

 

 = k3Ât / k3At (15)

 = Â / A.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 34

Thus, equation 6 can be rewritten to include Ω as follows:

 Mt = k3

ΩAt (in millimoles). (16)

The surface-reactivity test is based on a simple rearrangement 

of the equation 8 as follows:

 Ω =

k3At = k3At , Mt [Ca2+

]U

 (17)

where [Ca2+] is the Ca2+ concentration in U cm3 of test solution 

and t (in seconds) is the exposure time.

Laboratory Implementation

Apparatus

The laboratory implementation of the surface-reactivity 

test is simplicity itself. Two different laboratory setups were 

used to recycle a given volume (5–10 mL) of deionized water 

over the top surface of the sample. In each setup, the top surface of the sample was isolated by mounting each sample with 

silicone rubber adhesive in a Delrin (acetyl) ring, and the flow 

rate of the test solution over the core surface was controlled by 

an infusion pump to exceed the requisite threshold flow rate of 

0.01 cm 3/s per centimeter of contour.

The two different laboratory setups are diagrammed in 

fi gures 80 and 81. In each setup, a measured volume of water 

was recycled over the core surface. After the water had washed 

the core surface for a measured period, an aliquot of the 

homogenized test solution was analyzed for Ca2+ by inductively 

coupled plasma-emission spectrometry.

Equation 17 was then used to compute the Ω value of 

the core surface. In figure 76, the test solution was recycled 

through a dripline positioned about 3 cm above the test surface; 

in fi gure 77, the test solution was pumped over a vertically 

mounted core surface. With these simple techniques, we fi nd 

that the Ω value can vary by as much as 300 percent, depending on the type of calcareous stone and on the surface-soiling 

condition of the stone.

Test Procedure

The following procedure was followed in the surfacereactivity test. All core surfaces were (1) cleaned with compressed air to remove dust, (2) washed with 50 mL of deionized 

water, (3) dried at 110°C overnight, (4) mounted for isolation 

of the top surface, (5) soaked overnight in deionized water, 

(6) purged of loose droplets by shaking, (7) mounted in a test 

beaker, (8) conditioned by flowing deionized water over the 

core surface for 10 minutes, (9) washed with 20 mL of deionized water, (10) treated with 10 mL of deionized water , and 

(11) exposed to fl owing test solution for t seconds.

Results

The Ω values of sample surfaces in core series 3 are plotted in figure 82. An analysis of the average surface-reactivity 

values from replicate tests on these samples is presented in the 

section above entitled “Synthesis and Interpretation of Results.”
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 35

Footnotes

1

To define the state of a surface, a complete set of state 

variables is needed. In particular, a given state variable, such as 

roughness, does not uniquely define the state of the surface; the 

same level of roughness can reflect an infinite number of states. 

Although our present knowledge of a complete set of state 

variables for weathered stone is incomplete, the thermodynamic 

model is useful in developing a formalism for data analysis. 2

Primary deposition of a given weathering agent at a specific location can be represented by a spatially and temporally 

dependent flux-density function, a vector quantity with units of 

mass per unit area per unit time. 3

Secondary mobilization of a given weathering agent 

to a specific location can be represented by a spatially and 

temporally dependent surface-flow function, a vector quantity 

directed across the contour line with units of mass per unit time 

per unit length along the contour. 4

The following defi nitions are used in this report:

 nanoscale 0.1 to 100 nm

microscale 0.1 to 100 mm

 mesoscale 0.1 to 5 mm

macroscale 0.5 cm. 5

Because SEM measurements were used, core samples 

had to be taken from the building. 6

Although the laser-cleaned surfaces were visually examined by optical microscopy, no photomicrographs were prepared because of offschedule delivery of the cores. 7

Much of the soiling consists of carbon-based particulate 

matter that is not effectively imaged by backscatter SEM; only 

the gypsum in the soiling can be monitored with SEM energydispersive X-ray analysis. 8

We detected a fragment of gypsum in only one SEM 

image of a GCRG-series sample out of 20 such images examined for gypsum; in this particular SEM image, the gypsum 

may have been trapped by the L.R. white epoxy because of the 

location of the crevice near the protected area of the sample. 9

Most of the cores tested were found to be quite homogeneous below the surface, with a natural variation in fracture density 

of about ±0.25 percent. At the opposite extreme, we have seen 

variations of ±15 percent in several cores examined in this study. 10χ2

 analysis of a 2-by-2 contingency table of surface 

roughness (smooth, rough) versus friability index (FI 57, <57) 

gave the outcome χ2=6.8, with p=99 for DF=1. In terms of surface roughness, in figure 11C the shape factor falls mainly in 

the range 2–4 cmD for uncleaned smooth cores (LSS, GCS) and 

in the range 2–8 cmD for uncleaned rough cores (LSR, GCR). 11χ2

 analysis of a 2-by-2 contingency table of soiling 

(light, heavy) versus relation (yes, no) gave the outcome χ2=62, 

with p>99.9 for DF=1. 12The utility of a resource, an economic concept, provides 

a common quantitative unit, the “util,” for measuring the satisfaction provided by that resource. For buildings, this concept 

is useful for aggregating component utilities (shelter, emotional 

value, and so on) and for comparing the value of resources. 

For example, Philadelphia City Hall minimally provides structural utility, esthetic utility, and indirect economic utility on the 

basis of its effect on tourism. The importance of this concept 

2F/t

=

{40.103(μg/mmol)10−6.92

(mmol/cm2−s)π[(0.75/2)(in.)2.54(cm/in.)]

2

}

= 365 s

≈ 6 minutes.

1(μg/mL)5(mL)

t = [Ca2+]U/k3A

is not so much the absolute number of utility units assigned to 

each resource component but the relative magnitudes of the 

utility of each component in the data set under consideration. 13For a discussion of the Hausdorf measure, see Peitgen 

and others, 1992, p. 216–217. 14To compare the results of the surface-reactivity test 

from one sample to the next, we generally normalize the mass 

of Ca2+ released in a given test by the mass of Ca2+ that would 

theoretically appear in a test solution if the fractal dimension 

of the test sample were equal to 2 (Euclidean area); we refer to 

this ratio as the relative surface activity. 15As discussed in appendix 8, the surface-reactivity test 

is relatively insensitive to the dynamics of the delivery and 

collection of the test solution if the surface fl ow rate of the test 

solution exceeds ≈0.01 cm3/s per centimeter of contour. 16The notion of a limit state, sometimes called emergent 

criticality, is codified in the theory of disordered systems, popularly known as chaos theory. 17This definition is in keeping with the conventional statistical defi nition of accuracy as a measure of systematic error. 

The smaller the error, the higher the accuracy. 18In principle, the general-utility curve is actually a 

hypersurface because the utility space incorporates the effects 

of a multiplicity of state variables. 19Note that the confidence intervals for the curve as a 

whole, which imply joint statements about any number of data 

points across the curve, are larger by the ratio (where F

is the F-distribution and t is the table statistic) than corresponding intervals for any single data point. 20The photographic record of core sites has been submitted to the Philadelphia Department of Public Property in fieldnote format. This record documents the sites before they were 

contaminated by a film crew while making a movie and then, 

later, after the entire exterior of the building was recleaned by 

the gommage technique. 21When viewing a syntax diagram, read from left to right 

and from top to bottom. The command name and items required 

to make the command work appear on the baseline; the items 

below the baseline are optional. A line ending with an arrowhead means that the diagram is contiued. A line starting with an 

arrowhead means that the syntax is continued from the previous 

line. A vertical bar marks the end of the command syntax. 22In the data treatment shown in the main document (see 

fi g. 17), the surface-recession values obtained by the second 

method were used when the surface-recession values obtained 

by the first method were in the same order of magnitude as the 

noise level associated with the first method. 23The expected time needed to generate about 1 ppm Ca2+

in 5 mL of deionized water flowing over the surface of a 0.75-

in.-diameter calcite core sample is given by

≥
Figure 1.—Typical examples of surface-soiling conditions on Philadelphia City Hall. A, Lightly soiled, smooth. B,

Lightly soiled, rough. C, Gypsum crusted, smooth. D, Gypsum crusted, rough.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 36
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 37

Figure 2.—State diagram of stone surface, in which

each surface-soiling condition is viewed as a unique

thermodynamic state. Surface-soiling conditions:

GCR, gypsum crusted, rough; GCS, gypsum crusted,

smooth; LSR, lightly soiled, rough; LSS, lightly

soiled, smooth.

Figure 3.—Generalized cleaning process.

Figure 4.—Basic components of stone-weathering model.
Figure 5.—Primary deposition and secondary mobilization processes

in stone-weathering model.

Figure 6.—Experimental design for controlling extraneous variables. TB, epoxy protection layer applied

to control test surfaces; TA, cleaning treatment followed by the epoxy protection layer; OA-OB, effect of

interest in all measurements.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 38
Figure 7.—Photomicrographs of typical soiled surfaces in cores series 3 through 6 before and after application of cleaning techniques, showing morphologic features of surface and dissemination of soiling agents in

cracks and crevices of stone surface. Cleaning techniques: A, Armax; C, combination; D, dry ice; G,

gommage; J, JOS; L, laser; M, misting; P, power wash; V, virgin. Surface-soiling conditions: GCR, gypsum

crusted, rough; GCS, gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, lightly soiled, smooth.

Magnification, 67x—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 39
Figure 7 (Cont.)—Photomicrographs of typical soiled surfaces in cores series 3 through 6 before and after

application of cleaning techniques, showing morphologic features of surface and dissemination of soiling

agents in cracks and crevices of stone surface. Cleaning techniques: A, Armax; C, combination; D, dry ice; G,

gommage; J, JOS; L, laser; M, misting; P, power wash; V, virgin. Surface-soiling conditions: GCR, gypsum

crusted, rough; GCS, gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, lightly soiled, smooth.

Magnification, 67x.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 40
Figure 8.—Typical cross-sectional scanning-electron-microscope images of uncleaned

cores, showing substrate of various surface-soiling conditions: lightly soiled, smooth

(A, sample LSSV–1); lightly soiled, rough (B, sample LSRV–2C); gypsum crusted,

smooth (C, sample GCSV–1); and gypsum crusted, rough (D, sample GCRV–2)—

Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 41
Figure 8 (Cont.)—Typical cross-sectional scanning-electron-microscope images of

uncleaned cores, showing substrate of various surface-soiling conditions: lightly

soiled, smooth (A, sample LSSV–1); lightly soiled, rough (B, sample LSRV–2C); gypsum

crusted, smooth (C, sample GCSV–1); and gypsum crusted, rough (D, sample GCRV–2).

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 42
Figure 9.—Scanning-electron-microscope images of core samples with gypsumcrusted, rough surface-soiling condition. A, sample GCRD–1A. Note sugary texture of

matrix throughout bulk of core. Magnification, 10x. B, sample GCRC–1. Note relatively smooth surface. Magnification, 100x. C, sample GCRA–1D. Note highly

fractured surface. Magnification, 100x. D, sample GCRG–1D. Note highly fractured

surface. Magnification, 100x. Such GCR cores typically exhibit a complex morphology within ~250 m of surface and well-consolidated grains 200 to 300 μm below

surface—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 43
Figure 9 (Cont.)—Scanning-electron-microscope images of core samples with gypsumcrusted, rough surface-soiling condition. A, sample GCRD–1A. Note sugary texture of

matrix throughout bulk of core. Magnification, 10x. B, sample GCRC–1. Note relatively smooth surface. Magnification, 100x. C, sample GCRA–1D. Note highly

fractured surface. Magnification, 100x. D, sample GCRG–1D. Note highly fractured

surface. Magnification, 100x. Such GCR cores typically exhibit a complex morphology within ~250 μm of surface and well-consolidated grains 200 to 300 μm below

surface.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 44
Figure 10.—Surface reflectivity (grayness value) of soiled stone after cleaning. Cleaning techniques:

A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L, laser; M, misting; P, power wash; U,

uncleaned. Surface-soiling conditions: lightly soiled, smooth (A); lightly soiled, rough (B); gypsum

crusted, smooth (C); gypsum crusted, rough (D)—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 45
Figure 10 (Cont.)—Surface reflectivity (grayness value) of soiled stone after cleaning. Cleaning

techniques: A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L, laser; M, misting; P, power

wash; U, uncleaned. Surface-soiling conditions: lightly soiled, smooth (A); lightly soiled, rough (B);

gypsum crusted, smooth (C); gypsum crusted, rough (D).

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 46
Figure 11.—Friability of stone, showing near-surface fracture density versus shape factor for all cores

before (A) and after (B) cleaning, with friability index ±1σ (error bars) for various surface-soiling

conditions and two groups of cleaning techniques (C). Straight line is best-fit curve to data points.

Surface-soiling conditions: GCR, gypsum crusted, rough; GCS, gypsum crusted, smooth; LSR,

lightly soiled, rough; LSS, lightly soiled, smooth. Cleaning techniques: group I, JOS, gommage,

combination, and Armax; group II, dry ice, laser, misting, and power wash—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 47
Figure 11 (Cont.)—Friability of stone, showing near-surface fracture density versus shape factor

for all cores before (A) and after (B) cleaning, with friability index ±1σ (error bars) for various

surface-soiling conditions and two groups of cleaning techniques (C). Straight line is best-fit curve

to data points. Surface-soiling conditions: GCR, gypsum crusted, rough; GCS, gypsum crusted,

smooth; LSR, lightly soiled, rough; LSS, lightly soiled, smooth. Cleaning techniques: group I,

JOS, gommage, combination, and Armax; group II, dry ice, laser, misting, and power wash.

Figure 12.—Model of soiling-pattern development.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 48
Figure 13.—Fracture density versus shape factor (in units of cmD, where D is the surface fractal

dimension) before (A, C, E, G) and after (B, D, F, H) cleaning for various surface-soiling conditions:

lightly soiled, smooth (A, B); lightly soiled, rough (C, D); gypsum crusted, smooth (E, F); and

gypsum crusted, rough (G, H). Least-squares regression line (dashed) is best-fit curve to data points.

R2

 value, square of correlation coefficient. Textures (figs. 13G, 13H): squares, sugary; crossed

squares, fractured; solid squares, all others—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 49
Figure 13 (Cont.)—Fracture density versus shape factor (in units of cmD, where D is the surface

fractal dimension) before (A, C, E, G) and after (B, D, F, H) cleaning for various surface-soiling

conditions: lightly soiled, smooth (A, B); lightly soiled, rough (C, D); gypsum crusted, smooth (E, F);

and gypsum crusted, rough (G, H). Least-squares regression line (dashed) is best-fit curve to data

points. R2

 value, square of correlation coefficient. Textures (figs. 13G, 13H): squares, sugary; crossed

squares, fractured; solid squares, all others—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 50
Figure 13 (Cont.)—Fracture density versus shape factor (in units of cmD, where D is the surface

fractal dimension) before (A, C, E, G) and after (B, D, F, H) cleaning for various surface-soiling

conditions: lightly soiled, smooth (A, B); lightly soiled, rough (C, D); gypsum crusted, smooth (E, F);

and gypsum crusted, rough (G, H). Least-squares regression line (dashed) is best-fit curve to data

points. R2

 value, square of correlation coefficient. Textures (figs. 13G, 13H): squares, sugary; crossed

squares, fractured; solid squares, all others—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 51
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 52

Figure 13 (Cont.)—Fracture density versus shape factor (in units of cmD, where D is the surface

fractal dimension) before (A, C, E, G) and after (B, D, F, H) cleaning for various surface-soiling

conditions: lightly soiled, smooth (A, B); lightly soiled, rough (C, D); gypsum crusted, smooth (E, F);

and gypsum crusted, rough (G, H). Least-squares regression line (dashed) is best-fit curve to data

points. R2 value, square of correlation coefficient. Textures (figs. 13G, 13H): squares, sugary; crossed

squares, fractured; solid squares, all others.
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 53

Figure 14.—R2

 value (square of correlation coefficient) versus surface-soiling condition of stone for data

plotted in figure 13.

Figure 15.—Model of surface alteration by soiling load.
Figure 16.—Output of program EDGE (see app. 2) for cleaned side of sample GCRC–1 (left side of image),

showing ~278 μm of surface recession. Red, potting epoxy resin; green, fast-curing epoxy resin; blue and

light green, stone matrix. Magnification, 10x.

Figure 17.—Surface recession versus friability index for two groups of smooth and rough uncleaned

cores. Surface-soiling conditions: GCR, gypsum crusted, rough; GCS, gypsum crusted, smooth; LSR,

lightly soiled, rough; LSS, lightly soiled, smooth. Least-squares regression line (dashed) excludes data

point for sugary-textured sample GCRD–1.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 54
Figure 18.—Average friability index after application of each cleaning technique for all cores

tested. Cleaning techniques: A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L, laser; M,

misting; P, power wash. Black bars indicate 2σ. A, Cores with all surface-soiling conditions. B, All

lightly soiled, smooth cores. C, All lightly soiled, rough cores. D, All gypsum crusted, smooth cores.

E, All gypsum crusted, rough cores—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 55
Figure 18 (Cont.)—Average friability index after application of each cleaning technique for all

cores tested. Cleaning techniques: A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L,

laser; M, misting; P, power wash. Black bars indicate 2σ. A, Cores with all surface-soiling conditions. B, All lightly soiled, smooth cores. C, All lightly soiled, rough cores. D, All gypsum crusted,

smooth cores. E, All gypsum crusted, rough cores—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 56
Figure 19.—Model of laboratory configuration used for

implementation of surface-reactivity tests. Surface flow

rate, ≥0.01 cm3

/s per centimeter of contour.

Figure 18 (Cont.)—Average friability index after application of each cleaning technique for all

cores tested. Cleaning techniques: A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L,

laser; M, misting; P, power wash. Black bars indicate 2σ. A, Cores with all surface-soiling conditions. B, All lightly soiled, smooth cores. C, All lightly soiled, rough cores. D, All gypsum crusted,

smooth cores. E, All gypsum crusted, rough cores.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 57
Figure 20.—Absolute rate of release of Ca2+ ions for all cores tested, in descending order. Black bars

indicate 2σ.

Figure 21.—Absolute rate of release of Ca2+ ions versus shape factor for all cleaned cores. R2 value, square

of correlation coefficient. Two most erratic data points have been omitted from least-squares regression

line (dashed).

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 58
Figure 22.—Average absolute rate of release of Ca2+ ions for each cleaning technique for various

surface-soiling conditions: lightly soiled, smooth (A); lightly soiled, rough (B); gypsum crusted,

smooth (C); and gypsum crusted, rough (D). Cleaning techniques: A, Armax; C, combination; D, dry

ice; G, gommage; J, JOS; L, laser; M, misting; P, power wash; V, virgin. Black bars indicate 2σ—

Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 59
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 60

Figure 22 (Cont.)—Average absolute rate of release of Ca2+ ions for each cleaning technique for

various surface-soiling conditions: lightly soiled, smooth (A); lightly soiled, rough (B); gypsum crusted,

smooth (C); and gypsum crusted, rough (D). Cleaning techniques: A, Armax; C, combination; D, dry

ice; G, gommage; J, JOS; L, laser; M, misting; P, power wash; V, virgin. Black bars indicate 2σ.
Figure 23.—Change in absolute rate of release of Ca2+ ions versus surface color for all cores (A)

and for various surface-soiling conditions: lightly soiled, smooth (B); lightly soiled, rough (C);

gypsum crusted, smooth (D); and gypsum crusted, rough (E)—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 61

A

B
Figure 23 (Cont.)—Change in absolute rate of release of Ca2+ ions versus surface color for all cores

(A) and for various surface-soiling conditions: lightly soiled, smooth (B); lightly soiled, rough (C);

gypsum crusted, smooth (D); and gypsum crusted, rough (E)—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 62

C

D
Figure 24.—Model of physical alteration of stone surface by water. Panel labeled “Physicochemical

alteration” comprises various physical and chemical processes that can result in changes of surface

morphology from nanoscale to macroscale.

Figure 23 (Cont.)—Change in absolute rate of release of Ca2+ ions versus surface color for all

cores (A) and for various surface-soiling conditions: lightly soiled, smooth (B); lightly soiled,

rough (C); gypsum crusted, smooth (D); and gypsum crusted, rough (E).

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 63

E
Figure 25.—Model of factors controlling deposition of salts and particulate matter on stone surface.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 64
Figure 26.—Integrated model of dominant soiling-agent-delivery and stone-alteration processes. Boxes A, E, F, and

G represent collections of variables that drive the processes represented in panels B, C, and D. Box A includes

variables controlling extraneous inputs to the system, including ambient SO2 concentration; meteorologic and

geotechnical conditions to which system is subjected: rain, diurnal moisture, and rising damp-delivery patterns;

and level of airborne particulate matter. Implicit in connections between panels B and C is the assumption that

primary rate of deposition of SO2 and airborne particulate matter is closely linked to the total amount of soiling

agents available for secondary mobilization and deposition on the building. Otherwise stated, primary deposition

processes feed secondary mobilization processes. Panel D represents processes related to rate of loss of utility

resulting from mass loss, changes in microstructure, and changes in surface morphology. Boxes E, F, and G represent

state variables of the system, including a complete set of physicochemical variables required to define the state of

the system. For the purposes of this study, box E represents system output. Because delivery and transport processes

represented by box A and panels B and C control surface waterflow pattern, these variables define the physical

extent of the system and set an upper limit on the range of scale over which processes represented by panels B, C,

and D operate.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 65
Figure 27.—Utility versus surface cleanliness, showing general shape of esthetic-utility curve for the building.

Figure 28.—Finesse-classification diagrams for various surface-soiling

conditions, based on a range of target cleanliness (C*) values (in gray-scale

units): 3.0 (A), 2.5 (B), 2.0 (C), 1.6 (D), and 1.5 (E). Each diagram is plotted in

polar coordinate, with grayness value (accuracy), C*, as radius vector. Polar

angle is given by tan−1 (∆C*/∆C), if ∆C≥0, and by −tan−1 (∆C*/∆C) if

∆C<0, where ∆C* and ∆C are defined in equations 9 and 10. Surfacesoiling conditions: GCR, gypsum crusted, rough; GCS, gypsum crusted,

smooth; LSR, lightly soiled, rough; LSS, lightly soiled, smooth—

Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 66
Figure 28 (Cont.)—Finesse-classification diagrams for various surfacesoiling conditions, based on a range of target cleanliness (C*

) values (in

gray-scale units): 3.0 (A), 2.5 (B), 2.0 (C), 1.6 (D), and 1.5 (E). Each diagram

is plotted in polar coordinate, with grayness value (accuracy), C*, as

radius vector. Polar angle is given by tan−1 (∆C*/∆C), if ∆C≥0, and by −

tan−1 (∆C*/∆C) if ∆C<0, where ∆C* and ∆C are defined in equations 9

and 10. Surface-soiling conditions: GCR, gypsum crusted, rough; GCS,

gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, lightly soiled,

smooth—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 67
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 68

Figure 28 (Cont.)—Finesse-classification diagrams for various surfacesoiling conditions, based on a range of target cleanliness (C*

) values (in

gray-scale units): 3.0 (A), 2.5 (B), 2.0 (C), 1.6 (D), and 1.5 (E). Each diagram

is plotted in polar coordinate, with grayness value (accuracy), C*, as

radius vector. Polar angle is given by tan−1 (∆C*/∆C), if ∆C≥0, and by −

tan−1 (∆C*/∆C) if ∆C<0, where ∆C* and ∆C are defined in equations 9

and 10. Surface-soiling conditions: GCR, gypsum crusted, rough; GCS,

gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, lightly soiled,

smooth.
Figure 29.—Finesse-classification diagrams for gypsum-crusted, rough (GCR) stone, based on a

range of target-cleanliness (C*) values (in gray-scale units): 3.0 (A), 2.5 (B), 2.0 (C), and 1.5

(D)—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 69
Figure 29 (Cont.)—Finesse-classification diagrams for gypsum-crusted, rough (GCR) stone,

based on a range of target-cleanliness (C*) values (in gray-scale units): 3.0 (A), 2.5 (B), 2.0 (C),

and 1.5 (D).

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 70
Figure 30.—Finesse-classification diagrams for various surface-soiling conditions, based

on a range of target-roughness (R*) values (in shape-factor units): 3.5 (A), 3.0 (B), 2.5 (C),

2.0 (D), and 1.6 (E). Surface-soiling conditions: GCR, gypsum crusted, rough; GCS,

gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, lightly soiled, smooth—

Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 71
Figure 30 (Cont.)—Finesse-classification diagrams for various surface-soiling conditions,

based on a range of target-roughness (R*) values (in shape-factor units): 3.5 (A), 3.0 (B),

2.5 (C), 2.0 (D), and 1.6 (E). Surface-soiling conditions: GCR, gypsum crusted, rough; GCS,

gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, lightly soiled, smooth—

Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 72
Figure 31.—Approximate shape of projections of general utility curve onto utility/

roughness plane for gypsum crusted, rough (GCR) stone, based on various target cleanliness (C*

) values, in gray-scale units.

Figure 30 (Cont.)—Finesse-classification diagrams for various surface-soiling conditions,

based on a range of target-roughness (R*) values (in shape-factor units): 3.5 (A), 3.0 (B),

2.5 (C), 2.0 (D), and 1.6 (E). Surface-soiling conditions: GCR, gypsum crusted, rough; GCS,

gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, lightly soiled, smooth.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 73
Figure 32.—Utility versus surface roughness for various target-cleanliness (C*

) values for gypsumcrusted, rough stone after cleaning. Numbered curves denote various C*

 values, in gray-scale units.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 74
Figure 33.—Fracture density versus surface roughness for uncleaned (A, C, E, G, I, K, M) and cleaned (B,

D, F, H, J, L, N) sides of core samples cleaned by power-wash (A, B), gommage (C, D), combination (E, F),

Armax (G, H), JOS (I, J), laser (K, L), and dry-ice (M, N) techniques. Dashed line, least-squares regression

line; opposed dashed curves, confidence intervals for least-squares regression line; R2

 value, square of

correlation coefficient; ρ, confidence intervals for parent-population correlation coefficient. All confidence intervals were calculated at 95-percent-confidence level—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 75
Figure 33 (Cont.)—Fracture density versus surface roughness for uncleaned (A, C, E, G, I, K, M) and

cleaned (B, D, F, H, J, L, N) sides of core samples cleaned by power-wash (A, B), gommage (C, D), combination (E, F), Armax (G, H), JOS (I, J), laser (K, L), and dry-ice (M, N) techniques. Dashed line, least-squares

regression line; opposed dashed curves, confidence intervals for least-squares regression line; R2

 value,

square of correlation coefficient; ρ, confidence intervals for parent-population correlation coefficient. All

confidence intervals were calculated at 95-percent-confidence level—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 76
Figure 33 (Cont.)—Fracture density versus surface roughness for uncleaned (A, C, E, G, I, K, M) and

cleaned (B, D, F, H, J, L, N) sides of core samples cleaned by power-wash (A, B), gommage (C, D), combination (E, F), Armax (G, H), JOS (I, J), laser (K, L), and dry-ice (M, N) techniques. Dashed line, least-squares

regression line; opposed dashed curves, confidence intervals for least-squares regression line; R2

 value,

square of correlation coefficient; ρ, confidence intervals for parent-population correlation coefficient. All

confidence intervals were calculated at 95-percent-confidence level—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 77
Figure 33 (Cont.)—Fracture density versus surface roughness for uncleaned (A, C, E, G, I, K, M) and

cleaned (B, D, F, H, J, L, N) sides of core samples cleaned by power-wash (A, B), gommage (C, D), combination (E, F), Armax (G, H), JOS (I, J), laser (K, L), and dry-ice (M, N) techniques. Dashed line, least-squares

regression line; opposed dashed curves, confidence intervals for least-squares regression line; R2

 value,

square of correlation coefficient; ρ, confidence intervals for parent-population correlation coefficient. All

confidence intervals were calculated at 95-percent-confidence level—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 78
Figure 33 (Cont.)—Fracture density versus surface roughness for uncleaned (A, C, E, G, I, K, M) and

cleaned (B, D, F, H, J, L, N) sides of core samples cleaned by power-wash (A, B), gommage (C, D), combination (E, F), Armax (G, H), JOS (I, J), laser (K, L), and dry-ice (M, N) techniques. Dashed line, least-squares

regression line; opposed dashed curves, confidence intervals for least-squares regression line; R2

 value,

square of correlation coefficient; ρ, confidence intervals for parent-population correlation coefficient. All

confidence intervals were calculated at 95-percent-confidence level—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 79
Figure 33 (Cont.)—Fracture density versus surface roughness for uncleaned (A, C, E, G, I, K, M) and

cleaned (B, D, F, H, J, L, N) sides of core samples cleaned by power-wash (A, B), gommage (C, D), combination (E, F), Armax (G, H), JOS (I, J), laser (K, L), and dry-ice (M, N) techniques. Dashed line, least-squares

regression line; opposed dashed curves, confidence intervals for least-squares regression line; R2

 value,

square of correlation coefficient; ρ, confidence intervals for parent-population correlation coefficient. All

confidence intervals were calculated at 95-percent-confidence level—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 80
Figure 33 (Cont.)—Fracture density versus surface roughness for uncleaned (A, C, E, G, I, K, M) and

cleaned (B, D, F, H, J, L, N) sides of core samples cleaned by power-wash (A, B), gommage (C, D), combination (E, F), Armax (G, H), JOS (I, J), laser (K, L), and dry-ice (M, N) techniques. Dashed line, least-squares

regression line; opposed dashed curves, confidence intervals for least-squares regression line; R2

 value,

square of correlation coefficient; ρ, confidence intervals for parent-population correlation coefficient. All

confidence intervals were calculated at 95-percent-confidence level.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 81
Figure 34.—Summary of statistical data for various cleaning techniques plotted in figure 33. Cleaning techniques: A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L, laser; P, power wash.

Bars, change in sample correlation coefficient; tickmarks, R2 value for least-squares regression line;

vertical lines, 95-percent-confidence interval for parent-population correlation coefficient (soiled

stone to left of bar, cleaned stone to right of bar).

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 82
Figure 35.—Summary diagram showing criteria used to filter cleaning techniques applied to lightly

soiled, smooth and rough stone (A) and gypsum-crusted, smooth and rough stone (B). Cleaning techniques: A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L, laser; P, power wash. Classifications: H, hard; S, soft; ?, indeterminate—Continued.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 83
Figure 35 (Cont.)—Summary diagram showing criteria used to filter cleaning techniques applied to

lightly soiled, smooth and rough stone (A) and gypsum-crusted, smooth and rough stone (B). Cleaning

techniques: A, Armax; C, combination; D, dry ice; G, gommage; J, JOS; L, laser; P, power wash. Classifications: H, hard; S, soft; ?, indeterminate.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 84
Figure 36.—East façade of building, south of east portal,

showing locations of masonry-testing sites.

Figure 37.—Ledge outside Room 428, upper and

lower scaffold levels, showing lightly soiled marble on

ledge and gypsum-crusted areas on projecting cornice

and frieze below ledge.

Figure 38.—South wall, upper scaffold level, showing

area of lightly soiled, rough stone. Patches, from left to

right: soiled, gommage cleaned, soiled, power-wash

cleaned.

Figure 39.—West wall, upper scaffold level, showing

area of lightly soiled, smooth stone. Patches, from left to

right: power-wash cleaned, soiled, gommage cleaned,

soiled, Armax cleaned, soiled, JOS cleaned.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 85
Figure 40.—North wall, upper scaffold level. Patches, from left to right: soiled, JOS cleaned, soiled, powerwash cleaned.

Figure 41.—Lower scaffold level. Patches: 1, soiled; 2, misting cleaned; 3, soiled; 4, Armax cleaned; 5,

soiled; 6, gommage cleaned; 7, combination cleaned; 8, soiled; 9, gommage cleaned.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 86
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 87

Figure 42.—South side of building. A, Animal carving on cornice return. JOS cleaned, standard nozzle. B,

Animal carving to west of carving shown in figure 42A. JOS cleaned, piccolo nozzle.
Figure 43.—Ashlar on ledge outside Room 622, showing area of lightly

soiled, rough stone cleaned by laser cleaning technique. Within rectangle,

different-colored patches demonstrate operator’s control over extent of

cleaning: dark brown (center and top right), least cleaned, with only

superficial gray dirt removed; light brown (bottom), intermediate level of

cleaning; white (upper left), soiling completely removed.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 88
Figure 44.—Ledge outside Room 622, showing back of capital on

column. Gypsum crust has been removed to cleanest level on test areas.

Dark patches in white fields are epoxy resin, which preserves gypsumcrusted surfaces, permitting laboratory comparison of soiled and cleaned

areas under microscope.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 89
Figure 45.—Marble fragment from unknown location on building, showing gypsum-crusted, smooth

stone.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 90
Figure 46.—Marble fragment from unknown location on building, showing lightly soiled, smooth stone

after dry-ice cleaning. Numbers denote core samples in series LSSD.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 91
Figure 47.—North wall, upper scaffold level, showing locations of core samples in series LSRG,

LSRV, and LSRX.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 92
Figure 48.—South wall, upper scaffold level, showing locations of core samples in series LSRP, LSSP,

and LSSV.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 93
Figure 49.—West wall, upper scaffold level, showing locations of core samples in series LSSA, LSSG, and

LSSV.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 94
Figure 50.—West wall, upper scaffold level, showing locations of core

samples in series LSSJ and LSSV.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 95
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 96

Figure 51.—North wall, upper scaffold level, showing locations

of core samples in series LSRJ and LSRP.
Figure 52.—Entablature, lower scaffold level, showing locations of core samples in series GCRM, GCRV,

and GCSM.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 97
Figure 53.—Entablature, lower scaffold level, showing locations of core samples in series GCRV and GCSV.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 98
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 99

Figure 54.—Entablature, lower scaffold level, showing locations of core samples in series GCRV and GCSV.
Figure 55.—Entablature, lower scaffold level,

showing locations of cores samples in series GCRA

and GCSA.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 100
Figure 56.—Entablature, lower scaffold level, showing locations of core samples in series GCRC, GCSC,

and GCSG.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 101
Figure 57.—Entablature, lower scaffold level, showing locations of core samples in series GCRG and

GCRV.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 102
The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 103

Figure 58.—Entablature on south side, lower scaffold level, showing locations of core samples in series

GCRJ, GCRV, and GCSJ.
Figure 59.—Entablature, lower scaffold level, showing locations of core samples in series GCSJ.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 104
Figure 60.—Column base at 6th-floor level (see fig. 43), showing locations of core samples in series LSRL.

Figure 61.—Detail of column base at 6th-floor level, showing locations of core samples in series GCRL.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 105
Figure 62.—Marble fragment from unknown location on building, showing location of core

samples in series LSRD.

Figure 63.—Marble fragments from unknown locations on building,

showing material for core samples in series GCRD.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 106
Figure 64.—Sample-preparation protocol for core samples selected for SEM analysis.

Figure 65.—Summary of SEM image-analysis programs.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 107
Figure 66.—Syntax for program SEM2BIN as run from DOS command line.

Figure 67.—Syntax for program SHOWPIX as run from DOS command line.

Figure 68.—Syntax for program PROFILE as run from DOS command line.

Figure 69.—Typical output image from program PROFILE. Bimodal histogram shows high

occurrence frequency of both dark and light pixels. Location of bimodal peaks along gray scale

differs from image to image because SEM images are not produced with same average brightness.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 108
Figure 70.—Syntax for program EDGE as run from DOS command line.

Figure 71.—Brown stain (approximately 2.5 mm across) on sample LSRP–6.

Figure 72.—Brown stain (approximately 1 by 2 mm) on sample GCRM–3.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 109
Figure 73.—Zoning on sample GCRG–3.

Figure 74.—Typical stained area on sample GCRC–5.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 110
Figure 75.—Measured color values of core samples, in descending order from 10 to 0. A,

Electronic technique. B, Visual method.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 111
Figure 76.—Electronic exposure time versus F-M color value for grayness measurements on core series.

Straight line is a curve with a slope of 1.

Figure 77.—X-ray-fluorescence spectra of Devcon 5 epoxy resin (Dev 5) and L.R. white acrylic resin

(LRW). Leftmost peak is sulfur Kα line; rightmost peak is argon in ambient air.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 112
Figure 78.—Typical X-ray-fluorescence spectra of uncleaned samples in core series GCR. A, Silicate

minerals. B, Iron minerals.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 113
Figure 79.—Typical X-ray-fluorescence spectra of silicate and iron minerals in uncleaned

samples in core series GCS (A) and GCR (B).

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 114
Figure 80.—Schematic diagram of system used for surface-reactivity test.

Figure 81.—Configuration of alternative apparatus used for surface-reactivity test.

Figure 82.—Relative surface activity (W value) of samples in core series 3.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 115
Table 1.—Summary of technical details on four cleaning techniques selected during first phase of this study—Continued.

Symbol Description Comments

WW Water wash Hose pressure. Not useful as a cleaning or rinsing method.

PW Power wash Water delivered at approx 100 to 600 lb/in2 pressure, depending on s

tip of wand to stone surface. Effective on protected areas, such as thof the window at the top-platform level; not effective on exposed areadetergents. Streaks of clean stone appear when tip is held close to sdamage to stone when tip is used in this manner. Effective as a rins

ST Steam Deliver at 80 °C at approx 150 lb/in2 pressure. Uneven results. Diffic

delivering even flow. Streaky. Less effective than PW. Note problemon 3/16/95 and problems with equipment.

ADC Alkali detergent liquid concentrate pH 10.6. Brush-coat dwell time, 10 minutes. Even cleaning when fo

top-platform level, but not as effective as ADG. Not effective at the s

ADG Alkali detergent gel pH 9.7. Gel left on 10 to 15 minutes. Even cleaning when followed b

platform level. Easier to use than detergent in liquid-concentrate formsecond-level frieze. G.S. Wheeler thinks ADG/PW is the most practisystem if a water-based cleaning technique is selected but that approsurface will have to be recleaned by other techniques.

ACH Alkali gel with chelating agents pH 8.5. Potentially more aggressive than detergent solutions, produc

cleaning than detergents on the top-platform level but was as ineffecon the second-level frieze.

APW AR Alkali prewash/acid rinse A two-step system. The alkali pretreatment sits on the stone for 30 m

rinsed off. An acid cleaner is then applied for 3 to 5 minutes and rinswith PW, this technique produced very clean surfaces. Problems incsurface appearance, likely removal of stone, and potential chemical rrinse.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 116
Table 1 (Cont.)—Summary of technical details on four cleaning techniques selected during first phase of this study.

Symbol Description Comments

Misting Filtered water Delivered as a fine spray from a series of nozzles and played over th

hours. This technique works by dissolving gypsum crusts. Problems consuming to control so that maximum cleaning is achieved without from weakened substrates. Also, more water can enter the building as a result of any other water-based cleaning technique tested.

Thomann-Hanry --- An abrasive cleaning technique using a proprietary mixture of glass p

process

μ

m) and aluminum silicate crystals, delivered at very low pressure (a

Gypsum crusts and soiling layers are removed from the surface. The produces an even, clean appearance that is less rough than APW/AR rough to the touch than detergent-cleaner/PW techniques. Main advabsence of water, smoothness of finished surface, and effectiveness

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 117
Cleaning technique:

CriteriaCost High Moderate Lowest Moderate Sequence Light delivered from 

portable source connected to portable power unit. Work area by area in flexible patterns.

Soak 4 hours; scrub with detergent, scrub without detergent. Rinse by more watersoak. Work in vertical strips entire height of building or to drainage points.

Pretreat with water. Scrub with and without detergent. Water-rinse at medium (100 to 500 lb/in2) pressure. 

Rinse with vaporized water at low pressure (10 to 30 lb/in2). Work up area by 

area.

Pretreat with water. Appalkali prewash on marbApply acid wash. Rinse with low-pressure waterWork up area by area.

Selectivity Level of cleaning optional 

by electronic controls. Adjustment possible for varying thicknesses of soiling or to allow for fragile areas of stone.

Operator has little control over final appearance. Repetition of watersoak cycle if soiling is stubborn.

Operator controls by adjusting pressure, flow, standoff, and duration. Continuous evaluation.

Can control by testing foconcentration and dwelltime. Cannot controvercleaning in places. May appear spotty and require retreatment.

Sensitivity Removes dirt but not 

chemical or biological stains. Chemical or mechanical cleaning must follow in problem areas.

Removes surficial watersoluble dirt but not chemical or biological stains. Problem areas may require chemical or abrasive followup cleaning.

Removes superficial soiling and accretions and some entrapped matter because of water pressure. Removes some chemical and biological stains. Spot followup treatments with chemical or abrasives may be needed.

Removed surface and some subsurface soilingMost stains are removaby adjusting or adding chemical after rinsing cycles.

Protection of adjacent materials

Easily controlled because cleaning power is highly focused and exhaust plume can be directed.

Essential to seal well all cracks and open joints in stone and to seal windows.

Masking of windows, cracks, and open joints required.

Attention needed to assadequate masking of windows, cracks, and ojoints.

Duration Slow process; takes 

several minutes to clean 6 in2

.

Slow process; 4 to 24 hours.

As fast as number of workstations.

As fast as number of workstations. Each workstation should be relatively small area.

Table 2.—Summary of important operational features of four of the eight cleaning techniques evaluated in this study. Features not considered in future studies, include liability and cyclical maintenance—Continued. [Chemical: chemical cleaning techniques tested 

LASER Misting Power wash Chemical 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 118
Cleaning technique:

CriteriaStructural Risk to stone from heat 

burst appears less severe than force of water or abrasive media.

Some loss of sugarytextured stone must be accepted. Water soaking into building can cause problems.

Powdery and loose stone removed by pressure. Operator must note surfacesoiling condition forward of power-wash path.

Method removes surfacof stone. Potential chemical residues may cause damage.

Ease of operation Special training required. 

Technique is slow enough to allow adjustment of control settings as situation changes on stone surfaces.

Wet, messy, and labor intensive.

Operator likely to be engaged by obvious success and control of cleaning treatment.

Unpleasant but familiar work for operators.

Health and safety Not fully identified at 

present time.

Minimal Minimal Moderate 

Protection of pedestrians

Beam source must not be visible to pedestrians or operators.

Runoff channels and equipment on street must be monitored regularly.

Street barriers must be maintained.

Street barriers must include runoff paths and waste-collection areas.

Waste removal Material from plume must 

be caught by vacuum and collected.

Runoff can be directed into sewer system.

Runoff can be directed into sewer system.

Waste must be collecteand arrangements madfor required disposal system.

Adaptability to changing technology

Excellent because it causes minimal change at stone surface.

New technology used on another area of building will probably yield better results.

Yes Residues may react wit

future cleaning material

Annoyance factor Minor Moderate Moderate Considerable Public perception Developing technololgy 

unknown in the United States.

Familiar Familiar Accepted 

Availability of independent testing

Prelimary tests available. Yes Yes Yes 

Proprietary Yes No No No 

Table 2 (Cont.)—Summary of important operational features of four of the eight cleaning techniques evaluated in this study. Featurshould be considered in future studies, include liability and cyclical maintenance. [Chemical: chemical cleaning techniques tested in 

LASER Misting Power wash Chemical 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 119
Table 3.—Inventory of core samples from the building—Continued.

 Cleaning technique

Misting 

(M)

Power wash 

(P)

Gommage 

(G)

Combination 

(C)

Armax 

(A)

JOS 

(J)

Las

(L)

Lightly soiled, smooth (LSS)

LSSV-1, --- LSSP-1, LSSG-1, --- LSSA-1, LSSJ-1, --- 

LSSV-2 LSSP-2 LSSG-2 LSSA-2 LSSJ-2 

LSSV-3, --- LSSP-3, LSSG-3, --- LSSA-3 LSSJ-3 --- 

LSSV-4 LSSP-4 LSSG-4

LSSV-5, --- LSSP-5, LSSG-5, --- LSSA-5, LSSJ-5, --- 

LSSV-6 LSSP-6 LSSG-6 LSSA-6 LSSJ-6

Client sample LSSV-7 --- LSSP-7 LSSG-7 --- LSSA-7 --- --- Additional samples LSSV-3R --- --- LSSG-1A --- --- --- --- 

Virgin stone 

(uncleaned) 

(V)

Samples taken for deposition-velocity testsSamples taken for acceleratedweathering testsUSGS samples taken for surface-loss, surface-roughness, pore-space, and saltmapping tests

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 120
Table 3 (Cont.)—Inventory of core samples from the building—Continued.

 Cleaning technique

Misting 

(M)

Power wash 

(P)

Gommage 

(G)

Combination 

(C)

Armax 

(A)

JOS 

(J)

Las

(L

Lightly soiled, rough (LSR)

LSRV-1, --- LSRP-1, LSRG-1, --- --- LSRJ-1, LSR

LSRV-2 LSRP-2 LSRG-2 LSRJ-2 LSR

LSRV-3, --- LSRP-3, LSRG-3, --- --- LSRJ-3 LSR

LSRV-4 LSRP-4

LSRV-5, --- LSRP-5, LSRG-5, --- --- LSRJ-5, LSR

LSRV-6 LSRP-6 LSRG-6 LSRJ-6 LSR

Client sample LSRV-7 --- LSRP-7 LSRG-7 --- --- LSRJ-7 --

Additional samples LSRV-1E, --- LSRP-1A, --- --- --- --- --

LSRV-2A, LSRP-1B,

LSRV-2B, LSRP-2ALSRV-2E,

LSRV-3R,

LSRV-E,

LSRV-N,LSRV-W

Virgin stone 

(uncleaned) 

(V)

Samples taken for deposition-velocity tests.Samples taken for acceleratedweathering tests.USGS samples taken for surface-loss, surface-roughness, pore-space, and saltmapping tests.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 121
Table 3 (Cont.)—Inventory of core samples from the building—Continued.

 Cleaning technique

Misting 

(M)

Power wash 

(P)

Gommage 

(G)

Combination 

(C)

Armax 

(A)

JOS 

(J)

La

(

Gypsum crusted, smooth (GCS)

GCSV-1, GCSM-1, --- GCSG-1, GCSC-1, GCSA-1, GCSJ-1, -

GCSV-2 GCSM-2 GCSG-2 GCSC-2 GCSA-2 GCSJ-2 

GCSV-3 GCSM-3 --- GCSG-3, GCSC-3 GCSA-3 GCSJ-3 -

GCSG-4 

GCSV-5, GCSM-5, --- GCSG-5, GCSC-5, GCSA-5, GCSJ-5, -

GCSV-6 GCSM-6 GCSG-6 GCSC-6 GCSA-6 GCSJ-6

Client sample GCSV-7 --- --- GCSG-7 GCSC-7 --- --- -

Additional samples --- --- --- GCSG-X1, --- --- GCSJ-1P, -

GCSG-X2, GCSJ-2P,

GCSG-X3 GCSJ-3P,

GCSJ-4P,GCSJ-5P,

GCSJ-6P

Virgin stone 

(uncleaned) 

(V)

Samples taken for deposition-velocity tests.Samples taken for acceleratedweathering tests.USGS samples taken for surface-loss, surface-roughness, pore-space, and saltmapping tests.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 122
Table 3 (Cont.)—Inventory of core samples from the building.

 Cleaning technique

Misting 

(M)

Power wash 

(P)

Gommage 

(G)

Combination 

(C)

Armax 

(A)

JOS 

(J)

La

(L

Gypsum crusted, rough (GCR)

GCRV-1, GCRM-1, --- GCRG-1, GCRC-1, GCRA-1, GCRJ-1, GCR

GCRV-2 GCRM-2 GCRG-2 GCRC-2 GCRA-2 GCRJ-2 GCR

GCRV-3, GCRM-3 --- GCRG-3, GCRC-3 GCRA-3 GCRJ-3 GCR

GCRV-4 GCR

GCRV-5, GCRM-5, --- GCRG-5, GCRC-5, GCRA-5, GCRJ-5 -

GCRV-6 GCRM-6 GCRG-6 GCRC-6 GCRA-6

Client sample GCRV-7 GCRM-7 --- GCRG-7 --- GCRA-7 --- -Additional samples GCRV-3R --- --- --- --- --- --- -

Virgin stone 

(uncleaned) 

(V)

Samples taken for deposition-velocity tests.Samples taken for acceleratedweathering tests.USGS samples taken for surface-loss, surface-roughness, pore-space, and saltmapping tests.

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 123
 Cleaning technique

Surface-soiling 

condition and core 

series

Virgin stone 

(uncleaned) 

(V)

Power wash 

(P)

Misting 

(M)

Gommage 

(G)

Combination 

(C)

Sample--------------- LSSV LSSP LSSG

ABCDEFG ABCDEFG No ABCDEFG No

LSS-1 √√√−−−− −−−−−−− samples √√√√√√√ samples

LSS-2 √√−−−−− √√√√√√√ √√√√√√√

Sample--------------- LSRV LSRP LSRGABCDEFG ABCDEFG No ABCDEFG No

LSR-1 √√√√−−− √√√√√√√ samples −−−−−−− samples

LSR-2 √√√√−−− √√√√√√√ √√√√√√√

Sample--------------- GCSV GCSM GCSG GCSC

ABCDEFG No ABCDEFG ABCDEFG ABCDEFG

GCS-1 √√√−−−− samples −−−−−−− −−−−−−− −−−−−−−

GCS-2 −−−−−−− √√√√√√√ √√√√√√√ √√√√√√√

Sample--------------- GCRV GCRM GCRG GCRC

ABCDEFG No ABCDEFG ABCDEFG ABCDEFG

GCR-1 −−−−−−− samples −−−−−−− √√√√√√√ √√√√√√√

GCR-2 √√√−−−− √√√√√√√ −−−−−−− −−−−−−−

Table 4.—Inventory of SEM images for computer analysis. [Surface-soiling conditions: 

GCR, gypsum crusted, rough; GCS, gypsum crusted, smooth; LSR, lightly soiled, rough; 

LSS, lightly soiled, smooth.]

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 124
 Cleaning technique

Surface-soiling 

condition and core 

series

Virgin stone 

(uncleaned) 

(V)

Power wash 

(P)

Misting 

(M)

Gommage 

(G)

Combination 

(C)

Sample--------------- LSSV LSSP LSSG

LSS-3 3.0 1.4 No 1.4 No

LSS-4 3.0 1.4 test 1.4 test

LSS-5 2.0 1.9 1.5

LSS-6 2.0 1.9 --

Sample--------------- LSRV LSRP LSRG

LSR-3 4.0 2.0 No 1.4 No

LSR-4 4.0 2.0 test 1.4 test

LSR-5 4.0 1.8 1.4

LSR-6 4.0 1.8 --

Sample--------------- GCSV GCSM GCSG GCSC

GCS-3 8.0 No 4.0 1.4 1.4

GCS-4 -- test -- 1.4 --

GCS-5 8.0 4.0 1.4 1.3

GCS-6 8.0 4.0 1.4 1.4

Sample--------------- GCRV GCRM GCRG GCRC

GCR-3 10.0 No -- -- --

GCR-3R 8.0 test 3.2 2.5 1.5

GCR-4 10.0 -- -- --

GCR-5 9.0 4.0 3.0 1.5

GCR-6 9.0 5.0 4.0 1.5

Table 5.—Summary of visual grayness measurements. [Surface-soiling conditions: GCR, 

gypsum crusted, rough; GCS, gypsum crusted, smooth; LSR, lightly soiled, rough; LSS, 

lightly soiled, smooth.]

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 125
Soluble ion

H+

--------------- 143

NH4+----------- 58

Ca2+------------ 73

Mg2+----------- 30

Na+

------------- 31

K+

--------------- 11

Zn2+------------ 8

SO4

2----------- 219

NO3

-

----------- 98

Cl-

--------------- 24

pH-------------- 3.84

Concentration 

(μequiv/L)

Table 6.—Chemical analyses of rainwater in the Philadelphia area. [Averages of 31 

rain events in the 1980’s. Data from U.S. Environmental Protection Agency, provided 

by A. Lins, Conservation Department, Philadelphia Museum of Art.]

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 126
Table 7.—Semiquantitative analyses of silicate inclusions on soiled stone surfaces.

Semi-quantitative analysis: LSRP1

Element Norm. K-ratio

Mg-K 0.28186+-0.00408

Al-K 0.12470+-0.00225

Si-K 0.37794+-0.00340

K-K 0.20584+-0.00247

Ti-K 0.00966+-0.00060

ZAF correction 20.00 kV 14.14 Degs

No. of iterations: 24

--- K [Z] [A] [F] [ZAF] Atom. % Wt. %

Mg-K 0.282 0.979 1.481 0.989 1.434 24.49 20.79 *

Al-K 0.125 1.023 2.307 0.987 2.328 15.64 14.93 *

Si-K 0.378 0.990 2.446 0.999 2.418 47.43 46.97 *

K-K 0.206 1.037 1.512 1.000 1.567 12.02 16.59 *

Ti-K 0.010 1.113 1.289 1.000 1.434 0.42 0.71

* - high absorbance

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 127
 Cleaning technique

Power wash Misting Gommage Combination Armax JOS Laser 

Surface-recession measurements based on change in surface level

LSS 17*±65 n.t. n.d. (s=100) n.t. 122*±65 115*±99 n.t. 

LSR 131*±75 n.t. 52*±45 n.t. n.t. 194*±90 n.t. 

GCS n.t. n.t. n.t. 17*±65 100*±50 n.t. n.t. 

GCR n.t. n.t. n.d. (s=100) 278*±90 75*±50 200*±99 150*±99 

Surface-recession measurements based on surface smoothing

LSS 183±33 n.t. 115*±30 n.t. 88±40 77±30 n.t. 

LSR -46±33 n.t. 1193±90 n.t. n.t. 105±45 190*±90 

GCS n.t. n.t. n.t. 1136±200 70±35 n.t. n.t. 

GCR n.t. n.t. 1111*±35 146±33 55±35 85±35 100±50 

1Revised value averaged from multiple images.

Surface-soiling 

condition

Table 8.—Estimated surface recession caused by the various cleaning techniques, as measured by two methodsvalues in micrometers. n.d., not determined; n.t., not tested. Asterisk (*), value used in figure 17. 

σ, standard dev

Misting technique was not tested on LSS and LSR surfaces, and epoxy resin inadvertently was not applied to GCGCR surfaces.]

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 128
Table 9.—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in this stu

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSSVn

ABCD Avg(BCD) E F G 

Rcssn (μm) (D/S)

 (Dirct//Smooth)Rcssn (sigma)Ecldn Length (cm) 23.00 20.00 22.50 22.50 22.00

Ecldn Length (pix) 523 455 512 512 501(D-1)*100 8.80 9.30 7.60 3.10 7.20Crossings 4 17 5 10 9Dev (sigma) 330 78 344 335 272Pixel count 856 680 821 682 760Grn size (100 μm) 155 140 250 400 236

Grn size (300 μm) 155 140 250 400 236

FD (%) at surface (100 μm) 18 16 4 2 10FD (%) subsurface (300 μm) 18 8428

N/L 1.64 1.49 1.60 1.33 1.52FRghness = ((N/L)D-1)*100 71 55 66 34 57

Rel Hausdorf 1.01 1.01 1.00 0.98 1.00Shape factor 3.03 2.77 2.95 2.39 2.78

LSSV1B LSSV1C LSSV2A LSSV2B Avg 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 129
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSSA2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *122+-65//88+-40

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.5

Ecldn Length (pix) 000 0(D-1)*100 9.70 9.30 14.30 11.10 3.10 9.2Crossings 6 5 6 6 71Dev (sigma) 292 282 333 302 166 27Pixel count 842 822 1024 896 608 84Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 28 23 23 25 24 1FD (%) subsurface (300 μm) 15 5 6 9 12 

N/L 1.64 1.61 2.00 1.75 1.19 1.6FRghness = ((N/L)D-1)*100 73 68 121 87 19 7

Rel Hausdorf 0.99 0.99 1.02 1.00 0.99 0.9Shape factor 3.06 2.98 3.82 3.28 2.13 3.0

LSSA2A LSSA2B LSSA2C LSSA2D LSSAo LSSA2E LSSA2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 130
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSSD1

ABCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) No test

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.50 Ecldn Length (pix) 512 512 512 512 512 

(D-1)*100 4.70 5.00 14.80 8.17 7.30 6.00 Crossings 3 51168 10 Dev (sigma) 454 456 359 423 262 249 Pixel count 699 716 1049 821 710 726 Grn size (100 μm) * * *0 * * 

Grn size (300 μm) * * *0 * * 

FD (%) at surface (100 μm) * 18 8 49 8 14 FD (%) subsurface (300 μm) * 5 81237

N/L 1.37 1.40 2.05 1.60 1.39 1.42 FRghness = ((N/L)D-1)*100 39 42 128 70 42 45 

Rel Hausdorf 0.98 0.98 1.03 1.00 0.99 0.98 Shape factor 2.47 2.53 3.92 2.97 2.54 2.58 

LSSD1A LSSD1B LSSD1C LSSD1D LSSDo LSSD1E LSSD1F LS

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 131
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSSG2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) u.d.//*115+-30

Rcssn (sigma)Ecldn Length (cm) 19.50 22.50 20.00 20.67 22.50 19.50 Ecldn Length (pix) 444 512 455 470 512 444 (D-1)*100 19.00 11.10 14.50 14.87 6.40 9.60 Crossings 18 8 15 1497Dev (sigma) 387 238 232 286 322 280 Pixel count 1192 888 939 1006 777 737 Grn size (100 μm) 50 50 50 50 300 300 

Grn size (300 μm) 100 100 200 133 300 300 

FD (%) at surface (100 μm) 17 14 14 15 6 6 

FD (%) subsurface (300 μm) 10 77855

N/L 2.69 1.73 2.06 2.16 1.52 1.66 FRghness = ((N/L)D-1)*100 224 84 129 146 56 74 

Rel Hausdorf 1.14 1.03 1.07 1.08 1.00 1.02 Shape factor 5.26 3.25 3.94 4.15 2.77 3.09 

LSSG2A LSSG2B LSSG2C LSSG2D Avg LSSG2E LSSG2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 132
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSSJ2

A BCD Avg(BCD) E

F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *115+-100//77+-30

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22Ecldn Length (pix) 512 512 512 512 

(D-1)*100 12.30 17.00 24.20 17.83 7.90 12Crossings 18 14 10 14 15 

Dev (sigma) 195 278 354 276 251 Pixel count 919 1021 1400 1113 789 Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 35 22 39 32 27 

FD (%) subsurface (300 μm) 17 4 14 12 12 

N/L 1.79 1.99 2.73 2.17 1.54 0FRghness = ((N/L)D-1)*100 93 124 249 155 59 

Rel Hausdorf 1.01 1.04 1.14 1.06 0.99 0Shape factor 3.39 3.86 5.52 4.26 2.84 0

LSSJ2A LSSJ2B LSSJ2C LSSJ2D LSSJo LSSJ2E LSSJ2

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 133
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSSP2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *17+-65//183+-33

Rcssn (sigma)Ecldn Length (cm) 22.50 23.00 22.50 22.67 22.00 22.0Ecldn Length (pix) 512 523 512 516 501 50(D-1)*100 8.40 15.50 6.50 10.13 10.70 8.6Crossings 13 16 2 10 14 1

Dev (sigma) 369 327 532 409 283 38Pixel count 813 1014 849 892 873 67Grn size (100 μm) 190 220 191 200 75 25Grn size (300 μm) 250 220 191 220 200 25FD (%) at surface (100 μm) 26 10 10 15 35 1FD (%) subsurface (300 μm) 5 2 64.337

N/L 1.59 1.94 1.66 1.73 1.74 1.3FRghness = ((N/L)D-1)*100 65 115 71 84 85 3

Rel Hausdorf 1.01 1.07 1.00 1.02 1.02 1.0Shape factor 2.93 3.72 3.03 3.23 3.26 2.5

LSSP2A LSSP2B LSSP2C Avg LSSP2E LSSP2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 134
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRV1

ABCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth)Rcssn (sigma)Ecldn Length (cm) 22.50 22.50

Ecldn Length (pix) 512 512 0 0 

(D-1)*100 2.30 9.50Crossings 5 3Dev (sigma) 380 729Pixel count 673 951Grn size (100 μm) 172 165

Grn size (300 μm) 172 165

FD (%) at surface (100 μm) 12 12FD (%) subsurface (300 μm) 12 5N/L 1.31 1.86FRghness = ((N/L)D-1)*100 32 97

Rel Hausdorf 0.98 1.02Shape factor 2.34 3.45

Avg 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 135
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRV2

ABCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth)Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50

Ecldn Length (pix) 512 512 512 512(D-1)*100 13.00 12.70 6.80Crossings 883Dev (sigma) 482 363 343Pixel count 1318 972 754Grn size (100 μm) 94 350 266Grn size (300 μm) 229 350 266

FD (%) at surface (100 μm) 20 20 20FD (%) subsurface (300 μm) 669

N/L 2.57 1.90 1.47 2.00FRghness = ((N/L)D-1)*100 191 106 51 116

Rel Hausdorf 1.07 1.04 1.00 1.00Shape factor 4.88 3.59 2.69 3.72

Avg 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 136
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRAn

ABCD Avg(BCD) E F G 

Rcssn (μm) (D/S) (Dirct//Smooth) No testRcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.50 2

Ecldn Length (pix) 512 512 512 512 512 

(D-1)*100 *** 0.00 * * Crossings * * *0 * * Dev (sigma) * * *0 * * 

Pixel count * * *0 * * Grn size (100 μm) * * * 0 * * 

Grn size (300 μm) * * *0 * * FD (%) at surface (100 μm) * * *0 * * 

FD (%) subsurface (300 μm) * * *0 * * N/LFRghness = ((N/L)D-1)*100

Rel HausdorfShape factor

LSRAn LSRAn LSRAn LSRAn LSRAn LSRAn LSRAn LS

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 137
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRD2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) u.d.+-100//*250+-90

Rcssn (sigma)Ecldn Length (cm) 13.50 13.50 13.50 13.50 13.

Ecldn Length (pix) 307 307 307 307 3(D-1)*100 22.90 17.80 7.60 9.50 10.Crossings 16 8298

Dev (sigma) 408 309 540 419 314 3Pixel count 1312 951 790 1018 841 8Grn size (100 μm) * * *0 * 

Grn size (300 μm) * * *0 * 

FD (%) at surface (100 μm) 25 23 28 25 12 

FD (%) subsurface (300 μm) 25 7 3121

N/L 4.27 3.10 2.57 3.31 2.74 2.FRghness = ((N/L)D-1)*100 496 279 176 317 201 1

Rel Hausdorf 1.19 1.08 0.97 1.08 0.98 0.Shape factor 8.56 6.03 4.72 6.44 5.08 4.

LSRD2A LSRD2B LSRD2C LSRD2D LSRDo LSRD2E LSRD2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 138
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRG2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *52+-45//193+-150

Rcssn (sigma)Ecldn Length (cm) 21.00 21.00 21.00 21.00 21.00 22.Ecldn Length (pix) 478 478 478 478 478 5(D-1)*100 18.40 19.10 14.00 17.17 5.20 7.

Crossings 22 6 10 13 4

Dev (sigma) 640 537 264 480 335 2Pixel count 1700 971 919 1197 641 7Grn size (100 μm) 198 191 167 185 40 

Grn size (300 μm) 198 191 167 185 170 2

FD (%) at surface (100 μm) 28 21 28 26 33 

FD (%) subsurface (300 μm) 11 9 6 9 9 

N/L 3.56 2.03 1.92 2.50 1.34 1.FRghness = ((N/L)D-1)*100 349 133 111 198 36 

Rel Hausdorf 1.17 1.11 1.06 1.11 0.99 1.Shape factor 6.95 3.98 3.66 4.86 2.43 2.

LSRG2A LSRG2B LSRG2C LSRG2D Avg LSRG2E LSRG2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 139
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRJ1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *194+-90//105+-45

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.5Ecldn Length (pix) 512 512 512 512 51

(D-1)*100 15.80 10.30 14.60 13.57 9.80 7.7Crossings 8 6 7 7 81

Dev (sigma) 701 401 672 591 284 24Pixel count 1186 871 1242 1100 786 78Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 38 32 34 35 23 3

FD (%) subsurface (300 μm) 18 14 11 14 11 1N/L 2.32 1.70 2.43 2.15 1.54 1.5FRghness = ((N/L)D-1)*100 165 80 176 140 60 5

Rel Hausdorf 1.04 1.00 1.03 1.02 0.99 0.9Shape factor 4.46 3.17 4.64 4.09 2.86 2.8

LSRJ1A LSRJ1B LSRJ1C LSRJ1D LSRJo LSRJ1E LSRJ1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 140
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRL1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) u.d.+-100//*190+-90

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.

Ecldn Length (pix) 512 512 512 512 5

(D-1)*100 20.70 20.00 14.40 18.37 11.60 16.Crossings 8 21 11 13 10 Dev (sigma) 506 245 253 335 280 4Pixel count 1602 1049 833 1161 909 11Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 15 31 44 30 23 

FD (%) subsurface (300 μm) 10 24 20 18 14 

N/L 3.13 2.05 1.63 2.27 1.78 2.FRghness = ((N/L)D-1)*100 296 136 75 169 90 14

Rel Hausdorf 1.12 1.07 1.02 1.07 1.00 1.Shape factor 6.19 4.04 3.11 4.44 3.34 4.

LSRL1A LSRL1B LSRL1C LSRL1D LSRLo LSRL1E LSRL1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 141
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRP1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *122+-75//65+-25

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.50 22.5Ecldn Length (pix) 512 512 512 512 512 51(D-1)*100 1.2 0.4 6.6 2.7 8.5 9.Crossings 12 4 6 7 31Dev (sigma) 298 370 538 402 775 30Pixel count 605 594 811 670 971 87Grn size (100 μm) 150 150 87 129 215 4Grn size (300 μm) 150 150 120 140 215 13FD (%) at surface (100 μm) 40 9 26 25 9 2

FD (%) subsurface (300 μm) 2 515 713

N/L 1.18 1.16 1.58 1.00 1.90 1.7FRghness = ((N/L)D-1)*100 18 16 63 33 100 7

Rel Hausdorf 0.98 0.98 1.00 0.99 1.01 1.0Shape factor 2.09 2.05 2.89 2.34 3.50 3.1

LSRP1A LSRP1B LSRP1C Avg LSRP1E LSRP1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 142
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: LSRP2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *139+-75//27+-25

Rcssn (sigma)Ecldn Length (cm) 22.00 22.00 22.00 22.00 22.00 22.5

Ecldn Length (pix) 501 501 501 501 501 51

(D-1)*100 6.70 4.00 7.40 6.03 7.20 10.0Crossings 685 6 3 

Dev (sigma) 512 376 451 446 718 43Pixel count 770 672 819 754 839 90Grn size (100 μm) 100 121 118 113 100 16Grn size (300 μm) 100 121 118 113 100 16FD (%) at surface (100 μm) 7 14 26 16 18 1FD (%) subsurface (300 μm) 3 14 4 7 10 1N/L 1.54 1.34 1.64 1.51 1.68 1.7FRghness = ((N/L)D-1)*100 58 36 70 55 74 8

Rel Hausdorf 1.00 0.99 1.00 0.99 1.00 1.0Shape factor 2.81 2.42 3.00 2.74 3.07 3.3

LSRP2A LSRP2B LSRP2C Avg LSRP2E LSRP2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 143
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSV1

ABCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth)Rcssn (sigma)Ecldn Length (cm) 19.00 17.00 19.00 18.33

Ecldn Length (pix) 432 387 432 417(D-1)*100 11.50 18.10 4.00 11.20Crossings 6 15 10 10

Dev (sigma) 415 226 128 256Pixel count 767 914 552 744Grn size (100 μm) 73 14 148 78Grn size (300 μm) 73 14 148 78FD (%) at surface (100 μm) 41 33 27 34FD (%) subsurface (300 μm) 12 30 9 17N/L 1.77 2.36 1.28 1.80FRghness = ((N/L)D-1)*100 89 176 29 98

Rel Hausdorf 1.03 1.12 0.99 1.05Shape factor 3.33 4.61 2.30 3.41

GCSV1A GCSV1B GCSV1C GCSV1D Avg 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 144
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSA1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *100+-50//70+-35

Rcssn (sigma)Ecldn Length (cm) 22.50 16.20 22.50 22.50 19.8Ecldn Length (pix) 512 369 512 512 45(D-1)*100 11.70 9.60 13.10 11.47 6.10 15.5Crossings 13 5 11 10 12 1Dev (sigma) 228 572 310 370 227 19Pixel count 853 696 1002 850 655 89Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 35 8 19 21 14 1FD (%) subsurface (300 μm) 88360N/L 1.67 1.89 1.96 1.84 1.28 1.9FRghness = ((N/L)D-1)*100 77 101 114 97 30 12

Rel Hausdorf 1.00 0.99 1.01 1.00 0.99 1.0Shape factor 3.13 3.51 3.71 3.45 2.33 3.8

GCSA1A GCSA1B GCSA1C GCSA1D GCSAo GCSA1E GCSA1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 145
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSC2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *17+-50//136+-200

Rcssn (sigma)Ecldn Length (cm) 22.00 20.00 22.00 21.33 22.00 22.0Ecldn Length (pix) 501 455 501 485 501 50(D-1)*100 5.40 5.60 7.40 6.13 3.00 4.2Crossings 5 9 7 710 Dev (sigma) 368 241 303 304 287 12Pixel count 704 609 716 676 645 6Grn size (100 μm) 121 160 68 116 196 8Grn size (300 μm) 121 160 68 116 196 8FD (%) at surface (100 μm) 8 72

4 13 0 

FD (%) subsurface (300 μm) 21423N/L 1.41 1.34 1.43 1.39 1.29 1.2FRghness = ((N/L)D-1)*100 43 36 47 42 30 2

Rel Hausdorf 0.99 0.99 1.00 1.00 0.98 0.9Shape factor 2.55 2.43 2.62 2.54 2.31 2.2

GCSC2A GCSC2B GCSC2C GCSC2D Avg GCSC2E GCSC2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 146
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSD1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) u.d.+-200//*65+-35

Rcssn (sigma)Ecldn Length (cm) 22.50 17.00 22.50 20.50 17.5Ecldn Length (pix) 512 387 512 466 39

(D-1)*100 13.90 21.80 8.00 14.57 22.60 22.8Crossings 15 9 3 9 15 1

Dev (sigma) 160 377 251 263 339 14Pixel count 904 1124 721 916 1292 90Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 9 25 25 20 45 4FD (%) subsurface (300 μm) 9 12 9 10 13 

N/L 1.77 2.91 1.41 2.03 2.77 2.2FRghness = ((N/L)D-1)*100 91 267 45 134 249 17

Rel Hausdorf 1.02 1.12 0.99 1.04 1.13 1.Shape factor 3.36 5.78 2.59 3.91 5.54 4.5

GCSD1A GCSD1B GCSD1C GCSD1D GCSDo GCSD1E GCSD1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 147
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSG2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) No test

Rcssn (sigma)Ecldn Length (cm) 22.25 22.25 Ecldn Length (pix) 506 506 (D-1)*100 5.00 9.60 Crossings 66Dev (sigma) 518 567 Pixel count 751 949 Grn size (100 μm) 212 118 

Grn size (300 μm) 212 118 

FD (%) at surface (100 μm) 15 15 

FD (%) subsurface (300 μm) 55N/L 1.48 1.87 FRghness = ((N/L)D-1)*100 51 99 

Rel Hausdorf 0.99 1.02 Shape factor 2.69 3.48 

GCSG2A GCSG2B GCSG2C GCSG2D Avg GCSG2E GCSG2F GC

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 148
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSJ1

ABCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) No test

Rcssn (sigma)Ecldn Length (cm) 12.15 22.50 22.50 22.50 22.50 Ecldn Length (pix) 276 512 512 512 512 

(D-1)*100 1.20 2.90 6.40 3.50 2.80 1.40 Crossings 18 13 9 13 18 11 

Dev (sigma) 71 135 177 128 101 94 Pixel count 300 578 669 516 573 537 Grn size (100 μm) * * *0 * * 

Grn size (300 μm) * * *0 * * 

FD (%) at surface (100 μm) 000084

FD (%) subsurface (300 μm) 000020

N/L 1.09 1.13 1.31 1.17 1.12 1.05 FRghness = ((N/L)D-1)*100 9 13 33 18 12 5 

Rel Hausdorf 0.99 0.99 0.99 0.99 0.99 0.99 Shape factor 1.92 2.02 2.38 2.11 2.00 1.86 

GCSJ1A GCSJ1B GCSJ1C GCSJ1D GCSJo GCSJ1E GCSJ1F GC

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 149
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSL

ABCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) No test

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.50 22.50 Ecldn Length (pix) 512 512 512 512 512 512 (D-1)*100 *** **

Crossings *** **Dev (sigma) *** **Pixel count *** **Grn size (100 μm) *** **

Grn size (300 μm) *** **

FD (%) at surface (100 μm) *** **

FD (%) subsurface (300 μm) *** **

N/LFRghness = ((N/L)D-1)*100

Rel HausdorfShape factor

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 150
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCSM2

A B C DAvg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) No test

Rcssn (sigma)Ecldn Length (cm) 22.50 Ecldn Length (pix) 512 (D-1)*100 13.60 

Crossings 10 

Dev (sigma) 173 Pixel count 892 Grn size (100 μm) 40 

Grn size (300 μm) 116 

FD (%) at surface (100 μm) 28 

FD (%) subsurface (300 μm) 12 

N/L 1.74 

FRghness = ((N/L)D-1)*100 88 

Rel Hausdorf 1.05 

Shape factor 3.41 3.41 3.41 3.31 

GCSM2A GCSM2B GCSM2C GCSM2D Avg GCSM2E GCSM2F GC

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 151
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRV2

A B C DAvg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth)Rcssn (sigma)Ecldn Length (cm) 22.50 22.00 22.50 22.33

Ecldn Length (pix) 512 501 512 508(D-1)*100 4.20 7.40 7.70 6.43Crossings 26 6 4 12Dev (sigma) 97 160 267 175Pixel count 602 667 509 593Grn size (100 μm) 76 132 104

Grn size (300 μm) 76 132 104FD (%) at surface (100 μm) 15 7 18 13FD (%) subsurface (300 μm) 5776N/L 1.18 1.33 0.99 1.17FRghness = ((N/L)D-1)*100 18 36 -1 18

Rel Hausdorf 0.99 1.00 1.00 1.00Shape factor 2.12 2.45 1.83 2.13

GCRV2A GCRV2B GCRV2C GCRV2D Avg GCRV2E GCRV2F GC

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 152
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRA1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *75+-50//55+-35

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.50 Ecldn Length (pix) 512 512 512 512 512 (D-1)*100 5.90 3.80 17.90 9.20 8.00 4.70 Crossings 4 615869Dev (sigma) 608 428 549 528 532 464 Pixel count 733 1050 1372 1052 834 696 Grn size (100 μm) * * *0 * * Grn size (300 μm) * * *0 * * FD (%) at surface (100 μm) 2 19 48 23 12 10 FD (%) subsurface (300 μm) 2 10 38 17 12 4 N/L 1.43 2.05 2.68 2.05 1.63 1.36 FRghness = ((N/L)D-1)*100 46 111 220 126 69 38 

Rel Hausdorf 0.98 0.95 1.07 1.00 0.98 0.98 Shape factor 2.61 3.69 5.22 3.84 3.00 2.46 

GCRA1A GCRA1B GCRA1C GCRA1D GCRAo GCRA1E GCRA1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 153
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRC1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *278+-90//146+-33

Rcssn (sigma)Ecldn Length (cm) 22.00 22.00 22.00 22.00 22.50 22.0Ecldn Length (pix) 501 501 501 501 512 50(D-1)*100 7.30 4.60 4.90 5.60 8.50 5.Crossings 9 5 4 616Dev (sigma) 488 401 456 448 243 33Pixel count 854 763 637 751 802 7Grn size (100 μm) 2000 0 1900 1300 240 180Grn size (300 μm) 2000 0 1900 1300 240 180FD (%) at surface (100 μm) 14 12 10 12 6 FD (%) subsurface (300 μm) 14 12 10 12 5 N/L 1.71 1.52 1.27 1.5 1.57 1.4FRghness = ((N/L)D-1)*100 77 55 29 54 63 4

Rel Hausdorf 1.00 0.99 0.99 0.99 1.01 0.9Shape factor 3.13 2.75 2.30 2.73 2.89 2.6

GCRC1A GCRC1B GCRC1C GCRC1D Avg GCRC1E GCRC1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 154
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRD1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) n.d.+-250//*175+-100

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.Ecldn Length (pix) 512 512 512 512 5(D-1)*100 17.10 13.70 22.10 17.63 11.90 6.Crossings 2 417 814 

Dev (sigma) 1173 606 200 660 203 4Pixel count 1577 1022 1205 1268 912 7Grn size (100 μm) * * *0 * 

Grn size (300 μm) * * *0 * 

FD (%) at surface (100 μm) 40 9 42 30 27 

FD (%) subsurface (300 μm) 40 28 37 35 22 

N/L 3.08 2.00 2.35 2.48 1.78 1.FRghness = ((N/L)D-1)*100 273 119 184 192 91 

Rel Hausdorf 1.07 1.02 1.10 1.06 1.00 0.Shape factor 5.97 3.80 4.69 4.82 3.35 2.

GCRD1A GCRD1B GCRD1C GCRD1D GCRDo GCRD1E GCRD1

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 155
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRG1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) u.d.+-100//*11+-35

Rcssn (sigma)Ecldn Length (cm) 16.00 22.00 21.00 19.67 22.00 22.Ecldn Length (pix) 364 501 478 448 501 50(D-1)*100 21.50 29.70 30.70 27.30 4.70 2.4

Crossings 11 6 12 10 2

Dev (sigma) 477 574 532 528 519 32Pixel count 1288 2308 1147 1581 629 64Grn size (100 μm) 44 24 24 31 150 15

Grn size (300 μm) 128 94 134 119 150 15FD (%) at surface (100 μm) 46 62 54 54 7 

FD (%) subsurface (300 μm) 8 7 12 9 7 N/L 3.54 4.61 2.40 3.52 1.26 1.2FRghness = ((N/L)D-1)*100 364 626 214 401 27 2

Rel Hausdorf 1.22 1.46 1.27 1.32 0.99 0.Shape factor 7.03 9.60 5.03 7.22 2.27 2.2

GCRG1A GCRG1B GCRG1C GCRG1D Avg GCRG1E GCRG1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 156
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRJ2

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *200+-100//85+-35

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 22.5Ecldn Length (pix) 512 512 512 512 51

(D-1)*100 11.40 29.00 12.30 17.57 5.30 0.0Crossings 22 26 10 19 8 

Dev (sigma) 142 344 418 301 183 31Pixel count 860 1916 1202 1326 629 57Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 41 29 47 39 8 1

FD (%) subsurface (300 μm) 25 20 47 31 5

N/L 1.68 3.74 2.35 2.59 1.23 1.1FRghness = ((N/L)D-1)*100 78 449 161 229 24 1

Rel Hausdorf 1.00 1.27 1.01 1.09 0.99 0.9Shape factor 3.15 7.76 4.43 5.11 2.23 1.9

GCRJ2A GCRJ2B GCRJ2C GCRJ2D GCRJo GCRJ2E GCRJ2F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 157
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRL1

A BCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) *150+-90//100+-50

Rcssn (sigma)Ecldn Length (cm) 22.50 22.50 22.50 22.50 18.41 22.5Ecldn Length (pix) 512 512 512 512 419 51(D-1)*100 3.80 17.00 14.70 11.83 27.70 15.3Crossings 6 7 9 716 1

Dev (sigma) 289 523 440 417 367 23Pixel count 628 1219 1123 990 1506 116Grn size (100 μm) * * *0 * Grn size (300 μm) * * *0 * FD (%) at surface (100 μm) 32 22 20 25 35 1

FD (%) subsurface (300 μm) 17 18 5 13 10

N/L 1.23 2.38 2.19 1.93 3.60 2.2FRghness = ((N/L)D-1)*100 24 176 146 115 412 15

Rel Hausdorf 0.98 1.05 1.03 1.02 1.24 1.0Shape factor 2.21 4.61 4.19 3.67 7.40 4.3

GCRL1A GCRL1B GCRL1C GCRL1D GCRLo GCRL1E GCRL1F 

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 158
Table 9 (Cont.)—Results of fracture-density measurements based on SEM-image analysis of core samples obtained in 

[Rcssn, surface recession; sigma, standard deviation. Ecldn Length, Euclidian length of trace of exposed surface, measline through trace from end to end. D, in (D-1)*100, Richardson dimension (see app. 7). Crossings, number of crossinsurface over regression line through trace. Dev, standard deviation of trace of exposed surface. Pixel count, number of exposed surface used in computing the shape factor. Grn size, estimated average grain size, using calipers provided by menu of program EDGE. Note that the distance between calipers as reported on monitor is based on the assumption thmagnification is 10X; if other than 10X images are used for analysis, values reported by program should by multiplied magnification. For most samples, two values are listed for grain size: the first is the average grain size in the first 50 mand the second is the average grain size in the region deeper than 150 mm below the surface. If only one value is listeddifference was noted in the perceived grain size with depth into the sample. FD, fracture density. Two values are listeddensity near the surface, and the second is the fracture density deep into the sample. N/L, pixel count divided by EucliFrgnness, fractal dimension of trace of exposed surface. Rel Hausdorf, for discussion of the Hausdorf measure, see PeShape factor, measure of surface roughness derived for this study (see app. 7).]

Image analysis data of core: GCRM2

ABCD Avg(BCD) E F 

Rcssn (μm) (D/S)

 (Dirct//Smooth) No test

Rcssn (sigma)Ecldn Length (cm) 22.00 22.00 Ecldn Length (pix) 501 501 (D-1)*100 6.50 9.80 Crossings 26Dev (sigma) 675 699 Pixel count 822 979 Grn size (100 μm) 135 230 

Grn size (300 μm) 135 230 

FD (%) at surface (100 μm) 14 49 

FD (%) subsurface (300 μm) 5 20 N/L 1.64 1.96 FRghness = ((N/L)D-1)*100 70 109 

Rel Hausdorf 1.00 1.02 Shape factor 3.00 3.64 

GCRM2A GCRM2B GCRM2C GCRM2D Avg GCRM2E GCRM2F GC

 The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall 159