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. 2017 May 31;10(6):601. doi: 10.3390/ma10060601

Annual Atmospheric Corrosion of Carbon Steel Worldwide. An Integration of ISOCORRAG, ICP/UNECE and MICAT Databases

Belén Chico 1, Daniel de la Fuente 1, Iván Díaz 1, Joaquín Simancas 1, Manuel Morcillo 1,*
Editor: Yong-Cheng Lin1
PMCID: PMC5553418  PMID: 28772966

Abstract

In the 1980s, three ambitious international programmes on atmospheric corrosion (ISOCORRAG, ICP/UNECE and MICAT), involving the participation of a total of 38 countries on four continents, Europe, America, Asia and Oceania, were launched. Though each programme has its own particular characteristics, the similarity of the basic methodologies used makes it possible to integrate the databases obtained in each case. This paper addresses such an integration with the aim of establishing simple universal damage functions (DF) between first year carbon steel corrosion in the different atmospheres and available environmental variables, both meteorological (temperature (T), relative humidity (RH), precipitation (P), and time of wetness (TOW)) and pollution (SO2 and NaCl). In the statistical processing of the data, it has been chosen to differentiate between marine atmospheres and those in which the chloride deposition rate is insignificant (<3 mg/m2.d). In the DF established for non-marine atmospheres a great influence of the SO2 content in the atmosphere was seen, as well as lesser effects by the meteorological parameters of RH and T. Both NaCl and SO2 pollutants, in that order, are seen to be the most influential variables in marine atmospheres, along with a smaller impact of TOW.

Keywords: atmospheric corrosion, carbon steel, damage function, ISOCORRAG, ICP/UNECE, MICAT

1. Introduction

The economic impact of corrosion of metallic structures is a matter of great relevance throughout the world. The World Corrosion Organisation (WCO) currently estimates the direct cost of corrosion worldwide at between €1.3 and 1.4 trillion, which is equivalent to 3.8% of the global Gross Domestic Product (GDP). More than half of the considerable damage due to corrosion is a result of atmospheric impacts on materials, which is logical considering that most metallic equipment and structures operate in the atmospheric environment. For this reason, the action of the atmosphere on metals is one of the major issues in corrosion science.

In a perfectly dry atmosphere, metallic corrosion progresses at an extremely low rate, and for practical purposes can be ignored. However, on wet surfaces, corrosion can be quite severe, as the atmospheric corrosion process is the sum of the individual corrosion processes that take place whenever an electrolyte layer forms on the metal surface. However, for the corrosion rate to be really significant, the atmosphere must also be polluted. Of all atmospheric pollutants, chlorides from marine aerosol and sulphur dioxide (SO2) mainly from the combustion of fossil fuels, are the most common aggressive agents in the atmosphere.

It is a well-known fact, which has been proven by practical experience with real structure behaviour and the results of numerous tests, that the corrosion rate of metals in the atmosphere can be tens or even hundreds of times higher in some places than in others. Thus, it is of great interest to understand the basic variables that operate in atmospheric corrosion and in order to establish a classification of the aggressiveness of an atmosphere. The best possible knowledge of the factors that affect atmospheric corrosivity would obviously help to plan anticorrosive measures for metals in a given environment.

In the 1980s, three different cooperative studies involving the participation of a large number of countries were carried out:

  • ISOCORRAG cooperative programme. This programme was designed by the Working Group/WG 4 of ISO 156 Technical Committee “Corrosion of metals and alloys”, with the aim of standardising atmospheric corrosion tests) [1]. The Programme began in the year 1986 and, as a result of the efforts of WG 4, four international standards were developed: ISO 9223 [2,3], ISO 9224 [4], ISO 9225 [5] and ISO 9226 [6]. These standards were based on an extensive review of atmospheric exposure programmes carried out in Europe, North America, and Asia. The aim of drawing up these documents was to establish simple and practical guidelines for the technicians responsible for designing structures to be exposed to the atmosphere and for corrosion engineers responsible for adopting anticorrosive protection measures. ISO 9223 [2] provided a general classification system for atmospheres based either on 1-year coupon exposures or on measurements of environmental parameters to estimate time of wetness (TOW), sulphur dioxide concentration or deposition rate, and sodium chloride deposition rate. ISO 9224 provided an approach to calculating the extent of corrosion damage from extended exposures for five types of engineering metals based on application of guiding corrosion values (average and steady-state corrosion rates) for each corrosivity categories in ISO 9223. ISO 9225 provided the measurements techniques for the sulphur dioxide concentration or deposition rate, and sodium chloride deposition rate, needed as classification criteria in ISO 9223. ISO 9226 provided the procedure for obtaining one-year atmospheric corrosion measurements on standard coupons.

  • MICAT cooperative programme: “Ibero-American Atmospheric Corrosivity Map” [7]. The MICAT programme was launched in 1988 as part of the Ibero-American CYTED “Science and Technology for Development” international programme and ended after six years of activities. Fourteen countries participated in the programme, whose goals were: (i) to obtain a greater knowledge of atmospheric corrosion mechanisms in the different environments of Ibero-America; (ii) to establish, by means of suitable statistical analysis of the results obtained, mathematical models that allow the calculation of atmospheric corrosion as a function of climate and pollution parameters; and (iii) to elaborate atmospheric corrosivity maps of the Ibero-American region.

  • ICP/UNECE cooperative programme [8]. Airborne acidifying pollutants are known to be one of the major causes of corrosion of different materials, including the extensive damage that has been observed on historic and cultural monuments. In order to fill some important gaps in the knowledge of this field, the Executive Body for the Convention on Long-Range Transboundary Air Pollution (CLRTAP) decided to launch an International Cooperative Programme within the United Nations Economic Commission for Europe (ICP/UNECE). The programme started in September 1987 and initially involved exposure at 39 test sites in 11 European countries and in the United States and Canada. The aim of the programme was to perform a quantitative evaluation of the effect of sulphur pollutants in combination with NOx and other pollutants as well as climatic parameters on the atmospheric corrosion of important materials.

Figure 1 shows the countries participating in each one of these programmes. The atmospheric corrosion stations are basically located in Europe, America and Asia, covering a broad range of meteorological and pollution conditions.

Figure 1.

Figure 1

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International Collaborative programmes on atmospheric corrosion and participant countries.

Though the three programmes, ISOCORRAG, ICP/UNECE and MICAT, each have their own particular characteristics, they nevertheless share a number of common objectives. The similarity of certain aspects of their methodologies allows a welcome meeting point between the three programmes, as was suggested by Morcillo in the 11th International Corrosion Congress held in Florence in April 1990, in the session on atmospheric corrosion where the three cooperative programmes were presented [9,10,11]. Such a meeting point would allow, for the first time, a worldwide perspective (38 countries) on the problem of atmospheric corrosion, covering a broad spectrum of climatological and atmospheric pollution conditions, never before considered in the abundant published literature on atmospheric corrosion. This idea was taken up at UNECE (Figure 2) by the “Working Group on Effects” of Executive Body for the Convention on Long-Range Transboundary Air Pollution [12].

Figure 2.

Figure 2

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Atmospheric corrosion stations networks: ISOCORRAG (+), ICP (°) and MICAT (●) [12].

The statistical analysis of data obtained in atmospheric corrosion studies in order to obtain correlation equations that allow the estimation of annual corrosion rates from meteorological and pollution parameters is a matter of great interest. Such equations are known as damage or dose/response functions. They often incorporate the SO2 concentration, the chloride concentration in areas close to the sea, and a parameter representing the wetness of the metallic surface (relative humidity, number of days of rain per year, time of wetness, etc.). Models for predicting the corrosion damage of metals in the atmosphere are useful when it comes to answering questions on the durability of metallic structures, determining the economic costs of damage associated with the degradation of materials, or acquiring knowledge about the effect of environmental variables on corrosion kinetics.

Abundant literature has been published on these models and damage functions. For instance, for long-term prediction of carbon steel atmospheric corrosion, mention may be made of the work of Benarie and Lipfert [13], Pourbaix et al. [14], Feliu et al. [15,16], Knotková and Barton [17], Kucera [18], Mc Cuen and Albrecht [19], Albrecht and Hall [20], Panchenko et al. [21,22], Melchers [23,24], etc. Recent reviews on corrosion models for long-term prediction of atmospheric corrosion has been made by Morcillo et al. [25] and Adikari and Munasinghe [26].

The purpose of this work is to bring together the three databases from the three international cooperative programmes (ICP/UNECE, MICAT and ISOCORRAG), carrying out a statistical analysis of the results they contain in order to establish mathematical expressions which allow an estimation of the extent of atmospheric corrosion of carbon steel during first-year exposure as a function of meteorological and pollution parameters.

2. Experimental

2.1. ICP/UNECE Programme

Twenty-four countries participated in the exposure programme with a total of 55 exposure sites. These sites included industrial, urban and rural atmospheres. Marine atmosphere exposures were not included. A list of the sites together with their code is given in Table 1.

Table 1.

Atmospheric corrosion test sites included in the ICP/UNECE Programme.

Code Country Test Site Code Country Test Site
P01 Czech Republic Praha P29 United Kingdom Clatteringshaws Loch
P02 Kasperske Hory P30 Stoke Orchard
P03 Kopisty P31 Spain Madrid
P04 Finland Espoo P32 Bilbao
P05 Ahtari P33 Toledo
P06 Helsinki P34 Russia Moscow
P07 Germany Waldhof-Langenbrugge P35 Estonia Lahemaa
P08 Aschaffenburg P36 Portugal Lisbon-Jeronimo Mon.
P09 Langenfeld-Reusrath P37 Canada Dorset
P10 Bottrop P38 USA Steubenville
P11 Essen-Leithe P39 Res. Triangle Park
P12 Garmisch-Partenkirchen P40 France Paris
P13 Italy Rome P41 Germany Berlin
P14 Casaccia P43 Israel Tel Aviv
P15 Milan P44 Norway Svanvik
P16 Venice P45 Switzerland Chaumont
P17 Netherlands Vlaardingen P46 United Kingdom London
P18 Eibergen P47 USA Los Angeles
P19 Vredepeel P49 Belgium Anvterps
P20 Wijnandsrade P50 Poland Katowice
P21 Norway Oslo P51 Greece Athens
P22 Borregaard P52 Latvia Riga
P23 Birkenes P53 Austria Vienna
P24 Sweden Stockholm S P54 Bulgaria Sophia
P25 Stockholm C P55 Russia St Petersburg
P26 Aspvreten P57 Finland Hameelina
P27 United Kingdom Lincoln Catch. P59 Slovakia Zilina
P28 Wells. Catch.
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Exposure always started in the autumn, typically from October of one year to September of the following year. The test site network originally consisted of 39 sites, which were all part of the original eight-year exposure between 1987 and 1995. Subsequently, in a four-year exposure programme carried out between 1997 and 2001, only part of the original sites were kept and eight new test sites were added. Since then, new sites have joined. Compared to the 2008–2009 exposure, the sites Lahemaa and Lincoln were withdrawn from the 2011 to 2012 exposure while a new site in St Petersburg (Russia) was added. In the 2014–2015 exposure, two new test sites, Hameenlina (Finland) and Zilina (Slovakia), were included.

Figure 3 shows a diagram of the exposure schedule. For each exposure and site, three identical flat samples were exposed. Average corrosion values for these three panels were obtained. A detailed description of the material and methods for measuring environmental parameters and the evaluation of corrosion attack is provided in Reference [8].

Figure 3.

Figure 3

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ICP/UNECE programme: Diagram showing the exposure sequences.

2.2. ISOCORRAG Programme

Fourteen countries participated in the exposure programme with a total of 53 exposure sites. These sites included industrial, urban, rural, marine and costal locations in temperate, tropical and arctic zones. A list of the sites together with their code is given in Table 2.

Table 2.

Atmospheric corrosion test sites included in the ISOCORRAG Programme.

Code Country Test Site Code Country Test Site
I01 Argentina Iguazu I29 Norway Birkenes
I02 Camet I30 Tannanger
I03 Buenos Aires I31 Bergen
I04 San Juan I32 Svanvik
I05 Jubany Base I33 Spain Madrid
I06 Canada Bourcherville I34 El Pardo
I07 Czech Republic Kasperske Hory I35 Lagoas-Vigo
I08 Praha-Bechovice I36 Baracaldo, Vizcaya
I09 Kopisty I37 Sweden Stockholm-Vanadis
I10 Germany Bergisch Gladbach I38 Bohus Malmon, Kattesand
I11 Finland Helsinki I39 Bohus Malmon, Kvarnvik
I12 Otaniemi I40 United Kingdom Stratford, East London
I13 Ahtari I41 Crowthorne, Berkshire
I14 France Saint Denis I42 Rye, East Sussex
I15 Ponteau Martigues I43 Fleet Hall
I16 Picherande I44 USA Kure Beach, N. Carolina
I17 Saint Remy I45 Newark-Kerney, New Jersey
I18 Salins de Giraud I46 Panama Fort Sherman Costal Site
I19 Ostende, Belgium I47 Research Triangle Park, N. Carolina
I20 Paris I48 Point Reyes, California
I21 Auby I49 Los Angeles, California
I22 Biarritz I50 USSR Mursmank
I23 Japan Choshi I51 Batumi
I24 Tokyo I52 Vladivostok
I25 Okinawa I53 Ojmjakon
I26 New Zealand Judgeford, Wellington
I27 Norway Oslo
I28 Borregaard
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Flat carbon steel specimens were exposed in triplicate, fixing their size and thickness in accordance with the provisions of standard ISO 8565 [27]. A detailed description of the exposed material is provided in Reference [1].

A set of specimens was initially exposed for one-year exposure at each site. After six months, another set of specimens was exposed for one-year exposure. After one year, the first set of one-year exposed specimens was removed and another set of one-year specimens was exposed. Every six months, this process was repeated until six sets of specimens had been exposed for one year. Figure 4 shows a diagram of the exposure schedule. The original exposure was planned to begin in the autumn of 1986, but several delays occurred at various sites.

Figure 4.

Figure 4

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ISOCORRAG and MICAT programmes: diagrams showing the exposure sequences.

A detailed description of the material and methods for measuring environmental parameters and the evaluation of corrosion attack is provided in Reference [1].

2.3. MICAT Programme

Fourteen countries participated in the exposure programme with a total of 75 exposure sites. These sites included industrial, urban, rural and marine atmospheres. A list of the sites together with their code is given in Table 3.

Table 3.

Atmospheric corrosion test sites included in the MICAT Programme.

Code Country Test Site Code Country Test Site
M01 Argentina Camet M38 Ecuador Esmeraldas
M02 Villa Martelli M39 San Cristóbal
M03 Iguazú M40 Spain León
M04 San Juan M41 El Pardo
M05 Jubany M42 Barcelona
M06 La Plata M43 Tortosa
M07 Brazil Caratinga M44 Granada
M08 Ipatinga M45 Lagoas-Vigo
M09 Arraial do Cabo M46 Labastida
M10 Cubatão M47 Arties
M11 Ubatuba M48 México Mexico
M12 São Paulo M49 Cuernavaca
M13 Río de Janeiro M50 San Luis Potosí
M14 Belem M51 Acapulco
M14 Fortaleza M52 Panamá Panamá
M16 Brasilia M53 Colon
M17 Paulo Afonso M54 Veraguas
M18 Porto Velho M55 Chiriquí
M19 Colombia Isla Naval M56 Perú Piura
M20 San Pedro M57 Villa Salvador
M21 Cotové M58 San Borja
M22 Costa Rica Puntarenas M59 Arequipa
M23 Limón M60 Cuzco
M24 Arenal M61 Pucallpa
M25 Sabanilla M62 Portugal Leixões
M26 Cuba Ciq M63 Sines
M27 Cojímar M64 Pego
M28 Bauta M65 Uruguay Trinidad
M29 Chile Cerrillos M66 Prado
M30 Valparaíso M67 Melo
M31 Idiem M68 Artigas
M32 Petrox M69 Punta del Este
M33 Marsh M70 Venezuela Tablazo
M34 Isla de Pascua M71 Punto Fijo
M35 Ecuador Guayaquil M72 Coro
M36 Riobamba M73 Matanzas
M37 Salinas M74 Barcelona, V
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Flat carbon steel panels were exposed in triplicate. A detailed description of the exposed material can be found in the book published with all the results of the project [7]. Figure 4 shows a diagram of the exposure schedule. The original exposure was planned to begin in 1989, but several delays occurred at various sites.

A detailed description of the material and methods for measuring environmental parameters and the evaluation of corrosion attack is provided in Reference [7].

2.4. Analysis of Data Properties

Before statistically analysing all the data collected from the various sources (data mining), data screening has been carried out. There follows a description of the criteria governing this screening:

  1. Extremely cold stations, with annual average temperatures below 0 °C, have been removed from the statistical analysis. Such is the case of the stations at Svanvik (Norway), Murmansk and Ojmjakon (USSR), Jubany (Argentina), Marsch (Chile) and Artigas (Uruguay), the latter three being Antarctic scientific bases. Low temperatures cause the metallic surface to be covered with an ice layer for long time periods during the year, considerably impeding the development of corrosion processes. This ice layer reduces oxygen access to the metallic surface and its time of wetness, decreasing corrosion rates to extremely low values [28,29,30,31].

  2. In stations characterised as rural environments where SO2 and Cl− deposition rates have not been determined due to being insignificant, values have been estimated for both pollutants. The figures indicated in Table 4, Table 5 and Table 6 correspond to the average value of the 0–3 mg Cl−/m2.d range (level S0) and the 0–4 mg SO2/m2.d range (level P0) according to standard ISO 9223 [3]. In those cases where both pollutants have been estimated, an average of the corrosion data from available annual series has been made.

  3. For test stations located in non-rural environments, all corresponding annual series data, or even the entirety of the available information, have been removed in those cases where, for some reason, meteorological or pollution data are not included.

  4. Chloride ion pollution data have not been determined for stations in the ICP/UNECE programme, which only considers non-marine test sites, unlike the other two exposure programmes (ISOCORRAG and MICAT). Therefore, the annual corrosion rate data and meteorological and SO2 deposition rate obtained are only included in the statistical analysis for non-marine environments. In this respect, the criteria adopted has been to remove from the ICP/UNECE database all stations located at a distance of less than 2 km from the seashore, supposing in these cases a chloride ion deposition level of more than 3 mg/m2.d (lower level S1 according to standard ISO 9223 [3]). Bilbao station (Spain), despite being characterised by high SO2 values, has been removed because of its location very close to the port.

Table 4.

ICP/UNECE data considered in the study.

Code 1st Year Corrosion, µm T, °C RH, % SO2 Deposition Rate mg/m2.d Precipitation, mm/y Code 1st Year Corrosion, µm T, °C RH, % SO2 Deposition Rate, mg/m2.d Precipitation, mm/y
P01 55.6 9.5 79 62 639 P23 12.09 6.8 77 0.16 1544
P01 34.48 9.1 73 32.96 684 P23 5.34 6.5 82 0.16 2195
P01 30.66 9.8 77 25.68 581 P24 33.97 7.6 78 13.44 531
P01 29.52 8.6 78 18.88 475 P24 15.27 7 70 4.56 577
P01 23.16 9.9 76 12.24 522 P24 13.1 7.5 73 3.36 581
P01 17.56 9.5 79 7.04 601 P24 13.61 7.4 68 2.64 556
P01 13.1 9.3 72 5.12 513 P24 15.9 6.7 76 2.08 463
P01 12.98 9.3 74 8.88 491 P24 14.76 8.1 81 1.52 635
P01 7.51 10.1 74 5.2 525 P24 10.31 7.1 80 1.28 384
P01 8.52 10.2 70 5.12 534 P24 11.7 8.9 74 1.44 273
P01 8.4 11 73 3.68 414 P24 7.76 7.8 76 0.64 270
P02 28.5 7 77 15.76 850 P24 5.47 7.8 77 0.72 428
P02 19.47 6.6 73 14.32 921 P24 7.38 8.3 81 0.4 330
P02 18.83 7.2 74 9.76 941 P25 33.46 7.6 78 15.68 531
P03 70.87 9.6 73 66.64 426 P25 13.1 7 70 3.76 577
P03 44.53 8.9 71 39.2 432 P25 12.09 7.5 73 2.72 581
P03 44.78 9.7 75 39.36 513 P26 18.7 6 83 2.64 543
P03 37.28 8.5 73 24.48 431 P26 9.54 6 81 1.04 468
P03 30.41 9.9 76 14.64 420 P26 10.31 6.8 82 0.88 525
P03 28.5 9.2 80 14.32 510 P26 8.78 6.5 83 0.64 409
P03 23.41 8.7 73 8.96 463 P26 7.89 5.9 86 0.48 479
P03 23.28 8.3 76 14.48 442 P26 8.78 7.2 86 0.48 772
P03 20.74 9.3 80 10.8 521 P26 5.09 5.6 82 0.48 562
P03 28.24 9.6 79 15.2 417 P26 5.22 6.3 84 0.48 435
P03 26.59 10.8 71 9.12 433 P26 3.56 7.1 82 0.32 452
P04 34.48 5.9 76 14.88 626 P26 2.54 6.7 86 0.32 511
P04 16.67 5.6 79 1.84 755 P26 14.89 7.2 82 0.24 784
P04 15.39 6 80 2.08 698 P27 40.08 9.2 84 14.16 365
P05 16.79 3.1 78 5.04 801 P27 39.31 9.6 82 14.24 530
P05 6.11 3.4 81 0.72 610 P27 30.15 10.5 78 5.44 515
P05 7.51 3.9 83 0.64 675 P27 34.35 10.2 81 7.5 708
P05 6.87 3.2 76 0.48 618 P27 24.81 9.7 81 6 831
P05 6.74 3.5 80 0.72 742 P27 22.9 10.4 78 3.92 548
P05 6.49 4.8 82 0.64 845 P28 32.19 10.8 86 5.76 447
P05 4.83 4.5 80 0.64 713 P28 25.95 10.5 82 2.56 614
P06 34.35 6.3 78 16.56 673 P28 25.19 11.2 79 2.64 696
P06 20.74 6.2 78 3.84 702 P30 39.06 10.2 78 12 610
P06 24.94 6.6 76 4.4 649 P30 29.26 10.3 76 7.44 549
P07 33.84 9.3 80 10.96 631 P31 28.24 14.1 66 14.72 398
P07 29.39 8.9 81 6.56 624 P31 20.61 14.3 67 6.56 360
P07 21.12 9.5 81 3.12 596 P31 19.21 15.7 68 6.24 224
P07 19.85 8.9 82 2.32 615 P31 20.23 14.8 67 9.12 401
P07 18.32 9.5 83 1.68 786 P31 9.16 12.9 61 9.44 765
P07 18.83 9.4 81 1.84 620 P31 9.8 15 62 0.96 560
P07 10.81 8.8 75 1.76 413 P31 7.38 15.3 60 2.08 447
P08 27.1 12.3 77 18.96 627 P31 5.6 15.3 56 1.28 399
P08 14.76 11.4 64 10.08 561 P31 2.29 15.1 53 2.96 267
P08 17.81 11.6 65 7.68 779 P31 0.51 16.2 43 0.48 283
P09 37.28 10.8 77 19.6 783 P31 2.54 16 63 0.56 303
P09 29.39 10.7 79 13.04 619 P33 5.73 14 64 2.64 785
P09 26.59 11.4 81 8.88 841 P33 3.31 13.4 61 1.36 433
P09 26.21 10 78 8.4 781 P33 4.58 14.8 57 3.36 327
P09 25.95 10.9 80 6.64 930 P33 4.58 14 61 0.88 603
P09 12.85 11.6 79 4 997 P33 6.87 14 59 1.2 872
P09 16.54 11.4 76 4.8 647 P33 5.98 12.2 71 0.96 739
P10 47.84 11.2 75 40.48 874 P33 4.2 12.2 78 0.88 411
P10 44.15 10.3 78 33.28 707 P33 5.73 12.1 69 0.72 689
P10 37.4 11.8 80 24.16 913 P33 1.78 14.7 61 0.32 828
P10 37.66 10.5 79 23.52 806 P33 0.51 15.4 58 0.32 430
P10 39.57 11.5 81 19.68 1044 P33 2.04 12.8 60 0.4 516
P10 37.28 11.7 81 14.32 791 P34 23.03 5.5 73 15.36 575
P10 28.24 11.3 77 13.52 780 P34 17.94 5.7 74 22.96 881
P10 27.48 10.8 81 8.88 663 P34 15.39 5.6 71 13.12 667
P10 28.63 11.1 75 7.44 849 P34 17.18 6.5 74 13.7 838
P10 30.92 11.4 78 7.04 880 P34 17.3 7.4 69 13.7 812
P11 43.51 10.5 79 24.24 713 P34 11.7 5.9 71 3.28 750
P11 37.28 10.1 79 18.32 684 P35 23.54 5.5 83 0.72 448
P11 30.66 10.9 78 12.96 889 P35 13.49 5.4 82 1.1 859
P12 17.56 8 82 7.52 1492 P35 12.09 6.9 81 1.04 668
P12 11.45 7.1 84 2.56 1552 P35 12.21 5 81 1.36 655
P12 10.81 7.4 83 1.92 1503 P35 11.2 5.2 80 3.2 403
P13 22.65 15.4 66 23.52 591 P35 7.38 8.8 81 0.88 640
P13 15.78 18.4 68 4.64 602 P36 28.5 12.1 64 5.44 972
P13 17.05 19.4 65 2.96 1125 P36 39.19 18 62 12.88 545
P13 8.02 17.8 53 0.88 625 P36 25.95 19.1 67 3.76 443
P13 8.02 18 66 0.64 1115 P36 27.23 17.9 63 14.16 252
P14 29.9 14.6 71 6.64 650 P37 18.96 5.5 75 2.64 961
P14 18.83 14.9 76 4.16 717 P37 13.99 4.3 80 1.68 1080
P14 15.9 14.5 74 4.16 742 P37 13.23 5.2 80 2.64 1023
P14 8.65 16.3 63 0.56 600 P37 14.76 7.4 75 1.92 788
P14 10.81 14.5 67 0.16 742 P37 11.96 7.2 76 0.48 964
P14 6.49 15.9 69 2.96 857 P38 27.23 14.6 69 7.68 847
P14 10.31 15.7 71 0.88 585 P38 23.54 15.5 64 8.08 982
P14 14.38 15.5 73 0.96 1114 P38 4.83 15.8 68 7.44 1038
P15 46.56 15.3 72 57.76 1125 P39 22.39 12.3 67 46.48 733
P15 25.06 14.3 69 17.68 1092 P39 36.9 11.8 65 34.48 729
P15 22.01 14.5 69 12.32 1077 P39 6.49 11.8 69 30.64 757
P15 23.41 15.9 71 10.32 932 P40 17.43 13.4 67 11.36 572
P15 11.45 15.1 66 9.84 619 P40 18.19 12.7 74 8.08 731
P15 14.63 14 56 5.92 632 P40 11.96 13.3 69 8.96 490
P15 11.96 15 56 3.84 1179 P40 11.58 12.6 73 5.28 571
P15 5.85 13.9 63 1.76 583 P40 7.51 12.7 70 2.48 427
P15 8.02 15.8 63 3.52 1037 P40 6.11 13.2 70 1.28 382
P16 31.17 14.9 77 16.88 714 P40 8.14 13.2 74 6.2 668
P16 26.97 13.2 82 5.04 500 P41 22.14 8.4 76 13.04 473
P16 26.84 13.5 83 5.92 742 P41 22.77 10.4 77 8.72 486
P16 18.96 14.9 83 6.24 638 P41 22.14 11.1 82 7.84 489
P16 13.23 13.7 79 3.36 795 P41 18.32 11.7 71 6.88 473
P16 8.78 14.5 77 1.44 588 P41 15.52 10.1 88 2.24 348
P16 9.8 15 77 0.96 881 P41 11.58 10 72 2.24 570
P17 43.77 10.5 84 28.24 978 P41 7.38 10.3 77 1.84 473
P17 38.55 10.3 83 20.4 860 P43 41.22 24.6 83 28 485
P17 32.57 11 84 16.4 996 P43 32.57 22 70 5.28 254
P18 32.32 9.9 83 8.08 904 P45 11.83 6.2 77 1.2 1135
P18 25.95 9.5 82 5.92 873 P45 8.52 6.9 77 1.04 1053
P18 18.32 10.3 83 3.76 987 P45 7.38 7.2 80 0.8 1281
P19 36.01 10.3 81 10.4 845 P45 4.58 7.3 75 1.04 1011
P19 30.41 10 82 6.64 749 P45 5.22 6.2 80 0.88 1404
P19 22.9 10.9 83 3.6 829 P45 3.31 6.3 80 0.56 950
P20 33.08 10.3 81 10.96 801 P45 2.8 7 79 0.32 1108
P20 26.08 10.1 81 7.44 680 P46 22.52 12.2 70 4.64 706
P20 21.88 11.1 82 4.64 790 P46 21.63 12.1 69 4.64 907
P21 29.13 7.6 70 11.52 1024 P46 19.08 12.7 66 4.64 494
P21 17.18 7.7 68 4.8 440 P47 17.3 17.4 61 0.48 33
P21 12.85 7.5 69 2.32 680 P49 21.76 11.4 76 18.24 834
P21 12.6 6.8 76 3.2 764 P49 23.54 11.7 75 10.8 993
P21 11.83 6.6 79 3.28 523 P49 13.99 11.9 65 11.04 674
P21 12.34 7.2 75 2.48 1050 P50 34.48 9.4 81 27.52 870
P21 7.12 6.4 74 1.36 794 P50 30.79 8.2 76 30.88 702
P21 9.54 7.2 74 1.04 869 P50 28.75 7.5 76 28.88 674
P21 7.51 6.9 76 1.6 737 P50 28.37 7.7 84 12.24 651
P21 5.22 7.4 74 0.48 715 P50 25.32 8.8 74 12.96 676
P21 7.89 7.5 76 3.36 805 P50 4.58 10.7 71 10.72 484
P22 54.71 6 78 28.64 1116 P51 10.05 18.7 62 11.36 461
P22 44.02 7 76 21.12 628 P51 6.87 18.5 56 3.36 325
P22 42.62 7.4 76 25.04 819 P51 19.85 18.7 62 6.32 570
P23 24.68 6.5 80 1.04 2144 P52 10.56 8.2 77 2.8 633
P23 16.79 5.9 75 0.56 1189 P52 8.02 7.8 75 0.8 589
P23 13.87 6.4 76 0.56 1420 P53 10.43 11.2 73 2 855
P23 14.38 5.6 75 0.32 1182 P53 5.73 11.3 73 2.64 555
P23 12.85 6.2 79 0.16 1744 P53 10.56 12 71 3.36 527
P23 14.5 6.6 83 0.24 2333 P54 8.91 11.5 70 10.8 651
P23 8.27 5.9 81 0.24 1390 P55 12.6 6.1 76 2.48 636
P23 13.61 6.2 79 0.4 1623 P59 13.74 9.7 74 5.2 664
P23 9.8 4.2 81 0.08 1392
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Table 5.

ISOCORRAG data considered in the study.

Code 1st Year Corrosion, μm T, °C RH, % TOW, Annual Fraction Deposition Rates, mg/m2.d Code 1st Year Corrosion, μm T, °C RH, % TOW, Annual Fraction Deposition Rates, mg/m2.d
SO2 Cl SO2 Cl
I01 5.8 22.9 0.615 2 1.5 I25 54.7 24 0.478 8.48 75.85
I02 24.9 14.1 0.682 2 18.21 I25 57.2 23.4 0.439 8.84 86.17
I02 54.8 13.9 0.708 2 24.39 I25 44.8 23.2 0.338 9.76 91.03
I02 78.2 14.3 0.725 2 33.38 I25 39.2 23.5 0.354 10.4 78.89
I02 66 14.5 0.736 2 42.48 I27 26.1 6.7 0.299 13.84 1.21
I02 68.3 14.2 0.711 2 32.16 I27 26.6 6.2 0.326 11.84 2.18
I03 14.7 17.1 0.529 9.7 1.5 I27 30.2 7.4 0.279 11.28 1.58
I04 4.6 19.2 0.104 2 1.5 I27 21.5 8.5 0.261 12.64 0.73
I06 25.5 8 0.287 11.28 33.38 I27 26.5 7.9 0.297 9.92 0.91
I06 21.5 7.6 0.267 12.8 42.48 I27 20.1 8.5 0.346 6.8 1.03
I06 28.3 8 0.238 12.4 33.38 I28 68.4 4.9 0.358 34.4 8.01
I06 21.3 7.5 0.283 12.24 37.02 I28 60.8 5.4 0.365 28.8 5.22
I06 25.5 7 0.287 12.72 35.2 I28 66 5.7 0.313 28.8 4.01
I06 21.6 7 0.317 14.8 33.98 I28 60 7.5 0.407 41.6 6.86
I07 27.1 5.5 76 0.347 20.24 2.31 I28 61.4 6.6 0.423 42.4 6.07
I07 23.1 7.1 77 0.414 13.68 1.64 I28 53.6 6.7 0.42 36.16 3.22
I07 26 6.8 77 0.409 12.88 2 I29 21.4 5.2 0.42 1.44 0.61
I07 23.3 7.1 76 0.353 10.48 2 I29 18.6 5.9 0.526 0.96 0.61
I07 30.7 7 77 0.454 10.72 2 I29 21.8 6.3 0.478 0.96 0.61
I07 25.7 7.3 77 0.503 13.92 2 I29 17.1 7.5 0.503 0.8 0.61
I08 62.4 7.5 81 0.272 71.6 2.91 I29 20.7 6.6 0.453 0.8 0.61
I08 44.3 8.8 79 0.285 52.32 1.21 I29 18.6 6.2 0.42 0.8 0.61
I08 43.3 9.3 75 0.238 56.4 2.1 I31 27.2 7.2 0.372 7.84 2.61
I08 42.1 9.7 74 0.226 52.64 2.1 I31 22.3 7.7 0.443 7.92 2.06
I08 53.3 9.9 77 0.264 47.76 2.1 I31 27.7 8.2 0.495 7.92 2.12
I08 38.9 10.1 77 0.299 43.04 2.1 I31 25.7 8.9 0.557 5.68 6.98
I09 87.9 7.7 76 0.355 84 2.31 I31 38 8.4 0.584 5.44 6.92
I09 66.1 9 73 0.279 66.64 1.09 I31 26.3 8.4 0.59 6.4 4.85
I09 57.7 9.5 73 0.256 65.84 1.7 I33 31.9 14.1 0.15 22 1.5
I09 59.1 9.8 72 0.266 71.6 1.7 I33 29.8 14.3 0.201 24.24 1.5
I09 84.1 9.6 74 0.271 76.88 1.7 I33 33.2 12.5 0.301 36.88 1.5
I09 69.2 9.9 74 0.235 66.48 1.7 I33 22.4 14.1 0.277 41.2 1.5
I10 38.5 10.4 0.545 18.72 1.52 I33 26.1 14.9 0.227 43.2 1.5
I10 40.6 10.8 0.535 17.84 1.09 I33 22.7 14.9 0.254 44.48 1.5
I10 35.3 11.1 0.506 12.08 1.09 I34 16.3 25.3 0.277 3.12 1.5
I10 37.4 10.8 0.486 10.56 1.4 I34 17 25.3 0.31 3.84 1.5
I10 31.8 9.6 0.428 14.64 1.03 I34 17.4 25.3 0.418 4.64 1.5
I10 33.8 9.7 0.424 12.48 0.61 I34 12.9 25.3 0.359 4.32 1.5
I11 37.5 3.3 0.339 17.12 2.18 I34 15.6 25.2 0.402 3.28 1.5
I11 33 5.1 0.395 17.12 2.49 I34 13.7 25.5 0.442 4.32 1.5
I11 41.2 6.4 0.394 16 2.55 I35 34.4 15.2 0.365 49.28 18.21
I11 28.3 6.8 0.42 14.72 2.43 I35 24.7 16.2 0.374 38.88 11.53
I11 31.4 6.7 0.439 13.28 2.41 I35 25.2 16.2 0.31 37.2 12.14
I11 28.6 6.8 0.464 12.24 2.41 I35 27.6 15.8 0.293 35.84 11.53
I12 30.9 3 0.297 16.24 2.55 I35 22.7 16.6 0.31 37.04 12.14
I12 21.4 4.9 0.325 13.04 1.52 I35 26.8 17.2 0.293 35.68 11.53
I12 34.6 5.4 0.388 15.2 1.09 I36 45.9 14.5 0.492 29.44 12.74
I12 19.9 5.3 0.348 11.2 1.72 I36 51.1 15.8 0.493 34.24 17.6
I12 26.2 5.9 0.434 8.48 1.72 I36 45 16.7 0.517 31.04 16.99
I12 20.8 6.4 0.491 9.04 1.72 I36 44.3 16.2 0.511 23.52 14.56
I13 16.7 0.3 0.378 4.72 1.94 I36 33.3 16.1 0.464 16.8 24.27
I13 11 2.2 0.345 4.24 1.5 I37 28 5 0.29 8.16 1.5
I13 15.7 3.4 0.313 4.08 1.5 I37 26.9 6.8 0.416 8.8 1.5
I13 9.7 4 0.347 2.8 1.5 I37 28.1 7.1 0.359 9.6 1.5
I13 12.5 4 0.357 2.48 1.5 I37 21.6 8 0.347 8 1.5
I13 11.3 4.1 0.386 1.52 1.5 I37 23.5 8.4 0.338 8.8 1.5
I14 40.7 12.3 0.473 42.24 15.17 I37 18.1 8.4 0.385 5.6 1.5
I14 34.5 13.1 0.546 37.04 15.17 I38 43 6.1 0.447 7.04 41.87
I14 44.2 13.5 0.52 31.04 18.21 I38 28.8 8 0.472 4 44.3
I14 35 13 0.511 32 18.81 I38 33.1 8.7 0.462 6.4 30.95
I15 83.5 14.6 0.423 120.8 125.01 I38 33.3 9.5 0.454 1.6 58.26
I15 68.1 16.2 0.488 77.04 155.96 I38 41.8 9.5 0.449 2.4 54.62
I15 70.7 16.1 0.427 61.04 158.38 I38 31.2 9.7 0.474 4 81.32
I15 66.4 15.6 0.349 64 154.14 I40 42.3 11.4 0.705 20.56 11.41
I15 72.6 15.6 0.503 35.84 138.36 I40 35.1 11.4 0.66 16.56 12.86
I16 19.6 6.5 0.493 14.4 4.85 I40 36 11.4 0.631 14.32 5.58
I16 15.5 6.5 0.474 9.04 3.64 I40 37.6 11.4 0.547 13.84 5.16
I16 19.6 7.1 0.542 8 4.25 I40 42.9 11.4 0.467 15.2 7.83
I16 12.3 6.7 0.47 6.48 3.03 I40 38 11.4 0.512 14.8 12.02
I18 82.1 13.6 0.352 32.5 83.74 I41 36.4 10.5 0.687 12.88 8.56
I18 70.2 14.2 0.37 32 149.89 I43 39.6 9 0.707 15.36 3.22
I18 70.5 15.4 0.45 31.44 101.95 I43 35.4 9 0.68 13.6 2.31
I19 118 9.7 0.691 8 95.27 I43 38.1 9 0.449 12.88 4.43
I19 95.8 9.7 0.664 24 112.26 I43 41.7 9 0.459 12.88 4.43
I19 83.5 9.7 0.728 25.6 107.41 I43 41.8 9 0.545 13.12 2.06
I20 37.6 13 0.494 42.88 1.5 I43 37.7 9 0.491 11.36 3.09
I20 39.7 13 0.38 42.88 1.5 I44 40.2 13.3 0.503 4.32 80.71
I20 48 13 0.218 42.4 1.5 I44 32.5 18.1 0.479 4.96 67.97
I21 101 9.6 0.471 171.68 8.92 I44 37.6 17.8 0.464 5.28 89.81
I21 95.1 11.9 0.527 147.68 8.5 I44 35.6 17.4 0.473 4.88 117.73
I21 126 12.8 0.567 133.6 14.93 I44 43.8 18.2 0.492 8.4 150.5
I23 44 16 0.654 5.84 36.41 I45 26.4 11.8 0.216 26.3 1.5
I23 40.9 15.9 0.639 6.48 47.94 I46 373 27.3 0.824 42.4 324.66
I23 45.2 15.5 0.644 6.64 41.87 I51 32.2 13.2 0.364 20 0.61
I23 39.7 15.8 0.643 6.48 37.62 I51 33.6 13.4 0.341 22.56 0.61
I23 48.2 16.1 0.644 6 39.44 I51 29.4 13.1 0.395 20.48 0.61
I23 42.1 16.2 0.683 5.68 40.05 I51 30.2 13.3 0.386 21.2 0.61
I24 38 14.1 0.18 11.28 2.61 I51 22.5 13.7 0.383 21.12 0.67
I24 28.6 14.1 0.221 11.6 2.49 I51 24.2 13.3 0.334 20.8 0.61
I24 48.8 13.9 0.275 11.28 2.85 I52 39 3.9 0.465 10.4 21.85
I24 32.1 14 0.262 11.36 3.34 I52 26.4 4.2 0.434 12.64 14.56
I24 55.8 14.2 0.258 12.24 3.16 I52 22.4 5.8 0.405 27.44 11.23
I24 33.8 14.6 0.291 12.4 3.09 I52 23.9 5.9 0.396 32.88 6.68
I25 118 22.8 0.538 8.88 80.1 I52 17.4 6.8 0.483 20.32 5.28
I25 138 23.9 0.525 6.64 60.08 I52 26.3 6.2 0.503 22.32 7.34
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Table 6.

MICAT data considered in the study.

Code 1st Year Corrosion, μm T, °C RH, % TOW, Annual Fraction Precipitation, mm/y Deposition Rates, mg/m2.d
SO2 Cl
M01 54.8 13.9 79 0.708 805 2 40.2
M01 66 14.5 80 0.736 1226 2 70
M02 14.73 16.9 74 0.538 1377 9 1.5
M03 5.7 21.2 75 0.643 2167 2 1.50
M04 4.9 18.8 50 0.103 80 2 1.50
M06 25.3 17 78 0.593 1178 6.2 1.5
M06 28.8 16.7 77 0.565 1263 8.2 1.5
M06 30.1 16.6 78 0.631 1361 6.2 1.5
M07 8.6 21.5 74 0.482 847 0.8 8.9
M07 11.5 20.9 75 0.482 1167 1.3 7.4
M07 13.1 21.2 75 0.482 996 1.7 1.60
M08 52.5 23.8 89 0.482 1122 23.8 8.6
M08 47.3 22.9 91 0.482 1471 20.7 6.8
M08 48.5 23 90 0.482 1444 24.5 5.2
M09 159.8 24.8 77 0.582 605 9.5 359.8
M09 194.7 24.5 79 0.582 985 5.3 174.8
M09 141.7 24.2 77 0.582 716 4.4 167.70
M10 98.7 22.7 73 0.579 960 40.4 4.50
M10 161.2 22.9 71 0.579 870 57.4 9.20
M10 216.9 22.6 79 0.579 1133 65.8 10.80
M11 301.9 22.1 80 0.579 1689 2.6 113.20
M13 127.1 20.1 80 0.598 1353 55.85 20.21
M13 61.2 23.1 78 0.598 1369 44.09 14.22
M13 73.1 21 82 0.598 1305 30.51 14.67
M14 19.4 26.1 88 0.682 2395 2 1.50
M16 12.9 20.4 69 0.442 1440 2 1.50
M17 17.3 25.9 77 0.172 1392 2 1.50
M18 4.9 26.6 90 - 2096 2 1.50
M19 16 27.6 85 0.989 940 7.8 43.60
M19 30.6 27.6 87 0.966 940 14.2 69.00
M19 54 28.2 87 0.975 940 8.9 69.50
M20 17 11.5 90 1 1800 0.6 1.50
M21 19.6 27 76 0.33 900 0.3 1.50
M22 61.6 27.6 80 0.562 1598 6.3 38.7
M23 371.5 25.3 88 0.763 3531 3.5 376
M24 69.3 22.9 88 0.838 3677 4 20.60
M25 16.6 18.9 83 0.695 1780 2.4 12.10
M26 36.1 25.2 80 0.571 1591 37.1 15.8
M26 26.4 25.4 79 0.571 1303 36.5 10.9
M26 29 24.7 79 0.571 1321 19.8 10.9
M26 32.3 25.5 79 0.571 1129 25.6 14.3
M26 31.3 25.4 79 0.571 1305 41 10.10
M26 29.2 24.7 79 0.571 1540 24.7 8.20
M26 27.3 25.2 79 0.571 1415 18.5 18.10
M26 32.8 25.1 79 0.571 1064 49.2 7.40
M27 391.1 25.2 80 0.571 1591 24.5 99.10
M27 213.7 25.4 79 0.571 1303 13.5 115.60
M27 173.6 24.7 79 0.571 1321 25.1 123.30
M27 171.9 25.5 79 0.571 1129 20.4 96.00
M27 391.2 25 79 0.571 1108 32.8 50.40
M27 126.5 25.4 79 0.571 1305 18.9 111.40
M27 71.6 25.7 79 0.571 1540 19.9 108.80
M27 84.8 24.2 79 0.571 1415 17.7 97.70
M27 188.9 25.1 80 0.571 1064 40.4 81.80
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On the other hand, a series of anomalous values have been observed at stations characterised as marine environments (ISOCORRAG and MICAT databases). Figure 5a shows the relationship between the variables of corrosion (µm/y) and salinity (mg Cl−/m2.d) in both databases. In this figure it is possible to see a cloud of points with very high salinity values (above 200 mg Cl−/m2.d) which does not seem to agree with the relatively low carbon steel corrosion values found (50–100 µm). It is also seen that a considerable rise in the marine chloride deposition rate (from 200 to 650 mg Cl−/m2.d) does not result in greater first-year corrosion of carbon steel, which is contradictory to the abundant literature on this matter recently reviewed by Alcántara et al. [32].These data have therefore been considered to be anomalous, and have been removed from the database. The corrosion stations removed for this reason are: Saint Remy (France), Tannanger (Norway) and Kvarnvik (Sweden). Figure 5b shows the linear relationship between these two variables after removing the aforementioned testing stations.

Figure 5.

Figure 5

Figure 5

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Relationship between annual steel corrosion and chloride deposition rate in marine test sites including in MICAT and ISOCORRAG databases (a); and the same relationship after anomalous data were eliminated (b).

2.5. Integration of ICP/UNECE, ISOCORRAG and MICAT Databases

Table 4, Table 5 and Table 6 present the databases finally considered for the ICP/UNECE, ISOCORRAG and MICAT programmes for statistical analysis after the screening mentioned in the preceding section.

ICP/UNECE: Table 4 presents the corrosion data obtained at the different testing stations along with the corresponding annual average values for the meteorological and pollution parameters measured in the programme: temperature (°C), precipitation (mm/y), relative humidity (%) and SO2 deposition rate (mg/m2.d).

ISOCORRAG: Table 5 presents the corrosion data obtained at the different testing stations along with the corresponding annual average values for the meteorological and pollution parameters measured in the programme: temperature (°C), relative humidity (%) (only for stations in the Czech Republic), time of wetness (annual fraction), SO2 deposition rate (mg/m2.d) and chloride ion deposition rate (mg/m2.d).

MICAT: Table 6 presents the average corrosion value obtained at the different testing stations along with the annual average values for the meteorological and pollution parameters measured in the programme: temperature (°C), relative humidity (%), time of wetness (annual fraction), precipitation (mm/y), SO2 deposition rate (mg/m2.d) and chloride ion deposition rate (mg/m2.d).

There follows an indication of the similarities and differences between the experimental methods used in the three collaborative programmes and how this has affected the integration of the three databases for statistical analysis:

  • (a)

    Evaluation of the first-year corrosion (mass loss) of carbon steel according to ISO 9226 [6].

  • (b)

    Measurement of meteorological parameters (T, RH, and precipitation) according to standard conventional procedures. The ISOCORRAG programme does not consider precipitation or RH (except at the Czech Republic stations).

  • (c)

    Estimation of TOW according to ISO 9223 [2,3]. The ICP/UNECE programme does not consider this parameter.

  • (d)

    Measurement of SO2 deposition rate according to ISO 9225 [5].

  • (e)

    Measurement of Cl deposition rate according to ISO 9225 [5]. The ICP/UNECE programme does not consider marine atmospheres.

3. Discussion

It is a well known fact that the atmospheric corrosion of metals is influenced by many factors: (a) external conditions, meteorology and air pollution; (b) exposure conditions; (c) construction conditions; (d) internal conditions, such as nature of the metal and characteristics of corrosion products; among others.

Over the years, many models have been developed to assess the corrosion of carbon steel in the atmosphere. The specialised literature offers a large range of damage functions that relate atmospheric corrosion of carbon steel with environmental data. However, most of them are of limited applicability as they were obtained with minimal variations in meteorological parameters (small geographic areas). Special mention should be made of the efforts of Benarie and Lipfert [13] to develop universal corrosion functions in terms of atmospheric pollutants, meteorological parameters and the rain pH, as well as the work of Feliu et al. [15] compiling a comprehensive literature survey of worldwide atmospheric corrosion and environmental data that were statistically processed to establish general corrosion damage functions in terms of simple meteorological and pollution parameters. Reviews on this subject can be found in [7,26].

In recent decades, several international exposure programmes (ISOCORRAG [1], ICP/UNECE [8], and MICAT [7]) have been carried out with the aim of more systematically obtaining relationships (dose/response functions) between atmospheric corrosion rates and pollution levels in combination with climate parameters. Integration of the databases obtained in these three exposure programmes may make it possible to obtain universal damage functions based on a worldwide variety of meteorological and pollution conditions. This has been the chief aim of the work reported here.

The data have been fitted to the following linear equation:

C = a1 + a2 RH + a3 P + a4 T + a5 TOW + a6 SO2 + a7 Cl, (1)

This equation is quite simple. Other combinations between the different variables or other more sophisticated statistical treatments would likely yield better fits, but the aim of this work has been to use as simple as possible a relation.

According to this model, the dependent variable C (carbon steel annual corrosion in µm) is interpreted as a linear combination of a set of independent variables: RH, annual average relative humidity, in per cent; T, annual average temperature, in °C; P, annual precipitation, in mm; TOW, time of wetness, annual fraction of number of hours/year in which RH > 80% and T > 0 °C [3]; SO2, SO2 pollution, in mg/m2.day; and Cl, chloride pollution, in mg/m2.day. Each independent variable is accompanied by a coefficient (a2–a7) which indicates the relative weight of that variable in the equation. The equation also includes a constant a1.

The minimum-quadratic regression equation is constructed by estimating the values of coefficients a1–a7 from the regression model. These estimates are obtained trying to keep the squared differences between the values observed and the forecast values to a minimum. In order to know the model’s fitting quality in relation to the experimental data, the statistic R2 is used, i.e., the square of the multiple correlation coefficient. R2 expresses the proportion of variance of the dependent variable which is explained by the independent variables.

There are different methods to select the independent variables that a regression model must include. The most widely accepted is the stepwise regression model. With this method, the best variable is firstly selected (always with a statistical criterion); then the best of the rest is taken; and so on, until no variables that fulfil the selection criteria remain. A great change in R2 when a new variable is inserted in the equation indicates that this variable provides unique information on the dependent variable that is not supplied by the other independent variables.

The study has been carried out with the assistance of a commercial computer programme (SSPS) [33]. Statistical processing has been carried out considering marine and non-marine atmospheres separately. The variability of the corrosion and environmental data is shown in Table 7.

Table 7.

Characteristics of the corrosion and environmental data used in the statistical treatment.

Type of Atmosphere Variable Smallest Value Largest Value
Non marine C (µm) 0.51 87.9
T (°C) 3.1 27
RH (%) 35 90
SO2 (mg/m2.d) 0.08 84
Marine C (µm) 8.6 411.2
T (°C) 3.9 28.2
TOW (annual fraction) 0.16 0.99
SO2 (mg/m2.d) 0.3 171.7
Cl (mg/m2.d) 3.03 376
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3.1. Non-Marine Atmospheres

The three databases have been analysed together: ICP/UNECE database (Table 4) (all corrosion stations), and ISOCORRAG (Table 5) and MICAT (Table 6) databases (only those stations with a chloride ion deposition rate of Cl− < 3 mg/m2.d).

The meteorological and pollution parameters common to all three databases and which have been included in the treatment are: temperature, relative humidity and SO2 pollution, though relative humidity is only available in the ISOCORRAG database for stations in the Czech Republic [34]. The stepwise method has been used to select what independent variables are included in the treatment and which are significant. Statistically all the variables are significant, with SO2 being the variable that contributes with an extremely high percentage (R2 = 0.671) in the total recorded variance (R2 = 0.725). RH and T also contribute, raising the R2 by 0.037 and 0.017 units, respectively.

The resulting regression equation is:

C = −26.32 + 0.43 T + 0.45 RH + 0.82 SO2; (R2 = 0.725) (N = 333), (2)

where N is the number of data. The model explains 72.5% of the dependent variable. This is the regression equation with non-standard coefficients, partial regression coefficients which define the regression equation at direct scores. Figure 6 shows the relationship between predicted and observed carbon steel corrosion values by applying Equation (2).

Figure 6.

Figure 6

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Relationship between predicted and observed carbon steel corrosion values by applying Equation (2).

The standardised partial regression coefficients are the coefficients that define the regression equation when it is obtained after standardising the original variables, i.e., after converting the direct scores into typical scores. These coefficients make it possible to evaluate the relative importance of each independent variable within the equation. The regression equation with standardised coefficients is shown in Equation (3):

C = 0.15 T + 0.26 RH + 0.82 SO2; (R2 = 0.725) (N = 333), (3)

Great caution must be used when making corrosion predictions with independent variable values that are much larger or smaller than those used to derive these equations (see Table 7).

The goodness of the fit is slightly higher than the damage function developed by ICP/UNECE for this type of atmospheres using a more sophisticated mathematical model (Table 8), with a notably lower number of data (N = 148) used in the statistical treatment.

Table 8.

Published dose/response (D/R) functions for first-year corrosion of carbon steel.

Type of Atmosphere Programme Ref. D/R Function N R2
Non marine ICP/UNECE (for weathering steels) [18] C = 34[SO2]0.13 exp{0.020 RH + f(T)}
where f(T) = 0.059(T − 10) when T ≤ 10 °C, otherwise f(T) = −0.036 (T − 10)		 148 0.68
All atmospheres (marine and non-marine atmospheres) ISOCORRAG [35] C = 0.091 [SO2]0.56 TOW0.52 exp(f(T)) + 0.158[Cl]0.58 TOW0.25 exp(0.050 T)
where f(T) = 0.103 (T − 10) when T ≤ 10 °C, otherwise f(T) = −0.059 (T − 10)		 125 0.85
C = 1.77 [SO2]0.52 exp(0.020 RH) exp(f(T)) +0.102[Cl]0.62 exp(0.033 RH +0.040 T)
where f(T) = 0.150 (T − 10) when T ≤ 10 °C, otherwise f(T) = −0.054 (T − 10)		 128 0.85
MICAT [7] C = −0.44 + 6.38 TOW + 1.58[SO2] + 0.96[Cl] 172 0.56
ISOCORRAG/MICAT
Including data from Russian sites in frigid regions		 [1] C = 0.085 × SO20.56 × TOW0.53 × exp(f) + 0.24 × Cl0.47 × TOW0.25 × exp(0.049 T)
f(T) = 0.098 (T − 10) when T ≤ 10 °C,
otherwise f(T) = −0.087 (T − 10)		 119 0.87
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N = number of data.

If, instead of RH, time of wetness (TOW) were to be considered (No. of hours in which RH > 80% and T > 0 °C, and therefore less precise than RH), taking into consideration the ISOCORRAG and MICAT databases the resulting regression equation would be:

C = 6.58 + 0.75 SO2 + 20.85 TOW; (R2 = 0.684) (N = 138), (4)

with a slightly lower regression coefficient than Equation (2). In this case, the temperature is a non-significant variable and the greatest specific weight again corresponds to SO2, which contributes to the total recorded variance with an R2 = 0.646 of a total R2 = 0.684.

Considering another related parameter, such as precipitation (P) (in mm/y), instead of RH, the following regression equation would be obtained:

C = −29.26 + 0.87 SO2 + 0.51 RH + 0.49 T − 0.003 P; (R2 = 0.625) (N = 315), (5)

in which the resulting regression coefficient decreases even more.

3.2. Marine Atmospheres

As in the previous case, the stepwise method has been used to select what independent variables are included in the statistical treatment and are significant. In this case, given that the ICP/UNECE programme did not measure the Cl ion deposition rate as it only considered non-marine testing stations, only stations from the ISOCORRAG and MICAT databases have been considered, taking as independent variables the chloride deposition rate (Cl), SO2 pollution, temperature and time of wetness. As has been noted above, data on relative humidity are not available for ISOCORRAG stations.

In this case Cl is the variable that contributes with the highest percentage (R2 = 0.411) to the total recorded variance (R2 = 0.474). SO2 also contributes, raising the R2 by 0.041 units. TOW is also a significant variable but raises the R2 by only 0.022 units. Temperature is excluded as a significant variable.

The resulting regression equation is:

C = −24.50 + 0.75 Cl + 0.67 SO2 + 77.32 TOW (R2 = 0.474) (N = 206), (6)

while in standard coefficients the resulting equation would be:

C = 0.62 Cl + 0.22 SO2 + 0.15 TOW (R2 = 0.474) (N = 206), (7)

Cl and SO2, in this order, are the variables with the greatest weight in the carbon steel corrosion rate. Figure 7 shows the relationship between predicted and observed carbon steel corrosion values by applying Equation (6).

Figure 7.

Figure 7

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Relationship between predicted and observed carbon steel corrosion values by applying Equation (6).

The goodness of this fit is slightly lower than in the MICAT programme and notably lower than those obtained in the ISOCORRAG programme (Table 8). On the other hand, the number of data used in the statistical treatment to obtain the damage function (Equations (6) and (7)) is somewhat higher.

Replacing the TOW variable with RH, information that is available in MICAT and perhaps in ISOCORRAG records, could possibly have improved the fitting quality achieved.

It would be also helpful to make new fits using more sophisticated mathematical models, and we encourage experts in statistics to do this. For this purpose, a good starting point could be the perfected broad database that are presented in this work (Table 4, Table 5 and Table 6).

3.3. Contribution of the Information Supplied for Each International Programme

Having established the integrated database, it is of interest to compare the damage functions and correlation coefficients obtained using the combined information of the three programmes (Equations (3) and (7)) with those obtained separately using the information supplied each individual programme. In this way, it may be possible to determine the contribution of each programme to the general damage functions. The results obtained with this treatment of the information are shown in Table 9.

Table 9.

D/R functions for first-year corrosion of carbon steel. Contribution of the information supplied by each of the international programmes to the overall D/R functions (Equations (3) and (7)) using the integrated database.

Type of Atmosphere Programme N Significant Variables D/R Function R2
Non-marine atmospheres Overall D/R function (Equation (3)) (ICP/UNECE + ISOCORRAG + MICAT) 333 SO2, RH, T C= 0.82 SO2 + 0.26 RH + 0.15 T 0.725
ICP/UNECE 269 SO2, RH, T C= 0.74 SO2 + 0.37 RH + 0.21 T 0.674
ISOCORRAG 18 SO2 C= 0.93 SO2 0.867
MICAT 46 SO2, T C= 0.49 SO2 + 0.46 T 0.398
Marine atmospheres Overall D/R function (Equation (7)) (ISOCORRAG + MICAT) 206 Cl, SO2, TOW C= 0.62 Cl + 0.22 SO2 + 0.15 TOW 0.474
ISOCORRAG 97 Cl, SO2, TOW C= 0.57 Cl + 0.31 SO2 + 0.31 TOW 0.582
MICAT 109 Cl, SO2, C= 0.69 Cl + 0.30 SO2 0.538
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N = number of data.

Considering non-marine atmospheres, it is seen that the greatest volume of information (81%) is supplied by the ICP/UNECE programme, which establishes SO2, RH and T as the most significant variables, the former with the greatest weight. The contribution of the ISOCORRAG programme (R2 = 0.867), which incorporates a relatively small amount of data as only the Czech Republic testing stations supplied information on these three variables, makes only a slight improvement to the general correlation coefficient (0.725) in relation to R2 supplied by the ICP/UNECE programme (0.674). With regard to the information provided by the MICAT programme, the low correlation coefficient obtained (0.398) may be an indication of poorer data quality than in the other programmes, perhaps due to the participation in MICAT of countries with little or no prior experience in the field of atmospheric corrosion [7]. In this respect, it should not be overlooked that one of the MICAT research programme’s main aims was precisely to promote the development of this line of research in some of the participating countries [11].

Considering marine atmospheres, information has been supplied only by the ISOCORRAG and MICAT programmes, with a similar amount of data in each case. These data show the important effect of Cl and SO2 pollutants (in this order) on the magnitude of the atmospheric corrosion of carbon steel. The greatest contribution to the general damage function seems to correspond to the ISOCORRAG programme, which includes TOW as an also significant variable. The joining of data from the two programmes, whose individual damage functions have only low correlation coefficients, yields a combined damage function with an even lower correlation coefficient.

4. Goodness of the Fits

As we have seen, the environmental parameters considered in this work only partly explain the corrosion data. The goodness of fit of experimental data to a proposed model is often measured by the statistical R2, i.e., the square of the correlation coefficient (R) between the observed values of the dependent variable and those predicted from the fitted line.

In previous work [15,16], the authors noted a series of causes that may affect the goodness of the fit obtained:

  • Oversimplification of the mathematical model. In this sense, the best fits obtained in the ISOCORRAG programme, including or excluding the MICAT databases and data from Russian sites in frigid regions [1] (see Table 8), may have been at least partly due to considering interactions between the meteorological and pollution variables. One example of complex interactions involves the RH (or TOW), which in addition to its effect on the wetting of the metal surface, plays a major role in the mechanisms whereby air pollutants take part in corrosion.

  • The lack of quality in corrosion and environmental data.

  • Probable occurrence of other variables with marked effects on corrosion that were not considered in the statistical treatment. For instance, besides sulphur dioxide and chlorides, other pollutants not considered in the study may have played an important role in the corrosion data. In this sense, mention should be made of the effort made by ICP/UNECE to consider in the new damage functions (for the multi-pollutant situation) other important pollutants in terms of their effect on the corrosion of weathering steel [18].

  • Many effects that have not been considered. To mention just a few: The magnitude of diurnal and seasonal changes in meteorological and pollution parameters, the frequency, duration and type of wetting and drying cycles, and the time of year when exposure is initiated.

Finally, it would be desirable, as noted by Leygraf et al. [36], to develop models on the basis of mechanistic considerations instead of statistical considerations, and recent work has attempted to take this into account [37,38,39]. Nevertheless, there is still a very long way to go to reach this desired goal. As Roberge et al. [40] note, the results obtained with mechanistic models reveal why statistical schemes have only a limited accuracy. There are many variables that can change from site to site which are not accounted for in the standard set of environmental variables. According to this researcher, a single transferable and comprehensive environmental corrosivity prediction model is yet to be published, and may ultimately not be possible due to the complexity of the issues involved.

5. Conclusions

The following may be considered the most relevant conclusions of this study:

  • A highly complete and perfected database has been obtained from published data from the ISOCORRAG, ICP/UNECE and MICAT programmes.

  • The number of data used in the statistical treatment has been much higher than that used in other damage functions previously published by the different programmes.

  • The statistical treatment carried out has differentiated between two types of atmospheres: non-marine and marine, which may represent a significant simplification for persons with little knowledge of the atmospheric corrosion process who wish to estimate the corrosion of carbon steel exposed at a given location. Moreover, having considered a highly simple polynomial function (Equation (2)) in the work may also be an advantage in this sense.

  • With regard to non-marine atmospheres, by joining the three databases (ISOCORRAG, ICP/UNECE and MICAT), the following damage function has been obtained:
    C = −26.32 + 0.43 T + 0.45 RH + 0.82 SO2 (R2 = 0.725) (N = 333),
    where SO2 is the variable of greatest significance. The inclusion of TOW (or precipitation) instead of RH leads to lower regression coefficients. The goodness of the fit obtained (R2) is slightly higher than that obtained in the ICP/UNECE programme with a more sophisticated function.
  • In relation with marine atmospheres, only the ISOCORRAG and MICAT databases have been considered (ICP/UNECE did not consider this type of atmospheres). The damage function obtained is:
    C = −24.50 + 0.75 Cl + 0.67 SO2 + 77.32 TOW (R2 = 0.474) (N = 206),
    where Cl and SO2, in this order, are the most significant variables. The goodness of the fit is slightly lower than that obtained in the MICAT programme, which uses a similar type of function, and notably lower than that obtained in the ISOCORRAG programme using a more sophisticated type of function.

Acknowledgments

The authors would like to express their gratitude to Johan Tidblad, from Swerea Kimab (Stockholm, Sweden) and Katerina Kreislova, from SVUOM (Prague, Czech Republic) for the information supplied about ICP/UNECE and ISOCORRAG programmes.

Author Contributions

Belén Chico and Manuel Morcillo conceived and designed the study; Iván Díaz and Joaquín Simancas collaborated to the data mining process; Daniel de la Fuente contributed to statistical analysis of data; Belén Chico and Manuel Morcillo analysed the data and wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest.

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