Five decades spatial hazard maps of atmospheric corrosion predict the rate of deterioration of steel beams in different environments of India

1.1 Steel and its impact on economy

Metals and alloys are deeply ingrained in human subsistence and considerably contribute to the world economy. All-natural metals exist as ores with other elements, and different procedures extract them to make the desired metal or alloy. Any natural ore that has been altered to an alloy by applying energy tends to reform into ore through a series of reactions by releasing energy. Corrosion is one such sluggish restoring process of alloys/metals into ore. This dawdling natural destruction process of metals in the presence of the atmosphere is referred to as atmospheric corrosion. The degradation of steel and other metals due to atmospheric corrosion is a substantial global economic problem. The corrosion costs are tantamount to natural hazards as global costs are estimated to be around 2.5 trillion USD and 3–5% GDP (Hays 2010) of any industrialized country. The corrosion costs in India are around 67 billion USD greater than the costs of the fatal Bhuj earthquake (2001) in India. The economic consequences and the loss of resources engross the necessity of assessing and monitoring atmospheric corrosion.

1.2 Atmospheric corrosion and its parameters

Steel is one of the most harnessed construction and engineering metal due to its enduring properties. It is utilized in many facets of human life, including automobile production, infrastructure building, mechanical and electrical machinery and housewares. Atmospheric corrosion facilitates the deterioration of atmospherically exposed infrastructure steel by accumulating sulphides, oxides and hydroxides through a series of electrochemical reactions. Atmospheric corrosion is an intricate process relatively dependent upon the environmental parameters and atmospheric pollutants. The relativity of environmental parameters tends to change quickly over time as a result of modernization. Furthermore, the upshot and change in climate succumbs the atmospheric corrosion to dynamic changes over time. However, atmospheric corrosion should be assessed and tracked spatially to assist governments, planners, and industries in planning and mitigating cost-effectively as per the severity.

Early research studies (Legault and Preban 1975; Ma et al. 2010; Townsend and Zoccola 2009) projected a power-law relation to consider the atmospheric corrosion kinetics. However, corrosion is mere thermodynamic chemical oxidation and reduction process associated with Faraday’s law. Furthermore, the fundamental thermodynamic or power-law functions are swapped with the current state of the art that environmental parameters influence corrosion dynamics. According to one study (Tice 1962), the trace of pollutants such as sulphur dioxide (SO₂) twitches atmospheric corrosion at lower meteorological parameters. Subsequently, many other studies (de la Fuente et al. 2011; Kusmierek and Chrzescijanska 2015; Morcillo et al. 2013; Oesch 1996; Stratmann et al. 1987; Syed 2013) have recognized the meteorological parameters temperature (T), relative humidity (RH) and atmospheric pollutants sulphur dioxide (SO₂) and chloride (cl⁻) as the most predominant parameters that govern the atmospheric corrosion dynamics. A thin electrolyte film formed due to the condensed water on the surface is the origin and sustenance of the fundamental corrosion mechanism. The corrosion mechanism gets triggered differently for the four environmental parameters to form the due and accelerate the mechanism. However, T and RH are the basic parameters that highly regulate atmospheric corrosion mechanisms, while the other two provide patinas and hygroscopic salts to accelerate the mechanism.

1.3 Renowned global/standard atmospheric corrosion studies

The authenticity of the influence of the environmental parameters T, RH, SO₂ and Cl⁻ is affirmed by the International standard organization over recognizing them as the critical parameters to measure corrosion rate. The ISO 9223 standard (ISO 9223 2012) categorizes factors depending on their location (rural, urban, industrial and maritime) and their corrosivity concerning intensity. Furthermore, Equation (1) to estimate atmospheric corrosion rate considering environmental factors was provided based on one-year experimental data. ISO 9223, on the other hand, is acknowledged as the primary standard for recognizing, classifying and comparing atmospheric corrosion approximations.

The ISO 9223 Equation (1), established based on one-year experimental data mimics the basic principle of power-law function, where T temperature (in °C), RH relative humidity (in %), p_d annual average rate of deposition of sulphur dioxide compounds in milligram/square decimetre/day (mdd), s_d is the annual average deposition rate of chloride (in mdd). f_st = 0.15 (T-10) when T ≤ 10 otherwise, −0.0154 (T-10) is a factor for temperature.

Despite the one-year experimental ISO 9223 equation, MICAT (Morcillo 2009) and ISOCORRAG (Knotkova et al. 2010) studies have conducted experiments across different continental locations for 2 y, 4 y and 8 y in 13 countries at 53 different locations. Subsequently, their experimental data is used to articulate empirical equations constituting environmental parameters to estimate atmospheric corrosion and further, ISOCORRAG mentioned enhancing the ISO 9223 classification system.

where c_st (µm/y) is the rate of corrosion, SO₂ (µg/m³) is the annual average sulphur dioxide deposition rate, Cl (mdd) is the annual average chloride deposition rate, TOW (h/year) is time of wetness defined as the time period during which RH is more than 80% and the temperature is more than 0 °C, T (^oC) is the annual average temperature and f_st is a function for temperature.

It can be interpreted that globally well-known experimental corrosion studies have projected different empirical equations by regression of experimental data. Inadequate experimental data may not provide a diverse range of probable worldwide ranges of environmental parameters that exist globally. The regression of limited experimental data may not perceive underlying corrosion dynamics that fluctuate with changes in the intensity of environmental parameters. A recent study (Kumar and Sil 2020; Sil and Kumar 2020) on the other side claims that its proposed empirical equation apprehends intensity changes and mimic the actual chemical corrosion dynamics in environmental parameters to estimate the atmospheric corrosion rate. The study generated adequate corrosion sample data within ISO 9223 ranges using very few available global expressions in the literature. The heterogeneity of the generated sample data is achieved by synthesizing it with inferences deduced from the parametric study such that the empirical equation approximates atmospheric corrosion as per the actual dynamics. It has been shown that the equation approximates are closer to the experimental data than ISO 9223 estimates and further reported that the ISO estimates are mostly uncertain.

where C_r is corrosion rate (µm/y), T is the annual average temperature in °C, RH in %, SO₂ is the annual average sulphur dioxide deposition rate in mdd, Cl is the annual average chloride content deposition rate in mdd and t is the time of exposure in years. The study used RH over TOW to evaluate wetness since previous research (Simillion et al. 2014; Wang et al. 2015) revealed that hygroscopic salts, MgCl₂ and NaCl, wetted metallic surfaces at lower RH. According to another study (Schindelholz et al. 2013), the ISO 80% RH demarcation of TOW does not represent the actual wetting circumstances as sea salt aerosols wetted the surface at 20% RH. The short-term and long-term equations (3) and (4) are statistically sound within 95% confidence limits, and the 0.998 R² is validated using actual field experimental data from ISOCOORAG and Fernández-Pérez et al. (Fernández-Pérez et al. 2015; Knotkova et al. 2010) to show that approximates are closer to the actual field atmospheric corrosion rates. This appropriate field approximation at any continental location, confidence limit and R² is probable due to the robustness of the equation established with diverse sample data.

1.4 Spatial atmospheric corrosion mapping studies

A study (Sica et al. 2007) conducted exposure experiments at 15 different locations in Sao Luis city on the north Brazilian coast to classify the city’s atmospheric corrosivity with experimental data and prepared a spatially classified corrosive map. Another experimental study (Portella et al. 2012) on the coast prepared a corrosion map for the city of Salvador and further went on to report that carbon steel is the most corrosive metal. It also reported that the coastal sites had recorded a higher corrosion rate due to the heavy chloride deposition. Furthermore, a global coastal corrosion spatial map was prepared in a study (Slamova 2012) by quantifying the global coastal corrosion rates using geographical information system to estimate airborne chloride salinity in two different ways. However, few experimental and spatial corrosion assessment studies (Kumar and Sil 2020; Morales et al. 2005; Natesan and Palaniswamy 2009; Pérez and Prado 2019; Ríos Rojas et al. 2015; Sil and Kumar 2020) at different places in numerous locations have reported that ISO 9223 had estimated higher and lower rates than the actual corrosion rate and further concluded that ISO 9223 estimates are uncertain.

The atmospheric corrosion studies (Natesan et al. 2005, 2006; Natesan and Palaniswamy 2009) in India are limited to Electrochemical Research Institute, exposing samples at 42 different locations for 1 y and continued to 5 y in 10 different locations across the country. The countries initial corrosion map was first proposed by the institute based on the 1 y exposure data at specific locations. The studies by the institute also reported that the experimental values differ from the ISO 9223 estimates and are uncertain with the real-time corrosion rates. A recent study (Kumar and Sil 2021) spatially zoned the whole country based on the intensity of corrosion rate into five zones. It also developed atmospheric corrosion maps prepared by the atmospheric rates assessed based on the real-time temperature (T) and relative humidity (RH) within the ISO 9223 specified pollutants (SO₂ the Cl⁻) ranges.

1.5 Deterioration of steel due to atmospheric corrosion

The impact of atmospheric corrosion on sectors vulnerable to atmospheric exposure is significant. The World Steel Association sectorises global steel demand as infrastructure, automotive, mechanical equipment, other transportation, domestic appliances and electrical equipment (Association 2021a). By their purpose, infrastructure, automotive and transportation industries are the most vulnerable to the atmosphere, accounting for around 69% of worldwide steel consumption. However, the infrastructure sector alone consumes around 52% of the demand and is a major victim of atmospheric corrosion. Furthermore, the lifespan of a typical structure is approximately 50 years, whereas the lifespan of an industrial or commercial structure is expected to be much longer. A study (Melchers and Jeffrey 2008) concluded that estimates for the probable future deterioration of steel due to corrosion are imperative for assessing existing infrastructure’s remaining life and the lifetime reliability design of physical infrastructure.

The deterioration of steel sections due to corrosion causes thickness loss and apparently, the reduction of thickness can modify the classification of a structural steel section (Rahgozar 2009). The loss of section thickness is mentioned (Rahgozar 2009) as reducing the member’s load-carrying capacity and changing the failure modes due to changes within the structural mechanism depending on relative thickness loss in various parts of the section. Further, a study (Xu et al. 2019) specified that the increase in mass loss ratio (reduction of mass/thickness) had decreased mechanical properties such as nominal strength, elongation, and elastic modulus. Another study (Wang et al. 2020) mentioned a significant decrease in flexural performance of steel beams and a reduction of bending capacity up to 26.7% over eight years of exposure. However, another study (Lignos and Krawinkler 2007) emphasized the deficiency of data on steel deterioration and its consequent limitations to systematic and reliable collapse prediction.

1.6 Importance of the study

The elucidation reveals that environmental parameters are crucial in governing corrosion dynamics and the rate of progression over time. It is well-known that climate change diversifies meteorological parameters spatially, resulting in significant intensity variation of environmental parameters from location to location. This diverse parametric distribution illustrates that the impact of corrosion across the regions (rural, urban, industrial and marine) changes with time, based on environmental parameters intensities. The assessment of atmospheric corrosion spatially signifies the impact of corrosion across a region. However, a study (Melchers and Jeffrey 2008) also reported that one-year losses could be misleading for future corrosion deterioration and require good quality long-term predictions. Therefore it is necessary to spatially assess and track the long-term atmospheric corrosion rates in different environments to adopt appropriate precautions and safety measures such that the steel sections or structures are guarded against losing their desired efficiency and sustain their design lifespan. Further, the assessment aids governments, planners and industries in reducing the economic impression of planning and mitigating any structure. It can be articulated that the lifespan of steel structures is essentially affected by atmospheric corrosion, and the percentage reduction is based on the rate of corrosion, which is further dependent on the structure’s location. The current study presumably connects the five decade spatial long-term corrosion rates and their impact on Indian standard structural steel sections’ deterioration.

1.7 Significance of the study in India

Global steel demand is skyrocketing due to various domestic, welfare and industrial applications. The global demand is expected (Association 2021b) to rise by 5.8% in 2021 and 2.7% in 2022, reaching 1874 Mt and 1924.6 Mt, respectively. China (1035.1 Mt) and India (112.3 Mt) account for a sizable portion of the demand and are expected to raise their consumption by 3.0 and 19.8%, respectively. With 106.1 Mt, India is the second-largest producer of steel and the second-largest consumer of steel. The majority of the country’s production is used to meet its own demand. However, the country’s demand is mostly shared by the construction (62%), railways (3%) and automotive (9%) sectors, which are all essentially exposed to the atmosphere. Consequently, 74% of total steel consumption is susceptible to atmospheric corrosion, emphasizing the need to assess and track its impact on these major sectors.

The country India has a diverse geography, with a 7516.6 km long coastal belt on three sides (S, E and W) and the Himalayas on the north, covering most of Asia between latitudes 8°N and 38°N longitudes 68°E and 98°E. It has a tropical monsoon-type climate diversified according to the seasons across the country. However, there are variances in climatic conditions and seasonal occurrences within the country itself as such due to their regional geographic climate control parameters (Himalayan mountains, latitude, distance from the sea, wind system, relief and altitude). Furthermore, all these climate parameters and seasonal occurrences, together with the type of region (rural, urban, marine and industrial), typically regulate any particular location’s ecosystem. Henceforth, it now allows environmental parameters to be dynamic from region to region based on diverse meteorological conditions and atmospheric pollutants. Therefore, the demand/consumption of the steel and the regional dynamics of the environmental parameters implicate importance of the current study about spatial hazard assessment, mapping of atmospheric corrosion and its effect on the structural steel sections across India. The objective of the current study is to prepare five decade corrosion maps and assess the cummulative corrosion hazard for the next 50 years in rural and urban environments of the Indian sub-continent and further evaluate the degradation of Indian standard structural steel sections in different corrosive zone of rural and urban environments.

2.1 Environmental parameters data

The meteorological parameters T and RH are responsible for the fundamental initiation and sustenance of the atmospheric corrosion process, while the other parameters are believed to act as catalysts for the process. Meteorological parameters (T and RH) data for the entire Indian subcontinent are spatially gathered in a 1 × 1 grid from various sources for 39 years (1979–2018) (Ministry of Environment and Forests, Data.Gov.In, n.d.; NOAA & Earth System Research Laboratory n.d.). However, data on the pollutant SO₂ are only available over the last 30 years (1979–2009). India stands top for global SO₂ emissions, accounting for 15% of total emissions (Dahiya and Myllyvirta 2019). Two-thirds of the India’s emissions are from the cement, fuel-burning industries and power plants. A spatial SO₂ concentration map (Figure 1a) in Dobson Unit made available by the European Space Agency (ESA) (European Space Agency 2017; European Space Agency 2020) locate the sources of SO₂ emissions and their concentrations. Further, another map (Figure 1b) signifies the closer presence of emissions around sources of emissions and their geographical dispersion around the source. However, both maps summarize that the emissions vary widely across India based on the source locations and are concentrated near the source locations.

Figure 1:

(a) Location of power plants in India and SO₂ distribution across the country (open source: European Space Agency). (b) Distribution of SO₂ around the source of emission in India (source: European Space Agency).

Consequently, it is inappropriate to generalize the SO₂ emissions across the county since many regions have very negligible emissions and are impossible to measure by the conventional methods for the entire India. Similarly, chloride concentrations or air salinity are significant mainly in coastal areas and are assessed using established methods. However, data on SO₂ and Cl deposition are not adequately measured across India and are insufficient to estimate atmospheric corrosion rate, so these are regarded in accordance with ISO 9223 categories (Table1) such that the appropriate atmospheric corrosion rate can be chosen based on locally measured SO₂ and Cl deposition.

The meteorological data collected for 39 years (1979–2018) is used to assess atmospheric corrosion rates for the entire Indian subcontinent. The spatial atmospheric corrosion rate maps are prepared using the collected (meteorological data T and RH obtained for 39 years) and considered environmental parameters data (Table 1). Long-term atmospheric corrosion rate maps are prepared using Equation (4) based on the short-term atmospheric corrosion rate. Long-term corrosion maps are prepared for 2, 5, 10, 20, 40 and 50 years. Further, the long-term effect of corrosion on the Indian standard structural steel (I sections) is assessed by calculating the area loss in regard to the corrosion rates for the specific type of region.

2.2 Map preparation process

The meteorological data collected for 39 years (1979–2018) is used to assess atmospheric corrosion rates for the entire Indian subcontinent. The spatial atmospheric corrosion rate maps are prepared using the collected (meteorological data T and RH obtained for 39 years) and considered environmental parameters data (Table 1). Firstly, Long-term atmospheric corrosion rates are approximated using Equation (4) based on the short-term atmospheric corrosion rate. The long-term atmospheric corrosion rate data is processed using the Kriging interpolation technique to obtain the contour long-term atmospheric corrosion data. Further, this contour data is used in a GIS application to develop the corrosion rate contour maps across India. Long-term corrosion maps are prepared for the next 2, 5, 10, 20, 40 and 50 years. Further, the long-term effect of corrosion on the Indian standard structural steel (I sections) is assessed by calculating the area loss in regard to the corrosion rates for the specific type of region.

2.3 Analysis

2.3.3 Fifty-year corrosion maps

The short-term (one-year) corrosion loss map PS₁₁₀₁ (Figure 2a) of the rural environment PS₁₁ signifies the initial corrosion rate of any steel member or structure in a rural area across India. Similarly, for the other two atmospheres PS₂₁ and PS₃₁, the short-term corrosion loss maps PS₂₁₀₁ (Figure 3a) and PS₃₁₀₁ (Figure 4a) are developed. Further, the maximum and minimum short-term corrosion rates of each atmosphere in each corrosion zone are recorded and presented in Table 2. The corrosion rates observed in Table 2 are used for further analysis. However, to observe the long-term trends of corrosion rates in different environments, the corrosion loss maps for 2, 5, 10, 20, 40 and 50 years are developed using equation (4). These maps signify the corrosion rate of the steel member or structure after a particular year of exposure to the environment. Furthermore, it is observed from the long-term corrosion maps that the rate of corrosion has greatly reduced due to a reduction in surface availability for electrochemical reactions after certain years of atmospheric exposure.

Figure 2:

Corrosion rates across India for PS11 environment at an exposure period of (a) 1st year – PS1101, (b) 2nd year – PS1102, (c) 5th year – PS1105, (d) 10th year – PS1110, (e) 20th year – PS1120 (f) 40th year – PS1140  and (g) 50th year – PS1150.

Figure 3:

Corrosion rates across India for PS21 environment at an exposure period of (a) 1st year – PS2101, (b) 2nd year – PS2102, (c) 5th year – PS2105, (d) 10th year – PS2110, (e) 20th year – PS2120, (f) 40th year – PS2140 and (g) 50th year – PS2150.

Figure 4:

Corrosion rates across India for PS31 environment at an exposure period of (a) 1st year – PS3101, (b) 2nd year – PS3102, (c) 5th year – PS3105, (d) 10th year – PS3110, (e) 20th year – PS3120, (f) 40th year – PS3140 and (g) 50th year – PS3150.

Long-term predictions of two (PS₁₁₀₂) and five years (PS₁₁₀₅) in PS₁₁ environment have a significant variation in their corrosion rates, while the change in the corrosion rate after 10 years of exposure is more diminutive and exhibits a gradual change for the next 40 years. Corrosion rates in zones 1, 2, and 3 are not more than 2.5 μm/y at any instant of exposure (Figure 2) in the PS₁₁ environment. However, only zones 4 and 5 show a shift (↓) after 10 years of exposure, while the changes in other zones are almost negligible. Further, only zone5 has much higher corrosion rates than other zones irrespective of the year of exposure. The increase in corrosion trends represented with dashed lines exhibits a backward moving tidal motion from lower intensity to higher intensity with an exposure period. It can also be observed that the coastal region and the northeast region of the country with high humidity levels than the other regions have high corrosion rates. However, the PS₂₁ atmosphere shows elevated trends in variations of corrosion rate intensities. In the initial year of exposure (PS₂₁₁Figure 2a), instant shifts of corrosion rates in zone3 can be observed and in the further exposure of two years (PS₂₁₀₂) to 5 (PS₂₁₀₅) years, the trends in zones 3 and 4 have gradually stabilized their corrosion rates up to 2.5 μm/y and 5 μm/y, respectively. Nevertheless, the shift in corrosion rates for the first two decades of exposure is progressive and the change in its trends is gradual, yet the shift for the next decades is negligible, while the corrosion rates are as low as 7 μm/y in zone 5 after 50 years of exposure.

The high pollution in PS₃₁ atmosphere has maneuvered corrosion rate increase up to 10 μm/y in zone 2 and 100 μm/y in zone 5, as well 56 μm/y in 2nd year of exposure comparable to first-year rates of PS₂₁ atmosphere. Moreover, the corrosion rates exhibited minimal reduction trends after the first decade of exposure, but the corrosion rates in all zones in the first five years are too high compared to the other two environments. However, as a summary of the corrosion trends in the three environments (PS₁₁, PS₂₁ and PS₃₁), the rates in PS₁₁ after the first decade of exposure are acquired by the PS₂₁ environment after two decades of exposure (PS₁₁₁₀, Figure 2d; PS₂₁₂₀, Figure 3e) as well the corrosion trends after two decades of exposure in PS₁₁ atmosphere is observed after four decades of exposure in PS₂₁ atmosphere (PS₁₁₂₀, Figure 2d; PS₂₁₄₀, Figure 3e), whereas the fifth-decade corrosion rate trends of PS₃₁ is closer to the trends in PS₂₁ after a decade exposure. Furthermore, the corrosion rates at 50 years of exposure are within a difference of 2–5 μm/y between the three environments, but there is a significant deviation in their spatial distribution of corrosion rates. Moreover, disparities in the corrosion rate trends among the three atmospheres throughout the five decades of exposure can be observed in Figures 2–4. It can be noted that a significant part of atmospheric corrosion has transpired for the first five years, the first decade of exposure in the PS₁₁ and PS₂₁ environments, respectively, while the corrosion rates are effective up to four decades in the PS₃₁ atmosphere.

Furthermore, the 50-year Indian corrosion hazard map is prepared for metal loss concerning urban and rural atmospheres. The cumulative metal loss for 50 years due to atmospheric corrosion is considered uniform along the I-section. It is used to approximate the percentage loss of area of the section with time such that designers, engineers, planners, industries and governments may adopt precaution and mitigation strategies. The area reduction estimates facilitate designers and the other stakeholders with the appropriate rate of deterioration and hazard levels over time according to the chosen category based upon the intensity of the environmental parameters (p_d and s_d). The environmental parameters categories used for assessing and preparing corrosion maps are designated (Table 1) as per the ISO 9223 classification of SO₂ and Cl (p_d and s_d).