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Reliability Evaluation of Pavement Life-Cycle Assessment Model.

1. Introduction

At present, energy conservation and emission reduction in transportation engineering have become the most important aim in China development planning. However, it is just a macroscopic one. Therefore, a data base about the C[O.sub.2] emission and energy consumption is needed to accurately assess the effect of technologies for energy conservation and emission reduction. Besides, developing an evaluation template on the bituminous pavement construction and the benefit of environmental protection of maintenance technology is also necessary [1]. Conducting studies on the evaluation system of technologies for energy conservation and emission reduction can provide theoretical foundation and technical support for evaluating the effect of technologies for energy conservation and emission reduction. Therefore, it is extremely necessary to carry out the relevant studies.

The rapid development of road construction brings about serious environmental issues, such as energy consumption, C[O.sub.2] emission, and land occupancy [2]. Thus, quantifying and reducing the influence of roadway on environment in its lifetime becomes gradually important and attracts wide attention [3, 4]. As a tool for assessing environmental implication, the life assessment (LCA) has been widely used in roadway engineering and becomes the main tool for analysis of influence of roadway on environment [5-7]. However, some weaknesses exist in the theory of LCA, particularly the uncertainness of data quality and model [8]. The accuracy of LCA for roadway or else objects depends on data and model parameters with high quality. Due to the absence of data and their reliability, the data utilized in LCA are not necessarily the expected one. Therefore, even though optimal data can be acquired or certain existing data can be combined, a trade-off has to be adopted. For example, the authors have collected numerous information and found that the consumption intensity for cement production is 4.6-7.3 MJ/kg and that for bitumen production is 0.7-6.0 MJ/kg. The great difference is shocking and resulted from the differences between system boundary, manufacturing technique, and manufacturing process in the local area. Besides, for bitumen material, the environmental influence depends on the distribution of different chemical products, such as petrol, diesel oil, and plastics. The difference of distribution style results in significantly different results [9-11]. In the process of LCA establishment, the assumption and simplification are involved, which also affect the reliability of assessment results of environmental influence.

Therefore, based on a practical case, a LCA model was developed and the influence of roadway on the environment was carried out. The uncertainness of assessment results of the LCA model is captured from the perspective of data quality and model assumption to establish a rigorous methodology for reliability assessment of results of the LCA model.

2. The Establishment of LCA Model

Roadway is a complex structure and susceptible to several external factors, such as climate, transportation intensity, and the production and transportation of material. Thus, the authors took advantage of the existing data model and established a LCA model which includes the following module:

(1) Material production module: consideration on the process involved in the material production

(2) Transportation module: description about the environmental influence during the material transportation

(3) Construction maintenance module: consideration on the oil consumption of facilities and its environmental influence

(4) Postprocessing module: different postprocessing scenes can be used according to the system boundary and the setup of consumption

The function unit of the LCA model is set in an analysis cycle of 30 years and the service performance is applicable to a roadway with single lane and a length of 1000 m.

3. Methodology for Reliability Evaluation

The application of LCA depends on large amounts of data input which defined the characters of the model such as specification for road design, input and output stream of material energy, and environmental impact. The results of LCA are highly dependent on the reliability of input parameters with a certain degree of uncertainty inevitably. This investigation establishes corresponding methodologies for quantitative assessment from data uncertainty and model uncertainty.

3.1. Data Uncertainty. The factors of data uncertainty are divided into experiential evaluation, measurement error, and nonrepresentativeness which includes data obsoleting and excessive technical differences, even data missing. Ideally, the solution to data uncertainty is establishing corresponding probability density function by collected data and putting it into LCA. However, the measurement data, samples, and experience that we need are usually rare. In this case, the descriptive index was employed to assess the quality of data, which forms the data quality evaluation matrix as shown in Table 1.

The data quality index (DQI) can be obtained by the established data quality system matrix. The DQI can be converted to probability density function as input parameters of uncertainty analysis, specifically shown as follows:

[mathematical expression not reproducible], (1)

where [alpha] and [beta] are the shape parameters of distributions and a and b are the interval end points.

The reasons why choosing beta function are shown as follows:

(1) The shape and probability of the distribution established by shape parameters are unknown, and endpoint parameters limit the range of prefabrication

(2) The distribution shape of the beta function is various due to the shape and endpoint parameters.

3.2. Model Uncertainty. The inherent uncertainty of the model associated with the decision affects the analysis results of the LCA. The model refers to the concept of generalization and has a wide range of extensions. It can be either a specific regression model, for example, prediction of asphalt mixing energy consumption, or input parameter assumptions, such as the amount of asphalt used in grading design and the organization of construction platoons. For the variability of LCA model input values, such as the ratio of oil and stone and transportation distance, it can be described by defining "uncertainty factor (UF)" where the uniform distribution and lognormal distribution are common distribution forms.

For the uniform distribution, the minimum and maximum values of the input parameters Mx are defined using the following equation:

[mathematical expression not reproducible], (2)

where [M.sub.x, min], [M.sub.x, max], and [M.sub.x,mod] are the minimum, maximum, and modus, respectively.

For the lognormal distribution, the fluctuation range of the input parameters M and UF are defined in the following equation:

[mathematical expression not reproducible], (3)

where the parameters are defined in Equation (2).

4. Case Analysis

This case analyzed the energy consumption and carbon emissions of highways cement pavement structure and asphalt pavement structure during their life cycle under the same traffic volume and weather conditions and demonstrated the reliability of the calculation results.

4.1. Waterway Asphalt Pavement Structure Design. For highways with asphalt pavement structures, the design period is generally 15 years. For cement highway structures, the design period is generally 30 years. In order to make the environmental impact analysis consistent, the functional unit is set to provide an acceptable road service performance for a single-lane one-kilometer-long road surface in a 30-year analysis cycle. The design index of the road structure tends to and does not exceed the design tolerance value. For asphalt pavement structural design, there are two situations:

(1) The design life of asphalt pavement structure is set to be 30 years to achieve a certain degree of long-life asphalt pavement design.

(2) The asphalt pavement design period is 15 years. Assuming that, in the 15th year, the basement structure of the asphalt pavement was not damaged, only the asphalt surface structure was resurfaced by milling.

In order to ensure the functional equivalence of the cement and asphalt pavement structures, the pavement structure needs to be carefully designed so that the control index of the pavement structure approaches its limit at the end of the design life cycle, ensuring that the pavement structure has neither "excessive design" nor "insufficient design". Under this guiding principle, the equivalent design parameters of cement and asphalt pavement structures are shown in Table 2.

Although the asphalt pavement designed for 30 years of life will not suffer structural damage, it is still necessary to periodically perform conservation actions to ensure the functionality of the surface structure. At present, China has not established a strict decision-making system for pavement maintenance. Therefore, the conservation manual in California is referred to maintain the three types of pavement structure [13]. The maintenance plan is as follows:

(1) For an asphalt pavement structure with a design life of 30 years, a 2.5 cm thick overlaying in thin layer is laid at the end of the 20th year, and a 4 cm-thick pavement structure is milling-resurfaced at the end of the 24th year

(2) For an asphalt pavement structure with a design life of 15 years, a 6cm-thick pavement structure is milling-resurfaced at the end of the 15th year, a 4 cm-thick overlaying in thin layer is laid at the end of the 20th year, and a 4 cm-thick pavement structure is milling-resurfaced at the end of the 25th year

(3) For a cement pavement structure, the pavement structure is replaced with the cement board at the end of the 15th year and the end of the 25th year, respectively

4.2. Calculation Based on LCA Model. The environmental impact on each module has been computed based on the LCA model constructed before. The calculation factors include energy consumption and C[O.sub.2] emissions, using unit mass standard coal (kgce) and kg-C[O.sub.2] as their unit, respectively.

4.2.1. Material Producing Module. Environmental impact of unit productivity on road materials has been determined through document retrieval, theoretical calculations, and on-site inspections, and the result is shown in Table 3.

According to the pavement structure design maintenance parameters and Table 3, the environmental impact on material producing module can be obtained.

4.2.2. Transportation and Construction Module. As for the calculation of the environmental impact on transportation and construction modules, we choose JTG/T B06-02-2007 and JTG/T B06-03-2007 as the fundamental documents. The main steps are as follows: first, determining the number of per-unit-production of mechanical shifts according to the construction process flow stipulated in the budget quota; second, determining the total energy consumption by the energy consumption parameters of the unit machinery and equipment specified by the mechanical station cost quota; the last but not the least, calculating per-unit energy consumption.

4.2.3. Postprocessing Module. The asphalt pavement and cement pavement reach the end of design life after the 30year life cycle. Thus, for the asphalt pavement, the pavement layer needs to be milled and the RAP needs to be transported to mixing building, and for the cement pavement, the pavement layer needs to be smashed and the RCM needs to be transported to the accumulation point, where the transportation distance is assumed to be 25 km.

4.2.4. Calculation Results of LCA Model. Calculation of the environmental impact of the cement asphalt pavement structure under life cycle with the LCA model is done, and the results are shown in Table 4.

It can be seen that the stage of producing pavement material consumed the most energy and released the most C[O.sub.2] in all structures with any pavement. By comparing the structure with the asphalt pavement (15-year or 30-year design) and the cement pavement, the structure with the cement pavement consumes less energy and emits less C[O.sub.2], which means the cement pavement structure has a greater probability of reducing the environmental impact during the life cycle.

In this paper, two life-cycle asphalt pavement structures were designed. The asphalt roads with a design life of 15 years have less impact on environment than those with a design life of 30 years, but the difference is not so significant. Also, such conclusion is closely dependent on the parameter assumptions in the LCA modeling process and needs further confirmation.

4.3. Reliability Analysis for LCA Model. There remains some uncertainty during modeling the LAC model due to data quality defects and model parameters assumptions, which are needed to be evaluated.

4.3.1. Data Uncertainty Analysis. With the data quality assessment methodology established in Section 3.1, the quality of road material environmental data was evaluated. Taking bituminous materials as an example, the energy consumption of matrix asphalt production is 170.34 kgce.

The calculation for energy consumption of base-asphalt production uses the calculation model of Eurobitume 2011 and the data of China Statistical Yearbook as reference. Therefore, the evaluation scores for the indicators according to Table 1 are {3.0, 5.0, 4.0, 4.0, 3.0, 5.0, 3.0}, and thus its DQI is 3.85. By using Table 5 as a reference, the parameter of beta function can be de defined. Its shape function [alpha], [beta] is (3,3), and the interval endpoint is (-20%, +20%). The probability density function of the production of matrix asphalt materials is shown in Figure 2.

According to the probability density function established in Figure 2, the energy consumption value and reliability of the matrix asphalt materials can be evaluated. Analogously, a probability density function curve can be established for the production of carbon emissions from matrix asphalt materials and other road material, providing a data source for the Monte Carlo analysis.

4.3.2. Model Uncertainty Analysis. During modeling, parameters like transportation distance and asphalt mixture mixing energy consumption are introduced, and the influence of those parameters on the LCA calculation results is needed to be examined. Those parameters and their fluctuation range are specified in Tables 6-7.

4.3.3. Results of LAC Model Uncertainty Analysis. The Monte Carlo analysis was performed based on the parameters inputted in Sections 4.3.1 and 4.3.2. After 50 times of iteration, the results are shown in Figure 3.

In Figure 3, the left Figure 3(a) shows the average energy consumption and its standard deviation for different scenarios, and Figure 3(b) shows the probability density function for each scenario. From Figure 3(a), the energy consumption of the PCC scheme is smaller than the HMA scheme generally. It can be found that the coefficient of variation of the PCC program, 30year, and 15-year design of the HMA program are 7.4%, 9.0%, and 9.1%, respectively, when it is used as the indicator of energy fluctuation range. The volatility of the PCC program is slightly less than HMA program. Since the maximum volatility of all programs is about 10%, which is quite limited, the energy consumption indicators of different programs have a good stability.

Define R as the ratio of the energy consumption of design 1 to the energy consumption of design 2, and then P (R<1) means the probability that R is less than 1. According to Figure 3(b), 50000 Monte Carlo analyses were performed, and the results are shown in Figure 4.

It can be seen from Figure 4 that the probability that the energy consumption of the cement pavement structure is less than the 15-year design asphalt pavement structure is 97%, and the probability that the energy consumption of the cement pavement structure is less than the 30-year design asphalt pavement structure is 99%. Under the conditions set in this study, the cement pavement structure consumes less energy during the life cycle at the 95% confidence level. Comparing these two asphalt pavement designs, the probability that the energy consumption of the 30-year design asphalt pavement structure is less than that of the 15-year design asphalt pavement structure is 47.5%. It is difficult to have sufficient reason to detail the difference in environmental impact between the two structural designs. Similar conclusions can be drawn for carbon emissions indicators and will not be repeated here.

5. Conclusion

(1) A road LCA model with complete system boundary has been constructed. This model can not only describe the environmental impact of the road during service, but also can be used to assess the environmental impact of road materials in a measurable way.

(2) A rigorous method for evaluating the reliability of LCA model calculation results is constructed, which can be used to evaluate the reliability of LCA model settlement results under data quality and model assumptions.

(3) Under the established research background, the environmental impact of the cement pavement structure is less than the asphalt pavement structure at the 95% confidence level. The average energy consumption and carbon emission of the cement pavement is 21.6%-28.2% lower than the asphalt pavement during the life cycle.

(4) The reliability analysis shows that the results of the LCA model are stable and the fluctuation range is about 10%.

(5) Under the research background setup, there is no obvious environmental difference between the 15-year design life asphalt pavement and the 30-year design asphalt pavement at the 95% confidence level.

https://doi.org/10.1155/2018/4172519

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

[1] N. J. Santero, Pavements and the environment: a life-cycle assessment approach, Ph.D. thesis, University of California, Berkeley, CA, USA, 2009.

[2] B. Yu, Environmental implications of pavement: a life cycle view, Ph.D. thesis, University of South Florida, Tampa, FL, USA, 2013.

[3] H. Zhang, G. A. Keoleian, M. D. Lepech, and A. Kendall, "Lifecycle optimization of pavement overlay systems," Journal of Infrastructure Systems, vol. 16, no. 4, pp. 310-322, 2010.

[4] Y. Huang, R. Bird, and O. Heidrich, "Development of a life cycle assessment tool for construction and maintenance of asphalt pavements," Journal of Cleaner Production, vol. 17, no. 2, pp. 283-296, 2009.

[5] B. Yu and Q. Lu, "Life cycle assessment of pavement: methodology and case study," Transportation Research PartD: Transport and Environment, vol. 17, no. 5, pp. 380-388, 2012.

[6] J. Santos, A. Ferreira, and G. Flintsch, "A life cycle assessment model for pavement management: methodology and computational framework," International Journal of Pavement Engineering, vol. 16, no. 3, pp. 268-286, 2015.

[7] L. Cheng, G. J. Yan, D. Z. Chen et al., "Quantitative investigation on energy conservation and emission reduction related to warm mix asphalt (WMA)," Chinese Journal of Environmental Engineering, vol. 4, no. 9, pp. 2151-2155, 2010.

[8] N. J. Santero, E. Masanet, and A. Horvath, "Life-cycle assessment of pavements part II: filling the research gaps," Resources, Conservation and Recycling, vol. 55, no. 9-10, pp. 810-881, 2011.

[9] A. L. Nicholson, E. A. Olivetti, J. R. Gregory et al., "End-of-life LCA allocation methods: open loop recycling impacts on robustness of material selection decisions," in Proceedings of IEEE International Symposium on Sustainable Systems and Technology, Phoenix, AZ, USA, December 2009.

[10] Y. Huang, A. Spray, and T. Parry, "Sensitivity analysis of methodological choices in road pavement LCA," International Journal of Life Cycle Assessment, vol. 18, no. 1, pp. 93-101, 2013.

[11] Eurobitume, Life Cycle Inventory: Bitumen, The European Bitumen Association, Brussels, Belgium, 2nd edition, 2012.

[12] K. G. Canter, D. J. Kennedy, D. C. Montgomery et al., "Screening stochastic life cycle assessment inventory models," International Journal of Life Cycle Assessment, vol. 7, no. 1, pp. 18-26, 2002.

[13] University of California Pavement Research Center (UCPRC), Life-Cycle Cost Analysis Procedures Manual, Prepared for the California Department of Transportation Division of Design and the Pavement Standards Team, University of California Pavement Research Center (UCPRC), Davis, CA, USA, 2007.

[14] Energy Statistics Division of National Bureau of Statistics, China Energy Statistical Yearbook 2013, China Statistics Press, Beijing, China, 2013.

[15] National Development and Reform Commission Resource Conservation and Environmental Protection Department, GB/T2589-2008, General Principles for Calculation of the Comprehensive Energy Consumption, China Standards Press, Beijing, China, 2008.

Jiale Huang (ID), (1) Fei Xiao (ID), (2) and Yang Zhang (ID) (2)

(1) School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China

(2) Jiangsu Research Institute of Building Science Co. Ltd, Nanjing 210008, China

Correspondence should be addressed to Fei Xiao; xiaofeihust@163.com

Received 10 May 2018; Accepted 26 September 2018; Published 18 October 2018

Academic Editor: Ricardo Perera

Caption: Figure 1: (a) Energy consumption and (b) C[O.sub.2] emission of the three pavement structures.

Caption: Figure 2: Probability density function of base asphalt production energy consumption.

Caption: Figure 3: Energy consumption comparisons of the three pavement structures.

Caption: Figure 4: Energy consumption ratio probability distribution of the three pavement structures.
Table 1: Data quality evaluation matrix.

Quality                               Technology
grade      Selection standard        correlation

5         Transparent,           Representative
          verified, and          process and
          generally applicable   representative
                                 technology

4         Transparent,           Representative
          verified, and no       process and similar
          generally applicable   technology

          Transparent, no        Representative
3         verified, and no       process and
          generally applicable   different technology

          Selective              Similar process and
2         transparent and no     similar technology
          selection standard

1         Unknown selection      Similar process and
          standard               different technology

                    Judgement standard

Quality
grade     Location correlation       Data sources

5                                Verified data
          Represent value of     from
          local area             independent
                                 source

4         Average value of       Verified data
          local area             from relevant
                                 source

          Other area with        Unverified data from
3         similar production     independent source
          process

          Other area with        Unverified data
2         slightly similar       from irrelevant
          production process     source

1         Unknown location       Unverified data from
                                 relevant source

Quality                                       Representation of
grade      Acquiring methods       Time              data

5
          Actual                 <3          Long time and
          measurement            years       large sample

4         Measurement-           <6 years    Long time and small
          based                              sample
          calculation

          Hypothesis-            <10 years   Short time and large
3         based                              sample
          calculation

          Expert estimation      <15 years   Short time and small
2                                            sample

1         Calculation            >15         Cannot be
          with no                years       obtained
          guaranteed
          quality

Table 2: Converting matrix of probability density function [12].

DQI                                       5.0     4.5     4.0     3.5

Parameters of beta   Shape function      (5,5)   (4,4)   (3,3)   (2,2)
distribution         Interval endpoint    10      15      20      25

DQI                                       3.0     2.5     2.0     1.5

Parameters of beta   Shape function      (1,1)   (1,1)   (1,1)   (1,1)
distribution         Interval endpoint    30      35      40      45

DQI                                       1.0

Parameters of beta   Shape function      (1,1)
distribution         Interval endpoint    50

Table 3: Design parameters of pavement structure.

Pavement structure   Upper layer   Middle layer   Lower layer   Base
                        (cm)           (cm)          (cm)       (cm)

Asphalt (30 years         6             8             12         40
of design)
Asphalt (15 years         4             6              8         30
of design)

Pavement structure   Subbase    Designed    Calculated
                      (cm)     deflection   deflection
                               (0.01 mm)    (0.01 mm)

Asphalt (30 years      25         14.0         13.5
of design)
Asphalt (15 years      25         17.2         16.8
of design)

Cement        Surface            Base course          Fatigue
            course (cm)             (cm)             stress of
                                                   surface course
                                                       (MPa)

                30                   20                 4.89

Cement   Maximum multiple     Flexural tensile
         stress of surface   strength of surface
           course (MPa)         course (MPa)

               2.94                  5.0

Table 4: Energy consumption and C[O.sub.2] emission of
pavement materials in China.

Material                          Energy      C[O.sub.2]
                                consumption   emissions
                                 (kgce/t)       (kg/t)

Asphalt                           170.34        287.51

Modified asphalt (3.5% SBS)       311.61        485.54

Emulsified asphalt (60% DPU)      118.55        205.98

Stone                              3.71          9.02
Comment                             136         377.06

Industrial water                  0.0857         0.24

Steel                               674        1868.67

Asphalt heating                    0.04          0.09
Aggregate heating                  10.9          24.2
Asphalt mixture mixing             1.62          3.99
Cement concrete mixing             4.09          8.93

Material                                    Data source

Asphalt                         << China Energy Statistical Yearbook
                                2013 >> [14]
Modified asphalt (3.5% SBS)     << China Energy Statistical Yearbook
                                2013 >> and
                                Eurobitume [11] calculation
Emulsified asphalt (60% DPU)    << China Energy Statistical Yearbook
                                2013 >> and Eurobitume [11]
                                calculation
Stone                           Investigation
Comment                         << China Energy Statistical Yearbook
                                2013 >>
Industrial water                << Comprehensive Energy Consumption
                                Calculation Principles 2008 >> [15]
Steel                           << China Energy Statistical
                                Yearbook 2013 >>
Asphalt heating                 Investigation
Aggregate heating               Investigation
Asphalt mixture mixing          Investigation
Cement concrete mixing          Investigation

Table 5: Life-cycle inventories of the three pavement structures.

Cement

Modules            Energy      C[O.sub.2]
                 consumption      (kg)
                   (kgce)

Material            90773        241737
production
Construction        4432          9416
Transport           2758          5859
Postprocessing      5958         12658

Asphalt (15 years of design)

Modules            Energy      C[O.sub.2]
                 consumption      (kg)
                   (kgce)

Material           129326        309219
production
Construction        2716          9887
Transport           6024         12799
Postprocessing      2931          6228

Asphalt (30 years of design)

Modules            Energy      C[O.sub.2]
                 consumption      (kg)
                   (kgce)

Material           135508        328646
production
Construction        2914         10373
Transport           6797         14443
Postprocessing      3908          8303

With the data in Table 5, the analysis chart
can be drawn, shown in Figure 1.

Table 6: Parameter uncertainties of the asphalt pavement LCA model.

Stage                 Parameters        Uncertainty (1)

Material         Asphalt production           --
  production     Aggregate production      UF = 1.62
                 Mixing                    UF = 1.47
                 SBS amount (2)            UF = 1.09
                 Asphalt amount            UF = 1.09
                 Asphalt aggregate         UF = 1.09
                   heating

Construction     Shift                     UF = 1.09
  stage          Transport distance        UF = 1.41
Transportation   Carbon emission rate      UF = 1.14
  stage            of transportation
                   energy

Postprocessing   Shift                     UF = 1.09
  stage          Transport distance        UF = 1.41

Stage                 Parameters             Distribution form

Material         Asphalt production     Probability density function
  production     Aggregate production   Lognormal distribution
                 Mixing                 Lognormal distribution
                 SBS amount (2)         Uniform distribution
                 Asphalt amount         Uniform distribution
                 Asphalt aggregate      Uniform distribution
                   heating

Construction     Shift                  Uniform distribution
  stage          Transport distance     Uniform distribution
Transportation   Carbon emission rate   Uniform distribution
  stage            of transportation
                   energy

Postprocessing   Shift                  Uniform distribution
  stage          Transport distance     Uniform distribution

Note. 1: uncertainty parameters determined by engineering
investigation (the subscripts are similar); 2: the asphalt amount
adjusted with SBS amount.

Table 7: Parameter uncertainties of the cement pavement LCA model.

Stage               Parameters        Uncertainty1

Material       Cement production      --
  production   Aggregate production   UF = 1.62
               Mixing                 UF = 1.47
               Cement amount          UF = 1.09
               Steel amount           UF = 1.09

Stage               Parameters             Distribution form

Material       Cement production      Probability density function
  production   Aggregate production   Lognormal distribution
               Mixing                 Lognormal distribution
               Cement amount          Uniform distribution
               Steel amount           Uniform distribution

Note. 1: uncertainty parameters in the remaining stages are
consistent with those in Table 2.
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Title Annotation:Research Article
Author:Huang, Jiale; Xiao, Fei; Zhang, Yang
Publication:Modelling and Simulation in Engineering
Article Type:Report
Date:Jan 1, 2018
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