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Sustainability 2016, 8(7), 621; https://doi.org/10.3390/su8070621

Article
Water and Carbon Footprint of Wine: Methodology Review and Application to a Case Study
1
CIRIAF, University of Perugia, Via G. Duranti, 67-06125 Perugia, Italy
2
Department of Engineering, University of Roma Tre, Via V. Volterra, 62-00146 Rome, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 12 May 2016 / Accepted: 27 June 2016 / Published: 2 July 2016

Abstract

:
Life cycle assessments (LCAs) play a strategic role in improving the environmental performance of a company and in supporting a successful marketing communication. The high impact of the food industry on natural resources, in terms of water consumption and greenhouse gases emission, has been focusing the attention of consumers and producers towards environmentally sustainable products. This work presents a comprehensive approach for the joint evaluation of carbon (CF) and water (WF) footprint of the wine industry from a cradle to grave perspective. The LCA analysis is carried out following the requirements of international standards (ISO/TS 14067 and ISO 14046). A complete review of the water footprint methodology is presented and guidelines for all the phases of the evaluation procedure are provided, including acquisition and validation of input data, allocation, application of analytic models, and interpretation of the results. The strength of this approach is the implementation of a side-by-side CF vs. WF assessment, based on the same system boundaries, functional unit, and input data, that allows a reliable comparison between the two indicators. In particular, a revised methodology is presented for the evaluation of the grey water component. The methodology was applied to a white and a red wine produced in the same company. A comparison between the two products is presented for each LCA phase along with literature results for similar wines.
Keywords:
Water Footprint (WF); Carbon Footprint (CF); Life Cycle Assessment (LCA); Wine Industry
PACS:
J0101

1. Introduction

The human influence on the climate system is evident, and present anthropogenic emissions of greenhouse gases are the highest in history [1].
Among all the production sectors, the food industry is one of those characterized by a significant impact in terms of greenhouse gas (GHG) emissions, which are estimated as 29% of all anthropogenic emissions [2]. Moreover, the food sector is also one of the major impacting sectors in terms of freshwater consumption, accounting for approximately 70% of all the human use [3,4].
Because of their success in reaching a large audience and their ease of understanding, Carbon Footprint (CF) and Water Footprint (WF) are two of the most widespread indicators for the evaluation of the total direct and indirect environmental impact related to food production and consumption. CF and WF analyses of products are carried out with a Life Cycle Assessment (LCA) approach [5,6], which allows the evaluation of impacts from a cradle to grave perspective, following the requirements of their respective reference international standards, ISO/TS 14067 [7] and ISO 14046 [8].
Since wine is one of the most relevant products in the economic production and in the world distribution market, the wine industry emerges as one of the most analyzed sectors. Literature regarding CF [9,10,11] is quite extensive and encompasses studies that deal with a complete LCA of a wine bottle, studies concerning specific phases of the production process [12,13,14,15,16,17,18,19,20,21], supply chain analyses [22,23] and also comparative analyses between conventional and unconventional viticulture activities [24]. In addition, studies regarding conceptual and methodological aspects [25,26] and review studies regarding the use of CF as an environmental indicator in the wine industry [27] are available. Regarding WF, some case studies are available for agriculture and agri-food sector [28,29,30,31,32,33], and for wine in particular, with a focus both on grape-wine production [34] and wine bottle [35,36,37]. Some studies regarding the critical review of WF methodology, with a particular focus on grey WF in the winemaking industry, are also available in literature [38,39,40]. With respect to such works, improvements on the evaluation of indirect blue, and direct and indirect grey water volumes are presented.
This paper presents a complete review of the WF methodology and provides guidelines for all the stages of the evaluation procedure, including data acquisition and validation, allocation, application of analytic models, and interpretation of the results. A WF assessment is then carried out in parallel with a CF analysis for a white and a red wine produced by the same company in Umbria, Italy, and results are presented side-by-side for each lifecycle phase.

2. Methodology

2.1. Impact Assessment Methodology

The water footprint (WF) of a product is the sum of freshwater volumes consumed during the product life cycle [41], including real (green and blue) and virtual (grey) volumes:
WF = WF green + WF blue + WF grey
Green (WFgreen, Section 2.1.1) and blue (WFblue, Section 2.1.2) components are the consumptive use of rain and freshwater, respectively. The grey component (WFgrey) is the amount of virtual water needed to dilute pollutants emitted to the natural water system during the process, quantified to guarantee that the quality of the ambient water remains beyond some reference water quality standards. Following the methodology presented in this paper for agriculture products, WFgrey is the sum of two components:
WF grey = WF grey,direct + WF grey,indirect
where WFgrey,direct (Section 2.1.3) is due to transport of pollutants applied to the crop (treatments and fertilizers), and WFgrey,indirect (Section 2.1.4) takes into account all the emission of pollutants in water during other processes involved in the product life cycle.
The carbon footprint (CF) of a product is computed using a standardized procedure as defined in Section 2.1.5.

2.1.1. Green Water Footprint

The green water footprint is defined as the total volume of rainwater used by the crop for evapotranspiration, and it is directly connected to site meteorological data and soil properties of a specific vineyard (i.e., territorial unit). The calculation, performed on a daily basis, follows the FAO methodology [42], which is the standard procedure for calculating crop evapotranspiration. For rain-fed cropping systems, the green water footprint is equivalent to the sum of daily volume of water effectively consumed by evaporation and transpiration over an entire year
WF green = i ET a,i
The effective evapotranspiration of the crop for the i-th day (ETa,i) is computed as a function of the water stress coefficient (ks,i) and the single crop coefficient (kc,i) according to
ET a,i = k s,i   ·   k c,i   ·   ET 0,i
The daily reference evapotranspiration (ET0,i) is the maximum amount of water (mm/day) that can be evapotranspirated considering meteorological conditions only. It is given by the FAO Penmant–Monteith equation
ET 0 =   0.408   ·   Δ   ·   R n   +   γ   ·   900 t   +   273.15   ·   u 2   ·   ( e s e a ) Δ + γ   ·   ( 1 + 0.34   ·   u 2 )
where Δ (kPa·°C−1) is the slope of the vapor pressure curve, Rn (MJ·m−2·day−1) is the net radiation at the crop surface, γ (kPa·°C−1) is the psychrometric constant, t (°C) is the mean temperature at 2 m height, u2 (m·s−1) is the wind speed at 2 m height, and es and ea (kPa) are the saturation and actual vapor pressures, respectively.
The single crop coefficient incorporates the characteristics of the crop to determine the crop evapotranspiration (ETc,i) as a function of ET0,i:
ET c,i = k c,i   ·   ET 0,i
It is given considering an interpolation of values for rest, initial, development, mid, and late stages of the crop cycle. Values for grapevines are reported in Table 1.
The water stress coefficient is a function of the water content in the root zone and it is used to compute the actual crop evapotranspiration:
ET a,i = k s,i   ·   ET c,i
As a function of the soil properties, the totally available water content (mm) can be computed from
TAW = 1000   ·   ( θ FC θ WP )   ·   z r
where θFC and θWP (m3·m−3) are the water content at field capacity and at wilting point, respectively, and zr (m) is the rooting depth. The water a crop can uptake is reduced before the wilting point is reached. The fraction of TAW that a crop can extract without suffering water stress (mm) is given by
RAW = p   ·   TAW
where p is the critical depletion factor and it is equal to 0.45 for grapevines. When the water content in the rooting zone is above RAW the crop is not water-stressed (ks,i = 1), otherwise
k s,i = TAW D r,i ( 1 p )   ·   TAW
The water depletion in the rooting zone (Dr,i) can be obtained from a balance equation
D r,i = D r,i - 1 P eff,i I i + ET a,i + DP i
where Peff,i, Ii, and DPi (mm) are the effective precipitation, irrigation, and deep percolation, respectively. The initial depletion (Dr,0) is equal to the depletion at the end of the previous year, and it is 0 for the cases presented in this work. Deep percolation is zero if the water content in the rooting zone is below field capacity, otherwise it is given by
DP i = P eff,i + I i ET a,i D r,i - 1
The effective precipitation (mm) is the fraction of rainwater that reaches the rooting zone [43]
P eff,i = P i   α   ·   LAI   ·   ( 1 1 1 + f sc   ·   P i α   ·   LAI )
where LAI is the leaf area index (Table 1), Pi (mm) is the daily observed rain, and α is an empirical parameter (equal to 0.6 for grapevines). The soil cover fraction (fsc) is given by [44]
f sc = 1 e k e · LAI
where the empirical value ke = 0.385 was used for the extinction coefficient [45].
Results are in agreement with the output from the CropWat software [46].

2.1.2. Blue Water Footprint

The blue water footprint is the consumption of freshwater (surface and groundwater) resources of a product during the entire life cycle. The WFblue is evaluated as the sum of freshwater withdrawal using the EcoInvent database (ecoinvent, Zurich, Switzerland) [47]. All the freshwater volumes (lake, ground, river, and unspecified natural origin) classified as raw material in the LCI are considered. WFblue includes both the direct contributions (i.e., tap/well water used in field and cellar activities) and the indirect contributions (e.g., leakage of the distribution grid, water for production of raw materials, transportations, etc.).
This methodology is applied to rain-fed cultures. In case of irrigation, blue water evapotranspirated by the crop (WFblue,irrigation) must be included within WFblue.

2.1.3. Direct Grey Water Footprint

The WFgrey,direct is the virtual water volume needed to dilute the pollutant load applied in the vineyard, due to runoff, leaching and drift
WF grey,direct = V runoff + V drift + V leaching
Runoff is the transport of pollutants dissolved in the water that flows over the soil surface; the amount of pollutant that reaches the water body via runoff depends on slope, texture, amount and timing of rainfall and irrigation, if used, and the characteristic of active ingredient used [48]. The dilution volume for the pollutant load that reaches the surface water body via runoff (Vrunoff) is the sum of the volumes to dilute each i-th pollutant load
V runoff = i ( V runoff,i )
The virtual water volume Vrunoff,i (m3·ha−1) is estimated as follows
V runoff,i = Runoff i C NOEC,i
where C NOEC,i (kg·m−3) is the minimum value of No-Observed-Effect-Concentration (NOEC) limit among Daphia, Algae and Fish for the i-th pollutant [49]. Runoffi (kg·ha−1) is the predicted amount of active ingredient in surface water due to runoff and it is given by
Runoff i = RATE i   ·   ( 1 f int )   ·   f runoff   ·   f slope   ·   f buffer   ·   f degradation,i
where RATEi (kg·ha−1) is the application dose of the i-th active ingredient. The canopy intercepted fraction (fint) depends on the phenological phase of the crop, as reported in Table 2.
The fraction of active ingredient that participates to runoff (frunoff) is a function of BBCH [51], perimeter P (m) and surface S (ha) of the vineyard, and it is calculated as follows [52]:
f runoff = { 1 0.758173   ·   P 2   ·   10 4   ·   S ,   60 < BBCH < 79 1 0.250698   ·   P 2   ·   10 4   ·   S ,   elsewhere
The slope factor (fslope) is equal to 1 if the field slope (s) is higher than 20% [53]:
f slope = { 0.02153   ·   s + 0.001423   ·   s 2 , s   <   20 % 1 , s 20 %
The factor fbuffer depends on the distance between the vineyard and the nearest surface water body z (m) [53]:
f buffer = 0.83 z
The fraction of the i-th active ingredient (fdegradation,i) that survives long enough to reach the surface water body is given by:
f degradation,i = e - Δ t ln   ( 2 ) t 1 / 2 , i 1 + K OC,i   ·   OC
where t1/2,i (days) is the half time of active ingredient in soil, Koc is the sorption coefficient of active ingredient to organic carbon (m3·kg−1), OC is the organic carbon content in the soil (kg·m−3), and Δt (days) is the time between application and rain event. In this study an average value of three days is considered for Δt.
Drift is the airborne movement of droplet spray away from the field during application. The dilution volume for the pollutant load that reaches the surface water body via drift (Vdrift) is the sum of the volumes to dilute each i-th pollutant:
V drift = i ( V drift,i )
The virtual water volume Vdrift,i (m3·ha−1) is given by:
V drift,i = Drift i C NOEC,i
The predicted pollutant load (Drifti) depends on the application dose RATEi (kg·ha−1) of the i-th active ingredient
Drift i = RATE i   ·   f drift
where fdrift is a fraction representing the drift deposit at a certain distance from the field, and depends on crop type, stage and distance from water body (z). The drift curves [54] were used to predict drift at certain distance downwind the field, as a percentage of the applied dose [55]
f drift = { 0.157926   ·   z 1.608 ,   BBCH < 60 0.44769   ·   z 1.563   ,   BBCH 60
where z (m) is the distance from the nearest water body.
Leaching is the movement of pollutants through the soil. The dilution volume for the pollutant load that reaches the groundwater via leaching (Vleaching) is the sum of the following two terms:
V leaching   =   V leaching,N   +   max ( V leaching,i ; V leaching,tot )
The virtual water needed to dilute nitrates (Vleaching,N) is given by
V leaching,N = Leaching N C legal,N
where C legal,N (kg·m−3) is the maximum allowed concentration of nitrogen in groundwater [56]. LeachingN (kg·ha−1) is the amount of nitrogen that reaches the groundwater reservoir and it is computed according to
Leaching N = Q fert   ·   f N   ·   f leaching,N
where Qfert (kg·ha−1) is the amount of nitrogen fertilizer used in the field, fN is the fraction of nitrogen in the fertilizer, and fleaching,N is the fraction of nitrogen that reaches the groundwater reservoir. A constant value of 0.06 is used for fleaching,N [38].
Vleaching,i and Vleaching,tot are the volumes to dilute the i-th pollutant and the total of pollutants other than nitrates, respectively.
The virtual water volume Vleaching,i (m3·ha−1) is estimated as follows
V leaching,i = Leaching i C legal,i
where C legal,i is the legal limit, which represents the maximum allowed concentration of the i-th pollutant in groundwater [56]. The pollutant load is given by
Leaching i = RATE i   ·   AF i   ·   ( 1 f int )   ·   f runoff
where RATEi is the active ingredient applied (kg·ha−1), and fint and frunoff are the fractions defined above.
The attenuation factor AFi is a function of half-life of the pollutant considered, field capacity of the soil, depth of soil layer [57]
AF i = e t d,i · ln   ( 2 ) t 1 / 2 , i
where t1/2,i is the pollutant half-life in the soil (day). The travel time td,i (day) is defined as
t d,i = L   ·   θ FC   ·   RF i J w
where L (m) is the soil depth. The soil average daily water net recharge Jw (m·day−1) is given by
J W   = - 0.2855   +   0.0008637   ·   P year
where Pyear (mm) is the annual precipitation. The retardation factor (RF) represents the delay of the pesticides leaching with regard to the water flow in the soil and it is given by [58]
RF i = 1 + ρ   ·   f OC   ·   K OC,i θ FC + δ   ·   H i θ FC
where ρ (kg·m−3) is the soil bulk density, ϴFC is the field capacity, Hi is the Henry’s constant water-air pesticide partition coefficient, fOC is the soil carbon volumetric fraction, KOC,i (m3·kg−1) is the soil-carbon pesticide partition coefficient. The soil air volumetric fraction (δ) is given by:
δ =   ( ϕ θ FC )
where ϕ , the soil porosity.

2.1.4. Indirect Grey Water Footprint

This component is defined as the virtual water volume needed to dilute the pollutants emitted in water during all the processes involved in the product life cycle other than ones already taken into account in Section 2.1.3. WFgrey,indirect is evaluated considering two pollution indicators that assess the water quality: Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD). The WFgrey,indirect is the maximum value between the volumes (VCOD and VBOD) needed to dilute COD and BOD. They are computed considering EU legal limits (CCOD and CBOD) for pollutant concentration [56]
WF grey,indirect = max ( V COD ; V BOD )
V COD = COD C COD ;   V BOD = BOD C BOD
In this analysis, only dilution volumes for COD and BOD are considered, in the assumption that other pollutant emission in water requires lower dilution volumes.
Unlike different approaches (e.g., [38,41]), both direct and indirect grey volumes are computed every time a pollutant reaches a water body and not just when some limit value is exceeded. This choice was adopted in order to avoid underestimations in the case of multiple processes insisting on the same water body.

2.1.5. Carbon Footprint

CF is a single-issue indicator commonly used to express the pressure of human activities on the environment. CF quantifies the impact of a given activity/process/product in terms of equivalent carbon dioxide (CO2eq) emissions, considering the total amount of direct and indirect GHG emissions related to activity/process/product itself. The carbon footprint of a product (CFP) is evaluated with a Life Cycle Assessment (LCA) approach [5,6], according to the ISO/TS 14067 standard [7], which details principles, requirements, and guidance for the quantification and communication of the CFP, including goods and services. Among the different quantification methodologies, an approach based on activity data multiplied by appropriate emission/removal factors has been adopted in this study. Therefore, the CF related to i-th process included in the lifecycle of the product (CFi) is computed using the following equation:
CF i = EF i   ·   A i
where Ai is the activity data and EFi is the emission factor of the i-th process.
The emission factors used are in compliance with the IPCC methodology [59], computed considering each GHG emission generated by the process and characterizing them through their Global Warming Potential (GWP), which relates the impact generating by the emission of a generic gas to that of an equivalent mass of CO2:
EF i = j GWP j   ·   e j,i
where ej,i is the emission (in mass unit) of the j-th GHG associated to the i-th process per unitary amount. As an example, Table 3 shows the GWP of some relevant GHGs (considering the time horizon of 100 years recommended for CF assessments).
Site-specific activity data were as far as possible used to implement the calculation methodology for the studied product, using the PRé Consultants SimaPro 8.0 software [60] and the associated EcoInvent database [47].

2.2. Boundaries and Functional Unit

The system boundaries represent the interface between the product system and the environment and their definition determines which unit processes shall be included within the assessment. Consistently with the goal of the study, the system boundaries include grapes production, vinification, and marketing of the final product, while the final transportation of the product from the retailer to the end consumer is not included. According to point 6.2.1 of the ISO/TS 14067 that suggests adopting existing relevant Product Category Rules (PCR), the product lifecycle was modeled considering three main modules: upstream, core, and downstream [61]. Within the upstream module are included all the inflow of raw materials and energywares required for the wine production, the core module includes the production and the packaging of the final product (including internal transportation and external transportation of raw materials and energywares), while the downstream module comprises the transportation to a distribution platform and the handling (recycling or disposal) of packaging materials (Figure 1).
The functional unit (FU) is defined as a quantified performance of a product system for use as a reference unit in a LCA study and its primary purpose is to provide a reference to which the inputs and outputs are related. The FU used in this study is a 0.75 L wine bottle.

2.3. Data Collection

As mentioned above, the Life Cycle Inventory (LCI) was built up with activity data directly gathered from the winery (Table 4 and Table 5), except for the end-of-life phase, which was modeled considering representative scenarios based on average national and international data (Table 6). Red and white wines have different end-of-life scenarios according to the different distribution of the two products. Recycling, landfill, and incineration rates were computed according to [62].

3. Results and Discussion

Results of the carbon and water footprint analysis are shown for the red wine (Figure 2 and Table 7) and for the white wine (Figure 3 and Table 8).
Total CF and WF of the red wine are 1.433 kgCO2eq/bottle and 504.1 L/bottle, respectively. The major impact is due to the upstream phase, representing 72.77% and 98.55% of total CF and WF, respectively. Most impacting phases, in terms of CF, are packaging (43.43%), distribution (29.65%), and grapes production (18.64%). The WF is almost entirely associated to grapes production (90.95%), followed by use of fertilizers (3.65%) and packaging (3.14%).
Total CF and WF of the white wine are 1.377 kgCO2eq/bottle and 551.0 L/bottle, respectively. As for the red wine, the major impact is due to the upstream phase, representing 72.09% and 98.72% of total CF and WF, respectively. Most impacting phases, in terms of CF, are packaging (39.12%), distribution (29.34%), and grapes production (21.54%). The WF is almost entirely due to grapes production (91.70%), followed by use of fertilizers (3.68%) and packaging (2.57%).
As a result of this study, it can be noted that some processes do not produce impacts on CF and all the WF components in a homogeneous way. For example, crop evapotranspiration is entirely responsible for the WFgreen, but no CF is associated to the process. Similarly, no CF is associated to WFgrey,direct. Absolute values of CF and WF phases, not taking into account WFgreen, are shown in Figure 4 (red wine) and Figure 5 (white wine).
A correlation analysis between CF and WF phases was performed testing CF vs. WFgrey,indirect and CF vs. WFblue+WGgrey,indirect for red (Figure 6) and white (Figure 7) wine. Values were grouped considering a 0.1 kgCO2eq bin size and data were fitted using a linear regression. Fit results for CF vs. WFgrey,indirect are 15.38 L/kgCO2eq (red wine) and 15.29 L/kgCO2eq (white wine), with a fit probability of 70% and 73%, respectively. Fit results for CF vs. WFblue+WGgrey,indirect are 20.32 L/kgCO2eq (red wine) and 20.17 L/kgCO2eq (white wine), with a fit probability of 42% in both cases. As a result, data show a reasonable correlation probability (above the 1-sigma threshold) for CF vs. WFgrey,indirect, while it is below the acceptance level when testing CF vs. WFblue+WGgrey,indirect.
Results were finally tested against different cut-off criteria applied at a phase level. If a 1% cut-off rule is applied, the resulting CF and WF of the red wine are 1.427 kgCO2eq/bottle (−1.14%) and 497.7 L/bottle (−1.26%), respectively. CF and WF of the white wine are 1.374 kgCO2eq/bottle (−0.25%) and 539.7 L/bottle (−2.05%), respectively. The variation of both CF and WF of the two products is consistent with the cut-off, with a maximum of approximately −2% with respect to the reference case. However, a general cut-off criterion for the proposed methodology and its effect on final results could only be established after more products are evaluated.

4. Conclusions

An original and comprehensive methodology for the joint assessment of carbon and water footprint is presented. The methodology was setup in order to include all the phases of the life cycle of a wine product in a cradle-to-grave approach and it could be easily adapted for application to other agricultural products. The main advantage of a comprehensive approach is the use of the same system boundaries, allocation procedure, and product modeling, guaranteeing the uniformity of final results between CF and WF and hence a reliable comparison. The functional unit is a 0.75 L wine bottle. Impacts are computed in terms of GHG emission (kg of equivalent CO2) and water intensity (L of freshwater consumed). The product life cycle was divided in a total of 11 phases, grouped into three modules (upstream, core, and downstream).
The water footprint is defined as the sum of green, blue, and grey volumes of freshwater consumed during the product life cycle. A detailed review of the assessment methodology is presented for the evaluation of evapotranspirated water (WFgreen), ground and surface freshwater withdrawal (WFblue), water pollution generated by the use of treatments and fertilizers (WFgrey,direct), and water pollution generated by other processes (WFgrey,indirect).
The methodology was applied for the evaluation of CF and WF of two wines (red and white) produced by the same winery during vintage year 2012. CF and WF of the red wine are 1.433 kgCO2eq/bottle and 504.1 L/bottle, respectively. CF and WF of the white are 1.377 kgCO2eq/bottle and 551.0 L/bottle, respectively. The CF of the red wine is higher than the white wine because of the heavier bottle used (0.45 vs. 0.39 kg). The WF of the white wine is higher than the red wine because of the lower productivity of white grapes per unit surface (5440 vs. 6000 L/ha).
A correlation analysis was finally performed to test the proportionality between CF and WF results from the 11 phases. A good probability (>70%) is found when fitting WFgrey,indirect vs. CF for both wines. The result is 15.38 L/kgCO2eq (red wine) and 15.29 L/kgCO2eq (white wine). A more robust estimate of correlation parameters will require the evaluation of larger number of products.

Acknowledgments

The authors would like to gratefully thank the staff of the Umbrian winery “Azienda Agraria e Cantina Chiorri” for making available all the data needed for the CF and WF evaluation.

Author contributions

Sara Rinaldi and Emanuele Bonamente were responsible of the methodology revision, lifecycle modelling, results interpretation, and general coordination. Flavio Scrucca and Maria Cleofe Merico supported data acquisition and modelling activities. Francesco Asdrubali and Franco Cotana supervised the project.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. IPCC (Intergovernmental Panel on Climate Change). IPCC Fifth Assessment Report (Ar5), Climate Change 2013: The Physical Science Basis. Available online: http://www.ipcc.ch/report/ar5/wg1/ (accessed on 7 March 2016).
  2. Vermeulen, S.J.; Campbell, B.M.; Ingram, J.S.I. Climate Change and Food Systems. Annu. Rev. Environ. Resour. 2012, 37, 195–222. [Google Scholar] [CrossRef]
  3. FAO. Introduction to Agricultural Water Pollution; Food and Agriculture Organization of United Nations (FAO): Rome, Italy, 1996. [Google Scholar]
  4. UN—Sustainable Development Solutions Network, 2013. Solutions for Sustainable Agriculture and Food Systems—Technical Report for the Post-2015 Development Agenda. Available online: http://unsdsn.org/wp-content/uploads/2014/02/130919-TG07-Agriculture-Report-WEB.pdf (accessed on September 2015).
  5. ISO. ISO 14040:2006. Environmental Management—Life Cycle Assessment—Principles and framework; ISO: Geneva, Switzerland.
  6. ISO. ISO 14044:2006. Environmental Management—Life cycle assessment—Requirements and Guidelines; ISO: Geneva, Switzerland.
  7. ISO. ISO/TS 14067:2013. Greenhouse Gases-Carbon Footprint of Products-Requirements and Guidelines for Quantification and Communication; ISO: Geneva, Switzerland.
  8. ISO. ISO 14046:2014. Environmental Management—Water Footprint—Principles, Requirements and Guidelines; ISO: Geneva, Switzerland.
  9. Bonamente, E.; Cotana, F. Carbon and Energy Footprint of Prefabricated Industrial Buildings: A systematic Life Cycle Assessment Analysis. Energies 2015, 8, 12685–12701. [Google Scholar] [CrossRef]
  10. Bonamente, E.; Pelliccia, L.; Merico, M.C.; Rinaldi, S.; Petrozzi, A. The Multifunctional Environmental Energy Tower: Carbon Footprint and Land Use Analysis of an Integrated Renewable Energy Plant. Sustainability 2015, 7, 13564–13584. [Google Scholar] [CrossRef]
  11. Rossi, F.; Bonamente, E.; Nicolini, A.; Anderini, E.; Cotana, F. A Carbon Footprint and energy consumption assessment methodology for UHI-affected lighting systems in built areas. Energy Buildings 2016, 114, 96–103. [Google Scholar] [CrossRef]
  12. Aranda, A.; Scarpellini, S.; Zabalza, I. Economic and environmental analysis of the wine bottle production in Spain by means of life cycle assessment. Int. J. Agric. Res. Gov. Ecol. 2005, 4, 178–191. [Google Scholar] [CrossRef]
  13. Petti, L.; Ardente, F.; Bosco, S.; De Camillis, C.; Masotti, P.; Pattara, C.; Raggi, A.; Tassielli, G. State of the art of Life Cycle Assessment (LCA) in the Wine Industry. In Proceedings of the International Conference on Life Cycle Assessment in the Agri-food Sector, Bari, Italy, 22–24 September 2010.
  14. Carballo-Penela, A.; García-Negro, M.C.; Doménech-Quesada, J.L. A methodological proposal for corporate carbon footprint and its application to a wine-producing company in Galicia, Spain. Sustainability 2009, 1, 302–318. [Google Scholar] [CrossRef]
  15. Gazulla, C.; Raugei, M.; Fullana, P. Taking a life cycle look at crianza wine production in Spain: Where are the bottlenecks? Int. J. Life Cycle Assess. 2010, 15, 330–337. [Google Scholar] [CrossRef]
  16. Bosco, S.; Di Bene, C.; Galli, M.; Remorini, D.; Massai, R.; Bonari, E. Greenhouse gas emissions in the agricultural phase of wine production in the Maremma rural district in Tuscany, Italy. Ital. J. Agronomy 2011, 6, 93–100. [Google Scholar] [CrossRef][Green Version]
  17. Vázquez-Rowe, I.; Villanueva-Rey, P.; Moreira, M.T.; Feijoo, G. Environmental analysis of Ribeiro wine from a timeline perspective: Harvest year matters when reporting environmental impacts. J. Environ. Manag. 2012, 98, 73–83. [Google Scholar] [CrossRef] [PubMed]
  18. Point, E.; Tyedmers, P.; Naugler, C. Life cycle environmental impacts of wine production and consumption in Nova Scotia, Canada. J. Cleaner Prod. 2012, 27, 11–20. [Google Scholar] [CrossRef]
  19. Fusi, A.; Guidetti, R.; Benedetto, G. Delving into the environmental aspect of a Sardinian white wine: From partial to total life cycle assessment. Sci. Total Environ. 2014, 472, 989–1000. [Google Scholar] [CrossRef] [PubMed]
  20. Iannone, R.; Miranda, S.; Riemma, S.; De Marco, I. Improving environmental performances in wine production by a life cycle assessment analysis. J. Cleaner Prod. 2015. [Google Scholar] [CrossRef]
  21. Asdrubali, F.; Bonamente, E.; Merico, M.C.; Scrucca, F.; Lunghi, F. Carbon Footprint nel settore vitivinicolo umbro: Implementazione di una metodologia di calcolo e applicazione ad alcuni casi studio. In Proceedings of the 15th CIRIAF National Congress, Perugia, Italy, 9–11 April 2015.
  22. Cholette, S.; Kumar, V. The energy and carbon intensity of wine distribution: A study of logistical options for delivering wine to consumers. J. Cleaner Prod. 2009, 17, 1401–1413. [Google Scholar] [CrossRef]
  23. Amienyo, D.; Camilleri, C.; Azapagic, A. Environmental impacts of consumption of Australian red wine in the UK. J. Clean. Prod. 2014, 72, 110–119. [Google Scholar] [CrossRef]
  24. Villanueva-Rey, P.; Vázquez-Rowe, I.; Moreira, M.T.; Feijoo, G. Comparative life cycle assessment in the wine sector: Biodynamic vs. conventional viticulture activities in NW Spain. J. Clean. Prod. 2014, 65, 330–341. [Google Scholar] [CrossRef]
  25. Vázquez-Rowe, I.; Villanueva-Rey, P.; Iribarren, D.; Moreira, M.T.; Feijoo, G. Joint life cycle assessment and data envelopment analysis of grape production for vinification in the RíasBaixas appellation (NW Spain). J. Clean. Prod. 2012, 27, 92–102. [Google Scholar] [CrossRef]
  26. Vázquez-Rowe, I.; Rugani, B.; Benetto, E. Tapping carbon footprint variations in the European wine sector. J. Clean. Prod. 2013, 43, 146–155. [Google Scholar] [CrossRef]
  27. Rugani, B.; Vázquez-Rowe, I.; Benedetto, G.; Benetto, E. A comprehensive review of carbon footprint analysis as an extended environmental indicator in the wine sector. J. Clean. Prod. 2013, 54, 61–77. [Google Scholar] [CrossRef]
  28. Cazcarro, I.; Duarte, R.; Martín-Retortillo, M.; Pinilla, V.; Serrano, A. How sustainable is the increase in the water footprint of the Spanish agricultural sector? A Provincial Analysis between 1955 and 2005–2010. Sustainability 2015, 7, 5094–5119. [Google Scholar] [CrossRef]
  29. Flores Lopez, L.I.; Bautista-Capetillo, C. Green and blue water footprint accounting for dry beans (Phaseolus vulgaris) in primary region of Mexico. Sustainability 2015, 7, 3001–3016. [Google Scholar] [CrossRef]
  30. Ridoutt, B.G.; Sanguansri, P.; Harper, G.S. Comparing carbon and water footprints for beef cattle production in Southern Australia. Sustainability 2011, 3, 2443–2455. [Google Scholar] [CrossRef]
  31. De Miguel, Á.; Hoekstra, A.Y.; García-Calvoa, E. Sustainability of the water footprint of the Spanish pork industry. Ecol. Indic. 2015, 57, 465–474. [Google Scholar] [CrossRef]
  32. Bocchiola, D. Impact of potential climate change on crop yield and water footprint of rice in the Po valley of Italy. Agric. Syst. 2015, 139, 223–237. [Google Scholar] [CrossRef]
  33. Pellegrini, G.; Ingrao, C.; Camposeo, S.; Tricase, C.; Contò, F.; Huisingh, D. Application of Water Footprint to olive growing systems in the Apulia region: A comparative assessment. J. Clean. Prod. 2016, 112, 2407–2418. [Google Scholar] [CrossRef]
  34. Herath, I.; Green, S.; Singh, R.; Horne, D.; van der Zijpp, S.; Clothier, B. Water footprinting of agricultural products: A hydrological assessment for the water footprint of New Zealand’s wines. J. Clean. Prod. 2013, 44, 232–243. [Google Scholar] [CrossRef]
  35. Herath, I.; Green, S.; Horne, D.; Singh, R.; McLaren, S.; Clothier, B. Water footprinting of agricultural products: Evaluation of different protocols using a case study of New Zealand wine. J. Clean. Prod. 2013, 44, 159–167. [Google Scholar] [CrossRef]
  36. Quinteiro, P.; Dias, A.C.; Pina, L.; Neto, B.; Ridoutt, B.G.; Arroja, L. Addressing the freshwater use of a Portuguese wine (‘vinhoverde’) using different LCA methods. J. Clean. Prod. 2014, 68, 46–55. [Google Scholar] [CrossRef]
  37. Ene, S.A.; Teodosiu, C.; Robu, B.; Volf, I. Water footprint assessment in the winemaking industry: A case study for a Romanian medium size production plant. J. Clean. Prod. 2013, 43, 122–135. [Google Scholar] [CrossRef]
  38. Lamastra, L.; Suciu, N.A.; Novelli, E.; Trevisan, M. A new approach to assessing the water footprint of wine: An Italian case study. Sci. Total Environ. 2014, 490, 748–756. [Google Scholar] [CrossRef] [PubMed]
  39. Bonamente, E.; Scrucca, F.; Asdrubali, F.; Cotana, F.; Presciutti, F. The Water Footprint of the Wine Industry: Implementation of an Assessment Methodology and Application to a Case Study. Sustainability 2015, 7, 12190–12208. [Google Scholar] [CrossRef]
  40. Bonamente, E.; Scrucca, F.; Rinaldi, S.; Merico, M.C.; Asdrubali, F.; Lamastra, L. Environmental impact of an Italian wine bottle: Carbon and water footprint assessment. Sci. Total Environ. 2016, 560–561, 274–283. [Google Scholar] [CrossRef] [PubMed]
  41. Hoekstra, A.Y.; Chapagain, A.K.; Aldaya, M.M.; Mekonnen, M.M. The Water Footprint Assessment Manual: Setting the Global Standard; Earthscan Ltd: London, UK, 2011. [Google Scholar]
  42. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements. Available online: https://appgeodb.nancy.inra.fr/biljou/pdf/Allen_FAO1998.pdf (accessed on 28 June 2016).
  43. Moricz, N.; Matyas, C.; Berki, I.; Rasztovits, E.; Vekerdy, Z.; Gribovszky, Z. Comparative water balance study of forest and fallow plots. iForest Biogeosci. For. 2012, 5, 188–196. [Google Scholar] [CrossRef]
  44. Campbell, G.S. Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution. Agric. For. Meteorol. 1986, 36, 317–321. [Google Scholar] [CrossRef]
  45. Bindi, M.; Miglietta, F.; Gozzini, B.; Orlandini, S.; Seghi, L. A simple model for simulation of growth and development in grapevine. Vitis 1997, 2, 67–71. [Google Scholar]
  46. CropWat 8.0 Model. Available online: http://www.fao.org/nr/water/infores_databases_cropwat.html (accessed on 28 June 2016).
  47. Ecoinvent Version 3. Available online: http://www.ecoinvent.org/database/ecoinvent-version-3/ (accessed on 28 June 2016).
  48. Nollet, Leo M.L.; Rathore, H.S. Handbook of Pesticides: Methods of Pesticide Residues Analysis; CRC Press Taylor & Francis Group: Milton Park, Abingdon, 2010. [Google Scholar]
  49. Lewis, K.A.; Tzilivakis, J.; Warner, D.; Green, A. An international database for pesticide risk assessments and management. Human Ecol. Risk Assess. Int. J. 2016. [Google Scholar] [CrossRef]
  50. Linders, J.; Mensink, H.; Stephenson, G.; Wauchope, D.; Racke, K. Foliar interception and retention values after pesticide application. A proposal for standardized values for environmental risk assessment. Pure Appl. Chem. 2000, 72, 2199–2218. [Google Scholar] [CrossRef]
  51. BBCH Monograph. Growth Stages of Mono-and Dicotyledonous Plants. Available online: www.politicheagricole.it/flex/AppData/WebLive/Agrometeo/MIEPFY800/BBCHengl2001.pdf (accessed on 28 June 2016).
  52. Trevisan, M.; Di Guardo, A.; Balderacchi, M. An environmental indicator to drive sustainable pest management practices. Environ. Model. Softw. 2009, 24, 994–1002. [Google Scholar] [CrossRef]
  53. Strassemeyer, J.; Gutsche, V.; Brown, C.; Liess, M.; Schriever, C. Harmonised environmental Indicators for pesticide Risk: Aquatic Indicators; SSPE-CT-2003–501997; European Commission: Brussels, Belgium, 2007. [Google Scholar]
  54. Ganzelmeier, H.; Rautmann, D.; Spangenberg, R.; Streloke, M.; Herrmann, M.; Wenzelburger, H.J.; Walter, H.F. Studies on the Spray Drift of Plant Protection Products; Results of a Test Program Carried out Throughout the Federal Republic of Germany; Wissenschafts: VerlagBmbH, Berlin, 1995. [Google Scholar]
  55. FOCUS. FOCUS Surface Water Scenarios in the EU Evaluation Process under 91/414/EEC; Report of the FOCUS Working Group on Surface Water Scenarios; EC Document Reference SANCO/4802/2001-rev.2. Available online: http://esdac.jrc.ec.europa.eu/public_path/projects_data/focus/sw/docs/Generic%20FOCUS_SWS_1.2.pdf (accessed on 28 June 2016).
  56. European Community. Council Directive of 21 May 1991 Concerning Urban Waste Water Treatment (91/271/EEC). Available online: http://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX:31991L0271 (accessed on 28 June 2016).
  57. Paraiba, L.C.; Spadotto, C.A. Soil temperature effect in calculating attenuation and retardation factors. Chemosphere 2002, 48, 905–912. [Google Scholar] [CrossRef]
  58. Sanderson, I.D.; Lowe, M.; Wallace, J.; Kneedy, J.L. Ground-water Sensitivity and Vulnerability to Pesticides; Utah Geological Survey: Utah County, UT, USA, 2002; Volume 2, ISBN 1-55791-678-0. [Google Scholar]
  59. IPCC. Climate Change 2007: The Physical Science Basis; IPCC Fourth Assessment Report; IPCC: Cambridge, United Kingdom; New York, NY, USA, 2007. [Google Scholar]
  60. SimaPro LCA Software. Available online: http://www.pre-sustainability.com/simapro (accessed on 28 June 2016).
  61. Environdec, Product Category Category Rules (PCR) for the Assessment of the Environmental Performance of UN CPC 24212. In Wine of Fresh Grapes, Except Sparkling Wine; Grape Must; The International EPD ® System: Stockholm, Sweden, 2010; PCR 2010:02 Version 1.03, 23 July 2013.
  62. Eurostat—Data Explorer. Available oniline: http://appsso.eurostat.ec.europa.eu/nui/show.do (accessed on 28 June 2016).
Figure 1. System boundaries and flow diagram.
Figure 1. System boundaries and flow diagram.
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Figure 2. Carbon and water footprint results for the red wine.
Figure 2. Carbon and water footprint results for the red wine.
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Figure 3. Carbon and water footprint results for the white wine.
Figure 3. Carbon and water footprint results for the white wine.
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Figure 4. WF vs. CF for the red wine. WFgreen is not shown.
Figure 4. WF vs. CF for the red wine. WFgreen is not shown.
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Figure 5. WF vs. CF for the white wine. WFgreen is not shown.
Figure 5. WF vs. CF for the white wine. WFgreen is not shown.
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Figure 6. Correlation analysis for the red wine: CF vs. WFgrey,indirect (grey) and CF vs. WFblue+WFgrey,indirect (blue).
Figure 6. Correlation analysis for the red wine: CF vs. WFgrey,indirect (grey) and CF vs. WFblue+WFgrey,indirect (blue).
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Figure 7. Correlation analysis for the white wine: CF vs. WFgrey,indirect (grey) and CF vs. WFblue+WFgrey,indirect (blue).
Figure 7. Correlation analysis for the white wine: CF vs. WFgrey,indirect (grey) and CF vs. WFblue+WFgrey,indirect (blue).
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Table 1. Values of single crop coefficients (kc) and leaf area indices (LAI) for the red and white grapevines shown in this study (2012).
Table 1. Values of single crop coefficients (kc) and leaf area indices (LAI) for the red and white grapevines shown in this study (2012).
Stage RestInitialDevelop.MidLateRest
kc 0.20.30.3 to 0.70.70.7 to 0.450.2
LAI 0.50.50.5 to 1.61.61.6 to 0.50.5
Duration (red)(days)913060408065
Duration (white)(days)913060407372
Table 2. fint values for the phenological stages [50].
Table 2. fint values for the phenological stages [50].
BBCH0–811–1953–5760–6971–79>80
fint0.20.30.50.80.60.2
Table 3. Global Warming Potential of relevant GHGs [59].
Table 3. Global Warming Potential of relevant GHGs [59].
NameFormulaGWP
Carbon dioxideCO21kgCO2eq/kgCO2
MethaneCH425kgCO2eq/kgCH4
Nitrous oxideN2O298kgCO2eq/kgN2O
Table 4. Input data—1.
Table 4. Input data—1.
ProductSurfaceGrapes YieldWine YieldSoil TextureAltitude a.s.l.
Ha102 kg·ha−1l·10−2·kg−1 m
Company avg.24.1199.4378.19--
Red wine0.67100.0060.00loam271
White wine4.0080.0068.00loam-clay214
Table 5. Input data—2. All data are allocated to the functional unit.
Table 5. Input data—2. All data are allocated to the functional unit.
ParameterUnitRed WineWhite WineParameterUnitRed WineWhite Wine
N fertilizerkg2.80 × 10−33.10 × 10−3Diesel consumptionl3.00 × 10−23.30 × 10−2
P fertilizerkg1.20 × 10−31.30 × 10−3ElectricitykWh1.80 × 10−11.80 × 10−1
K fertilizerkg3.50 × 10−33.90 × 10−3Water use grape productionm35.50 × 10−46.10 × 10−4
Organic fertilizerkg2.30 × 10−32.60 × 10−3Water use cellar activitiesm32.20 × 10−32.20 × 10−3
Generic pesticidekg4.00 × 10−44.40 × 10−4Bottle (glass)kg4.50 × 10−13.90 × 10−1
Triazine compoundskg2.80 × 10−63.10 × 10−6Corkkg4.00 × 10−34.00 × 10−3
Fosetyl Aluminiumkg5.30 × 10−45.90 × 10−4Capsulekg1.00 × 10−31.00 × 10−3
Sulphurkg6.70 × 10−37.40 × 10−3Labelskg1.00 × 10−31.00 × 10−3
Acetamide compoundkg5.50 × 10−56.10 × 10−5Core board box distributionkg4.80 × 10−24.80 × 10−2
Copperkg5.10 × 10−45.70 × 10−4Packaging PETkg8.80 × 10−32.80 × 10−3
Dichlorokg6.20 × 10−56.90 × 10−5Packaging PET (Hazardous)kg1.10 × 10−41.20 × 10−4
Metalaxil mkg4.10 × 10−64.60 × 10−6Packaging Paper (Hazardous)kg3.00 × 10−43.30 × 10−4
Lubricating oilkg2.20 × 10−42.20 × 10−4Packaging Paperkg1.60 × 10−61.60 × 10−6
Propylene glycolkg3.00 × 10−63.00 × 10−6Packaging filmkg5.60 × 10−45.20 × 10−4
Potassium metabisulfitekg1.70 × 10−42.30 × 10−4Packaging Coreboard boxkg3.00 × 10−44.70 × 10−4
Enzymekg1.60 × 10−61.60 × 10−6Transport lorry < 3.5 ttkm4.60 × 10−34.60 × 10−3
Yeastkg4.00 × 10−54.00 × 10−5Transport lorry 3.5–7.5 ttkm3.20 × 10−22.90 × 10−2
Carbon dioxidekg3.40 × 10−33.40 × 10−3Transport lorry 16–32ttkm2.50 × 10−32.50 × 10−3
Acetic acidkg1.30 × 10−51.30 × 10−5Transport cartkm3.40 × 10−23.40 × 10−2
Diammonium Phosphatekg4.00 × 10−44.00 × 10−4Distribution Lorry < 3.5 ttkm1.50 × 10−11.40 × 10−1
Soapkg1.50 × 10−31.50 × 10−3Distribution lorry 3.5–7.5 ttkm2.80 × 10−12.60 × 10−1
R404A leakagekg8.40 × 10−78.40 × 10−7Distribution shiptkm04.20 × 10−1
Table 6. End of life scenario.
Table 6. End of life scenario.
Red WineWhite Wine
WasteMaterialRecyclingLandfillIncinerationRecyclingLandfillIncineration
%%%%%%
BoxCardboard77.50%22.50%-77.05%22.95%-
BottleGlass70.70%29.30%-68.95%31.05%-
CorkCork-100%--100%-
LabelPaper 29.30%70.70% 31.05%68.95%
CapsulePlastic34.72%65.28%-35.65%64.35%-
Table 7. CF and WF results for the red wine.
Table 7. CF and WF results for the red wine.
ModulePhaseCarbon FootprintWater FootprintGreen WFBlue WFGrey WF
kgCO2eq/Bottle%L/Bottle%%%%
UpstreamEnergywares0.12628.75%1.8230.36%0.00%0.12%0.24%
Field Water0.00020.01%0.60.12%0.00%0.12%0.00%
Grapes0.268918.64%458.4390.95%89.38%0.22%1.34%
Other Materials0.014911.03%1.6630.33%0.00%0.19%0.14%
Packaging0.6266043.43%15.8433.14%0.00%0.78%2.37%
Use of fertilizers0.01310870.91%18.37503.65%0.00%0.00%3.65%
CoreCellar Water0.0007670.05%2.45352880.49%0.00%0.48%0.00%
Materials0.002160.15%0.00020.00%0.00%0.00%0.00%
Transportation0.03363082.33%0.4700.09%0.00%0.02%0.08%
DownstreamDistribution0.427729.65%5.09111.01%0.00%0.19%0.82%
End-of-life−0.07136−4.95%−0.71−0.14%0.00%−0.14%0.00%
Upstream total1.049972.77%496.898.55%89.38%1.43%7.74%
Core total0.03662.53%2.92380.58%0.00%0.50%0.08%
Downstream total0.356424.70%4.380.87%0.00%0.05%0.82%
Total1.443100%504.1100%89.38%1.98%8.64%
Table 8. CF and WF results for the white wine.
Table 8. CF and WF results for the white wine.
ModulePhaseCarbon FootprintWater FootprintGreen WFBlue WFGrey WF
kgCO2eq/Bottle%l/bottle%%%%
UpstreamEnergywares0.12779.28%1.8970.34%0.00%0.11%0.23%
Field Water0.00020.02%0.70.13%0.00%0.13%0.00%
Grapes0.296521.53%505.3091.70%90.13%0.22%1.35%
Other Materials0.015001.09%1.6650.30%0.00%0.18%0.12%
Packaging0.5385439.11%14.1552.57%0.00%0.63%1.94%
Use of fertilizers0.01445831.05%20.26653.68%0.00%0.00%3.68%
CoreCellar Water0.0007670.06%2.45352880.45%0.00%0.44%0.00%
Materials0.002160.16%0.00020.00%0.00%0.00%0.00%
Transportation0.03216222.34%0.4530.08%0.00%0.02%0.07%
DownstreamDistribution0.404029.34%4.79980.87%0.00%0.16%0.71%
End-of-life−0.05479-3.98%−0.64−0.12%0.00%−0.12%0.00%
Upstream total0.992572.08%544.098.72%90.13%1.27%7.32%
Core total0.03512.56%2.90650.53%0.00%0.46%0.07%
Downstream total0.349225.36%4.160.75%0.00%0.05%0.71%
Total1.377100%551.0100%90.13%1.77%8.10%
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