Authors: Gioia, Garavini1; Alessandra, Zamagni1; Pier Luigi Porta2; Paolo, Masoni2; Gabriele Facibeni3; Valentina Fantin2; Serena Righi3
1Ecoinnovazione srl, ENEA spin-off. Via Guido Rossa 26, 35020 Ponte San Nicolò (Padova) 2ENEA, Via Martiri di Monte Sole 4, 40129 Bologna
3University of Bologna, CIRSA (Centro Interdipartimentale di Ricerca per le Scienze Ambientali), Via dell’Agricoltura 5, 48123 Ravenna
E-mail contact: a.zamagni@ecoinnovazione.it
1. Abstract
The choice of models to estimate emissions from pesticide use represents a key methological aspect but to date a common agreement in the scientific community has not been achieved yet. This paper discusses the application of the PestLCI2.0 model to the case study of refined sugar from sugar beets, evaluating its feasibility and robustness, and considering the main criticalities at the level of both inventory and impact assessment. The study points out that, despite the non-homogeneous coverage of the pesticide emissions and of their effects, their inclusion in the study is of paramount important. We suggest favouring completeness over precision in the study, as key aspect for operationalizing the materiality principle fostered by PEF.
2. Introduction
The harmonisation of methods and models to account for the potential environmental impact of products and organisation is at the core of many European and international initiatives. The European Commission’s initiative “A single market for green products” [1] promotes the Product and Organisation Environmental Footprint (PEF and OEF, respectively) methods, whose development is presently undergoing in several pilots. The harmonisation process is built upon previous initiatives such as the ENVIFOOD Protocol [2] and the Environmental Product Declaration (EPD) systems. A common aspect of all the initiatives is the product category rule (PCR) concept, i.e. the definition of technical criteria and data for a specific product-goup, which can increase consistency in LCA applications and support comparability.
A key methodological aspect, not implemented yet in any of the above-mentioned initiatives, is the choice of models to estimate emissions from pesticide use. In fact, different approaches have been developed, but a common agreement in the scientific community has not been achieved yet.
We have addressed the issue of pesticide emissions in the evaluation of the environmental footprint of refined sugar from sugar beet. Given that product environmental footprint category rules for sugar are not under development in the pilots and the PCR of the International EPD system does not account for emissions from pesticide use, we have adopted the PestLCI2.0 model to assess the pesticide emissions to the ecosphere. This paper discusses the application of the PestLCI2.0 model in terms of feasibility and robustness, considering the main criticalities at the level of both inventory and impact assessment.
46 3. Methods
The PestLCI 2.0 model [3] estimates the fraction of pesticide applied in the technosphere, which migrates to the environment (air, surface water and groundwater) by crossing the technosphere-environment borders. In PestLCI the technosphere boundaries are defined to be horizontaly the arable field borders, and vertically from 1 m soil depth up to 100 m up into the air column. The model does not take into account the emissions to soil outside the technosphere because they are assumed to occurr only indirectly after the emission of pesticide in the other compartments. Considering that the distribution of pesticide emissions between environmental compartments strongly depends on local climate and soil characteristics [3], the model has been adapted to allow the user to select different European climate scenarios and soil profiles as well as to adjust additional parameters such as field characteristics and pedo-climate values.
We have applied PestLCI 2.0 - which includes one climate scenario for the agricultural zone investigated in our study (considering the monthly fluctuation of temperature, precipitation, solar irradiation and the potential water balance) - in the framework of a PEF study of refined sugar from sugar beets, which is cultivated and processed in Italy. The unit of analysis is 1 kg of refined sugar from sugar beet packed into 1 kg carton box for sale by retailers (NACE code: C10.8.1). The system boundaries are from cradle to grave and the reference year is 2013. The study has been developed with the support of GaBi 6 software and Ecoinvent 2.2 database. All the impact categories required by the PEF methodology were considered, but this article will analyse only those related to toxicity.
The following input data have been collected and estimated: i) the pesticide active ingredient; ii) the crop on which they are applied, iii) the soil profile and the climate zone, iv) the period on which the pesticide is applied (month), v) the application rate; vi) the tillage type and the field dimensions (width, length and slope).
The other parameters (called “adjustable model parameters”) set by the model (ex. solid material density, fraction macropores) are assumed to be unchanged, even though, according to expert judgment, their default values cannot be considered representative of the agricultural land area under study [4]. As far as the completeness is concerned, the PestLCI 2.0 database does not have all the active ingredients of the pesticides used in the sugar beet cultivation (70% completeness as number of available pesticides). Therefore, proxies with the same pesticide’s function have been selected in those cases.
Regarding the impact assessment phase, the USEtox recommended method has been applied. Currently USEtox cannot handle groundwater emissions [5], therefore those emissions have been neglected in the impact assessment with an average mass loss from 2 to 6% of the relative pesticide emissions. Moreover, there is not a full coverage of the characterization factors (CFs) at environmental impact categories levels. In this case study, all the analysed pesticides’ CFs for Ecotoxicity freshwater are included, a few CFs are available for the Human toxicity non cancer effects (45% as number of CFs available), while none for Human toxicity cancer effects.
47 4. Results
The study points out that the cultivation of the sugar beet represents the most relevant phase in refined sugar’s life cycle for the majority of the analysed impact categories. Regarding the impact categories related to toxicity, the contribution of pesticides both to the whole life cycle and to the cultivation phase are illustrated in figure 1.
The contribution of pesticides to the total result of cultivation phase is equal to 37% for the Ecotoxicity for aquatic fresh water and 6% for the Human toxicity cancer and non-cancer effects. Other important contributions to these categories are related to the production and the use of NPK fertilisers (28% for the Ecotoxicity for aquatic fresh water, 42% for Human toxicity non cancer effects and 52% for the Human toxicity cancer effects) and the agricultural work processes (43% for the Ecotoxicity for aquatic fresh water, 68% for Human toxicity non cancer effects and 59% for the Human toxicity cancer effects), in particular ploughing and irrigation.
Among the pesticides, herbicides are those that affect most the Ecotoxicity freshwater results. Nevertheless, it should be noted that these results are underestimated, due to the non complete coverage of CFs discussed in section 2. Furthermore, in the LCA software there is not a full coverage of the CFs for all the environmental compartments due to the different level of robustness of the USEtox characterization flows (interim and recommended), therefore only the recommended factors have been implemented in the software.
5. Conclusion
The case study pointed out that a proper evaluation of the toxicity impact category within a PEF study is challenging due to the calculation of pesticide emissions at LCI level and to the LCIA modelling. Regarding the inventory, information about field characteristics in the sugar beets cultivation has been collected as well as that related to the pesticide application period, in order to have an estimation of the influence of spatial and temporal aspects on pesticide dynamics. However, the following limitations can be identified in the study and in PestLCI 2.0: i) the default values of the adjustable parameters - in particular for soil that are non-representative of the Italian agricultural area; ii) the limited number of pesticides included in the Figure 1: Contribution of the pesticides to the total results (on the right) and to the farming phase (on the left) of the impact categories related to toxicity
48 PestLCI model; iii) no possibility to manage the background parameters (such as buffer zone width in
pesticide database) that can be lead to inconsistencies in the model’s outputs. In particular, buffer zones are not considered in our study, but a deeper analysis on the Italian regulation related to the sugar beet cultivation areas should be done because their presence can affect the off-field emissions, in particular the emissions to air due to wind drift; iv) the assumption to consider only the distribution of the pesticide’s active ingredient, omitting the contribution of by-products used in pesticide formulations, such as adjuvants and solvents [6].
As far as the LCIA modelling is concerned, there is not a full match between PestLCI 2.0 model and USEtox: the former does not take into account the emissions to soil, while the latter has not developed yet CFs for groundwater emissions. Moreover, in USEtox there is a low availability of the pesticides’ CF for the impact categories of Human toxicity and the consideration of different levels of robustness for the same compound leads to an uncompleate implemention in LCA softwares.
However, despite the non-homogeneous coverage of the pesticide emissions and of their effects, their inclusion in the study is of paramount important and they need to be traced at least at inventory level. In fact, according to the materiality approach fostered by PEF, their contribution to the overall performance of the product is relevant and the company has a certain level of influence on them. Thus, the incomplete inventory and LCIA should not prevent the opportunity to intervene on the process: while working on making the models more accurate, we suggest favouring completeness over precision in the study, following the materiality principle.
6. Acknowledgment
We gratefully acknowledge the funding of Eridania Sadam and their support in providing the data for the study.
7. References
[1] COM/2013/0196 final. Building the Single Market for Green Products Facilitating better information on the environmental performance of products and organisations.
[2] Food SCP RT (2013), ENVIFOOD Protocol, Environmental Assessment of Food and Drink Protocol, European Food Sustainable Consumption and Production Round Table (SCP RT), Working Group 1, Brussels, Belgium. [3] Dijkman T. J., Birkved M., Hauschild M. Z., “PestLCI 2.0: a second generation model for estimating emissions of pesticides from arable land in LCA”. Int J Life Cycle Assess 17 (2012), 973–986
[4] Ecoinnovazione, 2015. Personal communication with ARPA Emilia-Romagna Agency – Piacenza Division – Environmental System Service
[5] van Zelm R., Larrey-Lassale P., Roux P., “Bridging the gap between life cycle inventory and impact assessment for toxicological assessment of pesticides used in crop production”. Chemosphere 100 (2014): 175-181
[6] Rosenbaum RK, Bachmann TK, Gold LS, Huijbregts MAJ, Jolliet O, Juraske R, Koehler A, Larsen HF,MacLeod M,Margni M, McKone TE, Payet J, Schuhmacher M, Van de Meent D, Hauschild MZ.
[7] “USEtox: The UNEP/SETAC-consensus model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int J Life Cycle Assess 13 (2008):532–546.
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