Regional modeling of freshwater eutrophication in Argentina for Life Cycle Assessment

Regional modeling of freshwater eutrophication in Argentina for Life Cycle Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Regional modeling of freshwater eutrophication in Argentina for Life Cycle Assessment Eliana Conci, Analía Rosa Becker, Alejandro Pablo Arena, Bárbara María Civit This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9418860/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Aquatic eutrophication is a significant impact category in Life Cycle Assessment in countries with high environmental heterogeneity, such as Argentina. However, the usual practice in these studies is to use global characterization factors (given the lack of regional factors), which leads to results that generally do not reflect local conditions. To address this issue, phosphorus-based regional characterization factors are developed at the midpoint level to improve the spatial representativeness of this category. The methodology adapts and improves an existing model by differentiating the impact between ecological regions and incorporating observed concentrations of total phosphorus into the calculation of the effect factor. The model integrates nutrient transport processes in soil and water and the response of the receiving ecosystem within the cause-effect chain. As a result, three types of factors were developed to represent the Regionalized Aquatic Eutrophication Potential (kg P eq): factors for four regions of the country, an arithmetic national average, and a national average weighted by land area. The regional factors obtained range from 0.331 to 0.678 kg P eq in water and from 0.145 to 0.296 kg P eq for inputs to soil, demonstrating spatial variability in eutrophication potential. The use of global factors may underestimate or overestimate local impacts, whereas regionalization improves environmental consistency and identifies regions with greater eutrophic susceptibility. The proposed approach constitutes a methodological advance transferable to regions with limited data availability and provides a tool for environmental assessment and water management. phosphorus emissions midpoint environmental impact aquatic ecosystems spatial differentiation regional characterization factors Figures Figure 1 Highlights o Regional characterization factors for aquatic eutrophication were developed for Argentina. o The model is based on the fate and transport of phosphorus to aquatic ecosystems. o The use of regional characterization factors improves environmental representativeness. o Global characterization factors underestimated the regional impact when applied to Argentina. o The methodology is transferable to other regions of the world. 1. Introduction 1.1. Concept and relevance of eutrophication Aquatic ecosystems are closely related to their environment, and their dynamics are influenced by various factors at different spatial and temporal scales (Rechencq et al. 2024 ). Eutrophication is the process of changing from one trophic state to a higher one as a result of the incorporation of nutrients (Schindler 2006 ; Paerl and Huisman 2008 ), which causes deterioration in water quality and biodiversity, modifies the trophic network and increases the biomass of certain communities (Wurtsbaugh et al. 2019 ). The nutrients that most influence the process are phosphorus (P) and nitrogen (N). In some ecosystems, the limiting factor is the phosphate ion (PO 4 3− ), as is the case in most continental lentic systems, while in marine ecosystems the limiting factor is N (Vásquez Zapata et al. 2012 ). 1.2. Current methods in Life Cycle Assessment (LCA) LCA is a widely used approach for assessing environmental impacts associated with product systems (Muralikrishna and Manickam 2017 ). In Life Cycle Impact Assessment (LCIA), there are various methods for characterizing eutrophication, which differ in their inventory requirements, geographic coverage, spatial resolution, and modeled emission pathways. Despite these differences, these methods have in common the assessment of the potential impact of eutrophication based on: the inventory of nutrients emitted, their environmental fate through fate factors (FF), which represent transport and attenuation to the receiving compartment (freshwater or coastal seawater), and the subsequent exposure of the ecosystem to nutrient enrichment (exposure factor) and the effect (effect factor, EF) on aquatic species. This process allows the calculation of a midpoint impact indicator representing an increase in nutrients in the water (Payen and Ledgard 2017 ). Initially, FFs for N and P were derived from European models, although in the absence of FFs for other continents, they have been used outside Europe (Struijs et al. 2009 ). The use of global CFs can lead to significant uncertainties when applied to specific environmental conditions in a region, hence the need for globally valid models, but with site-specific CFs (Payen and Ledgard 2017 ), especially in countries in the southern hemisphere where LCA studies are scarce, such as Argentina (Conci et al. 2022 ). 1.3. Geographical divide and the need for regionalization Argentina has a wide variety of water bodies 1 , although some of them suffer from severe eutrophication problems (Quirós 2000 ), which explains the variation in trophic status (Quirós 1991 ). Given that in countries such as Argentina, data on nutrient fate may be limited, the selection and regionalization of CFs poses a challenge. Although the development of methodologies has improved the description and assessment of aquatic eutrophication, there is still a need for improvements. Having damage indicators, as has been achieved for other categories such as human toxicity or ozone layer depletion, is a long-term goal, since certain constraints must first be overcome, such as the inability to calculate the bioavailability of nutrients. In the short term, a significant improvement could be the calculation of CFs with spatial differentiation for regions outside Europe or North America (Schmid 2008 ). In this context, the objective of this study is to develop regional indicators of aquatic eutrophication in freshwater ecosystems using the LCIA approach, following the environmental mechanism of the impact category and focusing on P emissions and the spatial differentiation of the impact. The research aims to provide methodological tools for the regional assessment of eutrophication in aquatic ecosystems in countries where site-dependent CFs are not available. 2. Development of the regional midpoint model The research is part of the LCIA within the LCA, in accordance with IRAM-ISO 14044 (2008). Since its inception, the LCA methodology has developed various LCIA methods aimed at both midpoint and end-point impacts (Civit 2009 ). Various forms of P can be included in eutrophication modeling, with the phosphate ion (PO 4 3− ) being the most commonly used reference substance. Midpoint CFs are the product of a fate factor (FF) and an exposure factor (XF) (Morelli et al. 2018 ). Endpoint CFs typically consist of a fate factor (FF), an exposure factor (XF), and an effect factor (EF), and characterize the potential damage that exposure to eutrophicating substances could cause to human health, ecosystem quality, or natural resource availability. In this research, midpoint CFs are addressed and an approximation of the EF is made, without actually measuring the damage. The methodological procedure followed these steps: definition of environmental variables, modeling of P fate and transport, calculation of CFs, and spatial aggregation at the regional scale. The proposed equation is based on the Schmid ( 2008 ) model. The potential contribution to eutrophication of system A, denoted I Eu (A) , is calculated as the result of nutrient inputs that cause increased biomass production in water bodies. Following the usual terminology in LCIA, the impact caused by aquatic eutrophication in a system (A) within a geographical area can be expressed as follows (adapted from Seppälä et al. 2004 2 ) (Eq. 1 ): $$\:{I}_{Eu}\left(A\right)={\sum\:}_{j=1}^{n}{C}_{j,i}\left(A\right)*{E}_{j}\left(A\right)$$ 1 where I Eu (A) represents the value of the aquatic eutrophication impact caused by system A, C j,i (A) represents the CF of substance j produced by system A that reaches a given aquatic zone i, and E j (A) represents the amount of substance j emitted by system A. The higher the score of the value I Eu (A) , the more undesirable the system is. The impact category indicator in the method is algae growth (Seppälä et al. 2004 ). If effect and transport factors are considered, the CF C j,i can be calculated as (Schmid 2008 , adapted from Seppälä et al. 2004 ) (Eq. 2 ): $$\:{C}_{j,i}\left(A\right)={n}_{j,i}\left(A\right)*{u}_{j,i}\left(A\right)*{Eqv}_{j}$$ 2 where \(\:{n}_{j,i}\left(A\right)\) is the transport factor for substance j (0≤ \(\:{n}_{j,i}\) ≤1) and indicates the portion of substance j that will reach a given aquatic zone i, and \(\:{u}_{j,i}\left(A\right)\) is the effect factor for substance j (0≤ \(\:{u}_{j,i}\) ≤1), indicating the amount of transported substance j that causes an increase in biomass production in zone i. Finally, \(\:{Eqv}_{j}\) is the equivalence factor for substance j. The equivalence factors for N and P can be expressed as PO 4 - equivalents (Seppälä et al. 2004 ). Air emissions of P have not been taken into account as they have little significance in the generation of eutrophication (Potting et al. 2005 ). This paper proposes an extension of the model developed by Schmid ( 2008 ), based on the methodology of Seppälä et al. ( 2004 ). The assessment of the EF is expanded by quantifying it based on the relationship between the regional TP concentration and a reference value corresponding to the average TP concentration in water bodies according to the classification of the Organization for Economic Cooperation and Development (OECD 1982), which indicates that the degree of trophic status is quantified as the average annual concentration of chlorophyll-a (Cl-a) in that environment (Table 1 ). Table 1 OECD threshold values for the trophic classification (OECD 1982) Trophic level Average TP (ug/L) Average Cl-a (ug/L) Max. Cl-a (ug/L) Average DS (m) Min. DS (m) Ultra-oligotrophic < 4 < 1 12 > 6 Oligotrophic < 10 < 2.5 6 > 3 Mesotrophic 10–35 2.5-8 8–25 6 − 3 3-1.5 Eutrophic 35–100 8–25 25–75 3-1.5 1.5–0.7 Hypereutrophic > 100 > 25 > 75 < 1.5 < 0.7 Average PT: average PT concentration in a water body; Average Cl−a: average chlorophyll−a concentration; Max Cl−a: peak chlorophyll−a concentration; Average DS: average Secchi disk transparency; Min DS: minimum Secchi disk transparency Finally, it is multiplied by the equivalence factor of the emitting substance. Thus, the proposed Eq. (3) is defined as: \(\:{C}_{j,i}\left(A\right)\) = η j,i (A) · μj,i (A) · Eqυj (3) \(\:{C}_{j,i}\left(A\right)\) = η j,i (A) · (Ppi / Pu) · Eqυj where ηj,i (A) is the transport factor for substance j and indicates the portion of substance j that will reach a given aquatic zone i, µj,i (A) is the effect factor of substance j and designates how much of the transported substance j causes an increase in biomass production in zone i, Ppi is the total P concentration present in zone i, Pu is the concentration of P in an eutrophicated water body, and Eqυj is the equivalence factor for substance j based on Heijungs et al. ( 1992 ). To reduce the variability and asymmetry of TP data between different water bodies, a base-10 logarithmic transformation (log 10 ) is applied to obtain the Ppi value. This transformation allows the concentration scale to be homogenized and improves comparability between regions. The transformed values are normalized with respect to the logarithm of the maximum value (log 10 (TPmax)), obtaining a dimensionless index between 0 and 1 that expresses the relative magnitude of the potential eutrophication effect, where 1 represents the condition of greatest impact and values close to 0 represent the best environmental reference situation. Eq. ( 4 ) for this calculation is defined as: $$\:{I\:norm}=\frac{{{log}}_{10}\left(TPi\right)}{{{log}}_{10}\left(TPmax\right)}$$ 4 where I norm is the normalized TP index, TP i is the P concentration at site i, and TPmax is the maximum recorded value. This dimensionless index varies between 0 and 1, with 1 representing the condition of greatest effect. The same calculation was performed to determine Pu. The Regionalized Aquatic Eutrophication Potential of substance j, \(\:{C}_{j,i}\left(A\right)\) , is used as CF, combining transport and normalized effect in aquatic ecosystems, expressed in kg P eq. The resulting impact, or category indicator, is interpreted as regionalized potential algal growth, representing the equivalent eutrophicating load attributable to a unit of P emission. 2.1. Transport routes Following Schmid ( 2008 ), Fig. 1 shows the main transport routes of P in continental waters and coastal seas considered in this research. Sources are grouped into direct nutrient inputs to soil (such as fertilizers and manure) and wastewater from urban or industrial sources. Considering agricultural inputs, nutrients can be added directly to the soil and subsequently transported to aquatic ecosystems where they cause eutrophication. The most important sources of this input are fertilizers and manure used in agriculture. Considering the transport route, Eq. 5 calculates the CF of P added to the soil: Cj, suelo, i = (τj · σj, i) · µj, i · Eqυj (5) where Cj, soil, i is the CF for substance j (P) contributed directly to the soil through different inputs (usually fertilizers or manure) that reaches a water body i (inland waters or coastal seas). In turn, τj describes the fraction of substance j present in the soil after consumption by plants and σj, i the fraction of substance j present in the soil that reaches water body i. As for wastewater emissions from industries and households that, after being treated or not in treatment plants, are discharged into aquatic ecosystems, the CF of the P emissions contained therein is estimated (Eq. 6): Cj, residuales, i = γj · µj, i · Eqυj (6) where Cj,residual,i is the CF for the eutrophying substance j (P) present in wastewater reaching an aquatic zone i (inland waters or coastal seas), and γj represents the fraction of the total substance j emitted that remains in the water after passing through treatment plants. Unlike the model proposed by Schmid ( 2008 ), which assigns a generic effect factor (value 1) to the receiving ecosystem, this approach introduces regionalized values based on P concentration data in water bodies within the national territory. Eq. 7 breaks down the effect factor: µ j, i (A) = (Ppi / Pu) (7) where µ j, i (A) is the effect factor of substance j and designates the amount of substance j transported that causes an increase in biomass production in zone i, Ppi is the total P concentration present in zone i, Pu is the P concentration of an eutrophicated water body, as detailed above. 2.2. Model validation Eutrophication represents a regional environmental impact, and it may be necessary to develop models based on location (Henryson et al. 2017 ). To validate the proposed model, the geographical regionalization of Argentina based on the proposal by Quirós ( 2004 ) was selected (Table 2 ). Table 2 Main average limnological characteristics of the water bodies studied (Quirós 2004 ) Water body districts N° of environments in each district Georegion Area (km 2 ) TP TN* TN:TP Cl-a (mg/m 3 ) Chaco-pampa (2) 25 2 Max Min 98 1984 1.4 290 1288 23 8583 28750 2660 56 145 6 63.8 405.3 1.6 West and Northwest (3) 27 3 Max Min 30.6 330 0.1 68 477 5 2008 11200 653 60 147 6 20.3 218.1 0.9 Andrean Patagonia (4) 33 4 Max Min 103.7 1466 0.6 7 33 1 697 2242 186 146 335 31 2.3 54.1 0.2 Patagonian Plain (5) 13 5 Max Min 252.3 816 0.6 111 608 4 2515 7900 223 48 75 4 9 41.8 0.4 *TN: Total N . Regional CFs for aquatic eutrophication in Argentina were estimated based on transport values and normalized TP values. A representative CF was obtained for each of the regions considered, reflecting local conditions and relative susceptibility to impact. In addition, two integrative values were calculated for the country: a national arithmetic mean and a surface-weighted mean. 1.1.1. Fertilizers and impact on water Argentine soils have a robust level of nutrients, where the low national demand for fertilizers is not consistent with Argentina's global relevance in the world grain markets. 2021 closed as the year with the highest fertilizer consumption, with a total of around 5.6 million tons (Rebora et al. 2023 ). Agriculture has altered the flow of phosphorus into the oceans (Andrade et al. 2017 ), promoting eutrophication, an increase in biomass, changes in the composition of algal species, and fluctuations in pH and dissolved oxygen, thereby affecting water quality (Smith et al. 1999 ). 1.1.2. Wastewater data in Argentina Seventy-five percent of Argentina's territory is arid or semi-arid (Abraham and Salomón 2011 ). Most of the water is used for agricultural and industrial production, while only 10% is used for human consumption. Katopodis et al. ( 2023 ) indicates that 63.1% of urban areas have sewerage coverage, meaning that around 15.7 million people lack access to sewerage networks and regional inequalities are evident. Many treatment plants have maintenance deficiencies, affecting effluent quality. Of 600 treatment plants, 376 (serving more than 10,000 inhabitants) serve some 22 million inhabitants. Seventy-one percent of the plants are in fair or poor condition, and lagoon systems show the greatest deterioration (81%). Only 27.6% of the wastewater collected is treated properly. Of the total effluent generated (3.2 billion m 3 /year), only 17.3% reaches plants in good condition, while 38.9% are partially treated and 6.9% are discharged untreated into receiving water bodies. 3. Results obtained for Argentina 2.1. Direct agricultural inputs to the soil According to Eq. 5, the variables for the CF of P contributed to the soil are calculated. To determine the fraction of substance j present in the soil after consumption by plants, the value given by Bonadeo et al. ( 2017 ) is taken, which indicates that of the total P in the plant, 50% or more is found as inorganic phosphates, which predominate in the conducting or reserve organs, while in the reproductive organs (e.g., seeds), they are mainly found in organic forms. Due to low requirements, generally within the soil-plant-animal system, less than 10% of total P enters the plant-animal life cycle, and more than 90% remains in the soil. That is, 90% of P will be available, establishing τj = 0.9. To calculate σj, i, the fraction of substance j present in the soil that reaches water body i, values available in SimaPro for three different crops are considered, using the ReCiPe 2016 Midpoint method, as no other more regional values are available. Thus, PO 4 3− emissions transported by water to the river for tomatoes (Spain) were 0.00001 kg, for soybeans (Argentina) 0.0002 kg, and for corn (Argentina) 0.00010 kg, obtaining an average and defining σj = 0.33. 2.2. Wastewater discharges into aquatic ecosystems Following Eq. 6, the fraction of total P emitted that remains in the water after passing through treatment plants is calculated based on data provided by INDEC (2025) and Katopodis et al. ( 2023 ). According to the latest national census, the Argentine population is estimated at 45,892,285 inhabitants for the year 2022 (INDEC 2025), with a production of 0.975 kg P/capita/year, an average value derived from different authors (Rodríguez et al. 2022), and an annual production of 44,744.98 t P is obtained. From this, the nutrients consumed in treatment plants are subtracted, taking into account that 17.3% of the population is connected to adequate treatment and 38.9% to inadequate treatment, leaving 6.9% without treatment (Katopodis et al. 2023 ). As the outfalls are primary treatment facilities, 30% (0.3) correspond to this type and the remaining 70% (0.7) to secondary treatment facilities. Considering that primary and secondary treatment facilities produce a removal rate of 10% (0.1) and 42% (0.42) of P, respectively (in the absence of specific data for Argentina, values published in Schmid 2008 are used), the amount of P treated in treatment plants for Argentina is calculated to be 14,497.37 t P/year (32.4% of the total amount produced). Based on this, 30,247.61 t P/year are discharged into the environment. Finally, it is obtained that in Argentina the fraction of the total P emitted that remains in the water after passing through the treatment plants is γj = 0.68. 2.3. Calculation of the regional effect factor To calculate Ppi, the maximum TP values in Table 1 of Quirós ( 2004 ) are used, and to calculate Pu, the maximum limit values defined by the OECD (1982) as the worst environmental situation (Eq. 7) are used. Table 3 shows the results of the log 10 applied and its normalization to obtain the effect factor in each region, where each unit in log 10 represents an order of magnitude in the concentration of P. Table 3 TP values to define the effect factor. Regions Chaco- pampa West and Northwest Andrean Patagonia Patagonian Plain Range for Argentina (Quirós 2004 ) TP present mean* 290 68 7 111 log 10 (TP mean)** 2.462 1.833 0.845 2.045 log 10 (TP mean) norm** 0.792 0.589 0.272 0.658 TP present max* 1288 477 33 608 log 10 (TP max)** 3.110 2.679 1.519 2.784 log 10 (TP max) norm** 1 0.861 0.488 0.895 TP present min* 23 5 1 4 log 10 (TP min)** 1.362 0.699 0 0.602 log 10 (TP min) norm** 0.438 0.225 0 0.194 OECD range (1982) TP threshold max* 100 100 100 100 log 10 (TP max)** 2 2 2 2 log 10 (TP max) norm** 1 1 1 1 TP threshold min* 35 35 35 35 log 10 (TP min)** 1.544 1.544 1.544 1.544 log 10 (TP min) norm** 0.772 0.772 0.772 0.772 µ Argentina FC final effect max** 1 0.861 0.488 0.895 * Min= minimum; max= maximum. TP expressed in mg/m3; **Dimensionless unit. Norm= normalized . 2.4. Transport routes of P to Argentine freshwater ecosystems Following equations 5 and 6, Table 4 shows the results of the regional CFs obtained for each of the transport routes considered. Table 4 P concentrations in wastewater and P concentrations from direct agricultural inputs to soil CFs Regions Chaco-pampa West and Northwest Andrean Patagonia Patagonian Plain γ* 0.68 0.68 0.68 0.68 µ max* 1 0.861 0.488 0.895 Eqυ** 3.06 3.06 3.06 3.06 Cj,residuals,i** 2.081 1.792 1.016 1.863 τ* 0.9 0.9 0.9 0.9 σ* 0.33 0.33 0.33 0.33 µ max* 1 0.861 0.488 0.895 Eqυ** 3.06 3.06 3.06 3.06 Cj,soil,i** 0.909 0.783 0.444 0.814 * Dimensionless unit; **Expressed in kg PO 4 3− eq/kg substance j emitted . 2.5. Regional midpoint CFs The CFs obtained for the study region in kg P eq are presented below. Table 5 shows the calculation for estimating the weighted average CF per area (data from Tables 2 and 4 ). Table 5 Calculation of the weighted average CF per area Regions Weighted average CF per area Substance j = P (water) Substance j = P (soil) Chaco-pampa West and Northwest Andrean Patagonia Patagonian Plain Regional CFs (kg P eq ) * regional area (km 2 ) 66.477 17.878 34.348 153.204 29.035 7.808 15.002 66.914 Weighted total sum (kg P eq * km 2 ) 271.907 118.759 Sum of areas (km 2 ) 484.6 484.6 Weighted average (kg P eq) 0.561 0.245 Table 6 shows the values from Table 5 and presents the final CFs for P emitted to water and soil: by region and national arithmetic mean. Table 6 Regional CFs, national arithmetic mean, and weighted by area. CFs (kg P eq) Substance j = P (water) Substance j = P (soil) For region Chaco-pampa West and Northwest Andrean Patagonia Patagonian Plain 0.678 0.584 0.331 0.607 0.296 0.255 0.145 0.265 National arithmetic mean 0.550 0.240 Area-weighted mean 0.561 0.245 An analysis of each region shows that the Chaco-pampa is the region with the greatest potential impact, due to a high average concentration of nutrients in water bodies, making it a critical area in terms of eutrophication. This is followed by the Patagonian Plain, the western and northwestern regions of the country, and finally the Andean Patagonia, which is the region with the lowest impact, possibly due to a low concentration of TP, lower ecological sensitivity, or lower connectivity of nutrients with vulnerable water bodies. 2.5.1. Application of CFs obtained in a specific LCA Following Eq. 1 , data from the Life Cycle Inventory and freshwater aquatic eutrophication CFs calculated in an LCA of tomatoes produced in the Uco Valley region, located in the central-western part of the province of Mendoza, Argentina (Piastrellini et al. 2024 ), were used. The LCA was carried out using SimaPro software, using the CML Baseline model (World 2000). The global CF for aquatic eutrophication is set at 0.000163 kg PO 4 3− eq. If the amount of fertilizer emitted during all stages of the production process of 1 kg of tomatoes is 0.000381 kg P and the outflow to surface water is 0.00000516 PO 4 3− eq, the impact is 0.0000000202 kg P eq for the input and 0.000000000274 kg P eq for the outflow. When applying the CF for the West and Northwest region, the results are as follows (Table 7 ): Table 7 Results of the application case. Substance Emissions (kg P) CFs (kg P eq) I Eu (A) (kg P eq) Global impact (kg P eq) Percentage variation between impacts P (water) 0.000381 0.584 0.000225 0.0000000202 0.00897% P (soil) 0.000381 0.255 0.0000971 0.0000000202 0.02080% If the national arithmetic mean CF is applied, the result is 0.000209 kg P eq for P in water and 0.0000914 kg P eq for P in soil. Finally, using the weighted average CF per surface area, we obtain 0.000213 kg P eq for P in water and 0.0000933 kg P eq for P in soil. 4. Discussions 4.1. Transport routes and regional characteristics for defining CFs The Chaco-Pampas regions exhibit higher values associated with eutrophic, hyper-eutrophic, or saline aquatic ecosystems, whereas the Patagonian Andes and Tierra del Fuego exhibit ultra-oligotrophic to oligotrophic conditions (Drago and Quirós 1995 ; Quirós et al. 2002 ). The trophic states of waters on the Patagonian plateau range from mesotrophic to eutrophic, as do most in the central-west and northwest (Quirós 2004 ). At higher nutrient concentrations and shallower depths, these ecosystems exhibit greater algal biomass (Quirós 2000 ). This supports the ability of the proposed approach to capture regional environmental heterogeneity, an aspect particularly relevant in countries with high hydrological variability such as Argentina. The differences observed between regions are consistent with their characteristic trophic states, and the calculated CFs represent the maximum potential contribution of P to biomass growth (Schmid 2008 ). Thus, the calculation of the regional EF highlights that, in the Patagonian Plain, the average TP (log10 = 2.045) is more than an order of magnitude higher than that of Andean Patagonia (0.845), which is reflected in the final CFs. This allows for a comparison of the regions and the expression of differences in impact without extreme values dominating the scale due to regions with very large differences in TP concentration. Tomato cultivation was selected as the case study due to its regional significance, intensive use of fertilizers, and availability of primary data. It follows that the use of global factors underestimates and fails to capture the potential impact of aquatic eutrophication in the study area. 4.2. Comparison with existing models Table 8 presents a comparison between the CFs obtained in this study (using national arithmetic mean CFs) and those available in the literature for Argentina, which are scarce and global in scope. Three different approaches are presented for calculating CFs for freshwater eutrophication caused by phosphorus in Argentina, derived from global, national, and regionally scaled national models. Table 8 Comparison of CFs CFs (kg P eq / kg substance emitted) Substance World* Argentina** Argentina regionalized*** Percentage variation between the model by Payen et al. ( 2021 ) and the CFs obtained P (water) 0.008 1.004 0.550 54.78% P (soil) 0.008 0.894 0.240 26.84% *IMPACT World+ Midpoint V1.05 (Bulle et al. 2019. Calculation of aquatic eutrophication based on the model by Helmes et al. 2012); **Global calculation (model by Payen et al. 2021); ***National arithmetic mean with regional CFs . The values obtained reveal a substantial difference between the calculated CFs (Table 8 ). The impact of eutrophication in the study by Bulle et al. ( 2019 ) is spatially assessed using the global model with a resolution of 0.5° × 0.5° developed by Helmes et al. ( 2012 ), based on global hydrological data. The IMPACT World+ global model (Bulle et al. 2019 ) assigns a value of 0.008 kg P eq/kg P, representing a global average. This value tends to underestimate the potential impact of eutrophication when applied to local contexts. Meanwhile, the model by Payen et al. ( 2021 ) incorporates explicit spatial differentiation and estimates a CF for Argentina of 1.004 kg P eq/kg P (water) and 0.894 kg P eq/kg P (soil). These values represent an improvement over global factors by including regional P export and fate parameters. The authors developed the fate factors for application to both diffuse soil emissions and point source nutrient emissions to freshwater, at the watershed scale with global coverage and at the national and global scales through weighted aggregation of emissions and the distinction between agricultural and non-agricultural emissions. The modeled fate processes include nutrient attenuation from land to water bodies and within them. However, their direct application in local studies requires a level of resolution for hydrological and emission data that is not always available. Finally, the regional CFs obtained in this study (0.550 kg P eq/kg P for water and 0.240 kg P eq/kg P for soil) show a reduction compared to the national values obtained by Payen et al. ( 2021 ). This suggests that incorporating local information, such as limnological quality, the effective surface area of water bodies, and regional variability in nutrient retention and transport processes, allows for a more accurate representation of the actual potential magnitude of the impact. The use of regional CFs improves environmental representativeness and avoids over- or underestimation of the impact that can occur when applying generic or global-scale factors in local LCA studies. The developed regional model, by using observed TP concentrations and normalizing the values relative to the maximum (critical case), more accurately reflects the eutrophic response of aquatic ecosystems, reducing the uncertainty associated with the use of generic factors. 4.3. Advantages and methodological innovation The methodological innovation lies in replacing the generic value with regionalized EFs derived from empirical data on TP concentrations in Argentine water bodies. The model retains the structure based on the environmental mechanisms of P (emission-transport-impact) but introduces an explicit representation of the sensitivity of the regional aquatic ecosystem without compromising compatibility with the LCA methodological framework. Due to the great diversity of its aquatic ecosystems, this approach is not implemented at the watershed scale because it would generate such a high number of CFs that the tool would become impractical (Schmid 2008 ); therefore, the regional scale is appropriate for Argentina. LCA end users are provided with CFs for specific regions, a national average, and a value weighted by land area. The methodology developed is replicable in other regions of the world provided that regional water quality data are available, addressing the growing need for spatial regionalization in LCA. For the end user, the approach provides CFs directly applicable in LCA studies, allowing the eutrophic impact of a given production system to be differentiated based on the geographic location of its emissions. The selection of the CF will be based on the type of study and the spatial aggregation level of the LCA. Thus, regional CFs are more suitable for assessments with a spatially differentiated approach. The national arithmetic average represents the country’s average performance without considering the size of the regions. In contrast, the area-weighted average shows that each CF contributes in proportion to the area it occupies within the national territory. 4.4. Limitations and opportunities for improvement The lack of data on diffuse sources of phosphorus is acknowledged, although it is more difficult to model discharges than emissions, and models are generally unable to calculate aquatic eutrophication with precision and accuracy (Resano Goizueta 2019 ). Furthermore, the study could be expanded to other regions of the country, and the TP values could be updated. However, when specific water bodies are analyzed, it is observed that TP values fall within the range for each region (e.g., Andén and Pizzolon 2025 ). In the future, land-use data could be included to improve the model’s regionalization. In a given body of water, eutrophication may be limited by P or N, or co-limited by both nutrients, depending on the ratio of P to N and their concentrations (Francoeur et al. 1999 ). Zhou et al. ( 2024 ) developed globally regionalized CFs for the impacts of P and N and indicate that eastern South America (Argentina) belongs to regions with P limitation and undesirable periphyton growth. Therefore, in this study, P is considered the limiting nutrient for freshwater eutrophication, and no CFs have been derived for N in water in Argentina; this could be a point to consider in future research, incorporating atmospheric deposition as well as transport pathways in soil and water. Likewise, other aspects not addressed in this study could be examined, such as the analysis of environmental conditions involving non-homogeneous or non-steady-state mixtures in aquatic ecosystems, since the regional parameters here represent average environmental behaviors and temporal variability is not modeled. 5. Conclusions This study proposes a methodological extension for assessing aquatic eutrophication within the framework of the LCIA using site-dependent CFs, which incorporate spatial differentiation based on regional empirical data and maintain the consistency of the LCA’s cause-effect chain. The developed CFs represent the Regionalized Aquatic Eutrophication Potential for P, integrating transport processes and the normalized effect of the receiving ecosystem. Their application in Argentina allowed for the definition of three levels of spatial aggregation (regional, national arithmetic mean, and area-weighted average) that facilitate the adaptation of the indicator to different study objectives. The results show that global factors may overestimate or underestimate impacts in specific contexts, highlighting the need for regionalization to improve environmental representativeness. The methodology is transferable to other regions of the world through the incorporation of local data and transforms the LCA-based eutrophication assessment into a tool sensitive to the environmental context, useful for supporting sustainability decisions and water resource management. The impact model identifies regions most susceptible to eutrophication processes and promotes responsible production and consumption strategies aligned with the Sustainable Development Goals related to water and the protection of aquatic ecosystems. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was funded by a doctoral grant from the National Scientific and Technical Research Council (CONICET, RESOL-2020-126-APN-DIR#CONICET) and was carried out within the Cliope Group “Energy, Environment, and Sustainable Development” with the support of the National Technological University, Mendoza Regional Faculty (UTN-FRM), Argentina. Author Contribution EC: conceptualization, methodology, research, formal organization and analysis, and original writing; ARB: writing-review and editing; APA: writing-review and editing; BMC: writing-review and editing and analysis. Acknowledgement The authors would like to thank Argentina's National Scientific and Technical Research Council (CONICET) for its support during the development of this work, carried out within the framework of an Internal Doctoral Scholarship awarded by the institution, as part of the Cliope group at UTN-FRM, Argentina. References Abraham EM, Salomón M (2011) Experiencias de combate a la desertificación en Mendoza. En: da Cunha Correia Lima R, de Mendonça Barreto Cavalcante A, Perez Marin AR (eds) Desertificação e mudanças climáticas no semiárido brasileiro. Instituto Nacional do Semiárido, Ministerio de Ciencia, Tecnología de Brasil y Embrapa Semiárido, pp 183–206. ISBN 978-85-64265-02-8 Andén DA, Pizzolon LA (2025) Procesos de eutrofización en los ríos Azul y Quemquemtreu, subcuenca del Lago Puelo. Argentina Ecología Austral 35(1):001–015. https://doi.org/10.25260/EA.25.35.1.0.2335 Andrade FH (Comp.), Taboada M, Lema D, Maceira N, Echeverría H, Posse G, Prieto D, Sánchez E, Ducasse D, Bogliani M, Gamundi JC, Trumper E, Frana J, Perotti E, Fava F, Mastrángelo M (eds) (2017) Los desafíos de la agricultura argentina. Ediciones INTA. 1ª ed. Ciudad Autónoma de Buenos Aires. ISBN 978-987-521-859-8 Bartrolí J (2003) Avaluació ambiental del cicle del nitrogen a Catalunya: aplicació de l´anàlisi del flux de substàncies. [Tesis de doctorado, Universidad de Gerona]. https://dugi-doc.udg.edu/bitstream/handle/10256/45/1%20MEM%C3%92RIA%20AVALUACIO%20NITROGEN%20CATALUNYA_2003.pdf?sequence=8 Bonadeo E, Moreno I, Bongiovanni M, Marzari R, Ganum Gorriz MJ (2017) El sistema suelo-planta. Principios generales. (1ª ed.). UniRío Editora. ISBN 978-987-688-204-0 Bulle C, Margni M, Patouillard L, Boulay AM, Bourgault G, De Bruille V, Cao V, Hauschild M, Henderson A, Humbert S, Kashef-Haghighi S, Kounina A, Laurent A, Levasseur A, Liard G, Rosenbaum RK, Roy PO, Dhaked S, Fantke P, Jolliet O (2019) IMPACT World+: a globally regionalized life cycle impact assessment method. Int J Life Cycle Assess 24(9):1653–1674. https://doi.org/10.1007/s11367-019-01583-0 Civit BM (2009) Sostenibilidad ambiental. Desarrollo de indicadores para su aplicación en estudios de análisis de ciclo de vida en la región árida del centro-oeste argentino. [Tesis de doctorado no publicada, Universidad Nacional de Cuyo] Conci E, Becker AR, Arena AP, Civit BM (2022) A literature review of eutrophication in Life Cycle Assessment. Relevance for pampean agroecosystems in Argentina. S Fla J Dev 3(1):618–643. https://doi.org/10.46932/sfjdv3n1-046 Drago E, Quirós R (1995) The hydrochemistry of the inland waters of Argentina: a review. Int J Salt Lake Res 4(4):315–325. https://doi.org/10.1007/BF01999115 Ecoinvent (20 de diciembre (2016) de Método de EICV-Datos: EF-Eutrophication, freshwater. https://ecoinvent.lca-data.com/datasetdetail/lciamethod.xhtml?uuid=b53ec18f-7377-4ad3-86eb-cc3f4f276b2b &version=01.00.009 Folke J (1996) Phosphate, zeolite and citrate in detergents-Technical and environmental aspects of detergent builder systems (Report nº 95002/06) Francoeur SN, Biggs BJF, Smith RA, Lowe RL (1999) Nutrient limitation of algal biomass accrual in streams: seasonal patterns and a comparison of methods. J North Am Benthol Soc 18:242–260. doi/10.2307/1468463# Gallego A, Hospido A, Moreira MT, Feijoo G (2008) Environmental performance of wastewater treatment plants for small populations. Resour Conserv Recycl 52(6):931–940. 10.1016/j.resconrec.2008.02.001 Heijungs R, Guineé JB, Huppes G, Lankreijer RM, Udo de Haes HA, Weneger A, Ansems P, Eggels PG, van Duin R, de Goede H (1992) Environmental life cycle assessment of products. Guide and Backgroud (Part 1). CML Leiden, Netherlands Helmes RJ, Huijbregts MA, Henderson AD, Jolliet O (2012) Spatially explicit fate factors of phosphorous emissions to freshwater at the global scale. Int J Life Cycle Assess 17(5):646–654. https://doi.org/10.1007/s11367-012-0382-2 Henryson K, Hansson PA, Sundberg C (2017) Spatially differentiated midpoint indicator for marine eutrophication of waterborne emissions in Sweden. Int J Life Cycle Assess 23(1):70–81. https://doi.org/10.1007/s11367-017-1298-7 Instituto Nacional de Estadística y Censos de la República Argentina (2025) Censos. https://www.indec.gob.ar/indec/web/Nivel3-Tema-2-41 IRAM-ISO (2008) Environmental management - Life cycle assessment - Requirements and guidelines 14044 Katopodis GN, Rodríguez CA, Zárate FJ, Regueira JM (2023) Diagnóstico y Prospectivas del Tratamiento de Aguas Residuales en Argentina. Dirección de Comunicación Estratégica-CAF. https://scioteca.caf.com/handle/123456789/2153 Morelli B, Hawkins TR, Niblick B, Henderson AD, Golden HE, Compton JE, Cooter EJ, Bare JC (2018) Critical Review of Eutrophication Models for Life Cycle Assessment. Environ Sci Technol 52(17):9562–9578. https://doi.org/10.1021/acs.est.8b00967 Muralikrishna I, Manickam V (2017) Life cycle assessment. In: Muralikrishna I, Manickam V (eds) Environmental Management, pp 57–75. 10.1016/B978-0-12-811989-1.00005-1 Organización para la Cooperación y el Desarrollo Económico (1982) Eutrophication of Waters. Monitoring, Assessment and Control. Cooperative Programmers on Monitoring of Inland Waters (Eutrophication Control), Environment Directorate, OECD Paris, Final Report Paerl HW, Huisman J (2008) Blooms like it hot. Sci 320(5872):57–58. 10.1126/science.1155398 Payen S, Cosme N, Elliott AH (2021) Freshwater eutrophication: spatially explicit fate factors for nitrogen and phosphorus emissions at the global scale. Int J Life Cycle Assess 26(2):388–401. https://doi.org/10.1007/s11367-020-01847-0 Payen S, Ledgard SF (2017) Aquatic eutrophication indicators in LCA: Methodological challenges illustrated using a case study in New Zealand. J Clean Prod 168:1463–1472. https://doi.org/10.1016/j.jclepro.2017.09.064 Piastrellini R, Rótolo GC, Arena AP, Civit BM, Curadelli S (2024) Evaluation of the environmental sustainability of agricultural production using the methodologies of emergy analysis and life cycle assessment. Case study, tomato grown in Mendoza (Argentina). Clean Circ Bioeconomy 8:100082. https://doi.org/10.1016/j.clcb.2024.100082 Potting J, Beusen A, Øllgaard H, Hansen OC, de Haan B, Hauschild M (2005) Aquatic eutrophication. In: Potting J, Hauschild M (eds) Background for spatial differentiation in life cycle impact assessment - The EDIP2003 methodology, Danish Environmental Protection Agency, pp 89–112 Quirós R (1991) Empirical relationships between nutrients, phyto-and zooplankton and relative fish biomass in lakes and reservoirs of Argentina. Verh Internat Verein Limnol 24(2):1198–1206 Quirós R (2000) 16 y 17 de marzo) La eutrofización de las aguas continentales de Argentina. I Reunión de la Red Temática sobre Eutrofización de Lagos y Embalses. Subprograma XVII. Cooperación Iberoamericana. Ciencia y Tecnología para el Desarrollo (CYTED), Mar del Plata, Buenos Aires, Argentina Quirós R (2004) Cianobacterias en lagos y embalses de Argentina: década del 80. Serie de documentos de trabajo del Área de sistemas de producción acuática. Departamento de producción animal, Facultad de agronomía. Universidad de Buenos Aires, 2, pp 1–23 Quirós R, Rennella A, Boveri M, Rosso JJ, Sosnovsky A (2002) Factores que afectan la estructura y el funcionamiento de las lagunas pampeanas. Ecología Austral 12:175–185 ISSN 1667-782X Rebora C, Bertona A, Ibarguren L (2023) Uso de fertilizantes en la producción de granos en Argentina: situación actual. Experticia, 1(14) Rechencq M, Fernández MV, Lallement ME, Alonso MF, Macchi PJ, Sosnovsky A (2024) El estado trófico de los arroyos andino-patagónicos en un gradiente de urbanización. Ecología Austral 34(3):422–434. https://doi.org/10.25260/EA.24.34.3.0.2342 Resano Goizueta P (2019) Revisión del análisis del ciclo de vida como herramienta de evaluación de impactos ambientales [Trabajo Fin de Estudios de Grado, Universidad Pública de Navarra]. https://academica-e.unavarra.es/entities/publication/ba71d387-3a09-4d81-8294-8cb7ecab46a2 Rodríguez A, Avena M, Rodríguez MI, Cossavella A, Oroná C, del Olmo S, Larrosa N, Bazán R, Corral M (2002) Estimación de aportes de nutrientes de fósforo a los embalses San Roque y Los Molinos en Córdoba, Argentina e implicancias en su gestión. Ingeniería Sanitaria y Ambiental 60:45–51 Schmid AG (2008) Diferenciación espacial en la metodología de Análisis del Ciclo de Vida: desarrollo de factores regionales para eutrofización acuática y terrestre [Tesis de doctorado, Universidad Santiago de Compostela]. https://biogroup.usc.es/sites/default/files/AlejandroGallego.pdf Seppälä J, Knuuttila S, Silvo K (2004) Eutrophication of aquatic ecosystems a new method for calculating the potential contributions of nitrogen and phosphorus. Int J Life Cycle Assess 9(2):90–100. https://doi.org/10.1007/BF02978568 Schindler DW (2006) Recent advances in the understanding and management of eutrophication. Limnol oceanogr 51:356–363. https://doi.org/10.4319/lo.2006.51.1_part_2.0356 Smith VH, Tilman GD, Nekola JC (1999) Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ pollut 100(1–3):179–196. https://doi.org/10.1016/S0269-7491(99)00091-3 Struijs J, Beusen A, van Jaarsveld H, Huijbregts MAJ (2009) Eutrophication. In: Goedkoop M, Heijungs R, Huijbregts MJA, De Schryver A, Struijs J, van Zelm R (eds) ReCiPe 2008. A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level, 1st ed. Report 1: Characterisation, pp 59–67 Vásquez Zapata GL, Herrera Orozco L, Cantera Kintz JR, Galvis Castaño A, Cardona Zea DA, Hurtado Sánchez IC (2012) Metodología para determinar niveles de eutrofización en ecosistemas acuáticos, vol 24. Revista de la Asociación Colombiana de Ciencias Biológicas, pp 112–128 Wurtsbaugh WA, Paerl HW, Dodds WK (2019) Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum. Wiley Interdisciplinary Reviews: Water 6(5):1373. https://doi.org/10.1002/wat2.1373 Zhou J, Mogollón JM, van Bodegom PM, Beusen AH, Scherer L (2024) Global regionalized characterization factors for phosphorus and nitrogen impacts on freshwater fish biodiversity. Sci Total Environ 912:169108. https://doi.org/10.1016/j.scitotenv.2023.169108 Footnotes Freshwater bodies include rivers and streams, lakes and ponds, reservoirs, and wetlands, and they support aquatic ecosystems that, in this study, are the recipients of environmental impacts. Seppälä et al. ( 2004 ) estimated CFs that took into account nutrient bioavailability for 11 sectors in Finland, based on three different scenarios, noting that this was an approach subject to considerable uncertainty and that further research was needed on the roles of different nutrient forms in aquatic eutrophication (Schmid 2008 ). The conversion from kg PO 4 3− eq (results from Table 3 ) to kg P eq was performed using a stoichiometric factor of 0.326, corresponding to the mass fraction of P present in the phosphate ion, calculated from the ratio between their molar masses. This calculation is shown in Table 5 . Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 14 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9418860","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630605045,"identity":"0e6039de-fe51-4438-8caf-1baef1afb62d","order_by":0,"name":"Eliana Conci","email":"data:image/png;base64,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","orcid":"","institution":"Universidad Tecnológica Nacional-Facultad Regional Mendoza","correspondingAuthor":true,"prefix":"","firstName":"Eliana","middleName":"","lastName":"Conci","suffix":""},{"id":630605046,"identity":"cb6ebe82-7264-44eb-b409-64d3a52650af","order_by":1,"name":"Analía Rosa Becker","email":"","orcid":"","institution":"I. A. P. de Cs. Básicas y Aplicadas, Universidad Nacional de Villa María","correspondingAuthor":false,"prefix":"","firstName":"Analía","middleName":"Rosa","lastName":"Becker","suffix":""},{"id":630605047,"identity":"1f192b8f-0b97-4970-ad1e-26a4cab1d280","order_by":2,"name":"Alejandro Pablo Arena","email":"","orcid":"","institution":"Universidad Tecnológica Nacional-Facultad Regional Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"Pablo","lastName":"Arena","suffix":""},{"id":630605048,"identity":"f4e70c29-17d4-401e-bcf9-7643844f3e03","order_by":3,"name":"Bárbara María Civit","email":"","orcid":"","institution":"Universidad Tecnológica Nacional-Facultad Regional Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Bárbara","middleName":"María","lastName":"Civit","suffix":""}],"badges":[],"createdAt":"2026-04-14 18:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9418860/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9418860/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108228992,"identity":"63136068-0b02-4b92-8d71-2f2fee8d0ada","added_by":"auto","created_at":"2026-04-30 16:59:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19845,"visible":true,"origin":"","legend":"\u003cp\u003eMain transport routes for P considered in the model.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9418860/v1/7f2c6f54f6f89230fda5e3f7.png"},{"id":108491158,"identity":"9db28eee-715a-43b9-8cda-9f58fb1edb5c","added_by":"auto","created_at":"2026-05-05 09:52:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":523647,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9418860/v1/cd80c387-a17a-4384-b44c-abaec19615c3.pdf"},{"id":108228991,"identity":"94116bb0-89a9-45cf-ac37-9211b1005664","added_by":"auto","created_at":"2026-04-30 16:59:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":212084,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-9418860/v1/2b4c1d32d5024345619733d3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Regional modeling of freshwater eutrophication in Argentina for Life Cycle Assessment","fulltext":[{"header":"Highlights","content":"\u003cp\u003eo Regional characterization factors for aquatic eutrophication were developed for Argentina.\u003c/p\u003e\u003cp\u003eo The model is based on the fate and transport of phosphorus to aquatic ecosystems.\u003c/p\u003e\u003cp\u003eo The use of regional characterization factors improves environmental representativeness.\u003c/p\u003e\u003cp\u003eo Global characterization factors underestimated the regional impact when applied to Argentina.\u003c/p\u003e\u003cp\u003eo The methodology is transferable to other regions of the world.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1. Concept and relevance of eutrophication\u003c/h2\u003e\n \u003cp\u003eAquatic ecosystems are closely related to their environment, and their dynamics are influenced by various factors at different spatial and temporal scales (Rechencq et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Eutrophication is the process of changing from one trophic state to a higher one as a result of the incorporation of nutrients (Schindler \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Paerl and Huisman \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which causes deterioration in water quality and biodiversity, modifies the trophic network and increases the biomass of certain communities (Wurtsbaugh et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The nutrients that most influence the process are phosphorus (P) and nitrogen (N). In some ecosystems, the limiting factor is the phosphate ion (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e), as is the case in most continental lentic systems, while in marine ecosystems the limiting factor is N (V\u0026aacute;squez Zapata et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2. Current methods in Life Cycle Assessment (LCA)\u003c/h2\u003e\n \u003cp\u003eLCA is a widely used approach for assessing environmental impacts associated with product systems (Muralikrishna and Manickam \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Life Cycle Impact Assessment (LCIA), there are various methods for characterizing eutrophication, which differ in their inventory requirements, geographic coverage, spatial resolution, and modeled emission pathways. Despite these differences, these methods have in common the assessment of the potential impact of eutrophication based on: the inventory of nutrients emitted, their environmental fate through fate factors (FF), which represent transport and attenuation to the receiving compartment (freshwater or coastal seawater), and the subsequent exposure of the ecosystem to nutrient enrichment (exposure factor) and the effect (effect factor, EF) on aquatic species. This process allows the calculation of a midpoint impact indicator representing an increase in nutrients in the water (Payen and Ledgard \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eInitially, FFs for N and P were derived from European models, although in the absence of FFs for other continents, they have been used outside Europe (Struijs et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The use of global CFs can lead to significant uncertainties when applied to specific environmental conditions in a region, hence the need for globally valid models, but with site-specific CFs (Payen and Ledgard \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), especially in countries in the southern hemisphere where LCA studies are scarce, such as Argentina (Conci et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e1.3. Geographical divide and the need for regionalization\u003c/h2\u003e\n \u003cp\u003eArgentina has a wide variety of water bodies\u003csup\u003e1\u003c/sup\u003e, although some of them suffer from severe eutrophication problems (Quir\u0026oacute;s \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), which explains the variation in trophic status (Quir\u0026oacute;s \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Given that in countries such as Argentina, data on nutrient fate may be limited, the selection and regionalization of CFs poses a challenge. Although the development of methodologies has improved the description and assessment of aquatic eutrophication, there is still a need for improvements. Having damage indicators, as has been achieved for other categories such as human toxicity or ozone layer depletion, is a long-term goal, since certain constraints must first be overcome, such as the inability to calculate the bioavailability of nutrients. In the short term, a significant improvement could be the calculation of CFs with spatial differentiation for regions outside Europe or North America (Schmid \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this context, the objective of this study is to develop regional indicators of aquatic eutrophication in freshwater ecosystems using the LCIA approach, following the environmental mechanism of the impact category and focusing on P emissions and the spatial differentiation of the impact. The research aims to provide methodological tools for the regional assessment of eutrophication in aquatic ecosystems in countries where site-dependent CFs are not available.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. Development of the regional midpoint model","content":"\u003cp\u003eThe research is part of the LCIA within the LCA, in accordance with IRAM-ISO 14044 (2008). Since its inception, the LCA methodology has developed various LCIA methods aimed at both midpoint and end-point impacts (Civit \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Various forms of P can be included in eutrophication modeling, with the phosphate ion (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e) being the most commonly used reference substance. Midpoint CFs are the product of a fate factor (FF) and an exposure factor (XF) (Morelli et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Endpoint CFs typically consist of a fate factor (FF), an exposure factor (XF), and an effect factor (EF), and characterize the potential damage that exposure to eutrophicating substances could cause to human health, ecosystem quality, or natural resource availability. In this research, midpoint CFs are addressed and an approximation of the EF is made, without actually measuring the damage.\u003c/p\u003e\n\u003cp\u003eThe methodological procedure followed these steps: definition of environmental variables, modeling of P fate and transport, calculation of CFs, and spatial aggregation at the regional scale. The proposed equation is based on the Schmid (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) model. The potential contribution to eutrophication of system A, denoted \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eEu\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(A)\u003c/em\u003e, is calculated as the result of nutrient inputs that cause increased biomass production in water bodies. Following the usual terminology in LCIA, the impact caused by aquatic eutrophication in a system \u003cem\u003e(A)\u003c/em\u003e within a geographical area can be expressed as follows (adapted from Sepp\u0026auml;l\u0026auml; et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003csup\u003e2\u003c/sup\u003e\u003c/span\u003e\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e) (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{I}_{Eu}\\left(A\\right)={\\sum\\:}_{j=1}^{n}{C}_{j,i}\\left(A\\right)*{E}_{j}\\left(A\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eEu\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(A)\u003c/em\u003e represents the value of the aquatic eutrophication impact caused by system A, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej,i\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(A)\u003c/em\u003e represents the CF of substance j produced by system A that reaches a given aquatic zone i, and \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(A)\u003c/em\u003e represents the amount of substance j emitted by system A. The higher the score of the value \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003eEu\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(A)\u003c/em\u003e, the more undesirable the system is. The impact category indicator in the method is algae growth (Sepp\u0026auml;l\u0026auml; et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIf effect and transport factors are considered, the CF \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej,i\u003c/em\u003e\u003c/sub\u003e can be calculated as (Schmid \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, adapted from Sepp\u0026auml;l\u0026auml; et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{C}_{j,i}\\left(A\\right)={n}_{j,i}\\left(A\\right)*{u}_{j,i}\\left(A\\right)*{Eqv}_{j}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{j,i}\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e is the transport factor for substance j (0\u0026le; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{j,i}\\)\u003c/span\u003e\u003c/span\u003e \u0026le;1) and indicates the portion of substance j that will reach a given aquatic zone i, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{j,i}\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e is the effect factor for substance j (0\u0026le; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{j,i}\\)\u003c/span\u003e\u003c/span\u003e \u0026le;1), indicating the amount of transported substance j that causes an increase in biomass production in zone i. Finally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Eqv}_{j}\\)\u003c/span\u003e\u003c/span\u003e is the equivalence factor for substance j. The equivalence factors for N and P can be expressed as PO\u003csub\u003e4\u003c/sub\u003e- equivalents (Sepp\u0026auml;l\u0026auml; et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Air emissions of P have not been taken into account as they have little significance in the generation of eutrophication (Potting et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThis paper proposes an extension of the model developed by Schmid (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), based on the methodology of Sepp\u0026auml;l\u0026auml; et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The assessment of the EF is expanded by quantifying it based on the relationship between the regional TP concentration and a reference value corresponding to the average TP concentration in water bodies according to the classification of the Organization for Economic Cooperation and Development (OECD 1982), which indicates that the degree of trophic status is quantified as the average annual concentration of chlorophyll-a (Cl-a) in that environment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOECD threshold values for the trophic classification (OECD 1982)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTrophic level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAverage TP (ug/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAverage Cl-a (ug/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMax. Cl-a (ug/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAverage DS\u003c/p\u003e\n \u003cp\u003e(m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eMin. DS\u003c/p\u003e\n \u003cp\u003e(m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUltra-oligotrophic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOligotrophic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMesotrophic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.5-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEutrophic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e35\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e8\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e25\u0026ndash;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.5\u0026ndash;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypereutrophic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003csup\u003eAverage PT: average PT concentration in a water body; Average Cl\u0026minus;a: average chlorophyll\u0026minus;a concentration; Max Cl\u0026minus;a: peak chlorophyll\u0026minus;a concentration; Average DS: average Secchi disk transparency; Min DS: minimum Secchi disk transparency\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFinally, it is multiplied by the equivalence factor of the emitting substance. Thus, the proposed Eq. (3) is defined as:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{j,i}\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e= \u0026eta; j,i (A) \u0026middot; \u0026mu;j,i (A) \u0026middot; Eq\u0026upsilon;j\u003c/em\u003e(3)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{j,i}\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e= \u0026eta; j,i (A) \u0026middot; (Ppi / Pu) \u0026middot; Eq\u0026upsilon;j\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u0026eta;j,i (A) is the transport factor for substance j and indicates the portion of substance j that will reach a given aquatic zone i, \u0026micro;j,i (A) is the effect factor of substance j and designates how much of the transported substance j causes an increase in biomass production in zone i, Ppi is the total P concentration present in zone i, Pu is the concentration of P in an eutrophicated water body, and Eq\u0026upsilon;j is the equivalence factor for substance j based on Heijungs et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). To reduce the variability and asymmetry of TP data between different water bodies, a base-10 logarithmic transformation (log\u003csub\u003e10\u003c/sub\u003e) is applied to obtain the Ppi value. This transformation allows the concentration scale to be homogenized and improves comparability between regions. The transformed values are normalized with respect to the logarithm of the maximum value (log\u003csub\u003e10\u003c/sub\u003e (TPmax)), obtaining a dimensionless index between 0 and 1 that expresses the relative magnitude of the potential eutrophication effect, where 1 represents the condition of greatest impact and values close to 0 represent the best environmental reference situation. Eq. (\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) for this calculation is defined as:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:{I\\:norm}=\\frac{{{log}}_{10}\\left(TPi\\right)}{{{log}}_{10}\\left(TPmax\\right)}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere I norm is the normalized TP index, TP\u003csub\u003ei\u003c/sub\u003e is the P concentration at site i, and TPmax is the maximum recorded value. This dimensionless index varies between 0 and 1, with 1 representing the condition of greatest effect. The same calculation was performed to determine Pu.\u003c/p\u003e\n\u003cp\u003eThe Regionalized Aquatic Eutrophication Potential of substance j, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{j,i}\\left(A\\right)\\)\u003c/span\u003e\u003c/span\u003e, is used as CF, combining transport and normalized effect in aquatic ecosystems, expressed in kg P eq.\u0026nbsp;The resulting impact, or category indicator, is interpreted as regionalized potential algal growth, representing the equivalent eutrophicating load attributable to a unit of P emission.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Transport routes\u003c/h2\u003e\n \u003cp\u003eFollowing Schmid (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the main transport routes of P in continental waters and coastal seas considered in this research. Sources are grouped into direct nutrient inputs to soil (such as fertilizers and manure) and wastewater from urban or industrial sources.\u003c/p\u003e\n \u003cp\u003eConsidering agricultural inputs, nutrients can be added directly to the soil and subsequently transported to aquatic ecosystems where they cause eutrophication. The most important sources of this input are fertilizers and manure used in agriculture. Considering the transport route, Eq. 5 calculates the CF of P added to the soil:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eCj, suelo, i = (\u0026tau;j \u0026middot; \u0026sigma;j, i) \u0026middot; \u0026micro;j, i \u0026middot; Eq\u0026upsilon;j\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere Cj, soil, i is the CF for substance j (P) contributed directly to the soil through different inputs (usually fertilizers or manure) that reaches a water body i (inland waters or coastal seas). In turn, \u0026tau;j describes the fraction of substance j present in the soil after consumption by plants and \u0026sigma;j, i the fraction of substance j present in the soil that reaches water body i.\u003c/p\u003e\n \u003cp\u003eAs for wastewater emissions from industries and households that, after being treated or not in treatment plants, are discharged into aquatic ecosystems, the CF of the P emissions contained therein is estimated (Eq. 6):\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eCj, residuales, i\u0026thinsp;=\u0026thinsp;\u0026gamma;j \u0026middot; \u0026micro;j, i \u0026middot; Eq\u0026upsilon;j\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere Cj,residual,i is the CF for the eutrophying substance j (P) present in wastewater reaching an aquatic zone i (inland waters or coastal seas), and \u0026gamma;j represents the fraction of the total substance j emitted that remains in the water after passing through treatment plants.\u003c/p\u003e\n \u003cp\u003eUnlike the model proposed by Schmid (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which assigns a generic effect factor (value 1) to the receiving ecosystem, this approach introduces regionalized values based on P concentration data in water bodies within the national territory. Eq. 7 breaks down the effect factor:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026micro; j, i (A) = (Ppi / Pu)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u0026micro; j, i (A) is the effect factor of substance j and designates the amount of substance j transported that causes an increase in biomass production in zone i, Ppi is the total P concentration present in zone i, Pu is the P concentration of an eutrophicated water body, as detailed above.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Model validation\u003c/h2\u003e\n \u003cp\u003eEutrophication represents a regional environmental impact, and it may be necessary to develop models based on location (Henryson et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To validate the proposed model, the geographical regionalization of Argentina based on the proposal by Quir\u0026oacute;s (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) was selected (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMain average limnological characteristics of the water bodies studied (Quir\u0026oacute;s \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWater body districts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eN\u0026deg; of environments in each district\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eGeoregion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eTN*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eTN:TP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eCl-a (mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChaco-pampa\u003c/p\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003cp\u003e1984\u003c/p\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003cp\u003e1288\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e8583\u003c/p\u003e\n \u003cp\u003e28750\u003c/p\u003e\n \u003cp\u003e2660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e63.8\u003c/p\u003e\n \u003cp\u003e405.3\u003c/p\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWest and Northwest\u003c/p\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003cp\u003e11200\u003c/p\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003cp\u003e218.1\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAndrean Patagonia (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e103.7\u003c/p\u003e\n \u003cp\u003e1466\u003c/p\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e697\u003c/p\u003e\n \u003cp\u003e2242\u003c/p\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003cp\u003e54.1\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePatagonian Plain (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e252.3\u003c/p\u003e\n \u003cp\u003e816\u003c/p\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2515\u003c/p\u003e\n \u003cp\u003e7900\u003c/p\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e*TN: Total N\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eRegional CFs for aquatic eutrophication in Argentina were estimated based on transport values and normalized TP values. A representative CF was obtained for each of the regions considered, reflecting local conditions and relative susceptibility to impact. In addition, two integrative values were calculated for the country: a national arithmetic mean and a surface-weighted mean.\u003c/p\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e1.1.1. Fertilizers and impact on water\u003c/h2\u003e\n \u003cp\u003eArgentine soils have a robust level of nutrients, where the low national demand for fertilizers is not consistent with Argentina\u0026apos;s global relevance in the world grain markets. 2021 closed as the year with the highest fertilizer consumption, with a total of around 5.6\u0026nbsp;million tons (Rebora et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Agriculture has altered the flow of phosphorus into the oceans (Andrade et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), promoting eutrophication, an increase in biomass, changes in the composition of algal species, and fluctuations in pH and dissolved oxygen, thereby affecting water quality (Smith et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e1.1.2. Wastewater data in Argentina\u003c/h2\u003e\n \u003cp\u003eSeventy-five percent of Argentina\u0026apos;s territory is arid or semi-arid (Abraham and Salom\u0026oacute;n \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Most of the water is used for agricultural and industrial production, while only 10% is used for human consumption. Katopodis et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicates that 63.1% of urban areas have sewerage coverage, meaning that around 15.7\u0026nbsp;million people lack access to sewerage networks and regional inequalities are evident. Many treatment plants have maintenance deficiencies, affecting effluent quality. Of 600 treatment plants, 376 (serving more than 10,000 inhabitants) serve some 22\u0026nbsp;million inhabitants. Seventy-one percent of the plants are in fair or poor condition, and lagoon systems show the greatest deterioration (81%). Only 27.6% of the wastewater collected is treated properly. Of the total effluent generated (3.2\u0026nbsp;billion m\u003csup\u003e3\u003c/sup\u003e/year), only 17.3% reaches plants in good condition, while 38.9% are partially treated and 6.9% are discharged untreated into receiving water bodies.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results obtained for Argentina","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Direct agricultural inputs to the soil\u003c/h2\u003e\n \u003cp\u003eAccording to Eq.\u0026nbsp;5, the variables for the CF of P contributed to the soil are calculated. To determine the fraction of substance j present in the soil after consumption by plants, the value given by Bonadeo et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) is taken, which indicates that of the total P in the plant, 50% or more is found as inorganic phosphates, which predominate in the conducting or reserve organs, while in the reproductive organs (e.g., seeds), they are mainly found in organic forms. Due to low requirements, generally within the soil-plant-animal system, less than 10% of total P enters the plant-animal life cycle, and more than 90% remains in the soil. That is, 90% of P will be available, establishing \u0026tau;j\u0026thinsp;=\u0026thinsp;0.9.\u003c/p\u003e\n \u003cp\u003eTo calculate \u0026sigma;j, i, the fraction of substance j present in the soil that reaches water body i, values available in SimaPro for three different crops are considered, using the ReCiPe 2016 Midpoint method, as no other more regional values are available. Thus, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e emissions transported by water to the river for tomatoes (Spain) were 0.00001 kg, for soybeans (Argentina) 0.0002 kg, and for corn (Argentina) 0.00010 kg, obtaining an average and defining \u0026sigma;j\u0026thinsp;=\u0026thinsp;0.33.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Wastewater discharges into aquatic ecosystems\u003c/h2\u003e\n \u003cp\u003eFollowing Eq.\u0026nbsp;6, the fraction of total P emitted that remains in the water after passing through treatment plants is calculated based on data provided by INDEC (2025) and Katopodis et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to the latest national census, the Argentine population is estimated at 45,892,285 inhabitants for the year 2022 (INDEC 2025), with a production of 0.975 kg P/capita/year, an average value derived from different authors (Rodr\u0026iacute;guez et al. 2022), and an annual production of 44,744.98 t P is obtained.\u003c/p\u003e\n \u003cp\u003eFrom this, the nutrients consumed in treatment plants are subtracted, taking into account that 17.3% of the population is connected to adequate treatment and 38.9% to inadequate treatment, leaving 6.9% without treatment (Katopodis et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As the outfalls are primary treatment facilities, 30% (0.3) correspond to this type and the remaining 70% (0.7) to secondary treatment facilities. Considering that primary and secondary treatment facilities produce a removal rate of 10% (0.1) and 42% (0.42) of P, respectively (in the absence of specific data for Argentina, values published in Schmid \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e are used), the amount of P treated in treatment plants for Argentina is calculated to be 14,497.37 t P/year (32.4% of the total amount produced). Based on this, 30,247.61 t P/year are discharged into the environment. Finally, it is obtained that in Argentina the fraction of the total P emitted that remains in the water after passing through the treatment plants is \u0026gamma;j\u0026thinsp;=\u0026thinsp;0.68.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Calculation of the regional effect factor\u003c/h2\u003e\n \u003cp\u003eTo calculate Ppi, the maximum TP values in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of Quir\u0026oacute;s (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) are used, and to calculate Pu, the maximum limit values defined by the OECD (1982) as the worst environmental situation (Eq. 7) are used. Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of the log\u003csub\u003e10\u003c/sub\u003e applied and its normalization to obtain the effect factor in each region, where each unit in log\u003csub\u003e10\u003c/sub\u003e represents an order of magnitude in the concentration of P.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTP values to define the effect factor.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eChaco-\u003c/p\u003e\n \u003cp\u003epampa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWest and\u003c/p\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAndrean Patagonia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ePatagonian Plain\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003eRange for Argentina (Quir\u0026oacute;s \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTP present mean*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP mean)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP mean) norm**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTP present max*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP max)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP max) norm**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTP present min*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP min)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP min) norm**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003eOECD range (1982)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTP threshold max*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP max)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP max) norm**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTP threshold min*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP min)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003elog\u003csub\u003e10\u003c/sub\u003e(TP min) norm**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003e\u0026micro; Argentina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFC final effect max**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e* Min= minimum; max= maximum. TP expressed in mg/m3; **Dimensionless unit. Norm= normalized\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Transport routes of P to Argentine freshwater ecosystems\u003c/h2\u003e\n \u003cp\u003eFollowing equations 5 and 6, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of the regional CFs obtained for each of the transport routes considered.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eP concentrations in wastewater and P concentrations from direct agricultural inputs to soil\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eCFs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eChaco-pampa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWest and\u003c/p\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAndrean Patagonia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ePatagonian Plain\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026gamma;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026micro; max*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEq\u0026upsilon;**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCj,residuals,i**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026tau;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026sigma;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026micro; max*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEq\u0026upsilon;**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCj,soil,i**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e* Dimensionless unit; **Expressed in kg PO\u003c/sup\u003e\u003csub\u003e4\u003c/sub\u003e \u003csup\u003e3\u0026minus; eq/kg substance j emitted\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Regional midpoint CFs\u003c/h2\u003e\n \u003cp\u003eThe CFs obtained for the study region in kg P eq are presented below. Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the calculation for estimating the weighted average CF per area (data from Tables \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCalculation of the weighted average CF per area\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWeighted average CF per area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSubstance j\u0026thinsp;=\u0026thinsp;P (water)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSubstance j\u0026thinsp;=\u0026thinsp;P (soil)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChaco-pampa\u003c/p\u003e\n \u003cp\u003eWest and\u003c/p\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003cp\u003eAndrean Patagonia\u003c/p\u003e\n \u003cp\u003ePatagonian Plain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRegional CFs (kg P eq\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e) * regional area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e66.477\u003c/p\u003e\n \u003cp\u003e17.878\u003c/p\u003e\n \u003cp\u003e34.348\u003c/p\u003e\n \u003cp\u003e153.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e29.035\u003c/p\u003e\n \u003cp\u003e7.808\u003c/p\u003e\n \u003cp\u003e15.002\u003c/p\u003e\n \u003cp\u003e66.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWeighted total sum (kg P eq * km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e271.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e118.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSum of areas (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e484.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e484.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWeighted average\u003c/p\u003e\n \u003cp\u003e(kg P eq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the values from Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and presents the final CFs for P emitted to water and soil: by region and national arithmetic mean.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegional CFs, national arithmetic mean, and weighted by area.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCFs (kg P eq)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSubstance j\u0026thinsp;=\u0026thinsp;P (water)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSubstance j\u0026thinsp;=\u0026thinsp;P (soil)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFor region\u003c/p\u003e\n \u003cp\u003eChaco-pampa\u003c/p\u003e\n \u003cp\u003eWest and\u003c/p\u003e\n \u003cp\u003eNorthwest\u003c/p\u003e\n \u003cp\u003eAndrean Patagonia\u003c/p\u003e\n \u003cp\u003ePatagonian Plain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNational arithmetic mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eArea-weighted mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAn analysis of each region shows that the Chaco-pampa is the region with the greatest potential impact, due to a high average concentration of nutrients in water bodies, making it a critical area in terms of eutrophication. This is followed by the Patagonian Plain, the western and northwestern regions of the country, and finally the Andean Patagonia, which is the region with the lowest impact, possibly due to a low concentration of TP, lower ecological sensitivity, or lower connectivity of nutrients with vulnerable water bodies.\u003c/p\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e2.5.1. Application of CFs obtained in a specific LCA\u003c/h2\u003e\n \u003cp\u003eFollowing Eq. \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, data from the Life Cycle Inventory and freshwater aquatic eutrophication CFs calculated in an LCA of tomatoes produced in the Uco Valley region, located in the central-western part of the province of Mendoza, Argentina (Piastrellini et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), were used. The LCA was carried out using SimaPro software, using the CML Baseline model (World 2000). The global CF for aquatic eutrophication is set at 0.000163 kg PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e eq. If the amount of fertilizer emitted during all stages of the production process of 1 kg of tomatoes is 0.000381 kg P and the outflow to surface water is 0.00000516 PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e eq, the impact is 0.0000000202 kg P eq for the input and 0.000000000274 kg P eq for the outflow. When applying the CF for the West and Northwest region, the results are as follows (Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of the application case.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSubstance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEmissions\u003c/p\u003e\n \u003cp\u003e(kg P)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCFs\u003c/p\u003e\n \u003cp\u003e(kg P eq)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eI\u003csub\u003eEu\u003c/sub\u003e (A)\u003c/p\u003e\n \u003cp\u003e(kg P eq)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eGlobal impact\u003c/p\u003e\n \u003cp\u003e(kg P eq)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003ePercentage variation between impacts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eP (water)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.000381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.000225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.0000000202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.00897%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eP (soil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.000381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.0000971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.0000000202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.02080%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIf the national arithmetic mean CF is applied, the result is 0.000209 kg P eq for P in water and 0.0000914 kg P eq for P in soil. Finally, using the weighted average CF per surface area, we obtain 0.000213 kg P eq for P in water and 0.0000933 kg P eq for P in soil.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Transport routes and regional characteristics for defining CFs\u003c/h2\u003e \u003cp\u003eThe Chaco-Pampas regions exhibit higher values associated with eutrophic, hyper-eutrophic, or saline aquatic ecosystems, whereas the Patagonian Andes and Tierra del Fuego exhibit ultra-oligotrophic to oligotrophic conditions (Drago and Quir\u0026oacute;s \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Quir\u0026oacute;s et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The trophic states of waters on the Patagonian plateau range from mesotrophic to eutrophic, as do most in the central-west and northwest (Quir\u0026oacute;s \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). At higher nutrient concentrations and shallower depths, these ecosystems exhibit greater algal biomass (Quir\u0026oacute;s \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This supports the ability of the proposed approach to capture regional environmental heterogeneity, an aspect particularly relevant in countries with high hydrological variability such as Argentina. The differences observed between regions are consistent with their characteristic trophic states, and the calculated CFs represent the maximum potential contribution of P to biomass growth (Schmid \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Thus, the calculation of the regional EF highlights that, in the Patagonian Plain, the average TP (log10\u0026thinsp;=\u0026thinsp;2.045) is more than an order of magnitude higher than that of Andean Patagonia (0.845), which is reflected in the final CFs. This allows for a comparison of the regions and the expression of differences in impact without extreme values dominating the scale due to regions with very large differences in TP concentration.\u003c/p\u003e \u003cp\u003eTomato cultivation was selected as the case study due to its regional significance, intensive use of fertilizers, and availability of primary data. It follows that the use of global factors underestimates and fails to capture the potential impact of aquatic eutrophication in the study area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Comparison with existing models\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents a comparison between the CFs obtained in this study (using national arithmetic mean CFs) and those available in the literature for Argentina, which are scarce and global in scope. Three different approaches are presented for calculating CFs for freshwater eutrophication caused by phosphorus in Argentina, derived from global, national, and regionally scaled national models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of CFs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCFs (kg P eq / kg substance emitted)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWorld*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eArgentina**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eArgentina regionalized***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePercentage variation between the model by\u003c/b\u003e Payen et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) \u003cb\u003eand the CFs obtained\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP (water)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP (soil)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e*IMPACT World+ Midpoint V1.05 (Bulle et al. 2019. Calculation of aquatic eutrophication based on the model by Helmes et al. 2012); **Global calculation (model by Payen et al. 2021); ***National arithmetic mean with regional CFs\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe values obtained reveal a substantial difference between the calculated CFs (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The impact of eutrophication in the study by Bulle et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) is spatially assessed using the global model with a resolution of 0.5\u0026deg; \u0026times; 0.5\u0026deg; developed by Helmes et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), based on global hydrological data. The IMPACT World+ global model (Bulle et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) assigns a value of 0.008 kg P eq/kg P, representing a global average. This value tends to underestimate the potential impact of eutrophication when applied to local contexts. Meanwhile, the model by Payen et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) incorporates explicit spatial differentiation and estimates a CF for Argentina of 1.004 kg P eq/kg P (water) and 0.894 kg P eq/kg P (soil). These values represent an improvement over global factors by including regional P export and fate parameters. The authors developed the fate factors for application to both diffuse soil emissions and point source nutrient emissions to freshwater, at the watershed scale with global coverage and at the national and global scales through weighted aggregation of emissions and the distinction between agricultural and non-agricultural emissions. The modeled fate processes include nutrient attenuation from land to water bodies and within them. However, their direct application in local studies requires a level of resolution for hydrological and emission data that is not always available. Finally, the regional CFs obtained in this study (0.550 kg P eq/kg P for water and 0.240 kg P eq/kg P for soil) show a reduction compared to the national values obtained by Payen et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that incorporating local information, such as limnological quality, the effective surface area of water bodies, and regional variability in nutrient retention and transport processes, allows for a more accurate representation of the actual potential magnitude of the impact. The use of regional CFs improves environmental representativeness and avoids over- or underestimation of the impact that can occur when applying generic or global-scale factors in local LCA studies. The developed regional model, by using observed TP concentrations and normalizing the values relative to the maximum (critical case), more accurately reflects the eutrophic response of aquatic ecosystems, reducing the uncertainty associated with the use of generic factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Advantages and methodological innovation\u003c/h2\u003e \u003cp\u003eThe methodological innovation lies in replacing the generic value with regionalized EFs derived from empirical data on TP concentrations in Argentine water bodies. The model retains the structure based on the environmental mechanisms of P (emission-transport-impact) but introduces an explicit representation of the sensitivity of the regional aquatic ecosystem without compromising compatibility with the LCA methodological framework. Due to the great diversity of its aquatic ecosystems, this approach is not implemented at the watershed scale because it would generate such a high number of CFs that the tool would become impractical (Schmid \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); therefore, the regional scale is appropriate for Argentina.\u003c/p\u003e \u003cp\u003eLCA end users are provided with CFs for specific regions, a national average, and a value weighted by land area. The methodology developed is replicable in other regions of the world provided that regional water quality data are available, addressing the growing need for spatial regionalization in LCA. For the end user, the approach provides CFs directly applicable in LCA studies, allowing the eutrophic impact of a given production system to be differentiated based on the geographic location of its emissions. The selection of the CF will be based on the type of study and the spatial aggregation level of the LCA. Thus, regional CFs are more suitable for assessments with a spatially differentiated approach. The national arithmetic average represents the country\u0026rsquo;s average performance without considering the size of the regions. In contrast, the area-weighted average shows that each CF contributes in proportion to the area it occupies within the national territory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Limitations and opportunities for improvement\u003c/h2\u003e \u003cp\u003eThe lack of data on diffuse sources of phosphorus is acknowledged, although it is more difficult to model discharges than emissions, and models are generally unable to calculate aquatic eutrophication with precision and accuracy (Resano Goizueta \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, the study could be expanded to other regions of the country, and the TP values could be updated. However, when specific water bodies are analyzed, it is observed that TP values fall within the range for each region (e.g., And\u0026eacute;n and Pizzolon \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the future, land-use data could be included to improve the model\u0026rsquo;s regionalization.\u003c/p\u003e \u003cp\u003eIn a given body of water, eutrophication may be limited by P or N, or co-limited by both nutrients, depending on the ratio of P to N and their concentrations (Francoeur et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Zhou et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) developed globally regionalized CFs for the impacts of P and N and indicate that eastern South America (Argentina) belongs to regions with P limitation and undesirable periphyton growth. Therefore, in this study, P is considered the limiting nutrient for freshwater eutrophication, and no CFs have been derived for N in water in Argentina; this could be a point to consider in future research, incorporating atmospheric deposition as well as transport pathways in soil and water. Likewise, other aspects not addressed in this study could be examined, such as the analysis of environmental conditions involving non-homogeneous or non-steady-state mixtures in aquatic ecosystems, since the regional parameters here represent average environmental behaviors and temporal variability is not modeled.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study proposes a methodological extension for assessing aquatic eutrophication within the framework of the LCIA using site-dependent CFs, which incorporate spatial differentiation based on regional empirical data and maintain the consistency of the LCA\u0026rsquo;s cause-effect chain. The developed CFs represent the Regionalized Aquatic Eutrophication Potential for P, integrating transport processes and the normalized effect of the receiving ecosystem. Their application in Argentina allowed for the definition of three levels of spatial aggregation (regional, national arithmetic mean, and area-weighted average) that facilitate the adaptation of the indicator to different study objectives. The results show that global factors may overestimate or underestimate impacts in specific contexts, highlighting the need for regionalization to improve environmental representativeness. The methodology is transferable to other regions of the world through the incorporation of local data and transforms the LCA-based eutrophication assessment into a tool sensitive to the environmental context, useful for supporting sustainability decisions and water resource management. The impact model identifies regions most susceptible to eutrophication processes and promotes responsible production and consumption strategies aligned with the Sustainable Development Goals related to water and the protection of aquatic ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded by a doctoral grant from the National Scientific and Technical Research Council (CONICET, RESOL-2020-126-APN-DIR#CONICET) and was carried out within the Cliope Group \u0026ldquo;Energy, Environment, and Sustainable Development\u0026rdquo; with the support of the National Technological University, Mendoza Regional Faculty (UTN-FRM), Argentina.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEC: conceptualization, methodology, research, formal organization and analysis, and original writing; ARB: writing-review and editing; APA: writing-review and editing; BMC: writing-review and editing and analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Argentina's National Scientific and Technical Research Council (CONICET) for its support during the development of this work, carried out within the framework of an Internal Doctoral Scholarship awarded by the institution, as part of the Cliope group at UTN-FRM, Argentina.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbraham EM, Salom\u0026oacute;n M (2011) Experiencias de combate a la desertificaci\u0026oacute;n en Mendoza. En: da Cunha Correia Lima R, de Mendon\u0026ccedil;a Barreto Cavalcante A, Perez Marin AR (eds) Desertifica\u0026ccedil;\u0026atilde;o e mudan\u0026ccedil;as clim\u0026aacute;ticas no semi\u0026aacute;rido brasileiro. Instituto Nacional do Semi\u0026aacute;rido, Ministerio de Ciencia, Tecnolog\u0026iacute;a de Brasil y Embrapa Semi\u0026aacute;rido, pp 183\u0026ndash;206. ISBN 978-85-64265-02-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnd\u0026eacute;n DA, Pizzolon LA (2025) Procesos de eutrofizaci\u0026oacute;n en los r\u0026iacute;os Azul y Quemquemtreu, subcuenca del Lago Puelo. Argentina Ecolog\u0026iacute;a Austral 35(1):001\u0026ndash;015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.25260/EA.25.35.1.0.2335\u003c/span\u003e\u003cspan address=\"10.25260/EA.25.35.1.0.2335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade FH (Comp.), Taboada M, Lema D, Maceira N, Echeverr\u0026iacute;a H, Posse G, Prieto D, S\u0026aacute;nchez E, Ducasse D, Bogliani M, Gamundi JC, Trumper E, Frana J, Perotti E, Fava F, Mastr\u0026aacute;ngelo M (eds) (2017) Los desaf\u0026iacute;os de la agricultura argentina. Ediciones INTA. 1\u0026ordf; ed. Ciudad Aut\u0026oacute;noma de Buenos Aires. ISBN 978-987-521-859-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartrol\u0026iacute; J (2003) Avaluaci\u0026oacute; ambiental del cicle del nitrogen a Catalunya: aplicaci\u0026oacute; de l\u0026acute;an\u0026agrave;lisi del flux de subst\u0026agrave;ncies. [Tesis de doctorado, Universidad de Gerona]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dugi-doc.udg.edu/bitstream/handle/10256/45/1%20MEM%C3%92RIA%20AVALUACIO%20NITROGEN%20CATALUNYA_2003.pdf?sequence=8\u003c/span\u003e\u003cspan address=\"https://dugi-doc.udg.edu/bitstream/handle/10256/45/1%20MEM%C3%92RIA%20AVALUACIO%20NITROGEN%20CATALUNYA_2003.pdf?sequence=8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonadeo E, Moreno I, Bongiovanni M, Marzari R, Ganum Gorriz MJ (2017) El sistema suelo-planta. Principios generales. (1\u0026ordf; ed.). UniR\u0026iacute;o Editora. ISBN 978-987-688-204-0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulle C, Margni M, Patouillard L, Boulay AM, Bourgault G, De Bruille V, Cao V, Hauschild M, Henderson A, Humbert S, Kashef-Haghighi S, Kounina A, Laurent A, Levasseur A, Liard G, Rosenbaum RK, Roy PO, Dhaked S, Fantke P, Jolliet O (2019) IMPACT World+: a globally regionalized life cycle impact assessment method. Int J Life Cycle Assess 24(9):1653\u0026ndash;1674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-019-01583-0\u003c/span\u003e\u003cspan address=\"10.1007/s11367-019-01583-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCivit BM (2009) Sostenibilidad ambiental. Desarrollo de indicadores para su aplicaci\u0026oacute;n en estudios de an\u0026aacute;lisis de ciclo de vida en la regi\u0026oacute;n \u0026aacute;rida del centro-oeste argentino. [Tesis de doctorado no publicada, Universidad Nacional de Cuyo]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConci E, Becker AR, Arena AP, Civit BM (2022) A literature review of eutrophication in Life Cycle Assessment. Relevance for pampean agroecosystems in Argentina. S Fla J Dev 3(1):618\u0026ndash;643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.46932/sfjdv3n1-046\u003c/span\u003e\u003cspan address=\"10.46932/sfjdv3n1-046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrago E, Quir\u0026oacute;s R (1995) The hydrochemistry of the inland waters of Argentina: a review. Int J Salt Lake Res 4(4):315\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF01999115\u003c/span\u003e\u003cspan address=\"10.1007/BF01999115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEcoinvent (20 de diciembre (2016) de M\u0026eacute;todo de EICV-Datos: EF-Eutrophication, freshwater. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ecoinvent.lca-data.com/datasetdetail/lciamethod.xhtml?uuid=b53ec18f-7377-4ad3-86eb-cc3f4f276b2b\u003c/span\u003e\u003cspan address=\"https://ecoinvent.lca-data.com/datasetdetail/lciamethod.xhtml?uuid=b53ec18f-7377-4ad3-86eb-cc3f4f276b2b\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026amp;version=01.00.009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFolke J (1996) Phosphate, zeolite and citrate in detergents-Technical and environmental aspects of detergent builder systems (Report n\u0026ordm; 95002/06)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancoeur SN, Biggs BJF, Smith RA, Lowe RL (1999) Nutrient limitation of algal biomass accrual in streams: seasonal patterns and a comparison of methods. J North Am Benthol Soc 18:242\u0026ndash;260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi/10.2307/1468463#\u003c/span\u003e\u003cspan address=\"doi/10.2307/1468463#\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallego A, Hospido A, Moreira MT, Feijoo G (2008) Environmental performance of wastewater treatment plants for small populations. Resour Conserv Recycl 52(6):931\u0026ndash;940. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.resconrec.2008.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.resconrec.2008.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeijungs R, Guine\u0026eacute; JB, Huppes G, Lankreijer RM, Udo de Haes HA, Weneger A, Ansems P, Eggels PG, van Duin R, de Goede H (1992) Environmental life cycle assessment of products. Guide and Backgroud (Part 1). CML Leiden, Netherlands\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelmes RJ, Huijbregts MA, Henderson AD, Jolliet O (2012) Spatially explicit fate factors of phosphorous emissions to freshwater at the global scale. Int J Life Cycle Assess 17(5):646\u0026ndash;654. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-012-0382-2\u003c/span\u003e\u003cspan address=\"10.1007/s11367-012-0382-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenryson K, Hansson PA, Sundberg C (2017) Spatially differentiated midpoint indicator for marine eutrophication of waterborne emissions in Sweden. Int J Life Cycle Assess 23(1):70\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-017-1298-7\u003c/span\u003e\u003cspan address=\"10.1007/s11367-017-1298-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Nacional de Estad\u0026iacute;stica y Censos de la Rep\u0026uacute;blica Argentina (2025) Censos. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.indec.gob.ar/indec/web/Nivel3-Tema-2-41\u003c/span\u003e\u003cspan address=\"https://www.indec.gob.ar/indec/web/Nivel3-Tema-2-41\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIRAM-ISO (2008) Environmental management - Life cycle assessment - Requirements and guidelines 14044\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatopodis GN, Rodr\u0026iacute;guez CA, Z\u0026aacute;rate FJ, Regueira JM (2023) Diagn\u0026oacute;stico y Prospectivas del Tratamiento de Aguas Residuales en Argentina. Direcci\u0026oacute;n de Comunicaci\u0026oacute;n Estrat\u0026eacute;gica-CAF. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scioteca.caf.com/handle/123456789/2153\u003c/span\u003e\u003cspan address=\"https://scioteca.caf.com/handle/123456789/2153\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorelli B, Hawkins TR, Niblick B, Henderson AD, Golden HE, Compton JE, Cooter EJ, Bare JC (2018) Critical Review of Eutrophication Models for Life Cycle Assessment. Environ Sci Technol 52(17):9562\u0026ndash;9578. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.est.8b00967\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.8b00967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuralikrishna I, Manickam V (2017) Life cycle assessment. In: Muralikrishna I, Manickam V (eds) Environmental Management, pp 57\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-12-811989-1.00005-1\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-811989-1.00005-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganizaci\u0026oacute;n para la Cooperaci\u0026oacute;n y el Desarrollo Econ\u0026oacute;mico (1982) Eutrophication of Waters. Monitoring, Assessment and Control. Cooperative Programmers on Monitoring of Inland Waters (Eutrophication Control), Environment Directorate, OECD Paris, Final Report\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaerl HW, Huisman J (2008) Blooms like it hot. Sci 320(5872):57\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1155398\u003c/span\u003e\u003cspan address=\"10.1126/science.1155398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePayen S, Cosme N, Elliott AH (2021) Freshwater eutrophication: spatially explicit fate factors for nitrogen and phosphorus emissions at the global scale. Int J Life Cycle Assess 26(2):388\u0026ndash;401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-020-01847-0\u003c/span\u003e\u003cspan address=\"10.1007/s11367-020-01847-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePayen S, Ledgard SF (2017) Aquatic eutrophication indicators in LCA: Methodological challenges illustrated using a case study in New Zealand. J Clean Prod 168:1463\u0026ndash;1472. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2017.09.064\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2017.09.064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiastrellini R, R\u0026oacute;tolo GC, Arena AP, Civit BM, Curadelli S (2024) Evaluation of the environmental sustainability of agricultural production using the methodologies of emergy analysis and life cycle assessment. Case study, tomato grown in Mendoza (Argentina). Clean Circ Bioeconomy 8:100082. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clcb.2024.100082\u003c/span\u003e\u003cspan address=\"10.1016/j.clcb.2024.100082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotting J, Beusen A, \u0026Oslash;llgaard H, Hansen OC, de Haan B, Hauschild M (2005) Aquatic eutrophication. In: Potting J, Hauschild M (eds) Background for spatial differentiation in life cycle impact assessment - The EDIP2003 methodology, Danish Environmental Protection Agency, pp 89\u0026ndash;112\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuir\u0026oacute;s R (1991) Empirical relationships between nutrients, phyto-and zooplankton and relative fish biomass in lakes and reservoirs of Argentina. Verh Internat Verein Limnol 24(2):1198\u0026ndash;1206\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuir\u0026oacute;s R (2000) 16 y 17 de marzo) La eutrofizaci\u0026oacute;n de las aguas continentales de Argentina. I Reuni\u0026oacute;n de la Red Tem\u0026aacute;tica sobre Eutrofizaci\u0026oacute;n de Lagos y Embalses. Subprograma XVII. Cooperaci\u0026oacute;n Iberoamericana. Ciencia y Tecnolog\u0026iacute;a para el Desarrollo (CYTED), Mar del Plata, Buenos Aires, Argentina\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuir\u0026oacute;s R (2004) Cianobacterias en lagos y embalses de Argentina: d\u0026eacute;cada del 80. Serie de documentos de trabajo del \u0026Aacute;rea de sistemas de producci\u0026oacute;n acu\u0026aacute;tica. Departamento de producci\u0026oacute;n animal, Facultad de agronom\u0026iacute;a. Universidad de Buenos Aires, 2, pp 1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuir\u0026oacute;s R, Rennella A, Boveri M, Rosso JJ, Sosnovsky A (2002) Factores que afectan la estructura y el funcionamiento de las lagunas pampeanas. Ecolog\u0026iacute;a Austral 12:175\u0026ndash;185 ISSN 1667-782X\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRebora C, Bertona A, Ibarguren L (2023) Uso de fertilizantes en la producci\u0026oacute;n de granos en Argentina: situaci\u0026oacute;n actual. Experticia, 1(14)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRechencq M, Fern\u0026aacute;ndez MV, Lallement ME, Alonso MF, Macchi PJ, Sosnovsky A (2024) El estado tr\u0026oacute;fico de los arroyos andino-patag\u0026oacute;nicos en un gradiente de urbanizaci\u0026oacute;n. Ecolog\u0026iacute;a Austral 34(3):422\u0026ndash;434. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.25260/EA.24.34.3.0.2342\u003c/span\u003e\u003cspan address=\"10.25260/EA.24.34.3.0.2342\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResano Goizueta P (2019) Revisi\u0026oacute;n del an\u0026aacute;lisis del ciclo de vida como herramienta de evaluaci\u0026oacute;n de impactos ambientales [Trabajo Fin de Estudios de Grado, Universidad P\u0026uacute;blica de Navarra]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://academica-e.unavarra.es/entities/publication/ba71d387-3a09-4d81-8294-8cb7ecab46a2\u003c/span\u003e\u003cspan address=\"https://academica-e.unavarra.es/entities/publication/ba71d387-3a09-4d81-8294-8cb7ecab46a2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez A, Avena M, Rodr\u0026iacute;guez MI, Cossavella A, Oron\u0026aacute; C, del Olmo S, Larrosa N, Baz\u0026aacute;n R, Corral M (2002) Estimaci\u0026oacute;n de aportes de nutrientes de f\u0026oacute;sforo a los embalses San Roque y Los Molinos en C\u0026oacute;rdoba, Argentina e implicancias en su gesti\u0026oacute;n. Ingenier\u0026iacute;a Sanitaria y Ambiental 60:45\u0026ndash;51\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmid AG (2008) Diferenciaci\u0026oacute;n espacial en la metodolog\u0026iacute;a de An\u0026aacute;lisis del Ciclo de Vida: desarrollo de factores regionales para eutrofizaci\u0026oacute;n acu\u0026aacute;tica y terrestre [Tesis de doctorado, Universidad Santiago de Compostela]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biogroup.usc.es/sites/default/files/AlejandroGallego.pdf\u003c/span\u003e\u003cspan address=\"https://biogroup.usc.es/sites/default/files/AlejandroGallego.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSepp\u0026auml;l\u0026auml; J, Knuuttila S, Silvo K (2004) Eutrophication of aquatic ecosystems a new method for calculating the potential contributions of nitrogen and phosphorus. Int J Life Cycle Assess 9(2):90\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02978568\u003c/span\u003e\u003cspan address=\"10.1007/BF02978568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindler DW (2006) Recent advances in the understanding and management of eutrophication. Limnol oceanogr 51:356\u0026ndash;363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4319/lo.2006.51.1_part_2.0356\u003c/span\u003e\u003cspan address=\"10.4319/lo.2006.51.1_part_2.0356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith VH, Tilman GD, Nekola JC (1999) Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ pollut 100(1\u0026ndash;3):179\u0026ndash;196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0269-7491(99)00091-3\u003c/span\u003e\u003cspan address=\"10.1016/S0269-7491(99)00091-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStruijs J, Beusen A, van Jaarsveld H, Huijbregts MAJ (2009) Eutrophication. In: Goedkoop M, Heijungs R, Huijbregts MJA, De Schryver A, Struijs J, van Zelm R (eds) ReCiPe 2008. A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level, 1st ed. Report 1: Characterisation, pp 59\u0026ndash;67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026aacute;squez Zapata GL, Herrera Orozco L, Cantera Kintz JR, Galvis Casta\u0026ntilde;o A, Cardona Zea DA, Hurtado S\u0026aacute;nchez IC (2012) Metodolog\u0026iacute;a para determinar niveles de eutrofizaci\u0026oacute;n en ecosistemas acu\u0026aacute;ticos, vol 24. Revista de la Asociaci\u0026oacute;n Colombiana de Ciencias Biol\u0026oacute;gicas, pp 112\u0026ndash;128\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWurtsbaugh WA, Paerl HW, Dodds WK (2019) Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum. Wiley Interdisciplinary Reviews: Water 6(5):1373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wat2.1373\u003c/span\u003e\u003cspan address=\"10.1002/wat2.1373\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Mogoll\u0026oacute;n JM, van Bodegom PM, Beusen AH, Scherer L (2024) Global regionalized characterization factors for phosphorus and nitrogen impacts on freshwater fish biodiversity. Sci Total Environ 912:169108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2023.169108\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2023.169108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Freshwater bodies include rivers and streams, lakes and ponds, reservoirs, and wetlands, and they support aquatic ecosystems that, in this study, are the recipients of environmental impacts.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Sepp\u0026auml;l\u0026auml; et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) estimated CFs that took into account nutrient bioavailability for 11 sectors in Finland, based on three different scenarios, noting that this was an approach subject to considerable uncertainty and that further research was needed on the roles of different nutrient forms in aquatic eutrophication (Schmid \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The conversion from kg PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e eq (results from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to kg P eq was performed using a stoichiometric factor of 0.326, corresponding to the mass fraction of P present in the phosphate ion, calculated from the ratio between their molar masses. This calculation is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-processes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enpr","sideBox":"Learn more about [Environmental Processes](https://www.springer.com/journal/40710)","snPcode":"40710","submissionUrl":"https://submission.nature.com/new-submission/40710/3","title":"Environmental Processes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"phosphorus emissions, midpoint environmental impact, aquatic ecosystems, spatial differentiation, regional characterization factors","lastPublishedDoi":"10.21203/rs.3.rs-9418860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9418860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Aquatic eutrophication is a significant impact category in Life Cycle Assessment in countries with high environmental heterogeneity, such as Argentina. However, the usual practice in these studies is to use global characterization factors (given the lack of regional factors), which leads to results that generally do not reflect local conditions. To address this issue, phosphorus-based regional characterization factors are developed at the midpoint level to improve the spatial representativeness of this category. The methodology adapts and improves an existing model by differentiating the impact between ecological regions and incorporating observed concentrations of total phosphorus into the calculation of the effect factor. The model integrates nutrient transport processes in soil and water and the response of the receiving ecosystem within the cause-effect chain. As a result, three types of factors were developed to represent the Regionalized Aquatic Eutrophication Potential (kg P eq): factors for four regions of the country, an arithmetic national average, and a national average weighted by land area. The regional factors obtained range from 0.331 to 0.678 kg P eq in water and from 0.145 to 0.296 kg P eq for inputs to soil, demonstrating spatial variability in eutrophication potential. The use of global factors may underestimate or overestimate local impacts, whereas regionalization improves environmental consistency and identifies regions with greater eutrophic susceptibility. The proposed approach constitutes a methodological advance transferable to regions with limited data availability and provides a tool for environmental assessment and water management.","manuscriptTitle":"Regional modeling of freshwater eutrophication in Argentina for Life Cycle Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 16:59:14","doi":"10.21203/rs.3.rs-9418860/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"284205397897133587090273969180424789383","date":"2026-05-04T15:01:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123456896807485775194341083696834043465","date":"2026-05-04T11:53:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T11:02:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T08:02:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T01:12:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Processes","date":"2026-04-14T18:24:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-processes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enpr","sideBox":"Learn more about [Environmental Processes](https://www.springer.com/journal/40710)","snPcode":"40710","submissionUrl":"https://submission.nature.com/new-submission/40710/3","title":"Environmental Processes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"25a3b7f4-9afa-4499-a353-f6c92e0eae19","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"284205397897133587090273969180424789383","date":"2026-05-04T15:01:27+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"123456896807485775194341083696834043465","date":"2026-05-04T11:53:57+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T16:59:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 16:59:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9418860","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9418860","identity":"rs-9418860","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}