Ligia B. Azevedo

Assessing the importance of spatial variability versus model choices in life cycle impact assessment: The case of freshwater eutrophication in europe

In Life Cycle Impact Assessment (LCIA) both spatial variability and model choices may be influential. In the case of the effect model, the effect factors differ with respect to their assumption of linear/nonlinear responses to increases in environmental stressor levels, and whether or not they account for the current stressor levels in the environment. Here, we derived spatially explicit characterization factors of phosphorus emissions causing eutrophication based on three different effect models (depicted by marginal, linear, and average effect factors) and two freshwater types (lakes and streams) and we performed an analysis of variance (ANOVA) to investigate how the selection of the effect models and the freshwater types influence the impacts of phosphorus emissions to freshwater on heterotrophic species. We found that 56% of the variability of ecological impacts per unit of phosphorus emission was explained, primarily, by the difference between freshwater types and, to a lesser extent, by the difference between effect models. The remaining variability was attributed to the spatial variation between river basins, mainly due to the variability in fate factors. Our study demonstrates the particular importance of accounting for spatial variability and model choices in LCIA.

Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations.

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.

Species richness–phosphorus relationships for lakes and streams worldwide

Aim We investigated the patterns of autotrophic and heterotrophic relative species richness along a total phosphorus (TP) concentration gradient. The relative species richness–TP relationships were calculated separately for four different regions [(sub)tropical, xeric, temperate and cold] and two types of water bodies (lakes and streams). Location Global Methods Using data from peer-reviewed articles reporting the occurrence of freshwater species at specific TP concentrations, we determined the species richness along a TP gradient. Using log-logistic regressions, we then estimated the TP concentration at which the potential decrease of relative species richness (RSR) equals 0.5 and the slope at which the decrease occurs (β). The RSR is given as the ratio of species richness to maximized species richness along a TP gradient. Results The RSR of streams generally decreased more rapidly than that of lakes with increasing P, as illustrated by the steeper slope of the log-logistic functions for streams (βlakes < βstreams). Although there was no consistent trend between autotrophs and heterotrophs in the different regions, we found that the TP concentration at which the RSR equals 0.5 was lower in cold regions (0.04–0.22 mg P/L) than in warmer regions (0.28–1.29 mg P/L). Main Conclusions The log-logistic relationships between RSR and TP concentration vary considerably among regions of the world, between freshwater types (lakes and streams) and between species groups (autotrophs and heterotrophs). This variability may be attributed to differences between the two freshwater types in respect to their species groups and evolutionary patterns, nutrient demand, biogeochemical and hydrological processes. We were not able to derive log-logistic regressions for all combinations of freshwater type or species type and region [e.g. (sub)tropical lakes]. For other areas, our results can be used to assess the potential impact of phosphorus eutrophication on freshwater biota.

Calcifying species sensitivity distributions for ocean acidification

Increasing CO2 atmospheric levels lead to increasing ocean acidification, thereby enhancing calcium carbonate dissolution of calcifying species. We gathered peer-reviewed experimental data on the effects of acidified seawater on calcifying species growth, reproduction, and survival. The data were used to derive species-specific median effective concentrations, i.e., pH50, and pH10, via logistic regression. Subsequently, we developed species sensitivity distributions (SSDs) to assess the potentially affected fraction (PAF) of species exposed to pH declines. Effects on species growth were observed at higher pH than those on species reproduction (mean pH10 was 7.73 vs 7.63 and mean pH50 was 7.28 vs 7.11 for the two life processes, respectively) and the variability in the sensitivity of species increased with increasing number of species available for the PAF (pH10 standard deviation was 0.20, 0.21, and 0.33 for survival, reproduction, and growth, respectively). The SSDs were then applied to two climate change scenarios to estimate the increase in PAF (ΔPAF) by future ocean acidification. In a high CO2 emission scenario, ΔPAF was 3 to 10% (for pH50) and 21 to 32% (for pH10). In a low emission scenario, ΔPAF was 1 to 4% (for pH50) and 7 to 12% (for pH10). Our SSDs developed for the effect of decreasing ocean pH on calcifying marine species assemblages can also be used for comparison with other environmental stressors.

Combined ecological risks of nitrogen and phosphorus in European freshwaters.

Eutrophication is a key water quality issue triggered by increasing nitrogen (N) and phosphorus (P) levels and potentially posing risks to freshwater biota. We predicted the probability that an invertebrate species within a community assemblage becomes absent due to nutrient stress as the ecological risk (ER) for European lakes and streams subjected to N and P pollution from 1985 to 2011. The ER was calculated as a function of species-specific tolerances to NO3(-) and total P concentrations and water quality monitoring data. Lake and stream ER averaged 50% in the last monitored year (i.e. 2011) and we observed a decrease by 22% and 38% in lake and stream ER (respectively) of river basins since 1985. Additionally, the ER from N stress surpassed that of P in both freshwater systems. The ER can be applied to identify river basins most subjected to eutrophication risks and the main drivers of impacts.

Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty.

Characterization factors (CFs) are used in life cycle assessment (LCA) to quantify the potential impact per unit of emission. CFs are obtained from a characterization model which assess the environmental mechanisms along the cause-effect chain linking an emission to its potential damage on a given area of protection, such as loss in ecosystem quality. Up to now, CFs for acidifying emissions did not cover the global scale and were only representative of their characterization model geographical scope. Consequently, current LCA practices implicitly assume that all emissions from a global supply chain occur within the continent referring to the characterization method geographical scope. This paper provides worldwide 2°×2.5° spatially-explicit CFs, representing the change in relative loss of terrestrial vascular plant species due to an emission change of nitrogen oxides (NOx), ammonia (NH3) and sulfur dioxide (SO2). We found that spatial variability in the CFs is much larger compared to statistical uncertainty (six orders of magnitude vs. two orders of magnitude). Spatial variability is mainly caused by the atmospheric fate factor and soil sensitivity factor, while the ecological effect factor is the dominant contributor to the statistical uncertainty. The CFs provided in our study allow the worldwide spatially explicit evaluation of life cycle impacts related to acidifying emissions. This opens the door to evaluate regional life cycle emissions of different products in a global economy.

Plant Species Sensitivity Distributions for ozone exposure

This study derived Species Sensitivity Distributions (SSD), representing a cumulative stressor-response distribution based on single-species sensitivity data, for ozone exposure on natural vegetation. SSDs were constructed for three species groups, i.e. trees, annual grassland and perennial grassland species, using species-specific exposure-response data. The SSDs were applied in two ways. First, critical levels were calculated for each species group and compared to current critical levels for ozone exposure. Second, spatially explicit estimates of the potentially affected fraction of plant species in Northwestern Europe were calculated, based on ambient ozone concentrations. We found that the SSD-based critical levels were lower than for the current critical levels for ozone exposure, with conventional critical levels for ozone relating to 8-20% affected plant species. Our study shows that the SSD concept can be successfully applied to both derive critical ozone levels and estimate the potentially affected species fraction of plant communities along specific ozone gradients.

Global assessment of nitrogen losses and trade-offs with yields from major crop cultivations.

Agricultural application of reactive nitrogen (N) for fertilization is a cause of massive negative environmental problems on a global scale. However, spatially explicit and crop-specific information on global N losses into the environment and knowledge of trade-offs between N losses and crop yields are largely lacking. We use a crop growth model, Python-based Environmental Policy Integrated Climate (PEPIC), to determine global N losses from three major food crops: maize, rice, and wheat. Simulated total N losses into the environment (including water and atmosphere) are 44TgNyr-1. Two thirds of these, or 29TgNyr-1, are losses to water alone. Rice accounts for the highest N losses, followed by wheat and maize. The N loss intensity (NLI), defined as N losses per unit of yield, is used to address trade-offs between N losses and crop yields. The NLI presents high variation among different countries, indicating diverse N losses to produce the same amount of yields. Simulations of mitigation scenarios indicate that redistributing global N inputs and improving N management could significantly abate N losses and at the same time even increase yields without any additional total N inputs.

Analyzing and modelling the effect of long-term fertilizer management on crop yield and soil organic carbon in China

This study analyzes the influence of various fertilizer management practices on crop yield and soil organic carbon (SOC) based on the long-term field observations and modelling. Data covering 11 years from 8 long-term field trials were included, representing a range of typical soil, climate, and agro-ecosystems in China. The process-based model EPIC (Environmental Policy Integrated Climate model) was used to simulate the response of crop yield and SOC to various fertilization regimes. The results showed that the yield and SOC under additional manure application treatment were the highest while the yield under control treatment was the lowest (30%-50% of NPK yield) at all sites. The SOC in northern sites appeared more dynamic than that in southern sites. The variance partitioning analysis (VPA) showed more variance of crop yield could be explained by the fertilization factor (42%), including synthetic nitrogen (N), phosphorus (P), potassium (K) fertilizers, and fertilizer NPK combined with manure. The interactive influence of soil (total N, P, K, and available N, P, K) and climate factors (mean annual temperature and precipitation) determine the largest part of the SOC variance (32%). EPIC performs well in simulating both the dynamics of crop yield (NRMSE = 32% and 31% for yield calibration and validation) and SOC (NRMSE = 13% and 19% for SOC calibration and validation) under diverse fertilization practices in China. EPIC can assist in predicting the impacts of different fertilization regimes on crop growth and soil carbon dynamics, and contribute to the optimization of fertilizer management for different areas in China.