Environmental performance evaluation of agro-industrial innovations – Part 2: methodological approach for performing vulnerability analysis of watersheds

Environmental vulnerability analysis has been sparsely used in environmental performance evaluation (EPE) of technological innovations. The present paper proposes a methodological approach to carry out vulnerability analysis of watersheds and to integrate this analysis into methods of environmental performance evaluation of agro-industrial innovations. This approach is applied to the Ambitec-Life Cycle method, described in Part 1 (this issue) of this study. The case study of green coconut substrate compared to ripe coconut substrate, also described in Part 1 (this issue), is now presented considering the vulnerability analysis of the watersheds where the life cycle stages of these products occur. The integration of vulnerability analysis in Ambitec-Life Cycle contributes to a better understanding of the environmental aspects of agro-industrial technological innovations with potential to cause significant impacts in watersheds where these innovations are implemented.     

Farm factors associated with reducing Cryptosporidium loading in storm runoff from dairies

A systems approach was used to evaluate environmental loading of Cryptosporidium oocysts on five coastal dairies in California. One aspect of the study was to determine Cryptosporidium oocyst concentrations and loads for 350 storm runoff samples from dairy high use areas collected over two storm seasons. Selected farm factors and beneficial management practices (BMPs) associated with reducing the Cryptosporidium load in storm runoff were assessed. Using immunomagnetic separation (IMS) with direct fluorescent antibody (DFA) analysis, Cryptosporidium oocysts were detected on four of the five farms and in 21% of storm runoff samples overall. Oocysts were detected in 59% of runoff samples collected near cattle less than 2 mo old, while 10% of runoff samples collected near cattle over 6 mo old were positive. Factors associated with environmental loading of Cryptosporidium oocysts included cattle age class, 24 h precipitation, and cumulative seasonal precipitation, but not percent slope, lot acreage, cattle stocking number, or cattle density. Vegetated buffer strips and straw mulch application significantly reduced the protozoal concentrations and loads in storm runoff, while cattle exclusion and removal of manure did not. The study findings suggest that BMPs such as vegetated buffer strips and straw mulch application, especially when placed near calf areas, will reduce environmental loading of fecal protozoa and improve stormwater quality. These findings are assisting working dairies in their efforts to improve farm and ecosystem health along the California coast.     

Modeling biodiversity dynamics in countryside landscapes

The future of biodiversity hinges to a great extent on the conservation value of countryside, the growing fraction of Earth's surface heavily influenced by human activities. How many species, and which species, can persist in such landscapes (and analogous seascapes) are open questions. Here we explore two complementary theoretical frameworks to address these questions: species-area relationships and demographic models. We use the terrestrial mammal fauna of Central America to illustrate the application of both frameworks. We begin by proposing a multi-habitat species-area relationship, the countryside species-area relationship, to forecast species extinction rates. To apply it, we classify the mammal fauna by affinity to native and human-dominated habitats. We show how considering the conservation value of countryside habitats changes estimates derived from the classic species-area approach We also examine how the z value of the species-area relationship affects extinction estimates. Next, we present a framework for assessing the relative vulnerability of species to extinction in the countryside, based on the Skellam model of population dynamics. This model predicts the minimum area of contiguous native habitat required for persistence of a species, which we use as an indicator of vulnerability to habitat change. To apply the model, we use our habitat affinity classification of mammals and we estimate life-history parameters by species and habitat type. The resulting ranking of vulnerabilities is significantly correlated with the World Conservation Union (IUCN) Red List assessment.     

Statistical evaluation of chronic toxicity data on aquatic organisms for the hazard identification: the chemicals toxicity distribution approach

Presently, in the Globally Harmonised System of Classification and Labelling of Chemicals the classification of substances for long-term effects to aquatic life is based on acute toxicity in combination with degradation and/or bioaccumulation potential. Recently an OECD Working Group was created to develop the classification scheme to accommodate chronic toxicity data related to aquatic organisms for assigning a chronic hazard category. This study focuses on a new approach for setting chronic toxicity cut-off values based on Chemicals Toxicity Distributions (CTDs). A CTD is obtained through statistical fitting of the data used by regulatory bodies for setting hazard-based classifications. The CTDs were made using the lowest aquatic NOEC value of each chemical. A review of different toxicological sources reporting acute aquatic toxicities was carried out. Initially, the data were arranged according to the specific source and distributions for key taxonomic groups (i.e. fishes, crustaceans and algae) were evaluated separately. In most cases, no significant departures from normality were observed. Thereafter, a compiled database containing >900 values was developed and the CTDs were constructed for each taxonomic group. Significant deviation from normality (P < 0.05) was observed in the fishes and crustaceans' CTDs. However, this deviation was apparently produced by the presence of only seven values with NOECs <1 x 10(-5) mg l(-1), while high correlation between the data and the normal scores (r-values>or= 0.989) indicated that the data were samples from normal distributions. From these observations, potential cut-off values would allow quantitative estimations of the percentage of chemicals falling into each specific category. This approach results in a simple classification hazard scheme where most chemicals are covered in one of the categories, allowing a clear distribution of the chemicals among three categories for chronic toxicity.     

Acute effects of 3,4-dichloroaniline on biomarkers and spleen histology of the common goby Pomatoschistus microps

The aromatic amine 3,4-dichloroaniline (DCA) is a model environmental contaminant, precursor for synthesis and degradation product of several herbicides, which is commonly found in European estuarine ecosystems. In this work, the possibility of using biochemical and histological markers to assess sub-lethal effects of DCA in natural populations of Pomatoschistus microps juveniles was investigated. Alterations on the activities of the enzymes acetylcholinesterase (AChE), lactate dehydrogenase (LDH) and glutathione S-transferase (GST) and histological alterations on spleen were investigated after 96 h of exposure to sublethal concentrations of DCA (0.50-1.49 mg/l). At the concentrations tested, DCA had no effect on AChE activity. LDH and GST activities were significant altered in treated animals when compared to control groups. As already described for mammals, DCA induced splenic histological alterations in P. microps, including expansion of red pulp and deposition of hemosiderin granules in a concentration-dependent manner. This suggests that DCA is a xenobiotic of concern in estuaries receiving agricultural effluents.     

Prediction of ozone concentrations in Oporto city with statistical approaches

The performance of three statistical methods: time-series, multiple linear regression and feedforward artificial neural networks models were compared to predict the daily mean ozone concentrations. The study here reported was based on data from one urban site with traffic influences and one rural background site. The studies were performed for the year 2002 and the respective four trimesters separately. In the multiple linear regression and feedforward artificial neural network models, the concentrations of ozone, the concentrations of its precursors (nitrogen oxides) and some meteorological variables for one and two days before the prediction day were used as predictors. For the application of these models in the validation step, the inputs of ozone concentration for one and two days before were replaced by the ozone concentrations predicted by the models. The results showed that time-series modelling was not profitable. In the development step, similar performances were obtained with multiple linear regression and feedforward artificial neural network. Better performance indexes were achieved with feedforward artificial neural network models in validation step. Concluding, feedforward artificial neural network models were more efficient to predict ozone concentrations.     

Contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area, Portugal since the 19th century

The main purpose of this study was to evaluate the contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area (Portugal) since the 19th century. The study was based on pre-industrial and recent data series, the results being analyzed according to the atmospheric chemistry. The treatment of ozone and meteorological data was performed by classical statistics and by time-series analysis. It was concluded that in the 19th century the ozone present in the troposphere was not of photochemical origin, being possible to consider the respective concentrations as reference values. For recent data a cycle of 8h for ozone concentrations could be related to traffic. Compared to the 19th century, the current concentrations were 147% higher (252% higher in May) due to the increased photochemical production associated with the increased anthropogenic emissions.