Managing environmental quality at the enterprise: the role of project management

An exploratory field study conducted in Europe and the U.S. examines the challenges of managing environmental quality at the enterprise level. The results show that effective environmental quality management depend to a considerable degree on the ability to deal with the multidisciplinary organizational, technical, social, and cultural issues involved in the integration of environmental cost accounting at the enterprise level. The study suggests that the tools and techniques of modern project management can provide an important framework for developing and implementing environmental quality control systems. The recommendations put considerable emphasis on the human side, focusing on the criteria under which environmental quality is best achievable. Strong senior management leadership, people skills, organizational linkages, and alliances, all are critical for dealing with the complexities of managing responsibly toward sustained developments in today’s organizations.     

The influence of symbiont type on photosynthetic carbon flux in a model cnidarian–dinoflagellate symbiosis

We measured the relationship between symbiont diversity, nutritional potential, and symbiotic success in the cnidarian–dinoflagellate symbiosis, by infecting aposymbiotic (i.e. symbiont-free) specimens of the model sea anemone Aiptasia sp. with a range of Symbiodinium types. Four cultured heterologous Symbiodinium types (i.e. originally isolated from other host species) were used, plus both cultured and freshly isolated homologous zooxanthellae (i.e. from Aiptasia sp.). Rates of photosynthesis, respiration, and symbiont growth were measured during symbiosis establishment and used to estimate the contribution of the zooxanthellae to the animal’s respiratory carbon demands (CZAR). Anemones containing Symbiodinium B1 (both homologous and heterologous) tended to attain higher CZAR values and hence benefit most from their symbiotic partners. This was despite Symbiodinium B1 not achieving the highest cell densities, though it did grow more quickly during the earliest stages of the infection process. Rather, the heterologous Symbiodinium types A1.4, E2, and F5.1 attained the highest densities, with populations of E2 and F5.1 also exhibiting the highest photosynthetic rates. This apparent success was countered, however, by very high rates of symbiosis respiration that ultimately resulted in lower CZAR values. This study highlights the impact of symbiont type on the functionality and autotrophic potential of the symbiosis. Most interestingly, it suggests that certain heterologous symbionts may behave opportunistically, proliferating rapidly but in a manner that is energetically costly to the host. Such negative host–symbiont interactions may contribute to the host–symbiont specificity seen in cnidarian–dinoflagellate symbioses and potentially limit the potential for partner switching as an adaptive mechanism.     

Development of a dynamic model to predict PM10 emissions from swine houses

Influences on dust emissions from livestock operations are number, weight, and kind of animals and characteristics of the housing system. Differences between facilities cannot be explained solely by mechanistic input variables. The objective of this study was to characterize the main input variables for modeling emissions of particulate matter with a mass median diameter < or = 10 microm (PM10) from swine facilities using a data-based model. Investigations were performed in mechanically ventilated facilities for weaning, growing-finishing, and sows in Italy and Germany. The measurements included inside and outside concentration of airborne PM10 particles (scatter light photometry), ventilation rate (calibrated measuring fans), indoor air climate at a measuring frequency of 60 s, feeding times, and animal-related data such as weight and animal activity. Dust concentration and emission were simulated using a dynamic transfer function. The results indicated that the average PM10 emission rate was influenced considerably by housing system. The simulation of the PM10 emission rate resulted in a mean percentage error per data set of 21 to 39%, whereas the average simulated and measured emission rate per data set differed by about 4 to 19%. High prediction errors occurred especially during situations in which the absolute level and spatial location of the measured activity peaks did not correspond with the measured dust peaks. Further recommendations of the study were to improve continuous and accurate measurements of input variables, such as the activity level in animal houses, and to optimize the amount of measuring days in relation to the model accuracy.