Atmospheric Pollutant Flow and Precipitation: Modeling Effects on the Vegetation Ecosystem

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation

Atmospheric Pollutant Flow and Precipitation: Modeling Effects on the Vegetation Ecosystem

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation

Screening of Antimicrobial and Antioxidant Secondary Metabolites from Endophytic Fungi Isolated from Wheat (Triticum Durum)

Abstract: The emergence of antibiotic-resistant micro-organisms calls for inventive research and development strategies. Inhibition of these pathogenic micro-organisms may be a promising therapeutic approach. The screening of antimicrobial compounds from endophytes is a promising way to meet the increasing threat of drug-resistant strains of human and plant pathogens. In the present study, a total of 20 endophytic fungi and 23 endophytic actinomycetes have been isolated from wheat (Triticum durum). Mohamed Ben Bachir variety collected from Bordj Bou Arreridj region (Algeria) during winter 2010. The isolates were screened and evaluated for their antimicrobial and antioxidant activities. Antimicrobial activity was evaluated for crude ethyl acetate extracts using an agar dif-fusion assay against twelve pathogenic bacteria, yeast, and two phytopathogenic fungi. All extracts showed inhibitory activity on at least one or more pathogenic microorganisms, with an average zone of inhibition varied between 7 mm to 25 mm, and the largest zone was of 25 and 25.3 mm against candida albicans and Escherichia coli respectively. The antioxidant capacity of the extracts was evalu-ated by β-carotene/linoleic acid assay. Results showed that 60 % of these extracts have antioxidant activity, exhibiting 50, 57 % to 78, 96 % inhibitions. While the inhibitory activity for oxidation of linoleic acid of 40 % of them was less than 50%. From the present work it is possible to conclude that these microorganisms could be promising source of bioactive compounds, and warrant further study. Key words: endophytic microorganisms, antimicrobial activity, antioxidant activity, Triticum duru