Effects of increased CO2 levels on growth, photosynthesis, ammonium uptake and cell composition in the macroalga Hypnea spinella (Gigartinales, Rhodophyta)

[EN] The red seaweed Hypnea spinella (Gigartinales, Rhodophyta), was cultured at laboratory scale under three different CO2 conditions, non-enriched air (360 ppm CO2)and CO2-enriched air at two final concentrations (750 and 1,600 ppm CO2), in order to evaluate the influence of increased CO2 concentrations on growth, photosynthetic capacity, nitrogen removal efficiency, and chemical cellular composition. Average specific growth rates of H. spinella treated with 750 and 1,600 ppm CO2-enriched air increased by 85.6% and 63.2% compared with non-enriched air cultures. CO2 reduction percentages close to 12% were measured at 750 ppm CO2 with respect to 5% and 7% for cultures treated with air and 1,600 ppm CO2, respectively. Maximum photosynthetic rates were enhanced significantly for high CO2 treatments, showing Pmax values 1.5-fold higher than that for air-treated cultures. N–NH4+ consumption rates were also faster for algae growing at 750 and 1,600 ppm CO2 than that for non-enriched air cultures. As a consequence of these experimental conditions, soluble carbohydrates increased and soluble protein contents decreased in algae treated with CO2-enriched air. However, internal C and N contents remained constant at the different CO2 concentrations. No significant differences in data obtained with both elevated CO2 treatments, under the assayed conditions, indicate that H. spinella is saturated at dissolved inorganic carbon concentrations close by twice the actual atmospheric levels. The results show that increased CO2 concentrations might be considered a key factor in order to improve intensively cultured H. spinella production yields and carbon and nitrogen bioremediation efficiencies.

Application of genetic algorithms for the calibration of an air quality model and its validation using pollutant measures from the surroundings of an electric power plant

[EN]This work presents the calibration and validation of an air quality finite element model applied to emissions from a thermal power plant located in Gran Canaria. The calibration is performed using genetic algorithms. To calibrate and validate the model, the authors use empirical measures of pollutants concentrations from 4 stations located nearby the power plant; an hourly record per station during 3 days is available. Measures from 3 stations will be used to calibrate, while validation will use measures from the remaining station…