J. C. M. Pires

Wastewater treatment to enhance the economic viability of microalgae culture.

Microalgae culture is still not economically viable and it presents some negative environmental impacts, concerning water, nutrient and energy requirements. In this context, this study aims to review the recent advances on microalgal cultures in wastewaters to enhance their economic viability. We focused on three different culture concepts: (1) suspended cell systems, (2) cell immobilization, and (3) microalgae consortia. Cultures with suspended cells are the most studied. The nutrient removal efficiencies are usually high for wastewaters of different sources. However, biomass harvesting is difficult and a costly process due to the small cell size and lower culture density. On the other hand, the cell immobilization systems showed to be the solution for this problem, having as main limitation the nutrient diffusion from bulk to cells, which results in a reduced nutrient removal efficiency. The consortium between microalgae and bacteria enhances the growth of both microorganisms. This culture concept showed to be a promising technology to improve wastewater treatment, regarding not only nutrient removal but also biomass harvesting by bioflocculation. The aggregation mechanism must be studied in depth to find the process parameters that would lead to an effective and cheap harvesting process.

Atmospheric CO2 capture by algae: Negative carbon dioxide emission path.

Carbon dioxide is one of the most important greenhouse gas, which concentration increase in the atmosphere is associated to climate change and global warming. Besides CO2 capture in large emission point sources, the capture of this pollutant from atmosphere may be required due to significant contribution of diffuse sources. The technologies that remove CO2 from atmosphere (creating a negative balance of CO2) are called negative emission technologies. Bioenergy with Carbon Capture and Storage may play an important role for CO2 mitigation. It represents the combination of bioenergy production and carbon capture and storage, keeping carbon dioxide in geological reservoirs. Algae have a high potential as the source of biomass, as they present high photosynthetic efficiencies and high biomass yields. Their biomass has a wide range of applications, which can improve the economic viability of the process. Thus, this paper aims to assess the atmospheric CO2 capture by algal cultures.

Short-term effects of air pollution on respiratory morbidity at Rio de Janeiro--Part II: health assessment.

The effects of air pollution on health have been studied worldwide. Given that air pollution triggers oxidative stress and inflammation, it is plausible that high levels of air pollutants cause higher number of hospitalisations. This study aimed to assess the impact of air pollution on the emergency hospitalisation for respiratory disease in Rio de Janeiro, Brazil. The study was divided in two parts: Part I specifically addressing the air pollution assessment and Part II addressing the health assessment. Accordingly, this Part II aimed to estimate the association between the concentrations of PM₁₀, SO₂ and CO observed in Rio de Janeiro and the number of emergency hospitalisations at a central hospital due to respiratory diseases. The pollutant concentrations were measured at two different sites in Rio de Janeiro, but the excess relative risks were calculated based on the concentrations observed at one of the sites, where limits were generally exceeded more frequently, between September 2000 and December 2005. A time series analysis was performed using the number of hospitalisations, divided in three categories (children until 1 year old, children aged between 1 and 5 years old and elderly with 65 years old or more) as independent variable, the concentrations of pollutants as dependent variables and temperature, relative humidity, long term trend, and seasonality as confounders. Data were analysed using generalised additive models with smoothing for some of the dependent variables. Results showed an excess risk of hospitalisation for respiratory disease higher than 2% per 10 μg m⁻³ increase in PM₁₀ concentrations for children under 5 years old, of 2% per 10 μg m⁻³ increase in SO₂ for elderly above 65 years old and around 0.1% per 10 μg m⁻³ increase in CO for children under 1 year and elderly. Other studies have found associations that are in agreement with the results achieved in this study. The study suggests that the ambient levels of air pollutants experienced in Rio de Janeiro between 2000 and 2005 were linked to the number of hospitalisations for respiratory diseases among children and elderly.

Green fuel production: processes applied to microalgae

The continuous increase in world energy demand will lead to an energy crisis due to the limited availability of fossil fuels. Furthermore, the use of this energetic resource is responsible for the accumulation of greenhouse gases in atmosphere that is associated with several negative effects on environment. Therefore, it is worth to search for different energy supplies that are renewable and environmentally friendly—carbon neutral fuel. Microalgae are photosynthetic microorganisms that can achieve high oil contents. This oil is suitable for producing biodiesel; thus, microalgae are considered a promising sustainable energetic resource that can reduce the dependence on fossil fuel. Biodiesel production from microalgae includes several steps, such as cell cultivation and harvesting, oil extraction and biodiesel synthesis. Although several attempts have been made to improve biodiesel yields from microalgae, further studies are required to improve biodiesel production rates and to reduce the associated costs. This review shows the recent developments on biodiesel production from microalgae, emphasizing two process concepts: (1) indirect route, in which, after a facultative cell wall disruption method, microalgal oil is recovered in an appropriate solvent and then converted into biodiesel through transesterification and (2) direct route, in which biodiesel is produced directly from the harvested biomass. High biodiesel yields are obtained when both routes are preceded by a cell wall disruption method. In the indirect route, it is possible to apply three different types of solvents to recover microalgal oil. Although there are several concerns about the application of organic solvents, the most promising and cost-effective alternative for lipid recovery is n-hexane. Comparing direct and indirect routes, this study demonstrates that although further studies are required to optimize biodiesel production from microalgae, the available information proposes that the direct route is the most efficient.

Surface Water Quality Assessment of Lis River Using Multivariate Statistical Methods

This study presents the application of multivariate statistical tools for the evaluation of spatial variations and the interpretation of water quality data obtained in a monitoring program of Lis river basin surface water, Portugal. Twenty-seven physicochemical and microbiological parameters were determined in six water sampling campaigns at 16 monitoring sites during the period from September 2003 to November 2006. Correlation analysis, principal component analysis, and cluster analysis were performed to evaluate the main water pollution sources and to characterize the spatial distribution of water pollution profiles in river basin. The results achieved with the statistical methodologies led to distinguish natural and anthropogenic pollution sources. Additionally, monitoring sites with similar water pollution profile were identified, indicating that some monitoring locations can be changed to improve the spatial characterization of water quality in the river basin. CBO, CQO, P, and N were identified as significant variables affecting spatial variations, namely in the Lis river middle reach. Besides the identification of main pollution sources, the applied statistical tools were able to identify spatial patterns of water pollution in Lis river basin, which further helps in the reassessment of the number and location of monitoring sites.

Biotechnological potential of Synechocystis salina co-cultures with selected microalgae and cyanobacteria: Nutrients removal, biomass and lipid production

Cultivation of microalgae and cyanobacteria has been the focus of several research studies worldwide, due to the huge biotechnological potential of these photosynthetic microorganisms. However, production of these microorganisms is still not economically viable. One possible alternative to improve the economic feasibility of the process is the use of consortia between microalgae and/or cyanobacteria. In this study, Chlorella vulgaris, Pseudokirchneriella subcapitata and Microcystis aeruginosa were co-cultivated with Synechocystis salina to evaluate how dual-species cultures can influence biomass and lipid production and nutrients removal. Results have shown that the three studied consortia achieved higher biomass productivities than the individual cultures. Additionally, nitrogen and phosphorus consumption rates by the consortia provided final concentrations below the values established by European Union legislation for these nutrients. In the case of lipid productivities, higher values were determined when S. salina was co-cultivated with P. subcapitata and M. aeruginosa.

The effects of light and temperature on microalgal growth and nutrient removal: an experimental and mathematical approach

Cultivation of microalgae and cyanobacteria has been intensified in the last decades, due to the numerous applications described for these microorganisms. However, the high process costs associated with biomass production systems reduce the economic feasibility of microalgal/cyanobacterial cultivation. A better understanding of the effects of light and temperature on growth kinetics will contribute to the improvement of biomass productivities and reduce the costs associated with the optimization of culture parameters. In this study, the effects of average daily light irradiance and temperature on growth and nutrient removal were assessed using Chlorella vulgaris, Pseudokirchneriella subcapitata, Synechocystis salina and Microcystis aeruginosa. Additionally, a mathematical model relating specific growth rates with these variables was developed. Both kinetic growth parameters and nutrient removal had similar responses to light and temperature: increasing light supply, higher specific growth rates, biomass productivities and nutrient removal efficiencies were achieved. Among the studied temperatures, all microorganisms presented higher biomass productivities and nutrient removal efficiencies at 25 °C. Regarding the results from the mathematical model, the optimal temperature for the selected microorganisms was 25.3 ± 1.1 °C. On the other hand, the optimal average daily light irradiances varied with the species, being 208, 258, 178 and 140 μE m−2 s−1 for C. vulgaris, P. subcapitata, S. salina and M. aeruginosa, respectively.

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as “bad” or “very bad”. PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting.

IntroductionThis study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O3) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons.MethodsTwo of the methodologies define threshold models, which assume that the behaviour of the dependent variable (O3 concentrations) changes when it enters in a different regime (two and four regimes were considered in this study). The change from one regime to another depends on a specific value (threshold value) of an explanatory variable (threshold variable), which is also defined by GAs. The predictor variables were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide, nitrogen dioxide (NO2), and O3 (recorded in the previous day at an urban site with traffic influence) and also meteorological data (hourly averages of temperature, solar radiation, relative humidity and wind speed). The study was performed for the period from May to August 2004.Results and discussionSeveral models were achieved and only the best model of each methodology was analysed. In threshold models, the variables selected by GAs to define the O3 regimes were temperature, CO and NO2 concentrations, due to their importance in O3 chemistry in an urban atmosphere.ConclusionIn the prediction of O3 concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.

Evolutionary procedure based model to predict ground–level ozone concentrations

Abstract This study aims to predict the next day hourly average ozone (O3) concentrations using threshold autoregressive (TAR) models in which the threshold value and the threshold variable are defined by genetic algorithms. The procedure is also able to generate models with statistically significant regression parameters. The performance of TAR models was then compared to the one obtained with autoregressive (AR) and artificial neural network (ANN) models. Different TAR models were generated, corresponding to different threshold variables and values. For the training period, ANN model presented better results than TAR and AR models. However, in the test period, AR and one of the TAR models achieved better predictions of O3 concentrations than the ANN model. The distinction between the applied models became greater when they were evaluated in the prediction of the extreme values, for which the TAR model presented the best performance. The performance with respect to extreme values is a useful implication for the protection of public health as this model can provide more reliable early warnings about high O3 concentration episodes.