BackgroundFreshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.ResultsThe development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. System's accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%.ConclusionsThis study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases.

Ingestion of microplastics by marine organisms is a common occurrence in marine ecosystems, but the experimental demonstration of the effects of ingested microplastics on marine organisms has only recently become an important subject of research. In this review, the ingestion of microplastics by marine organisms, its attendant potential consequences and specific hypothetical questions for further studies are discussed. The formation of heteroaggregates in the gut of prey organisms may delay microplastic clearance, potentially increasing the chances of microplastic trophic transfer to predators. Also, the survival and energetics of keystone species at lower trophic levels are negatively affected by ingestion of microplastics, thereby raising questions about the transfer of energy and nutrients to organisms at higher trophic levels. Further, since microplastics are able to adsorb and concentrate organic pollutants up to 1 million times more than the pollutant concentration in ambient waters, the ingestion of such small plastic fragments is, a probable route for the entrance and biomagnification of toxic chemicals in the marine food web. However, the equilibrium state between pollutant concentration in marine organisms and that of surrounding waters makes it unclear whether the ingestion of microplastics actually increases the pollutant load of organisms. Finally, microplastic ingestion can cause endocrine disorders in adult fish, which could result in neoplasia via epigenetic programming. Therefore, microplastic pollution may be a contributory cause of increased incidents of neoplasia in marine animals. The amount of microplastics in marine waters will steadily rise, and questions about their impact on marine ecosystems will linger.

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Problem statement: Conventional techniques for removing dissolved heavy metals are only practical and cost-effective when applied to high strength wastes with heavy metal ion concentrations greater than 100 ppm. The possibility of using a nonliving algal biomass to solve this problem was carried in this study. Lead (II) was used in this study because it had been reported to cause several disorders in human. Approach: The nonliving algal biomass was obtained from a filamentous green alga Spirogyra neglecta. The effects of initial concentration and contact time, pH and temperature on the biosorption of lead (II) by the nonliving algal biomass were studied. The equilibrium isotherms and kinetics were obtained from batch adsorption experiments. The surface characteristics of the nonliving algal biomass were examined using scanning electron microscope and Fourier Transformed Infrared. The maximum adsorption capacity of the nonliving algal biomass was also determined. Results: Maximum adsorption capacity of lead (II) was affected by its initial concentration. Adsorption capacity of lead (II) increased with the pH and temperature of lead (II) solution. Langmuir isothermic model fitted the equilibrium data better than the Freundlich isothermic model. The adsorption kinetics followed the pseudo-second-order kinetic model. The nonliving algal biomass exhibited acaves-like, uneven surface texture along with lot of irregular surface. FTIR analysis of the alga biomass revealed the presence of carboyl, amine and carboxyl group which were responsible for adsorption of lead (II). The maximum adsorption capacity (q max ) of lead (II) by the nonliving biomass of Spirogyra neglecta was 132 mg g −1 . Conclusion: The maximum adsorption capacity for lead (II) by the nonliving biomass of Spirogyra neglecta was higher than reported for other biosorbents. Therefore, it had a great potential for removing lead (II) from polluted water. Its use will also need to consider the various factors that affect biosorption process.

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