Biodiesel is biodegradable, less CO 2 and NO x emissions. Continuous use of petroleum sourced fuels is now widely recognized as unsustainable because of depleting supplies and the contribution of these fuels to the accumulation of carbon dioxide in the environment. Renewable, carbon neutral, transport fuels are necessary for environmental and economic sustainability. Algae have emerged as one of the most promising sources for biodiesel production. It can be inferred that algae grown in CO 2 -enriched air can be converted to oily substances. Such an approach can contribute to solve major problems of air pollution resulting from CO 2 evolution and future crisis due to a shortage of energy sources. This study was undertaken to know the proper transesterification, amount of biodiesel production (ester) and physical properties of biodiesel. In this study we used common species Oedogonium and Spirogyra to compare the amount of biodiesel production. Algal oil and biodiesel (ester) production was higher in Oedogonium than Spirogyra sp. However, biomass (after oil extraction) was higher in Spirogyra than Oedogonium sp. Sediments (glycerine, water and pigments) was higher in Spirogyra than Oedogonium sp. There was no difference of pH between Spirogyra and Oedogonium sp. These results indicate that biodiesel can be produced from both species and Oedogonium is better source than Spirogyra sp.
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.
Concentration, source, and ecological risk of polycyclic aromatic hydrocarbons (PAHs) were investigated in 22 stations from surface sediments in the areas of anthropogenic pollution in the Klang Strait (Malaysia). The total PAH level in the Klang Strait sediment was 994.02±918.1 µg/kg dw. The highest concentration was observed in stations near the coastline and mouth of the Klang River. These locations were dominated by high molecular weight PAHs. The results showed both pyrogenic and petrogenic sources are main sources of PAHs. Further analyses indicated that PAHs primarily originated from pyrogenic sources (coal combustion and vehicular emissions), with significant contribution from petroleum inputs. Regarding ecological risk estimation, only station 13 was moderately polluted, the rest of the stations suffered rare or slight adverse biological effects with PAH exposure in surface sediment, suggesting that PAHs are not considered as contaminants of concern in the Klang Strait.
Dioxin-like compounds (DLCs) have been classified by the World Health Organization (WHO) as one of the most persistent toxic chemical substances in the environment, and they are associated with several occupational activities and industrial accidents around the world. Since the end of the 1970s, these toxic chemicals have been banned because of their human toxicity potential, long half-life, wide dispersion, and they bioaccumulate in the food web. This review serves as a primer for environmental health professionals to provide guidance on short-term risk assessment of dioxin and to identify key findings for health and exposure assessment based on policies of different agencies. It also presents possible health effects of dioxins, mechanisms of action, toxic equivalency factors (TEFs), and dose-response characterization. Key studies related to toxicity values of dioxin-like compounds and their possible human health risk were identified through PubMed and supplemented with relevant studies characterized by reviewing the reference lists in the review articles and primary literature. Existing data decreases the scope of analyses and models in relevant studies to a manageable size by focusing on the set of important studies related to the perspective of developing toxicity values of DLCs.
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