Dioxin risk assessment: mechanisms of action and possible toxicity in human health.

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.

Human health risk of polycyclic aromatic hydrocarbons from consumption of blood cockle and exposure to contaminated sediments and water along the Klang Strait, Malaysia.

The concentration of carcinogenic poly aromatic hydrocarbons (c-PAHs) present in water and sediment of Klang Strait as well as in the edible tissue of blood cockle (Anadara granosa) was investigated. The human health risk of c-PAHs was assessed in accordance with the standards of the United States Environmental Protection Agency (US EPA). The cancer risks of c-PAHs to human are expected to occur through the consumption of blood cockles or via gastrointestinal exposure to polluted sediments and water in Kalng Strait. The non-carcinogenic risks that are associated with multiple pathways based on ingestion rate and contact rates with water were higher than the US EPA safe level at almost all stations, but the non-carcinogenic risks for eating blood cockle was below the level of US EPA concern. A high correlation between concentrations of c-PAHs in different matrices showed that the bioaccumulation of c-PAHs by blood cockles could be regarded as a potential health hazard for the consumers.

Polycyclic aromatic hydrocarbons in coastal sediment of klang strait, Malaysia: distribution pattern, risk assessment and sources.

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.

A review of strategies to monitor water and sediment quality for a sustainability assessment of marine environment.

The basic aim of this work is (1) to review and present practically operational requirements for a sustainability assessment of marine environment, such as describing the monitoring process, research approaches, objectives, guidelines, and indicators and (2) to illustrate how physico-chemical and biological indicators can be practically applied, to assess water and sediment quality in marine and coastal environment. These indicators should meet defined criteria for practical usefulness, e.g. they should be simple to understand and apply to managers and scientists with different educational backgrounds. This review aimed to encapsulate that variability, recognizing that meaningful guidance should be flexible enough to accommodate the widely differing characteristics of marine ecosystems.

A preliminary study on automated freshwater algae recognition and classification system.

BACKGROUND: Freshwater 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. RESULTS: The 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%. CONCLUSIONS: This 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.

Assessment of predictive models for chlorophyll-a concentration of a tropical lake.

BACKGROUND: This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. RESULTS: Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task. CONCLUSIONS: Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.