Seaweeds as bioindicators of heavy metals off a hot spot area on the Egyptian Mediterranean Coast during 2008–2010

Concentrations of Cu, Zn, Cd, Pb, Ni, Co, Fe, Mn, and Hg were measured successively in water, sediments, and six macroalgal species belonging to three algal classes during 3 years (2008–2010) from Abu Qir Bay, Alexandria, Egypt: Chlorophyceae (Enteromorpha compressa, Ulva fasciata), Phaeophyceae (Padina boryana), and Rhodophyceae (Jania rubens, Hypnea musciformis, Pterocladia capillacea). The study aimed to assess the bioaccumulation potential of the seaweeds, as well as to evaluate the extent of heavy metal contamination in the selected study site. Metals were analyzed using atomic absorption spectrophotometry coupled with MH-10 hydride system. The obtained data showed that the highest mean concentrations of Cu, Zn, Fe, and Mn were recorded in E. compressa; Cd, Ni, and Hg exhibited their highest mean concentrations in P. boryana, while Pb and Co were found in J. rubens. Abundance of the heavy metals in the algal species was as follow: Fe?>?Mn?>?Zn?>?Pb?>?Ni?>?Co?>?Cu?>?Cd?>?Hg. E. compressa showed the maximum metal pollution index (MPI) which was 11.55. Bioconcentration factor (BCF) for the metals in algae was relatively high with a maximum value for Mn. The Tomlinson pollution load index (PLI) values for the recorded algal species were low, which ranged between 1.00 in P. boryana and 2.72 in E. compressa. Enrichment factors for sediments were low fluctuating between 0.43 for Hg to 2.33 for Mn. Accordingly, the green alga E. compressa, brown alga P. boryana, and red alga J. rubens can be nominated as bioindicators. Based on MPI and PLI indices, Abu Qir Bay in the present study is considered as low-contaminated area.     

Climate change adaptation through local knowledge in the north eastern region of Bangladesh

Climate change adaptation in the low laying developing countries is becoming crucial at the present time. However, the local knowledge regarding climate change adaptation is not well focused. This study evaluates climate related perception and identifying various adaptation strategies in the low lying areas of North-Eastern Bangladesh. Six focus group discussions and 120 households?? survey were carried out to identify the major climate events in our study areas which were temperature change, drought, heavy rainfall and cyclone and storm surges. Furthermore, main livelihood problems arising from these events were lack of fish availability, scarcity of water in drought seasons and frequent flood in the rainy seasons. Results also revealed that a little portion (10%) of the respondents had well knowledge about the present climate change. However, seasonal livelihood and hazard calendar demonstrated that local people were increasingly changing their livelihood status with changing climatic hazards. At that situation local people tried to adapt themselves with the changing climate through changing their own behavior and introducing some adaptation strategies. We recognized total 16 adaptive measures in the study areas within which crop diversification, floating garden, duck rearing, cage aquaculture, wave protection walls, re-digging of canal and construction of embankments were popular. The present study revealed that local experiences in the face of climate change adaptation have merits which need special consideration. Continuous research and more incentives required for proper documentation and relegate the local adaptation knowledge in the tropic.     

Development of an integrated sensor to measure odors

Odorous air samples collected from several sources were presented to an olfactometer, an electronic nose, a hydrogen sulfide (H(2)S) detector and an ammonia (NH(3)) detector. The olfactometry measurements were used as the expected values while measurements from the other instrumentation values became input variables. Five hypotheses were established to relate the input variables and the expected values. Both linear regression and artificial neural network analyses were used to test the hypotheses. Principal component analysis was utilized to reduce the dimensionality of the electronic nose measurements from 33 to 3 without significant loss of information. The electronic nose or the H(2)S detector can individually predict odor concentration measurements with similar accuracy (R (2) = 0.46 and 0.50, respectively). Although the NH(3) detector alone has a very poor relationship with odor concentration measurements, combining the H(2)S and NH(3) detectors can predict odor concentrations more accurately (R (2) = 0.58) than either individual instrument. Data from the integration of the electronic nose, H(2)S, and NH(3) detectors produce the best prediction of odor concentrations (R (2) = 0.75). With this accuracy, odor concentration measurements can be confidently represented by integrating an electronic nose, and H(2)S and NH(3) detectors.     

Determining the robust optimal set of BMPs for urban runoff management in data-poor catchments

Mismanagement of urban runoff can result in inundation which causes serious problems in providing urban services. Best management practices (BMPs) are used for urban runoff management. In this study, a method is proposed to determine the robust optimal set of BMPs for runoff management in data-poor catchments in urban areas. This method includes five main steps: (1) Sensitivity analysis to determine effective parameters in rainfall-runoff simulation model, (2) Calibration of the rainfall-runoff model based on selected effective parameters, (3) Developing a multi-objective optimization model to obtain the optimal sets of BMPs, (4) Selecting the final solutions using the Nash approach for ranking, (5) Evaluation of the robustness of the selected solution using the Management Option Rank Equivalence method. The proposed method is examined in an urban basin located in the north of Tehran, Iran. The results show that the proposed approach provides reliable results for urban runoff management in data-poor areas.     

Loofa egyptiaca as a novel adsorbent for removal of direct blue dye from aqueous solution

In this paper, Loofa egyptiaca (LE), an agricultural plant cultivated in Egypt, was used to prepare low-cost activated carbon (LE_C1 and LE_C2) adsorbents. The adsorbents (LE, LE_C1 and LE_C2) were evaluated for their ability to remove direct blue 106 dye from aqueous solutions. Batch mode experiments were conducted using various parameters such as pH, contact time, dye concentration and adsorbent concentration. The surface chemistry of LE, LE_C1 and LE_C2 was analyzed by scanning electron microscopy (SEM). The experimental data were examined using Langmuir, Freundlich, Temkin and Harkins–Jura isotherms. The results showed that the adsorption of direct blue 106 was maximal at the lowest value of pH (pH = 2). Removal efficiency was increased with an increase in dye concentration and a decrease in amount of adsorbent. Maximum adsorption capacity was found to be 57.14, 63.3 and 73.53 mg/g for LE, LE_C1 and LE_C2 respectively. Kinetics were also investigated using pseudo-first-order, pseudo-second-order and intra-particle diffusion models. The experimental data fitted very well with the pseudo-first-order and pseudo-second-order kinetic models. The results indicate that LE, LE_C1 and LE_C2 could be employed as adsorbents for the removal of direct blue dye from aqueous solutions.     

Systematic Development of an Artificial Neural Network Model for Real-Time Prediction of Ground-Level Ozone in Edmonton,Alberta, Canada

Abstract

Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O₃) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O₃ events. The models are used to determine the meteorological conditions and precursors that most affect O₃ concentrations. O₃ time-series effects and the efficacy of the systematic approach are also assessed. The developed models showed good predictive success, with coefficient of multiple determination values ranging from 0.75 to 0.94 for forecasts up to 2 hr in advance. The inputs most important for O₃ prediction were temperature and concentrations of nitric oxide, total hydrocarbons, sulfur dioxide, and nitrogen dioxide.     

Sulfur speciation: Methodology and application to sulfide oxidation studies at the sediment-water interface

A single column ion chromatographic method for the determination of sulfide, sulfite, sulfate and thiosulfate was developed. It uses an anion exchange column (Waters, IC-Pak A) and a borate-gluconate buffer at pH 8.5 that contains EDTA and ascorbic acid to prevent sulfite oxidation. This eluent has relatively low background values for both conductivity and ultraviolet absorption, which allows determination of the above sulfur anions with high sensitivity. The mean percent recovery of the investigated anions in synthetic mixtures were 98.6, 100.0, 99.6 and 100.2 % for sulfide, sulfite, thiosulfate and sulfate, respectively. The method was applied to study sulfide ion oxidation at the water-sediment interface using six aquifer samples collected within the continental United States. Results indicated that sulfide ion disappearance follows a pseudo first-order profile and that the rate of disappearance correlates with the total organic carbon and clay content of the sediment.     

Preconcentration of cadmium and copper ions on magnetic core–shell nanoparticles for determination by flame atomic absorption

Magnetic core–shell nanoparticles modified by (3-aminopropyl) trimethoxy silane were prepared and used as adsorbent for the extraction and preconcentration of cadmium and copper ions. The ions were desorbed with nitric acid followed by determination with flame atomic absorption spectrometry. The extraction conditions were investigated systematically. The method was applied for the determination of Cd(II) and Cu(II) ions in different water samples. The accuracy was also evaluated through analysis of certified reference material.     

Systematic development of an artificial neural network model for real-time prediction of ground-level ozone in Edmonton, Alberta, Canada

Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O3) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O3 events. The models are used to determine the meteorological conditions and precursors that most affect O3 concentrations. O3 time-series effects and the efficacy of the systematic approach are also assessed. The developed models showed good predictive success, with coefficient of multiple determination values ranging from 0.75 to 0.94 for forecasts up to 2 hr in advance. The inputs most important for O3 prediction were temperature and concentrations of nitric oxide, total hydrocarbons, sulfur dioxide, and nitrogen dioxide.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.