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.     

Residential solid waste generation and classification study of ten headquarter cities of Azad Jammu & Kashmir,Pakistan

Journal of Material Cycles and Waste Management - Effective municipal solid waste collection and disposal system is not possible without reliable solid waste generation and classification data...     

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.     

Zingiber officinale ethanolic extract attenuates oxidative stress,steroidogenic gene expression alterations,and testicular histopathology induced by sodium arsenite in male rats

Environmental Science and Pollution Research - Arsenic (As) indelibly exists in the environment and may reach to a food chain. Flavors and herbs are recognized sources of natural antioxidants that...     

Distribution and ecological risk of organochlorine pesticides and polychlorinated biphenyls in sediments from the Mediterranean coastal environment of Egypt

Organochlorine contamination in the Mediterranean coastal environment of Egypt was assessed based on 26 surface sediments samples collected from several locations on the Egyptian coast, including harbors, coastal lakes, bays, and estuaries. The distribution and potential ecological risk of contaminants is described. Organochlorine compounds (OCs) were widely distributed in the coastal environment of Egypt. Concentrations of PCBs, DDTs, and chloropyrifos ranged from 0.29 to 377 ng g⁻¹ dw, 0.07 to 81.5 ng g⁻¹ dw, and below the detection limit (DL) to 288 ng g⁻¹ dw, respectively. Other organochlorinated pesticides (OCP) studied were 1–2 orders of magnitude lower. OCP and PCBs had higher concentrations at Burullus Lake, Abu Qir Bay, Alexandria Eastern Harbor, and El Max Bay compared to other sites. OCP and PCB contamination is higher in the vicinity of possible input sources such as shipping, industrial activities and urban areas. PCB congener profiles indicated they were derived from more than one commercially available mixture. The ratios of commercial chlordane and heptachlor metabolites indicate historical usage; however, DDT and HCHs inputs at several locations appear to be from recent usage. The concentrations of PCBs and DDTs are similar to those observed in sediments from coastal areas of the Mediterranean Sea. Ecotoxicological risk from DDTs and PCBs is greatest in Abu Qir Bay, Alexandria Harbor, and El-Max Bay.     

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.     

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.     

Impact of Silver Nanoparticles Synthesized by Irradiated Polyvinylpyrrolidone on Spodoptera littoralis Nucleopolyhedrosis Virus Activity

Journal of Polymers and the Environment - A Green nanomaterial synthesis could be considered as an alternative bio-controller source of insect pest management as well as saving the environment....     

Mechanochemical activation for lead extraction from spent cathode ray tube

Journal of Material Cycles and Waste Management - In this study, a mechanochemical activation technique to effectually extract lead from the funnel glass of cathode ray tube (CRT) is proposed. A...     

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.