Metabolic response under different salinity and temperature conditions for glass eel Anguilla japonica

Diadromous fish often enter freshwater directly from seawater via fish ladders or channels built in estuarine dams. The oxygen consumption rates (OCR) of glass eel, Anguilla japonica, were determined using an automatic intermittent flow respirometer under various salinity and temperature regimes to physiologically explain this direct movement. The endogenous rhythm of the OCR in wild glass eels, freshly collected from estuaries, was nearly synchronous with the tidal pattern at the estuarine collection site. When the salinity was changed from 20 psu (12°C) at a constant temperature to that of freshwater, the OCR of the glass eels decreased by 21.6±7.0% (mean ± SD) (P<0.05), showing a dampened rhythm for about 48 h. After this period of impediment, the glass eels resumed normal metabolic activity. Direct migration from seawater to freshwater under constant temperature would result in a severe physiological stress for these glass eels for about two days. When the glass eels were exposed to a cyclic change in water temperature of 2°C 26 h⁻¹, as they encounter in estuaries, and then were introduced to freshwater abruptly, the OCR rhythm corresponded to the cyclic changes in water temperature after exposure to freshwater. Under these conditions, the mean OCR of the glass eels had a small difference before and after exposure to freshwater. These data explained how glass eels can directly move from sea water into the freshwater without any apparent metabolic stress in the estuaries showing cyclic change in water temperature (Δt=2°C).     

Adaptations to the dynamics of rural water supply from natural sources: A village example in semi-arid Nigeria

Domestic and agro-economic activities in the semi-arid region of Nigeria rely exclusively on rainfall, streamflow and groundwater in deep and alluvial (fadama) aquifers. Such water supply systems are subject to considerable seasonal and inter-annual variability. However, a combination of the various sources may mitigate the effects of water scarcity. This study describes the dynamics of the water sources for a village in northeastern Nigeria as an example that demonstrates the linkages between rainfall, streamflow and groundwater. Such linkages are important for developing strategies to mitigate the effects of climatic variation. Long-term records of rainfall were not available at the site so that the short-term data was interpreted in the context of the long-term climatic experience of the region. The stratigraphic profile of the aquifer was developed from resistivity methods, to supplement information obtained from well level hydrographs. Field observations and analysis of the data reveal that the beginning of the rainy season is a precarious period because none of the water sources are reliable. Rainfall infiltration recharges the shallow aquifers while lateral water flux from the floodwater-saturated fadama also contributes to water level rises in the deep wells. A study of the water level changes in response to deepening of the wells suggests that the wells are fed by an assortment of water-bearing lenses, separated by layers of low transmissivity. One major finding of this study is that there exists a close association between the wells in this riparian community and the recharge processes within the fadama. Upstream diversion, abstraction or impoundment could therefore undermine the viability of aquifer exploitation by the communities in the riparian zone. Analysis of the interactions among the various sources of water leads to the identification of several potential adaptation strategies for confronting the problem of water scarcity.     

STREAM DISCHARGE PREDICTION VIA A GRID BASED SOIL WATER ROUTING WITH PADDY FIELDS1

ABSTRACT: A grid based daily hydrologic model for a watershed with paddy fields was developed to predict the stream discharge. ASCII formatted elevation, soil, and land use data supported by the GRASS Geographic Information System are used to generate distributed results such as surface runoff and subsurface flow, soil water content, and evapotranspiration. The model uses a single flow path algorithm and simulates a water balance at each grid element. A linear reservoir assumption was used to predict subsurface runoff components. The model was applied to a 75.6 km² watershed located in the middle of South Korea, and observed stream flow hydrographs from 1995 and 1996 were compared to model predictions. The stream flow predictions of 1995 and 1996 generally agreed with the observed flow, resulting in a Nash‐Sutcliffe efficiency R² of 0.60 and 0.62, respectively. The hydraulic conductivity for percolating water through the saturated layer affected baseflow generation. The levee height of the paddy influenced the time and magnitude of the surface runoff, depending on irrigation management. The model will be used for making low flow management decisions by evaluating the role of each land use to stream flow, especially in case of paddy decrease by gradual urbanization of a watershed.     

Estimating and comparing greenhouse gas emissions with their uncertainties using different methods: A case study for an energy supply utility

Energy supply utilities release significant amounts of greenhouse gases (GHGs) into the atmosphere. It is essential to accurately estimate GHG emissions with their uncertainties, for reducing GHG emissions and mitigating climate change. GHG emissions can be calculated by an activity-based method (i.e., fuel consumption) and continuous emission measurement (CEM). In this study, GHG emissions such as CO₂, CH₄, and N₂O are estimated for a heat generation utility, which uses bituminous coal as fuel, by applying both the activity-based method and CEM. CO₂ emissions by the activity-based method are 12–19% less than that by the CEM, while N₂O and CH₄ emissions by the activity-based method are two orders of magnitude and 60% less than those by the CEM, respectively. Comparing GHG emissions (as CO₂ equivalent) from both methods, total GHG emissions by the activity-based methods are 12–27% lower than that by the CEM, as CO₂ and N₂O emissions are lower than those by the CEM. Results from uncertainty estimation show that uncertainties in the GHG emissions by the activity-based methods range from 3.4% to about 20%, from 67% to 900%, and from about 70% to about 200% for CO₂, N₂O, and CH₄, respectively, while uncertainties in the GHG emissions by the CEM range from 4% to 4.5%. For the activity-based methods, an uncertainty in the Intergovernmental Panel on Climate Change (IPCC) default net calorific value (NCV) is the major uncertainty contributor to CO₂ emissions, while an uncertainty in the IPCC default emission factor is the major uncertainty contributor to CH₄ and N₂O emissions. For the CEM, an uncertainty in volumetric flow measurement, especially for the distribution of the volumetric flow rate in a stack, is the major uncertainty contributor to all GHG emissions, while uncertainties in concentration measurements contribute a little to uncertainties in the GHG emissions.

Implications:Energy supply utilities contribute a significant portion of the global greenhouse gas (GHG) emissions. It is important to accurately estimate GHG emissions with their uncertainties for reducing GHG emissions and mitigating climate change. GHG emissions can be estimated by an activity-based method and by continuous emission measurement (CEM), yet little study has been done to calculate GHG emissions with uncertainty analysis. This study estimates GHG emissions and their uncertainties, and also identifies major uncertainty contributors for each method.     

Assessment of waters and sediments impacted by drainage at the Young Dong coal mine site, South Korea

Introduction  

This study focused on the assessment of the geochemistry and hydrology of the Imgok Creek–Young Dong tributary for the design of a field coal mine drainage treatment system.     

Emission factors for high-emitting vehicles based on on-road measurements of individual vehicle exhaust with a mobile measurement platform

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO(x)), and particle mass with aerodynamic diameter below 2.5 microm (PM2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO(x), black carbon (BC), PM2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO(x) and CO fleet-average emission factors and more than 60% of PM2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM2.5, and NO(x) tended to cluster among the same vehicles.     

PARAMETER ESTIMATION OF THE NONLINEAR MUSKINGUM MODEL USING HARMONY SEARCH1

ABSTRACT: A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows), DPO (deviations of peak of routed and actual flows), and DPOT (deviations of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

Nationwide monitoring of atmospheric PCDD/Fs and dioxin-like PCBs in South Korea

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) were measured in ambient air samples collected from different parts of South Korea in 2008, and the measured levels were used for assessing the spatial and temporal distribution of atmospheric PCDDFs and DL-PCBs in South Korea. The average concentrations of atmospheric PCDD/Fs and DL-PCBs among the 37 sites were 28 fg I-TEQ m⁻³ (ND ∼ 617) and 1 fg WHO-TEQ m⁻³ (ND ∼ 0.016). Elevated atmospheric levels of PCDD/Fs and DL-PCBs observed at residential/industrial sites and in the north-west of Korea, indicated a potential contribution and impacts of anthropogenic sources of PCDD/Fs and DL-PCBs. These levels were similar or lower than those previously reported in other ambient air surveys. Average concentrations of PCDD/Fs showed small seasonal variations (ANOVA analysis, p = 0.144). The highest concentrations of PCDD/Fs were observed during winter, followed by spring, autumn and summer. Atmospheric PCDD/Fs and DL-PCBs in South Korea rapidly decreased during the last 10 years (1998-2008), demonstrating the efficiency of stricter regulations and the application of best available technologies/best environmental practices at emission sources. Comparison of the congener profiles and principal component analysis showed that current atmospheric PCDD/Fs are mostly influenced by industrial sources and PCBs from old commercial PCB uses. Nationwide POPs monitoring will continue and allows an effective evaluation of the implementation of the Stockholm Convention on POPs.     

The impacts of the waterfront development in Iskandar Malaysia

The increasing number of waterfront projects shows that the concept of waterfront development has contributed to the advancement of the Malaysian construction industry. This study seeks to assess the impacts of the implementation of waterfront development in Iskandar Malaysia eliciting the perceived relative importance of these impacts. A comprehensive literature review has been conducted to collate the potential impacts of the waterfront development. A total of 363 sets of questionnaires were distributed to the contractors and developers registered under G7 category of the Construction Industry Development Board in Malaysia. Results show that the most significant influence of the waterfront development is related to economic benefits, while environmental impacts are not considered as a priority for this category of stakeholders. In addition, this study also indicates that the implementation of the waterfront projects has generated numerous job opportunities within the developed region. Moreover, exchanging knowledge in the planning and managing of the waterfront development is imperative to the development of the waterfront projects. Nevertheless, the shortcomings of these developments, such as the negative environmental impact on natural beaches and the coastline (i.e. pollution) as well as human-related issues (i.e. excessive human settlement), should not be taken lightly. This research confirms that the reclamation of land for waterfront development is closely correlated with the destruction of these natural structures.