Small cetacean bycatch as estimated from stranding schemes: The common dolphin case in the northeast Atlantic

Death in fishing gear of non-target species (called ‘bycatch’) is a major concern for marine wildlife, and mostly worrying for long-lived species like cetaceans, considering their demographic characteristics (slow population growth rates and low fecundity). In European waters, cetaceans are highly impacted by this phenomenon. Under the Common Fishery Policy, the EC 812/2004 regulation constitutes a legal frame for bycatch monitoring on 5–10% of fishing vessels >15 m. The aim of this work was to compare parameters and bycatch estimates of common dolphins (Delphinus delphis) provided by observer programmes in France and UK national reports and those inferred from stranding data, through two approaches. Bycatch was estimated from stranding data, first by correcting effectives from drift conditions (using a drift prediction model) and then by estimating the probability of being buoyant. Observer programmes on fishing vessels allowed us to identify the specificity of the interaction between common dolphins and fishing gear, and provided low estimates of annual bycaught animals (around 550 animals year⁻¹). However, observer programmes are hindered by logistical and administrative constraints, and the sampling scheme seems to be poorly designed for the detection of marine mammal bycatches. The analyses of strandings by considering drift conditions highlighted areas with high levels of interactions between common dolphins and fisheries. Since 1997, the highest densities of bycaught dolphins at sea were located in the southern part of the continental shelf and slope of the Bay of Biscay. Bycatch numbers inferred from strandings suggested very high levels, ranging from 3650 dolphins year⁻¹ [2250–7000] to 4700 [3850–5750] dolphins year⁻¹, depending on methodological choices. The main advantage of stranding data is its large spatial scale, cutting across administrative boundaries. Diverging estimates between observer programmes and stranding interpretation can set very different management consequences: observer programmes suggest a sustainable situation for common dolphins, whereas estimates based on strandings highlight a very worrying and unsustainable process.     

Use of hydrodynamic models for the management of the Danube Delta wetlands: The case study of Sontea-Fortuna ecosystem

Due to its high biodiversity the Danube Delta, in Romania, is recognized as a UNESCO World Heritage Natural Site and it is listed as a RAMSAR wetland. The Danube River variable discharges have a great impact on the habitats and the overall ecological status of the delta. One of its most vulnerable parts, from both hydrodynamic and morphological point of view is the Sontea-Fortuna wetland located in the upstream of the Danube Delta. Sontea-Fortuna wetland is susceptible to both floods and droughts. On a long term, decision makers in the area need to know which measures to implement and how these will impact/improve the environment.This article presents how a 3D hydrodynamic model can be used as support for making sound decisions regarding the management of deltaic ecosystems. In particular, the methodology is applied on the Sontea-Fortuna wetland. The case study is part of a wider research in the area, which was developed within the EnviroGRIDS EU FP7 research project. EnviroGRIDS aimed at building capacity for scientists, decision-makers and the general public in the Black Sea Catchment, through collection and sharing of environmental data and models at the basin scale.     

Description and evaluation of a model of deposition velocities for routine estimates of dry deposition over North America. Part II: review of past measurements and model results

Numerical sensitivity tests and four months of complete model runs have been conducted for the Routine Deposition Model (RDM). The influence of individual model inputs on dry deposition velocity as a function of land-use category (LUC) and pollutant (SO₂, O₃, SO²⁻₄ and HNO₃) were examined over a realistic range of values for solar radiation, stability and wind speed. Spatial and temporal variations in RDM deposition velocity (V_d) during June – September 1996 time period generated using meteorological input from a mesoscale model run at 35 km resolution over north-eastern North America were also examined. Comparison of RDM V_d values to a variety of measurements of dry deposition velocities of SO₂, O₃, SO²⁻₄ and NHO₃ that have been reported in the literature demonstrated that RDM produces realistic results. Over northeastern NA RDM monthly averaged dry deposition velocities for SO₂ vary from 0.2 to 3.0 cm s⁻¹ with the highest deposition velocities over water surfaces. For O₃, the monthly averaged dry deposition velocities are from 0.05 to 1.0 cm s⁻¹ with the lowest values over water surfaces and the highest over forested areas. For HNO₃, the monthly averaged dry deposition velocities have the range of 0.5 to 6 cm s⁻¹, with the highest values for forested areas. For SO²⁻₄, they range from 0.05–1.5 cm s⁻¹, with the lowest values over water and the highest over forest. The monthly averaged dry deposition velocities for SO₂ and O₃ are higher in the growing season compared to the fall, but this behaviour is not apparent for HNO₃ and sulphate. In the daytime, the hourly averaged dry deposition velocities for SO₂, O₃, SO²⁻₄ and HNO₃ are higher than that in the nighttime over most of the vegetated area. The diurnal variation is most evident for surfaces with large values for leaf area index (LAI), such as forests. Based on the results presented in this paper, it is concluded that RDM V_d values can be combined with measured air concentrations over hourly, daily or weekly periods to determine dry deposition amounts and with wet deposition measurements to provide seasonal estimates of total deposition and estimates of the relative importance of dry deposition.     

多级模糊决策在地震预报中的应用

本文把多级模糊决策方法应用于地震预报当中。文中详细论证和推导了多级模糊决策方法与模型。该方法使决策部门可以根据输入前兆的变化随时作出合理的对策。文中还给出了地震预报多级模糊决策数字解实例,并用回顾法讨论了1976年松潘7.2级地震的模糊预报决策。     

Dynamic behaviour of direct spring loaded pressure relief valves in gas service: II reduced order modelling

A previous study of gas-service direct-spring pressure relief valves connected to a tank via a straight pipe is continued by deriving a reduced-order model for predicting oscillatory instabilities such as valve flutter and chatter. The reduction process uses collocation to take into account a finite number N of acoustic pressure waves within the pipe, resulting in a set of 2N+3 ordinary differential equations. Following a novel non-dimensionalization, it is shown analytically that the model can exhibit, at experimentally realistic parameter values, instabilities associated with coupling between the valve and acoustic waves in the pipe. The thresholds for each instability are such that for a given flow rate, the first mode to go unstable as the inlet pipe length increases is the quarter-wave mode, then a three-quarter wave, a 5/4-wave etc. Thus the primary mode of instability should always be due to the quarter wave. In the limit of low flow rates, a simple approximate expression is found for the quarter-wave instability threshold in the form of inlet pipe length against mass flow rate. This threshold curve is found to agree well with simulation of the full model. For higher flow rates there is a need to include fluid convection, inlet pressure loss and pipe friction in order to get good agreement. The reduced model enables the dependence of the stability curve on key dimensionless physical parameters to be readily computed.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

Methodology for Natech coupling risk assessment using correlative multi-criteria decision-making method

When a natural disaster occurs, it may damage multiple industrial facilities in a certain area at the same time, and the resulting Natech events may have an impact on the surrounding industrial facilities, generating coupling risk. In this study, the assessment of Natech events coupling risk is conducted using the method of correlated multi-criteria decision-making, and the knowledge of fuzzy measures is introduced to solve the uncertainty problem in Natech coupling risk. Natech Coupling Risk Index is constructed to involve physical and functional facilities. The concept of equivalent population is proposed to compare the risks generated by physical facilities and functional facilities. And economic indicators are added to calculate the comprehensive risk value. The purpose of this contribution is to enable local government managers to use their expertise and resources and the existing risk assessment of the plants themselves and rely on the scoring of experts limitedly to quickly and easily identify potential high Natech risk areas. In the calculation process of coupling risk, the government can also take the lead to promote information communication between different plants and other industrial subjects. The proposed method was applied in a realistic chemical industry area in Guangzhou, China and in a hypothetical town. The result shows that the physical risk may be transferred to the population and economy through the coupling between industrial facilities and the functional link between functional facilities and population and economy.     

Prioritizing groundwater remediation policies: a fuzzy compatibility analysis decision aid

The implementation of groundwater remediation strategies in contaminated areas includes not only a cost-benefit analysis and an environmental risk assessment but also another type of study called compatibility analysis. A compatibility analysis targets the interactions between remediation technologies and site characteristics, such as the types of active contaminants and their concentrations, soil composition and geological features, etc. The purpose of this analysis is to identify the most compatible remediation plan for the contaminated site. In this paper, we introduce a decision support system for the prioritization of remediation plans based on their estimated compatibility index. As this model receives data in terms of linguistic judgments and experts' opinions, we use fuzzy sets theory to deal with these uncertainties. First, we break down the concept of compatibility into the measurable factors. Then by using a multiple-attribute decision-making (MADM) outline, we compute a factorial, regional and overall compatibility indicator for each plan. Finally, by comparing these generated indicators, we rank the remediation policies.     

Air assimilative capacity-based environment friendly siting of new industries--a case study of Kochi region, India

Air pollution has become a matter of grave concern, particularly in mega-cities and urban areas, where the situation is alarming and becoming more and more severe day-by-day and warrants, therefore, careful planning to facilitate future industrial development. Site selection, with the objective of minimizing adverse environmental impacts based on environmental criteria is a vital prerequisite, particularly for air polluting industries. In order to locate any air polluting industry, the assimilative capacity of the region needs to be assessed carefully and planned accordingly, so that the receiving environment is not adversely affected. Assimilative capacity of a region/airshed, widely represented through the ventilation coefficient by many researchers in the past, does not give a clear picture about the amount of emission load that can be assimilated in a given region. The ventilation coefficient, at best, can only present a broad picture about the air pollution dispersion potential (low, medium or high) of the region. A modified approach, which utilizes air quality modelling as a tool to estimate the maximum allowable emission load that a region can assimilate without violating the stipulated standards, has been used for estimating the assimilative capacity of the air environment. Details of this approach have been presented in this paper through a case study carried out for the Kochi region, located in the Kerala State of India. A variety of emission and meteorological scenarios have been considered and critical emission loads have been estimated. This approach shall provide necessary technical guidance to the environmental regulatory authorities as well as to the industries in planning environment friendly industrial development.     

Water quality modelling of the river Yamuna (India) using QUAL2E-UNCAS

This paper describes the utility of QUAL2E as a modelling package in the evaluation of a water quality improvement programme. In this study, QUAL2E was applied to determine the pollution loads in the river Yamuna during its course through the national capital territory of Delhi, India. The study aimed at examining the influence of different scenarios on river water quality. Four different pollution scenarios were analysed besides the 'business as usual' situation. The study revealed that it was necessary to treat the discharge from drains to the river Yamuna and diversion of a substantial load to the Agra canal for further treatment was also essential. It was also established through this study that maintaining a flow rate of more than 10 m(3)/s in the river could also help preserve the river's water quality. To clearly display the model outcomes and demarcate polluted zones in the river stretch, model results were interfaced with a Geographical Information System (GIS) to produce cartographic outputs. In addition, uncertainty analysis in the form of first-order error analysis and Monte Carlo analysis was performed, to realise the effect of each model parameter on DO and BOD predictions. The uncertainty analysis gave satisfactory results on simulated data.