The evolution of mass balance models of persistent organic pollutant fate in the environment

Current approaches to modelling the fate of persistent organic pollutants (POPs) in the environment have evolved in response to four dominant characteristics of these substances; namely: (1) the presence of POPs in virtually all environmental phases and the ease with which they move from one to the other requires multi-compartmental modelling. Describing transport across phase boundaries becomes as, or even more, important as quantifying transport within the phases; (2) POPs may persist in the environment for many decades. For chemicals that 'have time', concepts such as equilibrium partitioning and steady-state become more important than for short-lived substances whose fate is more controlled by the rates of transformation; (3) measuring POPs is difficult and expensive and observed concentrations of POPs are not available in high spatial or temporal resolution. Consequently, high resolution tends not to be a high priority in POP models; and (4) detrimental effects of POPs often manifest themselves in top predators, which has led to a focus on modelling biotic uptake and transfer within food chains. The task of building a POPs model is viewed as combining the four 'building blocks' of partitioning, transport, transformation and source data with the help of the law of the conservation of mass. Process models, evaluative models, models of real local, regional and global fate, as well as biological uptake models are presented and references to numerous examples are provided. An attempt is made to forecast future directions in the field of POPs modelling. It is expected that modelling techniques that do not rely on quantitative emission estimates as well as approaches that take into account spatial, temporal and climatic variability as well as parameter uncertainty will increase in importance. Finally, the relationship between modelling POPs and models of other pollutant issues is addressed, as are potential interactions between POPs and pollutant issues such as eutrophication, acidification and global climate change.     

Research on the influencing factors of reverse logistics carbon footprint under sustainable development

With the concerns of ecological and circular economy along with sustainable development, reverse logistics has attracted the attention of enterprise. How to achieve sustainable development of reverse logistics has important practical significance of enhancing low carbon competitiveness. In this paper, the system boundary of reverse logistics carbon footprint is presented. Following the measurement of reverse logistics carbon footprint and reverse logistics carbon capacity is provided. The influencing factors of reverse logistics carbon footprint are classified into five parts such as intensity of reverse logistics, energy structure, energy efficiency, reverse logistics output, and product remanufacturing rate. The quantitative research methodology using ADF test, Johansen co-integration test, and impulse response is utilized to interpret the relationship between reverse logistics carbon footprint and the influencing factors more accurately. This research finds that energy efficiency, energy structure, and product remanufacturing rate are more capable of inhibiting reverse logistics carbon footprint. The statistical approaches will help practitioners in this field to structure their reverse logistics activities and also help academics in developing better decision models to reduce reverse logistics carbon footprint.     

Using advanced surface complexation models for modelling soil chemistry under forests: Solling forest, Germany

Various dynamic soil chemistry models have been developed to gain insight into impacts of atmospheric deposition of sulphur, nitrogen and other elements on soil and soil solution chemistry. Sorption parameters for anions and cations are generally calibrated for each site, which hampers extrapolation in space and time. On the other hand, recently developed surface complexation models (SCMs) have been successful in predicting ion sorption for static systems using generic parameter sets. This study reports the inclusion of an assemblage of these SCMs in the dynamic soil chemistry model SMARTml and applies this model to a spruce forest site in Solling Germany. Parameters for SCMs were taken from generic datasets and not calibrated. Nevertheless, modelling results for major elements matched observations well. Further, trace metals were included in the model, also using the existing framework of SCMs. The model predicted sorption for most trace elements well.     

Adapting a geographical information system-based water resource management to the needs of the Romanian water authorities

Background, aim, and scope  

The need for global and integrated approaches to water resources management, both from the quantitative and the qualitative point of view, has long been recognized. Water quality management is a major issue for sustainable development and a mandatory task with respect to the implementation of the European Water Framework Directive as well as the Swiss legislation. However, data modelling to develop relational databases and subsequent geographic information system (GIS)-based water management instruments are a rather recent and not that widespread trend. The publication of overall guidelines for data modelling along with the EU Water Framework Directive is an important milestone in this area. Improving overall water quality requires better and more easily accessible data, but also the possibility to link data to simulation models. Models are to be used to derive indicators that will in turn support decision-making processes. For this whole chain to become effective at a river basin scale, all its components have to become part of the current daily practice of the local water administration. Any system, tool, or instrument that is not designed to meet, first of all, the fundamental needs of its primary end-users has almost no chance to be successful in the longer term.     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.     

Modelling of the migration of trace elements along groundwater flowpaths by using a steady state approach application to the site at El Berrocal (Spain)

The applications of geochemical modelling to natural water systems mostly rely upon the equilibrium assumption. This is in principle justified for deep groundwater systems with long water residence times.In addition, trace element behaviour in these natural systems has been normally modelled by taking into account the precipitation and dissolution of individual trace element phases.Recent geochemical modelling work related to natural analogue systems would indicate that: (1) the equilibrium condition is restricted to a limited number of components in long water residence times and (2) the behaviour of trace metals is very much connected to the major component cycling in the system.There is a need to develop our geochemical modelling capabilities to take these two facts into account in order to be able to predict the behaviour of trace components (radionuclides) in a geological repository.In this work we report on the successful application of steady-state kinetics in conjunction with co-dissolution/co-precipitation approaches to model trace element geochemistry in the natural analogue system at El Berrocal. The evolution of major components of the system (Ca (II), Al (III), CO₃²⁻ and Si) as well the trace elements investigated (U, Ba and Mn) is well reproduced by using the coupling between steady-state kinetics and co-dissolution/co-precipitation approaches.     

SAGA: a decision support system for air pollution management around a coal-fired power plant

Air quality models are currently feasible approaches to prevent air pollution episodes. From one of the first source-oriented modelling approaches for air pollution forecasting (Souto et al., 1994, 1996, 1998), a new decision support system for air quality management, SAGA, was developed to provide support to As Pontes Power Plant (APPP) staff. SAGA can provide air pollution forecasts and manage meteorological and air quality measurements. Power plant decisions are supported by the results of a non-hydrostatic meteorological model (ARPS, Xue et al., 2001) to produce Meteorological Forecasts (MFs), and to be coupled to different Lagrangian dispersion models.     

Income inequality,ecological footprint,and carbon dioxide emissions in Asian developing economies: what effects what and how?

The reduction of income inequality and environmental vulnerability is the most important factor, through which we can achieve the target of Sustainable Development Goals (SDGs). The past papers have investigated the nexus between income inequality and carbon emissions; however, the relationship between income inequality and carbon emissions along with ecological footprint has not been studied in the case of developing countries. To this end, this study analyzed the impact of income inequality on both carbon emissions and ecological footprint as well as the impact of carbon emission and ecological footprint on income inequality by using the dataset from 2006 to 2017 for the 18 Asian developing economies. This study confirmed the positive relationship between carbon emissions, ecological footprint, and income inequality under the methodology of Driscoll and Kraay (D&K) standard error approach. Specifically, a higher-income gap is destructive for environmental degradation, whereas increasing level of carbon emissions and ecological footprint also leads to rising income inequality in the investigated region. Furthermore, foreign direct investment (FDI), easy access to electricity, and population growth control income inequality, but they have a detrimental effect on both ecological footprint and carbon emissions. The empirical findings also provide some important policy implications.

     

Uranium transport around the reactor zone at Bangombé and Okélobondo (Oklo): examples of hydrogeological and geochemical model integration and data evaluation

The sites at Bangombé and Okélobondo (Oklo) in Gabon provide a unique opportunity to study the behaviour of products from natural nuclear reactions in the vicinity of reactor zones which were active around two billion years ago. The Commission of the European Communities initiated the Oklo Natural Analogue Programme. One of the principal aims was to study indications of present time migration of elements from the reactor zones under ambient conditions. The hydrogeological and hydrochemical data from the Oklo sites were modelled in order to better understand the geochemical behaviour of radionuclides in the natural system, by using independent models and by comparing the modelling outcome. Two modelling approaches were used: M3 code (hydrochemical mixing and mass balance model), developed by the Swedish Nuclear Fuel and Waste Management Company (SKB) and HYTEC (reactive transport model) developed by Ecole des Mines de Paris.Two different reactor zones were studied: Bangombé, a shallow site, the reactor being at 11 m depth, and OK84 at Okélobondo, situated at about 450 m depth, more comparable with a real repository location. This allowed the validation of modelling tools in two different sedimentary environments: one shallow, with a more homogeneous layering situated in an area of meteoric alteration, and the other offering the opportunity to study radionuclide migration from the reaction zone over a distance of 450 m through very heterogeneous sedimentary layers.The modeling results indicate that the chemical reactions retarding radionuclide transport are very different at the two sites. At Bangombé, the decomposition of organic material consumes oxygen and at Okélobondo the oxygen is consumed by inorganic reactions resulting, in both cases, in uranium retardation. Both modelling approaches (statistic with M3 code and deterministic with HYTEC code) could describe this situation.The goal of this exercise is to test codes which can help to describe and understand the processes taking place at the sites, validate the models with in situ data, and thus build confidence in the tools used for future site characterization. Ultimately, this allows identifying and selecting processes and parameters that can be used as input into repository performance assessment calculations and modelling exercises.     

Uranium transport around the reactor zone at Bangombé and Okélobondo (Oklo): examples of hydrogeological and geochemical model integration and data evaluation

The sites at Bangombé and Okélobondo (Oklo) in Gabon provide a unique opportunity to study the behaviour of products from natural nuclear reactions in the vicinity of reactor zones which were active around two billion years ago. The Commission of the European Communities initiated the Oklo Natural Analogue Programme. One of the principal aims was to study indications of present time migration of elements from the reactor zones under ambient conditions. The hydrogeological and hydrochemical data from the Oklo sites were modelled in order to better understand the geochemical behaviour of radionuclides in the natural system, by using independent models and by comparing the modelling outcome. Two modelling approaches were used: M3 code (hydrochemical mixing and mass balance model), developed by the Swedish Nuclear Fuel and Waste Management Company (SKB) and HYTEC (reactive transport model) developed by Ecole des Mines de Paris. Two different reactor zones were studied: Bangombé, a shallow site, the reactor being at 11 m depth, and OK84 at Okélobondo, situated at about 450 m depth, more comparable with a real repository location. This allowed the validation of modelling tools in two different sedimentary environments: one shallow, with a more homogeneous layering situated in an area of meteoric alteration, and the other offering the opportunity to study radionuclide migration from the reaction zone over a distance of 450 m through very heterogeneous sedimentary layers. The modeling results indicate that the chemical reactions retarding radionuclide transport are very different at the two sites. At Bangombé, the decomposition of organic material consumes oxygen and at Okélobondo the oxygen is consumed by inorganic reactions resulting, in both cases, in uranium retardation. Both modelling approaches (statistic with M3 code and deterministic with HYTEC code) could describe this situation. The goal of this exercise is to test codes which can help to describe and understand the processes taking place at the sites, validate the models with in situ data, and thus build confidence in the tools used for future site characterization. Ultimately, this allows identifying and selecting processes and parameters that can be used as input into repository performance assessment calculations and modelling exercises.     

Passive air sampling theory for semivolatile organic compounds

The mathematical modelling underlying passive air sampling theory can be based on mass transfer coefficients or rate constants. Generally, these models have not been inter-related. Starting with basic models, the exchange of chemicals between the gaseous phase and the sampler is developed using mass transfer coefficients and rate constants. Importantly, the inter-relationships between the approaches are demonstrated by relating uptake rate constants and loss rate constants to mass transfer coefficients when either sampler-side or air-side resistance is dominating chemical exchange. The influence of sampler area and sampler volume on chemical exchange is discussed in general terms and as they relate to frequently used parameters such as sampling rates and time to equilibrium. Where air-side or sampler-side resistance dominates, an increase in the surface area of the sampler will increase sampling rates. Sampling rates are not related to the sampler/air partition coefficient (K(SV)) when air-side resistance dominates and increase with K(SV) when sampler-side resistance dominates.     

Moving toward effective ozone flux assessment

We present a comment about "Ozone risk assessment for plants: central role of metabolism-dependent changes in reducing power" by Dizengremel, Le Thiec, Bagard, and Jolivet. As tools for summarizing plant O(3) sensitivity in simple indices, Dizengremel et al. suggest: reducing power, as antioxidant regeneration through the Halliwell/Asada cycle requires NADPH from the photosynthetic light reaction; Rubisco/PEPc ratio, as an index of the energy balance between anabolic and catabolic reactions; and water-use efficiency as a time-integrated approximation of the carbon gain to stomatal O(3) uptake ratio. The scientific background is solid, and simple enough (although expensive) to be translated into modelling and routine use. In the last decade, several approaches have been developed, mostly by using photosynthesis as a metric of defence. All these approaches should be experimentally tested in different and realistic conditions, before the results are transferred to the field and used in effective O(3) flux modelling and assessment.     

A review and numerical assessment of the random walk particle tracking method

We review the basic mathematical concepts of random walk particle tracking (RWPT) and its advantages and limitations. Three different numerical approaches to overcome the local mass conservation problem of the random walk methodology are examined: (i) the interpolation method, (ii) the reflection principle, and (iii) the generalized stochastic differential equations (GSDE). Analytical solutions of the spatial moments for a two-layer system are compared to model predictions using the different techniques and results demonstrate that the interpolation method reproduces correctly average velocity, but fails to reproduce macrodispersion at higher hydraulic conductivity contrasts between the two layers. On the contrary, the reflection principle and the GSDE approach underestimate average velocity, but reproduce macrodispersion better for high contrasts. The different behavior is based on an artificial shift of mass for increasing heterogeneities for the GSDE approach and the reflection principle, whereas the interpolation method suffers from the smoothing of the dispersion tensor. The behavior of these approaches was furthermore analyzed in two-dimensional heterogeneous hydraulic conductivity fields, which are characterized by different random function models. Solute transport was simulated correctly by all three approaches for the reference fields having Gaussian structures or non-Gaussian structures with an isotropic spatial correlation, even for a variance of the natural log of hydraulic conductivity of sigma(lnK)(2)=4. However, for the non-Gaussian model with a strong anisotropic spatial correlation and a variance of sigma(lnK)(2)=2 and higher, the interpolation method was the only technique modelling solute transport correctly. Furthermore, we discuss the general applicability of random walk particle tracking in comparison to the standard transport models and conclude that in advection-dominated problems using a high spatial discretization or requiring the performance of many model runs, RWPT represents a good alternative for modelling contaminant transport.     

Stochastic model to forecast ground-level ozone concentration at urban and rural areas

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,1)24 has been developed for the rural area. In both sampling points, predictions of hourly ozone concentrations agree reasonably well with measured values. However, the prediction of hourly ozone concentrations in the rural point appears to be better than that of the urban point. The performance of ARIMA models suggests that this kind of modelling can be suitable for ozone concentrations forecasting.     

Bioeconomic modelling and salmon aquaculture: an overview of the literature

Bioeconomic models can be used to assist producers and decision-makers in identifying optimal production system designs, operation management strategies, and alternative development and policy approaches. This paper reviews the literature on bioeconomic modelling in aquaculture since 1993 and builds on an earlier article by Leung (1994) which examines this literature for the 1974-1993 period. In order to identify the papers reviewed in the present study, a thorough online search in various databases and some specific journals was conducted. Observations on the general state-of-the-art of bioeconomic modelling in aquaculture are discerned based on a comparative analysis of work in the field, with specific reference to salmon aquaculture. Implications for salmon aquaculture systems in Chile and elsewhere are discussed.     

Modeling habitat suitability for moose in coastal northern Sweden: empirical vs process-oriented approaches

Habitat models may provide viable tools for co-management of large ungulates and forest resources, yet their applicability has not been comprehensively evaluated in managed forest. We examined 2 inherently different approaches to model the relative winter habitat suitability for moose (Alces alces) in the coastal area of northern Sweden. An empirical approach based on GPS positions of 15 female moose was used to scrutinize the assumptions and functional mechanisms of a process-oriented, conceptual approach, based on published material on the species' preferences for habitat components related to food and cover. For both model approaches habitat was described using estimates of forest-stand characteristics based on satellite imagery. The empirical model also included variables relating to topographic properties of the landscape as well as distances to landscape features. The output from both models was a habitat suitability index (HSI) score, enabling the models to be compared with each other. The models showed different results, highlighting the need to include the spatially explicit distribution of environmental variables in future conceptual, process-oriented models.     

Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks

The region of the Bay of Algeciras is a very industrialized area where very few air pollution studies have been carried out. The main objective of this work has been the use of artificial neural networks (ANNs) as a predictive tool of high levels of ambient carbon monoxide (CO). Two approaches have been used: multilayer perceptron models (MLPs) with backpropagation learning rule and k-Nearest Neighbours (k-nn) classifiers, in order to predict future peaks of carbon monoxide. A resampling strategy with twofold cross-validation allowed the statistical comparison of the different topologies and models considered in the study. The procedure of random resampling permits an adequate and robust multiple comparisons of the tested models and allow us to select a group of best models.     

Does foreign direct investments impair the ecological footprint? New evidence from the panel quantile regression

This study examines the impact of foreign direct investments on ecological footprint along with other explanatory variables of 92 countries from the year 2001 to 2016. Here, we applied the panel quantile regression model to meet the purpose of our study as it considers unobserved country heterogeneity, unlike other statistical methods. The study reveals that foreign direct investment has a positive relationship with the ecological footprint in each quantile except one, which proves the constancy of the pollution haven hypothesis. Moreover, we also tried to detect the impact of economic growth, manufacturing value-added, the percentage of world exports, and institutional quality on the ecological footprint in this study. The findings of this study also reveal that economic growth and manufacturing value-added are negatively associated with the ecological footprint. With respect to the percentage of world exports and institutional quality, we found a positive relationship with the ecological footprint. From the result of our study, different policy implications have been proposed for host countries and foreign investors on improving the economy through foreign direct investment with minimal ecological footprint.

     

New approaches to study the relationship between stomatal conductance and environmental factors under Mediterranean climatic conditions

The most frequently models used (Ball–Berry and Jarvis-type models) to estimate stomatal conductance (Gs) values have limitations when applied to plants growing in Mediterranean climate. To overcome these limitations, several statistical methodologies (Multiple Linear Regression, Neural Net Analysis (NNA)) were used to build models to predict Gs. However, all these models were unable to integrate the physiological response of plants to the overall limiting environmental parameters in our Mediterranean site especially during the summer drought. With this in mind, it is relevant to find alternative approaches which link Gs response to environmental limitations of plants. In this paper, we demonstrate that: (1) the different linear and nonlinear statistical approaches used significantly affect the weights of the environmental variables which are utilized in semi-empirical Gs models; (2) a tight relationship exists between summer values of Gs and the rate of accumulated precipitations (α) in the first 5 months of the year, thus allowing to predict Gs in a quantitative way; and (3) the latter is also related to different water-use strategies adopted by plants in response to drought stress in the summer period. Because α is easily calculated, it is an interesting parameter for the Gs modelling addressed to understand many important aspects of the plant–environment interactions, such as water relations and pollutant uptake.     

On the influence of meteorological input on photochemical modelling of a severe episode over a coastal area

The modelling reconstruction of the processes determining the transport and mixing of ozone and its precursors in complex terrain areas is a challenging task, particularly when local-scale circulations, such as sea breeze, take place. Within this frame, the ESCOMPTE European campaign took place in the vicinity of Marseille (south-east of France) in summer 2001. The main objectives of the field campaign were to document several photochemical episodes, as well as to constitute a detailed database for chemistry transport models intercomparison.CAMx model has been applied on the largest intense observation periods (IOP) (June 21–26, 2001) in order to evaluate the impacts of two state-of-the-art meteorological models, RAMS and MM5, on chemical model outputs. The meteorological models have been used as best as possible in analysis mode, thus allowing to identify the spread arising in pollutant concentrations as an indication of the intrinsic uncertainty associated to the meteorological input.Simulations have been deeply investigated and compared with a considerable subset of observations both at ground level and along vertical profiles. The analysis has shown that both models were able to reproduce the main circulation features of the IOP. The strongest discrepancies are confined to the Planetary Boundary Layer, consisting of a clear tendency to underestimate or overestimate wind speed over the whole domain.The photochemical simulations showed that variability in circulation intensity was crucial mainly for the representation of the ozone peaks and of the shape of ozone plumes at the ground that have been affected in the same way over the whole domain and all along the simulated period. As a consequence, such differences can be thought of as a possible indicator for the uncertainty related to the definition of meteorological fields in a complex terrain area.