A Novel Formulation for Designing a Monitoring Strategy: Application to the Design of a River Quality Monitoring System

In this work, we propose a technique to automatically optimize the monitoring of any distributed indicator (concentration of a substance along a river, blood pressure of a patient over time, etc.) for which a reliable estimate is previously available. From a mathematical point of view, the problem is based on obtaining a reliable estimate of the chosen indicator (e.g., by numerical simulation), and then solving a multi-objective optimization problem (with mixed real and integer variables) whose solution must provide an efficient and satisfactory monitoring strategy. As an illustrative case, we show the steps to follow in order to implement that strategy when designing a system for monitoring water quality in a river. Finally, we present and analyze the results when applying the proposed technique to study a real case in the Neuse River (North Carolina, USA).     

A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling

Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.     

Application of Adjoint Approach to Oil Spill Problems

Adjoint method is applied to various oil spill problems. A three-dimensional model for describing the dispersion of a quasi-passive substance (a pollutant or a nutrient) and its adjoint model are considered in a limited sea region. Direct and adjoint estimates are used to get dual (equivalent) estimates of the mean concentration of the substance in important zones of the region. The role of dual estimates is illustrated with a few examples. They include such oil spill problems as the search of the most dangerous point of the oil tanker route, the oil dispersion with a climatic velocity, and the dependence of the oil concentration estimates on the oil spill rate. One more example is the application of optimal bioremediation strategy for cleaning a few zones polluted by oil. In this case, instead of oil, the model describes the dispersion of a nutrient released to marine environment. Balanced, unconditionally stable second-order finite-difference schemes based on the splitting method for the solution of the dispersion model and its adjoint are suggested. The main and adjoint difference schemes are compatible in the sense that at every fractional step of the splitting algorithm, the one-dimensional split operators of both schemes satisfy a discrete form of Lagrange identity. In the special unforced and non-dissipative case, each scheme has two conservation laws. Every split one-dimensional problem is solved by Thomas’ factorization method.     

Spatial Validation of Land Use Change Models Using Multiple Assessment Techniques: A Case Study of Transition Potential Models

Using land use and cover change (LUCC) models for the urban growth planning, environmental assessment, and decision-making needs the establishment of an appropriate level of confidence in their performance. The objective of this research is to explore the importance of using multiple assessment techniques in order to fairly evaluate the performance of land use models. An application is conducted by using the Land Change Modeler for Ecological Sustainability (LCM) which is an empirical and transition potential model. LCM is applied to model the agricultural to developed areas transition in Rennes metropolitan area (France). The land demand is estimated using the Markov Chain model; whereas, the transition potential map is implemented using the Multi-Layer Perceptron Neural Network (MLP) method based on historical changes and driving variables. The model performance is assessed based on a variety of the most commonly used validation techniques. At the study area level, the correctness and disagreement analysis shows that LCM performs better at predicting the amount than the allocation of developed areas. Additionally, landscape metrics reveal that LCM tends to predict a fragmented urban form, which seems evident because of the large number of the individual urban patches. At the municipality level, the error budget analysis shows that the model performance, which varies highly between different subareas, needs to be improved. Moreover, the cross-tabulation between the transition potential map and both the observed and the predicted agricultural to developed areas transitions reveals that the order of the transition potential values does not perfectly fit the observed change; whereas, the predicted change is not solely limited to areas with high potential.     

Mercury Air Emission from Coal-Fired Public Power Sector: Uncertainty and Its Monthly Distribution. Case Study from Poland

The paper presents two aspects concerned with the mercury air emission inventory from coal-fired public power and energy plants: an uncertainty analysis, using Monte Carlo simulation (Journal of the American Statistical Association, 44(247), 335–341 1949) and the monthly distributions applying the Denton-Cholette approach (Dagum & Cholette 2006). The analysis determines uncertainty about the estimates mercury air emission from 1990 to 2012 including the development of air pollution control (APC) technologies in the Polish public power and energy sector, also the monthly distributions in comparison with previously obtained results (H?awiczka 2008). The uncertainty of mercury (Hg) content in fuel is 31.6% for hard coal and 42.4% for brown coal. The confidence interval for the estimated emission changed from [kg] (16,082.2; 16,242.2) in 1990 to (10,525.9;10,671.1) in 2012. However, the Denton-Cholette approach overestimates the emissions for the warmer periods of the year, but it could, however, in our view, be applied to attain the monthly distributions.     

Equity rules for burden sharing in the mitigation process of climate change

Although international negotiation on the mitigation of climate change is a process of determining burden-sharing rules between countries, there has been no clear agreement on equity principles for burden sharing. During the negotiating process up to the Kyoto Protocol, various proposals were made on such burden-sharing rules, but an agreement on emission targets for Annex I countries was achieved without explicitly agree-ing to any rules. In the next phase of the negotiation, debates on emission targets are likely to shift from those between developed countries to those between all parties to the convention. In such a phase, debates on burden-sharing rules will be revisited. The purpose of this paper is: (1) to determine implicitly a formula for the rule for burden sharing between Annex I countries that was considered to be underlying the emission targets of the Kyoto Protocol, and (2) to examine plausible emission targets and timing of commitments for non-Annex I countries in the future by using the result of the analysis on the Kyoto Protocol. A multi-regression method is used for this purpose. It was concluded that the burden sharing between Annex I countries in the Kyoto Protocol can mostly be explained by three variables: the increase in the rate of CO₂ emission during the years 1990 to 2010, the increase in the rate of afforestation between 1990 and 1995, and the GDP per capita at the time of negotiation. The timing of future commitments of developing countries and the levels of targets differ widely, depending on which index or formula is agreed as “equitable”. Some of the developing countries would have to start limiting their emissions within several years if GDP per capita or CO₂ per capita were chosen as the burden-sharing indicator. Developing countries would not have to make commitments until the mid-late 21st century if population growth rate were chosen. If the inferred formula of the Kyoto Protocol were applied to developing countries, they would have had to start mild limitation from 1990.     

Heavy metals and soil microbes

Heavy metal pollution is a global issue due to health risks associated with metal contamination. Although many metals are essential for life, they can be harmful to man, animal, plant and microorganisms at toxic levels. Occurrence of heavy metals in soil is mainly attributed to natural weathering of metal-rich parent material and anthropogenic activities such as industrial, mining, agricultural activities. Here we review the effect of soil microbes on the biosorption and bioavailability of heavy metals; the mechanisms of heavy metals sequestration by plant and microbes; and the effects of pollution on soil microbial diversity and activities. The major points are: anthropogenic activities constitute the major source of heavy metals in the environment. Soil chemistry is the major determinant of metal solubility, movement and availability in the soil. High levels of heavy metals in living tissues cause severe organ impairment, neurological disorders and eventual death. Elevated levels of heavy metals in soils decrease microbial population, diversity and activities. Nonetheless, certain soil microbes tolerate and use heavy metals in their systems; as such they are used for bioremediation of polluted soils. Soil microbes can be used for remediation of contaminated soils either directly or by making heavy metals bioavailable in the rhizosphere of plants. Such plants can accumulate 100 mg g^?1 Cd and As; 1000 mg g^?1 Co, Cu, Cr, Ni and 10,000 mg g^?1 Pb, Mn and Ni; and translocate metals to harvestable parts. Microbial activity changes soil physical properties such as soil structure and biochemical properties such as pH, soil redox state, soil enzymes that influence the solubility and bioavailability of heavy metals. The concept of ecological dose (ED₅₀) and lethal concentration (LC₅₀) was developed in response to the need to easily quantify the influence of pollutants on microbial-mediated ecological processes in various ecosystems.     

Polyurethane for removal of organic dyes from textile wastewater

Synthetic organic dyes are extensively used in consumer products from textile to pharmaceuticals. A large amount of organic dyes is ultimately discharged as effluent into water bodies, thus posing a serious threat to environment and life. Therefore, removal of dyes from water bodies is needed. To address this problem, various synthetic and natural materials have been used to adsorb dyes. Here, we review the application of polyurethane for removal of organic dyes. First, we review the application of simple and modified polyurethane as efficient and economic adsorbents for dyes. Secondly, we review the polyurethane-based membranes for separation and adsorption of various dyes. Thirdly, we describe polyurethane composites with improved efficiency of dyes removal. Finally, we review the bioremediation of dyes where polyurethane has been proven as an excellent inert support.     

Enhanced degradation of the antibiotic tetracycline by heterogeneous electro-Fenton with pyrite catalysis

There is actually increasing concern about the accumulation of antibiotics, such as tetracycline, in soil and water bodies. There is therefore a need for efficient methods to degrade antibiotics and thus clean waters. Here we tested the degradation of tetracycline using the heterogeneous electro-Fenton-pyrite method and compared the results with the conventional electro-Fenton method. The reaction was performed with a boron-doped diamond or Pt anode and carbon-felt cathode allowing electrogeneration of H₂O₂ from O₂ reduction. Results show an increasing tetracycline mineralization using the following methods: anodic oxidation with electrogenerated H₂O₂, electro-Fenton and then electro-Fenton-pyrite using boron-doped diamond. Ion-exclusion HPLC revealed the complete removal of malic malonic, succinic, acetic, oxalic and oxamic acids. Nitrogen present in tetracycline was mainly mineralized in NH₄ ⁺. The higher efficiency of electro-Fenton-pyrite is explained by self-regulation of soluble Fe²⁺ and pH to 3.0 from pyrite catalyst favoring larger ^·OH generation from Fenton’s reaction.