Occurrence of naproxen,ibuprofen, and diclofenac residues in wastewater and river water of KwaZulu-Natal Province in South Africa

The present paper reports a detailed study that is based on the monitoring of naproxen, ibuprofen, and diclofenac in Mbokodweni River and wastewater treatment plants (WWTPs) located around the city of Durban in KwaZulu-Natal Province of South Africa. Target compounds were extracted from water samples using a multi-template molecularly imprinted solid-phase extraction prior to separation and quantification on a high-performance liquid chromatography equipped with photo diode array detector. The analytical method yielded the detection limits of 0.15, 1.00, and 0.63 μg/L for naproxen, ibuprofen, and diclofenac, respectively. Solid-phase extraction method was evaluated for its performance using deionized water samples that were spiked with 5 and 50 μg/L of target compounds. Recoveries were greater than 80% for all target compounds with RSD values in the range of 4.1 to 10%. Target compounds were detected in most wastewater and river water samples with ibuprofen being the most frequently detected pharmaceutical. Maximum concentrations detected in river water for naproxen, ibuprofen, and diclofenac were 6.84, 19.2, and 9.69 μg/L, respectively. The concentrations of target compounds found in effluent and river water samples compared well with some studies. The analytical method employed in this work is fast, selective, sensitive, and affordable; therefore, it can be used routinely to evaluate the occurrence of acidic pharmaceuticals in South African water resources.     

Variability in the phytoplankton community of Kavaratti reef ecosystem (northern Indian Ocean) during peak and waning periods of El Niño 2016

El Niño, an interannual climate event characterized by elevated oceanic temperature, is a prime threat for coral reef ecosystems worldwide, owing to their thermal threshold sensitivity. Phytoplankton plays a crucial role in the sustenance of reef trophodynamics. The cell size of the phytoplankton forms the “master morphological trait” with implications for growth, resource acquisition, and adaptability to nutrients. In the context of a strong El Niño prediction for 2015–2016, the present study was undertaken to evaluate the variations in the size-structured phytoplankton of Kavaratti reef waters, a major coral atoll along the southeast coast of India. The present study witnessed a remarkable change in the physicochemical environment of the reef water and massive coral bleaching with the progression of El Niño 2015–2016 from its peak to waning phase. The fluctuations observed in sea surface temperature, pH, and nutrient concentration of the reef water with the El Niño progression resulted in a remarkable shift in phytoplankton size structure, abundance, and community composition of the reef waters. Though low nutrient concentration of the waning phase resulted in lower phytoplankton biomass and abundance, the diazotroph Trichodesmium erythraeum predominated the reef waters, owing to its capability of the atmospheric nitrogen fixation and dissolved organic phosphate utilization.     

Probabilistic assessment of phenophase-wise agricultural drought risk under different sowing windows: a case study with rainfed soybean

A new methodology for crop-growth stage-specific assessment of agricultural drought risk under a variable sowing window is proposed for the soybean crop. It encompasses three drought indices, which include Crop-Specific Drought Index (CSDI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI). The unique features of crop-growth stage-specific nature and spatial and multi-scalar coverage provide a comprehensive assessment of agricultural drought risk. This study was conducted in 10 major soybean-growing districts of Madhya Pradesh state of India. These areas contribute about 60% of the total soybean production for the country. The phenophase most vulnerable to agricultural drought was identified (germination and flowering in our case) for each district across four sowing windows. The agricultural drought risk was quantified at various severity levels (moderate, severe, and very severe) for each growth stage and sowing window. Validation of the proposed new methodology also yielded results with a high correlation coefficient between percent probability of agricultural drought risk and yield risk (r?=?0.92). Assessment by proximity matrix yielded a similar statistic. Expectations for the proposed methodology are better mitigation-oriented management and improved crop contingency plans for planners and decision makers.     

Development and Testing of 2D Finite Difference Model in Open Channels

This study develops a depth-averaged two-dimensional (2D) numerical model using a finite difference method (FDM) on a staggered grid. The governing equations were solved using the Marker and Cell method that was developed at the Los Alamos laboratories by Harlow and Welch in 1965. In the paper, an explicit FDM was used to solve the governing equations. A first-order approximation was used for the temporal derivative. Second-order central difference approximations were used for space discretization. The time step is limited by the Courant–Friedrichs–Lewy (CFL) condition. The time step used in this study depends on the grid spacing and velocity components in the x- and y-directions. The study is divided into two steps: the first step is to develop a depth-averaged 2D numerical model to simulate the flow process. The second constructs a module to calculate the bed load transport and simulate the river morphology in the areas that have steep slopes, torrents, and mountain rivers. Developed model was applied to the artificial channel and a flood event in the Asungjun River section of the mountainous Yangyang Namdae River (South Korea). General simulation results showed that the developed model was in good agreement with the observed data.     

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.     

Empirical Estimation of Virtual Point Source Height Over a Bank of Coke Ovens

This paper addresses an empirical method of assessment of the height of a virtual point source. In order to achieve such assessment, three planes defining the location of a virtual point source in a space are created and formulas describing the geometry of observer’s sight and plume orientation are used. Data for calculations are obtained on a basis of plume photographs. The final position of a virtual emission point is described by a set of its coordinates in the Cartesian coordinate system. Presented method may be applied as a means to verify and validate the existing formulas describing the rise of plume above the stack exit or as a tool enabling acquisition of information for the sake of creating new formulas of this type. The calculation of the location of a virtual point source ascribed to the point emission from the roof of the bank of coke oven is presented and illustrated with photographs which reveal position of plume, adequate planes, axes and characteristic objects.     

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.