Foliar nutrient and metal levels of crops in the Mount Cameroon area—reference values for plant nutrition and environmental monitoring

The growing population number and traffic loads, increasing environmental pressures, agricultural intensification, and the establishment of Mount Cameroon National Park demand farsighted environmental management in the region and the definition of a favorable ecological status. Since plants grow in the interface between soils and the atmosphere they can be used as passive biomonitors for the environmental quality. At the same time, the accumulation of nutrients and pollutants in crops is linked to human health, so that foliar elemental levels can be used as an integrative measure for environmental pollution and impact assessment. In the present study, we collected leaf samples of plantain, cassava, cocoyam, and maize on 28 sites at the southern flanks of Mt. Cameroon and determined 20 chemical elements. Air pollution in the study area comes from biomass and waste burning mainly, but emissions from traffic and a large refinery were believed to also play a significant role. However, spatial patterns in foliar elemental concentrations reflected the geochemistry rather than specific sources of pollution. Significant differences in foliar metal and nutrient levels were observed between the four species, indicating a different demand and uptake of specific elements. The results were compared to published data on nutrient concentrations in the tested species and the so-called reference plant. The data can be used as a baseline for future studies in plant nutrition and the environmental monitoring in inner tropical regions where these crops are grown.     

Permeability of hair to cadmium,copper and lead in five species of terrestrial mammals and implications in biomonitoring

The capacity of mammal hair to absorb toxic metals and its utility in biomonitoring has been broadly studied. Though these metal-binding properties has generally been attributed to the sulphur contained in cysteine, an amino acid that forms part of keratin, there are not many experimental studies that analyze the role of sulphur in the external deposition of potentially toxic metallic elements in order to better understand the potential of hair in biomonitoring and generate better tools for differentiating between internal and external deposition of contaminants. In this study, an experimental analysis is carried out using a scanning electron microscope on hairs of five terrestrial mammal species (Peromyscus furvus, P. maniculatus, Glossophaga soricina, Artibeus jamaicensis and Marmosa mexicana) treated with cadmium, copper and lead salts. We quantified absorbed metals as well as natural elements of the hair by energy dispersive X-ray spectroscopy (EDS) to analyze using simple statistics the role of sulphur in the absorption Cd, Cu and Pb. Given the lack of studies comparing the mechanisms of deposition of metal elements among different orders of Class Mammalia, external morphology was considered to be an important factor in the deposition of metallic particles of Cd, Cu and Pb. Bat species (Glossophaga soricina, Artibeus jamaicensis) showed a high concentration of particles in their scales, however, no between-species differences in metal absorption were observed, and during the exogenous deposition metal particles do not permeate the medulla. These results suggest that the sulphur in hair itself cannot bind metals to hair cuticle and that hair absorption capacity depends on a variety of factors such as aspects of hair morphology.     

<Emphasis Type="Italic">Noctiluca</Emphasis> and copepods grazing on the phytoplankton community in a nutrient-enriched coastal environment along the southwest coast of India

The relative grazing impact of Noctiluca scintillans (hereafter referred only Noctiluca) and copepods (Acrocalanus gracilis, Paracalanus parvus, Acartia danae and Oithona similis) on the phytoplankton community in an upwelling–mudbank environment along the southwest coast India is presented here. This study was carried out during the Pre-Southwest Monsoon (April–May) to the Late Southwest Monsoon (August) period in 2014. During the sampling period, large hydrographical transformation was evident in the study area (off Alappuzha, Southwest coast of India); warmer Pre-Southwest Monsoon water column condition got transformed into cooler and nitrate-rich hypoxic waters during the Southwest Monsoon (June–August) due to intense coastal upwelling. Copepods were present in the study area throughout the sampling period with a noticeable increase in their abundance during the Southwest Monsoon. On the other hand, the first appearance of Noctiluca in the sampling location was during the Early Southwest Monsoon (mid-June) and thereafter their abundance increased towards the Peak Southwest Monsoon. The grazing experiments carried out as per the food removal method showed noticeable differences in the feeding preferences of Noctiluca and copepods, especially on the different size fractions of phytoplankton. Noctiluca showed the highest positive electivity for the phytoplankton micro-fraction (av. 0.49 ± 0.04), followed by nano-fraction (av. 0.17 ± 0.04) and a negative electivity for the pico-fraction (av. ?0.66 ± 0.06). In total ingestion of Noctiluca, micro-fraction contribution (83.7%) was significantly higher compared to the nano- (15.7%) and pico-fractions (0.58%). On the other hand, copepods showed the highest positive electivity for the phytoplankton nano-fraction (av. 0.38 ± 0.04) followed by micro- (av. -0.17 ± 0.05) and pico-fractions (av. ?0.35 ± 0.05). Similarly, in total ingestion of copepods, nano-fraction (69.7%) was the highest followed by micro- (28.9%) and pico-fractions (1.37%). The grazing pressure of Noctiluca on the total phytoplankton was found to be 27.7% of the standing stock and 45.6% of the production, whereas in the case of copepods, it was 9.95% of the standing stock and 16.6% of the production. The study showed that the grazing pressure of Noctiluca on the total phytoplankton as well as larger phytoplankton fraction was 2.8- and 8-folds higher than that of the copepods. This suggests the leading role of Noctiluca as an effective grazer of larger phytoplankton along the southwest west coast of India, especially during the Peak/Late Southwest Monsoon.     

Quality assessment of groundwater from the south-eastern Arabian Peninsula

Assessment of groundwater quality plays a significant role in the utilization of the scarce water resources globally and especially in arid regions. The increasing abstraction together with man-made contamination and seawater intrusion have strongly affected groundwater quality in the Arabia Peninsula, exemplified by the investigation given here from the United Arab Emirates, where the groundwater is seldom reviewed and assessed. In the aim of assessing current groundwater quality, we here present a comparison of chemical data linked to aquifers types. The results reveal that most of the investigated groundwater is not suitable for drinking, household, and agricultural purposes following the WHO permissible limits. Aquifer composition and climate have vital control on the water quality, with the carbonate aquifers contain the least potable water compared to the ophiolites and Quaternary clastics. Seawater intrusion along coastal regions has deteriorated the water quality and the phenomenon may become more intensive with future warming climate and rising sea level.     

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.     

我国流域水生态完整性评价方法构建

流域水生态完整性评价是指通过对水生态系统中不同水生态指标(生物和非生物)的监测以及由数学方法综合形成的综合评价指数,来反映水生态系统完整性状况。近年来,世界各国水环境管理政策发生了变化,开始强调生态保护,重视水体的生态质量。中国现行的常规理化监测指标(如COD、氨氮、BOD5)很难满足水环境管理的需求,难以全面准确地反映水环境质量变化的趋势。因此,在借鉴欧美发达国家流域水生态完整性评价方法的基础上,结合中国目前监测现状以及流域水环境管理需求,构建了包括物理生境指标、理化指标、水生生物指标在内的流域水生态完整性监测与评价方法,以期为中国流域水质目标管理技术体系的业务化运行提供可资借鉴的技术支撑,实现从单一的化学指标监测转向综合的水生态系统监测,实现流域水生态完整性的监测与评价。     

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.     

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325–1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R² (LL R²) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.