Trace metals biogeochemistry of Kumaun Himalayan Lakes,Uttarakhand, India

The increasing urbanization, along with tourism, has posed a major threat to the Kumaun Himalayan Lakes, Uttarakhand, India. The total metal concentration in the water, interstitial water, and sediments along with the metal fractionation studies were carried out to understand the remobilization of the trace metals from the sediments of the lakes. The high concentration of the metals in the water column of the lakes generally decreases with depth and the metals release from the sediment is mainly due to the prevalence of anoxic condition at the sediment–water interface and sediment column. The sediment shows that metals Fe and Cr are derived from detrital source, whereas Co, Ni, and Zn are derived mainly from the organic matter dissolution. The sparse correlation of the trace metals with Ti shows most of the metals have chiefly re-precipitated from the water column. The metals speciation studies also supports that metals experience a high rate of anoxic dissolution and their precipitation onto the sediments are determined by the sediment composition and organic matter content. The high concentration of manganese in the interstitial water in the lakes indicates dissolution of organic matter. The released manganese is adsorbed/precipitated as carbonate phase (Nainital Lake) and oxide pahse (in other lakes). The study shows that the trace metals are regenerated from the sediments due to oxyhydroxide dissolution and organic matter decomposition.     

Benthic foraminiferal assemblages as pollution proxies in the northern coast of Gabes Gulf,Tunisia

A study of chemical and sedimentological parameters integrated with benthic foraminifera investigation was conducted along the northern coast of Gabes Gulf. Thirty-two samples were studied and a total of 68 benthic foraminiferal species were identified. Heavy metals enrichment factors and total hydrocarbon concentrations showed both metal and petrogenic pollution related mainly to phosphogypsum, sewage, and fishing activities. Statistical analysis (bivariate correlation and hierarchical cluster analysis) show a possible control of these pollutants on density, diversity, as well as the taxonomic composition of the benthic foraminiferal assemblages. The extent to which the population was found less dense and less diversified corresponded to the degree to which the sediment was contaminated. In these contaminated sites, an increase in relative abundance of opportunistic species such Ammonia tepida and Haynesina germanica was found. Far from pollution, foraminiferal assemblages are dominated by species characteristic of Mediterranean shallow water (Ammonia beccarii, Ammonia parkinsoniana, Elphidium crispum, Elphidium williamsoni, Elphidium advenum, Peneroplis planatus, Peneroplis pertesus).     

Assessment of Spatiotemporal Varying Relationships Between Rainfall,Land Cover and Surface Water Area Using Geographically Weighted Regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R ² and lower corrected Akaike information criterion (AIC_C). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R ² and lower AIC_C. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.     

Anticipating Climate Threshold Damages

Several integrated assessment studies have concluded that future learning about the uncertainties involved in climate change has a considerable effect on welfare but only a small effect on optimal short-term emissions. In other words, learning is important but anticipation of learning is not. We confirm this result in the integrated assessment model “model of investment and technological development” for learning about climate sensitivity and climate damages. If learning about an irreversible threshold is included, though, we show that anticipation can become crucial both in terms of necessary adjustments of pre-learning emissions and resulting welfare gains. We specify conditions on the time of learning and the threshold characteristic, for which this is the case. They can be summarized as a narrow “anticipation window.”     

Ecosystem Viable Yields

The World Summit on Sustainable Development (Johannesburg, 2002) encouraged the application of the ecosystem approach by 2010. However, at the same summit, the signatory States undertook to restore and exploit their stocks at maximum sustainable yield (MSY), a concept and practice without ecosystemic dimension, since MSY is computed species by species, on the basis of a monospecific model. Acknowledging this gap, we propose a definition of “ecosystem viable yields” (EVY) as yields compatible (a) with biological safety levels (over which biomasses can be maintained for all times) and (b) with an ecosystem dynamics. The difference from MSY is that this notion is not based on equilibrium but on viability theory, which offers advantages for robustness. For a generic class of multispecies models with harvesting, we provide explicit expressions for the EVY. We apply our approach to the anchovy–hake couple in the Peruvian upwelling ecosystem.     

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p?<?0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p?<?0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management.     

Workplace aerosol mass concentration measurement using optical particle counters

Direct-reading aerosol measurement usually uses the optical properties of airborne particles to detect and measure particle concentration. In the case of occupational hygiene, mass concentration measurement is often required. Two aerosol monitoring methods are based on the principle of light scattering: optical particle counting (OPC) and photometry. The former analyses the light scattered by a single particle, the latter by a cloud of particles. Both methods need calibration to transform the quantity of scattered light detected into particle concentration. Photometers are simpler to use and can be directly calibrated to measure mass concentration. However, their response varies not only with aerosol concentration but also with particle size distribution, which frequently contributes to biased measurement. Optical particle counters directly measure the particle number concentration and particle size that allows assessment of the particle mass provided the particles are spherical and of known density. An integrating algorithm is used to calculate the mass concentration of any conventional health-related aerosol fraction. The concentrations calculated thus have been compared with simultaneous measurements by conventional gravimetric sampling to check the possibility of field OPC calibration with real workplace aerosols with a view to further monitoring particle mass concentration. Aerosol concentrations were measured in the food industry using the OPC GRIMM? 1.108 and the CIP 10-Inhalable and CIP 10-Respirable (ARELCO?) aerosol samplers while meat sausages were being brushed and coated with calcium carbonate. Previously, the original OPC inlet had been adapted to sample inhalable aerosol. A mixed aerosol of calcium carbonate and fungi spores was present in the workplace. The OPC particle-size distribution and an estimated average particle density of both aerosol components were used to calculate the mass concentration. The inhalable and respirable aerosol fractions calculated from the OPC data are closely correlated with the results of the particle size-selective sampling using the CIP 10. Furthermore, the OPC data allow calculation of the thoracic fraction of workplace aerosol (not measured by sampling), which is interesting in the presence of allergenic particles like fungi spores. The results also show that the modified COP inlet adequately samples inhalable aerosol in the range of workplace particle-size distribution.     

How Do River Nitrate Concentrations Respond to Changes in Land-use? A Modelling Case Study of Headwaters in the River Derwent Catchment,North Yorkshire,UK

A combined semi-distributed hydrological model (CASCADE/QUESTOR) is used to evaluate the steady-state that may be achieved after changes in land-use or management and to explore what additional factors need to be considered in representing catchment processes. Two rural headwater catchments of the River Derwent (North Yorkshire, UK) were studied where significant change in land-use occurred in the 1990s and the early 2000s. Much larger increases in mean nitrate concentration (55%) were observed in the catchment with significant groundwater influence (Pickering Beck) compared with the surface water-dominated catchment (13% increase). The increases in Pickering Beck were considerably greater than could be explained by the model in terms of land-use change. Consequently, the study serves to focus attention on the long-term increases in nitrate concentration reported in major UK aquifers and the ongoing and chronic impact this trend is likely to be having on surface water concentrations. For river environments, where groundwater is a source, such trends will mask the impact of measures proposed to reduce the risk of nitrate leaching from agricultural land. Model estimates of within-channel losses account for 15–40% of nitrate entering rivers.     

Inexact Fuzzy-Stochastic Programming for Water Resources Management Under Multiple Uncertainties

In this study, an interval-parameter fuzzy-stochastic two-stage programming (IFSTP) approach is developed for irrigation planning within an agriculture system under multiple uncertainties. A concept of the distribution with fuzzy-interval probability (DFIP) is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets, and probability distributions. IFSTP integrates the interval programming, two-stage stochastic programming, and fuzzy-stochastic programming within a general optimization framework. IFSTP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. IFSTP is applied to an irrigation planning in a water resources management system. Solutions from IFSTP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions are generated for objective function values and decision variables; thus, a number of decision alternatives can be generated under different levels of stream flows.     

Bioavailability of copper,cadmium, zinc,and lead in tropical savanna soils assessed by diffusive gradient in thin films (DGT) and ion exchange resin membranes

The technique of diffusion gradient in thin films (DGT) for assessing bioavailable metals has not been tested under field conditions. We assessed the relationships of DGT- and cation exchange resin-membrane-measured concentrations of Cd, Cu, Pb, and Zn with plant uptake of the metals under greenhouse and field conditions. In the greenhouse, the effective concentrations of Cu, Pb, and Zn by DGT correlated significantly with uptake by sorghum (Sorghum bicolor), but cation exchange resin-membrane-measured concentrations of Cd, Pb, and Zn did not correlate with sorghum uptake. In the field, the DGT-measured concentrations of Cd, Pb, and Zn were not linearly related to uptake Cd, Pb, and Zn by lettuce (Lactuca sativa) except for Cu uptake (r = 0.87, p < 0.05). Similarly, it was only the resin-membrane-extractable Pb that correlated with Pb uptake by lettuce (r = 0.77; p < 0.05). However, fitting non-linear regression models improved the plant metal uptake predictions by DGT-measured bioavailable Cd, Cu, Pb, and Zn under field conditions. In conclusion, the DGT technique was fairly predictive of bioavailability in the greenhouse, but not in the field.