Traffic and catalytic converter - related atmospheric contamination in the metropolitan region of the city of Rio de Janeiro, Brazil

In this work, 24-h PM10 samples were collected in Rio de Janeiro, Brazil, and analysed for trace elements (Cd, Ce, Cu, La, Mo, Ni, Pb, Pd, Rh, Sb and Sn). The sampling was carried out at five locations (Bonsucesso; Centro, downtown city; Copacabana; Nova Igua?u and Sumaré) with different traffic densities and anthropogenic activities. An analytical method based on the EPA method for the determination of trace elements in airborne particulate matter (PM), using ultrasonic-assisted extraction and inductively coupled plasma mass spectrometry (ICP-MS) was applied. Our results suggest that vehicular traffic is the most important source of environmental pollution at the studied sites. The presence of Mo, Pd and Rh in the analysed filters reflects an additional source of pollution caused by the erosion and deterioration of automotive catalytic converters.     

Application of Defined Co-Immobilized Microbial Consortium as a Ready-to-Use Seed Inoculum in Bod Analysis

A number of microorganisms were isolated from sewage. Biochemical Oxygen Demand (BOD-5 day) analysis was carried out by individual pure cultures. The cultures giving higher or equal BOD values as compared to reference GGA solution were selected for the formulation of a defined mixed microbial consortium. This microbial consortium was co-immobilized on calcium-alginate beads. Four synthetic and six industrial samples were tested for BOD by using immobilized beads as well as sewage as source of seeding materials. BOD values obtained with beads for all the synthetic as well as industrial samples were fairly comparable with those obtained with sewage. Reusability of prepared microbial beads was also checked with different synthetic and industrial samples and was compared with reference GGA solution. The same microbial beads can be reused three times for different BOD-5 day estimations. It is recommended that immobilized microbial beads can be used as a ready-to-use seeding material for BOD analysis.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Seasonal variation in heavy metal contamination in water and sediments of river Sabarmati and Kharicut canal at Ahmedabad, Gujarat

The objective of the study is to reveal the seasonal variations in the river water and sediment quality with respect to heavy metal contamination. To get the extent of trace metals contamination, water and sediment samples were collected from five different sites along the course of Sabarmati River and its tributary Kharicut canal in pre-monsoon, monsoon and post-monsoon seasons. The concentration of trace metals such as chromium, copper, lead, nickel and zinc was determined using inductively coupled plasma spectroscopy. The concentrations of heavy metals were found to be higher in the pre-monsoon season than in the monsoon and post-monsoon seasons in water samples. The pollution load index, contamination factor and degree of contamination (C _d) in sediments were calculated to know the extent of anthropogenic pressures. The values of C _d clearly indicated very high degree of contamination at Kharicut canal (S-4: 32.25 and S-5: 54.52) and considerable degree of contamination at three sites of Sabarmati river viz; S-1, S-2 and S-3 with values 14.30, 14.42 and 17.21, respectively. Lead and nickel could not be traced in any of the river water samples.     

Concentration of trace metals in sediments and soils from protected lands in south Florida: background levels and risk evaluation

A comprehensive environmental evaluation was completed on 20 metals: two reference metals (Fe, Al) and several minor trace metals (As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn) for surface soils and sediments collected from 50 sites in Everglades National Park (ENP), the coastal fringes of Biscayne National Park (BNP), and Big Cypress National Preserve. Samples were prepared by acid digestion (EPA3050) and analyzed by ICP/MS detection (EPA6020). Although no widespread contamination was detected across the two parks and one preserve, there were some specific areas where metal concentrations exceeded Florida’s ecological thresholds, suggesting that some metals were of concern. A screening-level evaluation based on a proposed effect index grouped trace metals by their potential for causing negligible, possible, and probable effects on the biota. For example, Cu in BNP and Cr and Pb in ENP were considered of concern because their adverse effect likelihood to biota was assessed as probable; consequently, these trace metals were selected for further risk characterization. Also, stations were ranked based on a proposed overall contamination index that showed that: site BB10 in BNP and sites E3 and E5 in ENP had the highest scores. The first site was located in a marina in BNP, and the other two sites were along the eastern boundary of ENP adjacent to current or former agricultural lands. An assessment tool for south Florida protected lands was developed for evaluating impacts from on-going Everglades restoration projects and to assist State and Federal agencies with resource management. The tool consists of enrichment plots and statistically derived background concentrations based on soil/sediment data collected from the two national parks and one preserve. Finally, an equally accurate but much simplified approach is offered for developing enrichment plots for other environmental settings.     

A Cumulative Energy Demand indicator (CED), life cycle based,for industrial waste management decision making

Life cycle thinking is a good approach to be used for environmental decision-support, although the complexity of the Life Cycle Assessment (LCA) studies sometimes prevents their wide use. The purpose of this paper is to show how LCA methodology can be simplified to be more useful for certain applications.In order to improve waste management in Catalonia (Spain), a Cumulative Energy Demand indicator (LCA-based) has been used to obtain four mathematical models to help the government in the decision of preventing or allowing a specific waste from going out of the borders. The conceptual equations and all the subsequent developments and assumptions made to obtain the simplified models are presented.One of the four models is discussed in detail, presenting the final simplified equation to be subsequently used by the government in decision making.The resulting model has been found to be scientifically robust, simple to implement and, above all, fulfilling its purpose: the limitation of waste transport out of Catalonia unless the waste recovery operations are significantly better and justify this transport.