Study of Precision,Deviations and Uncertainty in the Design of the Strategic Noise Map of the Macrocenter of the City of Buenos Aires,Argentina

This article aims to discuss the influence of input data on the simulation model when designing Strategic Noise Maps. The studied noise map was made in the Macrocenter of the Independent City of Buenos Aires (Argentina), which has an approximated extension of 20 km² and about 500,000 inhabitants. The several input data for the model are analyzed, for their quality and the lack of some of them could affect the final result. Also, the evolution and validity of experimental measurements are analyzed when validating a simulated map. Finally, a study of the uncertainty of the map based on the input data is made, comparing it with the recommendations internationally adopted.     

Essential and toxic elements in infant foods from Spain, UK, China and USA

Spanish gluten-free rice, cereals with gluten, and pureed baby foods were analysed for essential macro-elements (Ca and Na), essential trace elements (Fe, Cu, Zn, Mn, Se, Cr, Co and Ni) and non-essential trace elements (As, Pb, Cd and Hg) using ICP-MS and AAS. Baby cereals were an excellent source of most of the essential elements (Ca, Fe, Cu, Mn and Zn). Sodium content was high in pureed foods to improve their flavour; fish products were also rich in Se. USA pure baby rice samples had the highest contents of all studied essential elements, showing a different nutrient pattern compared to those of other countries. Mineral fortification was not always properly stated in the labelling of infant foods. Complementary infant foods may also contain significant amounts of contaminants. The contents of Hg and Cd were low enough to guarantee the safety of these infant foods. However, it will be necessary to identify the source and reduce the levels of Pb, Cr and As in Spanish foods. Pure baby rice samples contained too much: Pb in Spain; As in UK; As, Cr and Ni in USA; and Cr and Cd in China.     

Correlations in polycyclic aromatic hydrocarbon (PAH) concentrations in UK ambient air and implications for source apportionment

The extent of correlation in polycyclic aromatic hydrocarbon (PAH) concentration data obtained by the UK PAH Monitoring and Analysis Air Quality Network from March 2008 to November 2010 has been assessed. Application of principal component analysis (PCA) to the dataset has revealed that the concentrations of the vast majority of PAHs are very highly correlated. The use of diagnostic PAH ratios (including a new benzo[b]naph[2,1-d]thiophene/benzo[a]pyrene diagnostic ratio), and a novel 'combined diagnostic ratio - PCA' approach has revealed information about the main sources of PAH at individual Network sites, allowing the sites to be grouped in terms of those influenced by solid fuel use, industry and traffic, and those of an urban or rural nature. Solid fuel use has also been delineated from other fuel burning. Conclusions are drawn about a number of UK PAH Network sites - four sites are shown to be influenced significantly by solid fuel use, and the sites in South Wales and at London Marylebone Road are found to be located in distinct and unusual PAH pollution climates - the identification of a unique PAH pollution climate in South Wales is a key and novel conclusion of the work.     

A Multiple Criteria Decision Analysis Model Based on ELECTRE TRI-C for Erosion Risk Assessment in Agricultural Areas

This paper deals with a real-world decision-aiding problem for zoning the risk of erosion, total suspended solids emissions, and ecological consequences of their transfers towards the streams. One of these consequences is the decrease of fishes into the streams in agricultural watersheds, because of the clogging of spawning areas. Given the multiple criteria nature of the problem, the originality of our research is to adapt a new decision-aiding sorting method, ELECTRE TRI-C, for identifying risk zones in rural areas, where measures must be taken. The developed model was applied in a small watershed (Low Normandy, France) where the objective was to assess the most appropriate intervention for protecting the reproduction habitat of the salmonid fishes. Agricultural parcels were evaluated on multiple criteria for grouping them into four risk categories, which are related to risk levels as well as priorities on the improvement works. The decision-aiding sorting model is co-constructed, within a constructive approach, through an interaction process between decision-aiding analysts, environmental experts, and local actors for improving transparency and communication on the results. This model is linked with a geographical information system (GIS) for assessing a set of criteria and the visualization of the farming parcels along with their type of intervention they should be submitted to best practices. The assignment results were validated by the environmental experts. These results have a strong impact on the agricultural practices of the farmers into the watersheds. The model proposed in this paper can be considered as a useful decision aid tool in any regions for implementing public agricultural and environmental policies for protecting the ecological areas.     

Differences in the Spatial Distribution and Chemical Composition of PM10 Between the UK and Poland

The Fine Resolution Atmospheric Multi-pollutant Exchange Model was used to calculate the spatial distribution and chemical composition of PM₁₀ concentrations for two geographically remote countries in Europe—the UK and Poland—for the year 2007. These countries are diverse in terms of pollutant emissions as well as climate conditions. Information on the contribution of natural and anthropogenic as well as national and imported particles in total PM₁₀ concentrations in both countries is presented. The paper shows that the modelled national annual average PM₁₀ concentrations, calculated for the entire country area, are similar for the UK and Poland and close to 12 μg m^?3. Secondary inorganic aerosols dominate the total PM₁₀ concentrations in Poland. Primary particulate matter has the greatest contribution to total PM₁₀ in the UK, with large contribution of base cations. Anthropogenic sources predominate (81 %) in total PM₁₀ concentrations in Poland, whereas natural prevail in the UK—hence, the future reduction of PM₁₀ air concentrations by emissions reduction could be more difficult in the UK than in Poland.     

Potential effects on the marine environment of dredging of the Bonny channel in the Niger Delta

This study is an assessment of the possible effects of the dredging project on the water quality of the surrounding environment. Water quality data for the Bonny offshore river were collected. The parameters assessed were temperature, dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), total dissolved solids (TDS), total suspended solids (TSS), nitrogen as nitrate, and phosphate. The concentrations of TDS, TSS, and nitrate far exceeded the permissible standard. There were significant differences between the dry and rainy season values of BOD5, TDS, TSS, nitrate, and phosphate. Also, the sediment physicochemical analysis indicated the presence of heavy metals such as Pb, Fe, Cd, Zn, Cr, and elements such as Ca, K, and Na. Following an assessment of the potential impact of the project using interaction matrix and checklist questionnaires, results showed that the major physicochemical parameters influenced by dredging are DO, TDS, TSS, heavy metals and calcium, potassium, and sodium. Mitigation measures for eliminating or reducing the negative impacts of dredging are presented.     

Long-term seasonal changes of the Danube River eco-chemical status in the region of Serbia

Seasonal spatial and temporal changes of selected eco-chemical parameters in section of the Danube River flowing through Serbia were analyzed. Data for electrical conductivity (EC), dry and suspended matter, residue on ignition, chemical oxygen demand (COD), biochemical oxygen demand (BOD-5), ultraviolet extinction, dissolved oxygen (DO), oxygen saturation, pH, nitrates, total phosphorus, and nitrogen were collected between 1992 and 2006. The use of monthly medians combined with linear regression and two-sided t test has been proven to be the best approach for resolving trends from natural variability of investigated parameters and for determining trend significance. Patterns of temporal changes between different months were examined. It was also determined that spatial trends of some parameters oscillate in predictable manner, increasing in one part of the year and declining in the other. Regression slope coefficients, an excellent indicator for determining when the water quality is changing the most along the course of the Danube, reach their maximum during summer for temperature (t), electric conductivity, nitrates, and total N, while in the same season suspended matter, COD, BOD-5, DO, and oxygen saturation coefficients reach their minimum. Correlations for used data sets of selected parameters were analyzed for better understanding of their behavior and mutual relations. It was observed that as Danube flows through Serbia, its general eco-chemical status either stagnates or improves, but the rate of river self-purification often depends on the season of the year.     

A survey on the utility of the USEPA CADDIS stressor identification procedure

The Environmental Protection Agency (EPA) has made available on the worldwide web a systematic stream stressor identification procedure, the “Causal Analysis/Diagnosis Decision Information System” or CADDIS. We report here the results of a survey of regulators and scientists in 11 states who use CADDIS or another stressor identification procedure in their work. The 13 survey questions address guidelines as to what impairment scenarios to approach with stressor identification, what information is needed to perform stressor identification, and what the stakeholder role is in performing stressor identification. At the time of this survey (the summer of 2009), the EPA CADDIS website was less commonly used among the state regulators surveyed than the published EPA stressor identification document on which it is based. The respondents generally find the EPA stressor identification procedure useful and capable of being adapted to their individual needs. Survey respondents all use stressor identification in their Total Maximum Daily Load work, but also in a wide variety of other applications. All the “types of evidence” included in the CADDIS stressor identification procedure are used by the practitioners surveyed with the exception of the results of ecological simulation models. While the CADDIS documentation encourages the involvement of stakeholders in stressor identification, most respondents do not assemble stakeholder teams of local officials and citizens to participate in stressor analyses.     

Coral assemblages in Tonga: spatial patterns, replenishment capacities, and implications for conservation strategies

Coral reefs in Tonga have been confronted by multiple threats of various origins, including large-scale disturbances and human-induced stressors. These reef communities have been poorly studied, and efficient conservation actions are urgently needed. The aim of this study was to: (1) examine the spatial distribution of coral assemblages in the lagoon of Tongatapu; (2) determine the degree to which spatial heterogeneity of adult corals is influenced by recruitment processes; and (3) examine the implications of these results in terms of conservation actions. We recorded a total of 37 adult and 28 juvenile coral genera, a mean density of 11.6 adult and 5.5 juvenile colonies m^?2, and a dominance of Montipora, Acropora, and Porites. For seven of the 10 dominant genera, spatial patterns of adults were linked to the short-term recruitment pattern history. Despite a reduced diversity and abundance of adult corals in some areas, the lagoon of Tongatapu retains the potential for replenishment through recruitment of young corals. Consequently, we suggest that conservation actions should focus on reducing factors causing coral mortality and maintain suitable conditions for the establishment and growth of juvenile corals, thus increasing the probability that they will reach maturity and participate to the maintenance of local populations. Rather than establishing a large marine protected area, which will almost certainly suffer from a lack of control and poor enforcement, alternative conservation measures could be successfully implemented through the establishment of several small village-based marine reserves, as has been undertaken in other South Pacific islands with promising results.     

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC_e 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.