Thirty year monitoring of PCBs, organochlorine pesticides and tetrabromodiphenylether in eel from The Netherlands

Because of their excellent properties as a biomonitor, yellow eels (Anguilla anguilla) have been used for time-trend monitoring of polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs) and tetrabrominated diphenylether (tetra-BDE). The program has now lasted for thirty years and has delivered valuable information on trends and spatial differences of these compounds in the delta of the rivers Rhine and Meuse and other Dutch canals, rivers and lakes. Specific local PCB, HCH and dieldrin contaminations were identified. Temporal trends show a slow decrease of PCB concentrations since 1977. Eels from the rivers Rhine and Meuse still exceed present European maximum residue limits for dioxin-like PCBs. Apart from some exceptions, OCP and tetra-BDE concentrations have also decreased, and more than those of PCBs. Fat contents of eel have decreased from an average of 21 to ca. 13%. This decrease in fat contents, coincides with the strong reduction of the European eel stock.     

Appraisal of measurement methods, chemical composition and sources of fine atmospheric particles over six different areas of Northern Belgium

Daily and seasonal variation in the total elemental, organic carbon (OC) and elemental carbon (EC) content and mass of PM_2.5 were studied at industrial, urban, suburban and agricultural/rural areas. Continuous (optical Dustscan, standard tapered element oscillating micro-balance (TEOM), TEOM with filter dynamics measurement system), semi-continuous (Partisol filter-sampling) and non-continuous (Dekati-impactor sampling and gravimetry) methods of PM_2.5 mass monitoring were critically evaluated. The average elemental fraction accounted for 2-6% of the PM_2.5 mass measured by gravimetry. Metals, like K, Mn, Fe, Cu, Zn and Pb were strongly inter-correlated, also frequently with non-metallic elements (P, S, Cl and/or Br) and EC/OC. A high OC/EC ratio (2-9) was generally observed. The total carbon content of PM_2.5 ranged between 3 and 77% (averages: 12-32%), peaking near industrial/heavy trafficked sites. Principal component analysis identified heavy oil burning, ferrous/non-ferrous industry and vehicular emissions as the main sources of metal pollution.     

Distribution of polycyclic aromatic hydrocarbons in urban stormwater in Queensland, Australia

This paper reports the distribution of Polycyclic Aromatic Hydrocarbons (PAHs) in wash-off in urban stormwater in Gold Coast, Australia. Runoff samples collected from residential, industrial and commercial sites were separated into a dissolved fraction (<0.45 μm), and three particulate fractions (0.45-75 μm, 75-150 μm and >150 μm). Patterns in the distribution of PAHs in the fractions were investigated using Principal Component Analysis. Regardless of the land use and particle size fraction characteristics, the presence of organic carbon plays a dominant role in the distribution of PAHs. The PAHs concentrations were also found to decrease with rainfall duration. Generally, the 1- and 2-year average recurrence interval rainfall events were associated with the majority of the PAHs and the wash-off was a source limiting process. In the context of stormwater quality mitigation, targeting the initial part of the rainfall event is the most effective treatment strategy. The implications of the study results for urban stormwater quality management are also discussed.     

How life history contributes to stress response in the Manila clam <Emphasis Type="Italic">Ruditapes philippinarum</Emphasis>

Introduction  

Within the last decade, numerous studies have investigated the role of environmental history on tolerance to stress of many organisms. This study aims to assess if Manila clams Ruditapes philippinarum may react differently to cadmium exposure and trematode parasite infection (Himasthla elongata) depending on their origin and environmental history in Arcachon Bay (France).     

Kinetics of the tropospheric formaldehyde loss onto mineral dust and urban surfaces

The kinetics of the heterogeneous reaction between gaseous HCHO and TiO₂/SiO₂ mineral coatings were investigated using a coated-wall flow tube to mimic HCHO loss on mineral aerosol and TiO₂ coated depolluting urban surfaces. The measured uptake kinetics were strongly enhanced when the flow tube was irradiated with 340–420 nm UV light with an irradiance of 1.45 mW cm^?2. The associated BET uptake coefficients ranged from (3.00 ± 0.45) × 10^?9 to (2.26 ± 0.34) × 10^?6 and were strongly dependent on HCHO initial concentration, relative humidity, temperature, and TiO₂ content in the mineral coating, which ranged from 3.5 to 32.5 ppbv, 6–70%, 278–303 K, and 1–100 %wt, respectively. The measured kinetics were well described using a Langmuir–Hinshelwood type formalism. The estimated uptake coefficients were used to discuss the importance of heterogeneous HCHO surface loss, in terms of deposition lifetimes, as compared to major homogeneous gas-phase losses such as OH reaction and photolysis. It is found that deposition may compete with gas-phase removal of HCHO in a dense urban environment if more than 10% of the urban surface is covered with TiO₂ treated material.     

Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.     

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.     

Integrating Environmental and Socio-Economic Indicators of a Linked Catchment–Coastal System Using Variable Environmental Intensity

Can we develop land use policy that balances the conflicting views of stakeholders in a catchment while moving toward long term sustainability? Adaptive management provides a strategy for this whereby measures of catchment performance are compared against performance goals in order to progressively improve policy. However, the feedback loop of adaptive management is often slow and irreversible impacts may result before policy has been adapted. In contrast, integrated modelling of future land use policy provides rapid feedback and potentially improves the chance of avoiding unwanted collapse events. Replacing measures of catchment performance with modelled catchment performance has usually required the dynamic linking of many models, both biophysical and socio-economic—and this requires much effort in software development. As an alternative, we propose the use of variable environmental intensity (defined as the ratio of environmental impact over economic output) in a loose coupling of models to provide a sufficient level of integration while avoiding significant effort required for software development. This model construct was applied to the Motueka Catchment of New Zealand where several biophysical (riverine water quantity, sediment, E. coli faecal bacteria, trout numbers, nitrogen transport, marine productivity) models, a socio-economic (gross output, gross margin, job numbers) model, and an agent-based model were linked. An extreme set of land use scenarios (historic, present, and intensive) were applied to this modelling framework. Results suggest that the catchment is presently in a near optimal land use configuration that is unlikely to benefit from further intensification. This would quickly put stress on water quantity (at low flow) and water quality (E. coli). To date, this model evaluation is based on a theoretical test that explores the logical implications of intensification at an unlikely extreme in order to assess the implications of likely growth trajectories from present use. While this has largely been a desktop exercise, it would also be possible to use this framework to model and explore the biophysical and economic impacts of individual or collective catchment visions. We are currently investigating the use of the model in this type of application.     

What makes neighborhood different from home and city? Effects of place scale on place attachment

Several years ago Hidalgo and Hernandez reported a curvilinear, U-shaped, relationship between scale of place (apartment, neighborhood, city) and strength of attachment to the place. In this paper four studies are presented, carried out in four Central-European cities, that (1) confirmed the reported curvilinear relationship using five places (apartment, building, neighborhood, city district, city) in three out of four cities and for five items of the Place Attachment Scale, (2) revealed a consistent curvilinear, inverse U-shaped relationship between scale of place and percentage of variance of place attachment predicted by three groups of factors: physical (type of housing, size of building, upkeep and personalization of house precincts, etc.), social (neighborhood ties and sense of security in the residence place), and socio-demographic (age, education, gender, length of residence, family size), and (3) identified strength of direct and indirect effects of the three groups of predictors on attachment to the five types of places. The curvilinear relationship between place scale and place attachment was particularly strong in highly attractive cities and in those scale items that described people's emotional reactions to places whereas a linear relationship was obtained in the least attractive city and in the items that referred to sense of security, amount of control and knowledge of place. In all four cities the best predicted variable was attachment to middle ranges of the place scale (building and neighborhood). The overall best direct predictor of place attachment was neighborhood ties, followed by direct and indirect effects of length of residence, building size, and type of housing. In conclusion it is argued that the usual choice of predictors of place attachment is biased by researchers' interest in the middle scales of place (neighborhood) at the expense of other place scales. In the paper a claim is made that attachments to smaller (apartments, homes) and larger (city) scales of place along with their unique predictors deserve more attention from environmental psychologists.