Lead traceability along soil-melliferous flora-bee family-apiary products chain

Indirect measures were considered to study the lead transfer along the soil-melliferous flora-bee family-apiary products food chain. The concentrations of total and bioavailable lead in the soil were first investigated. Next, the lead level in different organs of melliferous flora and subsequently in the bee family and apiary products was measured. The results indicate a decrease in contamination as follows: honeybees > drones > propolis > wax > bee larvae > honey > royal jelly. The statistical analysis highlights the "lead pollution bioindicator" role of honeybees, honey and propolis. The risk factor parameter allows the prediction of the most impacted month and the least impacted of the melliferous season.     

Preferential sorption of some natural organic matter fractions to titanium dioxide nanoparticles: influence of pH and ionic strength

Natural organic matter (NOM) sorption to nanoparticles (NPs) can influence their transport and bioavailability in the aquatic environment. The sorption affinity of NOM to surfaces including NPs is size dependent, and depending on environmental conditions, NOM may enhance or mitigate NPs toxicity. The aim of this study was to investigate the preferential sorption of different-sized fractions of NOM to titanium dioxide (TiO₂) NPs. We specifically investigated the influence of pH, ionic strength, and NOM concentration on the extent of this preferential sorption using a constant sorbent concentration (400 mg/L TiO₂ NPs). Additionally, sorption of NOM to TiO₂ NPs at varying pH was investigated. The nonsorbed NOM was separated from the sorbed, by 50 nm polycarbonate membrane filters and ultracentrifugation. High-performance size exclusion chromatography (HPSEC) was used to determine the average molecular weights of NOM (MWw). Corroborative evidence of preferential sorption of different-sized molecular weight fractions of NOM was obtained from optical techniques such as absorbance and fluorescence spectrophotometry. The total organic carbon was measured by the Total Organic Carbon Analyzer—Shimadzu (TOC-VCPH). The results indicated that there is preferential sorption of larger sized fractions of NOM to TiO₂ NPs irrespective of NOM concentration. It was observed that the sorption of larger sized fractions of NOM was much enhanced at lower pH and at higher ionic strength. Both absorbance and fluorescence spectrophotometric techniques gave credible corroborative evidence on the extent of preferential sorption of lager sized fractions of NOM with respect to pH and ionic strength. The sorption results demonstrated higher sorption at lower pH than at higher pH. Overall, the results of this study suggest that the environmental conditions are key factors that can contribute to NOM’s fractional preferential sorption to NPs in the aquatic environment.     

The Impacts of Different Meteorology Data Sets on Nitrogen Fate and Transport in the SWAT Watershed Model

In this study, we investigated how different meteorology data sets impacts nitrogen fate and transport responses in the Soil and Water Assessment Tool (SWAT) model. We used two meteorology data sets: National Climatic Data Center (observed) and Mesoscale Model 5/Weather Research and Forecasting (simulated). The SWAT model was applied to two 10-digit hydrologic unit code watersheds in the Coastal Plain and Piedmont zones of North Carolina. Nitrogen cycling and loading response to these meteorological data were investigated by exploring 19 SWAT nitrogen outputs relating to landscape delivery, biogeochemical assimilation, and atmospheric deposition. The largest difference in model output using both meteorology data sets was for large loads/fluxes. Landscape delivery outputs (e.g., NO^? ₃ watershed discharge, groundwater NO^? ₃ flux, soil NO^? ₃ percolation) showed the largest difference across all values. Use of the two weather data sources resulted in a nearly twofold difference in NO^? ₃ watershed discharge and groundwater NO^? ₃ flux. Differences for many nitrogen outputs were greater than those for sub-basin flow. Nitrogen outputs showed the greatest difference for agricultural land covers and there was no flow-related pattern in output differences across sub-basins or over time (years). In general, nitrogen parameter models that had a greater number of nitrate concentration, flow, and temperature terms (equation variables) in each transport model showed the greatest difference between both meteorology applications.     

Potential Impact of Clean Air Act Regulations on Nitrogen Fate and Transport in the Neuse River Basin: a Modeling Investigation Using CMAQ and SWAT

There has been extensive analysis of Clean Air Act Amendment (CAAA) regulation impacts to changes in atmospheric nitrogen deposition; however, few studies have focused on watershed nitrogen transfer particularly regarding long-term predictions. In this study, we investigated impacts of CAAA NOx emissions on the fate and transport of nitrogen for two watersheds in the Neuse River Basin. We applied the Soil and Water Assessment Tool (SWAT) using simulated deposition rates from the Community Multiscale Air Quality (CMAQ) model. Two scenarios were investigated: one that considered CAAA emission controls in CMAQ simulation (with) and a second that did not (without). By 2020, results showed a 70 % drop in nitrogen discharge for the Little River watershed and a 50 % drop for the Nahunta watershed from 1990 levels under the with-CAAA scenario. Denitrification and plant nitrogen uptake played important roles in nitrogen discharge from each watershed. Nitrogen watershed response time to a change in atmospheric nitrogen deposition was 4 years for Nahunta and 2 years for Little River. We attribute these differences in nitrogen response time to contrasts in agricultural land use and diversity of crop types. Soybean, hay, and corn land covers had comparatively longer response times to changes in atmospheric deposition. The studied watersheds demonstrate relatively large nitrogen retention: ≥80 % of all delivered nitrogen.     

Assessing health impacts in complex eco-epidemiological settings in the humid tropics: Modular baseline health surveys

The quantitative assessment of health impacts has been identified as a crucial feature for realising the full potential of health impact assessment (HIA). In settings where demographic and health data are notoriously scarce, but there is a broad range of ascertainable ecological, environmental, epidemiological and socioeconomic information, a diverse toolkit of data collection strategies becomes relevant for the mainly small-area impacts of interest. We present a modular, cross-sectional baseline health survey study design, which has been developed for HIA of industrial development projects in the humid tropics. The modular nature of our toolkit allows our methodology to be readily adapted to the prevailing eco-epidemiological characteristics of a given project setting. Central to our design is a broad set of key performance indicators, covering a multiplicity of health outcomes and determinants at different levels and scales. We present experience and key findings from our modular baseline health survey methodology employed in 14 selected sentinel sites within an iron ore mining project in the Republic of Guinea. We argue that our methodology is a generic example of rapid evidence assembly in difficult-to-reach localities, where improvement of the predictive validity of the assessment and establishment of a benchmark for longitudinal monitoring of project impacts and mitigation efforts is needed.     

Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China

The Three-North Shelter Forest Program is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land use and land cover change, but it is still challenging to accurately quantify the change in forest extent from time-series satellite images. In this paper, 30 Landsat MSS/TM/ETM+ epochs from 1974 to 2012 were collected, and the high-quality ground surface reflectance (GSR) time-series images were processed by integrating the 6S atmosphere transfer model and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the time-series Landsat GSR images based on the integrated forest z-score (IFZ) model by Huang et al. (2009a), which was improved by multi-phenological IFZ models and the smoothing processing of IFZ data for afforestation mapping. The mapping result showed a large increase in the extent of forest, from 380,394 ha (14.8 % of total district area) in 1974 to 1,128,380 ha (43.9 %) in 2010. Finally, the land cover and forest change map was validated with an overall accuracy of 89.1 % and a kappa coefficient of 0.858. The forest change time was also successfully retrieved, with 22.2 % and 86.5 % of the change pixels attributed to the correct epoch and within three epochs, respectively. The results confirmed a great achievement of the ecological revegetation projects in Yulin district over the last 40 years and also illustrated the potential of the time-series of Landsat images for detecting forest changes and estimating tree age for the artificial forest in a semi-arid zone strongly influenced by human activities.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

Preparation, certification and interlaboratory analysis of workplace air filters spiked with high-fired beryllium oxide

Occupational sampling and analysis for multiple elements is generally approached using various approved methods from authoritative government sources such as the National Institute for Occupational Safety and Health (NIOSH), the Occupational Safety and Health Administration (OSHA) and the Environmental Protection Agency (EPA), as well as consensus standards bodies such as ASTM International. The constituents of a sample can exist as unidentified compounds requiring sample preparation to be chosen appropriately, as in the case of beryllium in the form of beryllium oxide (BeO). An interlaboratory study was performed to collect analytical data from volunteer laboratories to examine the effectiveness of methods currently in use for preparation and analysis of samples containing calcined BeO powder. NIST SRM(?) 1877 high-fired BeO powder (1100 to 1200 °C calcining temperature; count median primary particle diameter 0.12 μm) was used to spike air filter media as a representative form of beryllium particulate matter present in workplace sampling that is known to be resistant to dissolution. The BeO powder standard reference material was gravimetrically prepared in a suspension and deposited onto 37 mm mixed cellulose ester air filters at five different levels between 0.5 μg and 25 μg of Be (as BeO). Sample sets consisting of five BeO-spiked filters (in duplicate) and two blank filters, for a total of twelve unique air filter samples per set, were submitted as blind samples to each of 27 participating laboratories. Participants were instructed to follow their current process for sample preparation and utilize their normal analytical methods for processing samples containing substances of this nature. Laboratories using more than one sample preparation and analysis method were provided with more than one sample set. Results from 34 data sets ultimately received from the 27 volunteer laboratories were subjected to applicable statistical analyses. The observed performance data show that sample preparations using nitric acid alone, or combinations of nitric and hydrochloric acids, are not effective for complete extraction of Be from the SRM 1877 refractory BeO particulate matter spiked on air filters; but that effective recovery can be achieved by using sample preparation procedures utilizing either sulfuric or hydrofluoric acid, or by using methodologies involving ammonium bifluoride with heating. Laboratories responsible for quantitative determination of Be in workplace samples that may contain high-fired BeO should use quality assurance schemes that include BeO-spiked sampling media, rather than solely media spiked with soluble Be compounds, and should ensure that methods capable of quantitative digestion of Be from the actual material present are used.     

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.     

Selecting Variables for Habitat Suitability of <Emphasis Type="Italic">Asellus</Emphasis> (Crustacea,Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the ‘partial derivatives’ method; (2) the ‘weights’ method; (3) the ‘perturb’ method; (4) the ‘profile’ method; (5) the ‘classical stepwise’ method; (6) the ‘improved stepwise’ method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000–2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment.