Investigating Jacaranda mimosifolia tree as biomonitor of atmospheric trace metals

Studies on the use of tree bark as biomonitors for environmental pollutants are still very scarce. We evaluated the reliability of using Jacaranda mimosifolia, a common tree in Tshwane City of South Africa, as a suitable biomonitor of atmospheric trace metals. Bark samples were collected from ten different locations during two sampling periods. The concentrations of the metals were determined by inductively coupled plasma mass spectrometry. The concentrations of the metals were 33.2–1,795 μg/g (Pb), 21.4–210 μg/g (Cu), 68.4–490 μg/g (Zn), 30.6–2,916 μg/g (Cr), 0.12–1.34 μg/g (Cd), and 6.04–68.0 μg/g (V), respectively. The differences obtained for the results from different sites were significant (p?< 0.05). A significant difference was also observed between the two sampling periods. The trace metals concentrations suggested that automobile emissions are a major source of these metals. The study also confirms the suitability of J. mimosifolia as a biomonitor of atmospheric deposition of these metals.     

Multiscale satellite and spatial information and analysis framework in support of a large-area forest monitoring and inventory update

Many countries undertake a national forest inventory to enable statistically valid monitoring in support of national and international reporting of forest conditions and change. Canada’s National Forest Inventory (NFI) program is designed to operate on a 10-year remeasurement cycle, with an interim report produced at the 5-year mid-point. The NFI is a sample-based inventory, with approximately 18,850 2 ×2-km photo plots across the country, distributed on a 20×20-km grid of sample points; these photo plots are the primary data source for the NFI. Capacity to provide annual monitoring information is required to keep policy and decision makers apprised of current forest conditions. In this study, we implemented a multistage monitoring framework and used a Moderate Resolution Imaging Spectroradiometer (MODIS) change product to successfully identify 78% of the changes in forest cover area that were captured with a Landsat change detection approach. Of the NFI photo plots that were identified by both the Landsat and MODIS approaches as having changes in forest cover, the proportion of change area within the plots was similar (R ²?=?0.78). Approximately 70% of the Landsat-derived change events occupied less than 40% of a single MODIS pixel, and more than 90% of the change events of this size were successfully detected with the MODIS product. Finally, MODIS estimates of the proportion of forest cover change at the NFI photo plot level were comparable to change estimates for the ecoregions as a whole (R ²?=?0.95). High-temporal, low-spatial resolution imagery such as MODIS, in combination with other remotely sensed data sources, can provide information on disturbance events within a national forest inventory remeasurement cycle, thereby satisfying the interim information needs of policy and decision makers as well as the requirements of national and international reporting commitments.     

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.     

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.     

Evaluating Uncertain CO2 Abatement over the Very Long Term

Climate change research with the economic methodology of cost–benefit analysis is challenging because of valuation and ethical issues associated with the long delays between CO₂ emissions and much of their potential damages, typically of several centuries. The large uncertainties with which climate change impacts are known today and the possibly temporary nature of some envisaged CO₂ abatement options exacerbate this challenge. For example, potential leakage of CO₂ from geological reservoirs, after this greenhouse gas has been stored artificially underground for climate control reasons, requires an analysis in which the uncertain climatic consequences of leakage are valued over many centuries. We here present a discussion of some of the relevant questions in this context and provide calculations with the top–down energy-environment-economy model DEMETER. Given the long-term features of the climate change conundrum as well as of technologies that can contribute to its solution, we considered it necessary extending DEMETER to cover a period from today until the year?3000, a time span so far hardly investigated with integrated assessment models of climate change.     

The Impact of Uncertainty in Climate Targets and CO2 Storage Availability on Long-Term Emissions Abatement

A major characteristic of our global interactive climate-energy system is the large uncertainty that exists with respect to both future environmental requirements and the means available for fulfilling these. Potentially, a key technology for leading the transition from the current fossil fuel-dominated energy system to a more sustainable one is carbon dioxide capture and storage. Uncertainties exist, however, concerning the large-scale implementability of this technology, such as related to the regional availability of storage sites for the captured CO₂. We analyze these uncertainties from an integrated assessment perspective by using the bottom-up model TIAM-ECN and by studying a set of scenarios that cover a range of different climate targets and technology futures. Our study consists of two main approaches: (1) a sensitivity analysis through the investigation of a number of scenarios under perfect foresight decision making and (2) a stochastic programming exercise that allows for simultaneously considering a set of potential future states-of-the-world. We find that, if a stringent climate (forcing) target is a possibility, it dominates the solution: if deep CO₂ emission reductions are not started as soon as possible, the target may become unreachable. Attaining a stringent climate target comes in any case at a disproportionally high price, which indicates that adaptation measures or climate damages might be preferable to the high mitigation costs such a target implies.     

Influence of a portable air treatment unit on health-related quality indicators of indoor air in a classroom

During periods of two weeks in February and June 2010 the performance of portable air treatment units (PATUs) was evaluated in a primary school classroom using indicators of indoor air quality. Air samples were collected in an undisturbed setting on weekend days and in an occupied setting during teaching hours. In the first week PATUs were turned off and in the second week they were turned on. On weekend days PATUs reduced indoor levels of PM-10 by 87% in February and by 70% in June compared to weekend days when PATUs were turned off. On schooldays, indoor PM-10 was increased by 6% in February and reduced by 42% in June. For PM-2.5 reductions on weekend days were 89% in February and 80% in June. On school days PM-2.5 was increased by 15% in February and reduced by 83% in June. Turning on the PATUs reduced total VOC by 80% on weekend days and by 57% on school days (but not in June). No influence on formaldehyde, NO(2), O(3) and molds was observed. PATUs appeared to be less effective in removal of air pollutants when used in an occupied classroom compared to an unoccupied setting. Our study suggests that such devices should be tested in real-life settings to evaluate their influence on indoor air quality.     

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.     

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.