A process-based model designed for filling of large data gaps in tower-based measurements of net ecosystem productivity

In this paper we present a simple hybrid gap-filling model (GFM) designed with a minimum number of parameters necessary to capture the ecological processes important for filling medium-to-large gaps in Flux data. As the model is process-based, the model has potential to be used in filling large gaps exhibiting a broad range of micro-meteorological and site conditions. The GFM performance was evaluated using “Punch hole” and extrapolation experiments based on data collected in west-central New Brunswick. These experiments indicated that the GFM is able to provide acceptable results (r² > 0.80) when >500 data points are used in model parameterization. The GFM was shown to address daytime evolution of NEP reasonably well for a wide range of weather and site conditions. An analysis of residuals indicated that for the most part no obvious trends were evident; although a slight bias was detected in NEP with soil temperature. To explore the portability of the GFM across ecosystem types, a transcontinental validation was conducted using NEP and ancillary data from seven ecosystems along a north-south transect (i.e., temperature–moisture gradient) from northern Europe (Finland) to the Middle East (Israel). The GFM was shown to explain over 75% of the variability in NEP measured at most ecosystems, which strongly suggests that the GFM maybe successfully applied to forest ecosystems outside Canada.     

Steel slag quality control for road construction aggregates and its environmental impact: case study of Vietnamese steel industry—leaching of heavy metals from steel-making slag

Environmental Science and Pollution Research - Steel slag is an industrial by product of steel manufacturing processes and has been widely utilized within civil and construction materials for road...     

Catalytic hydrodechlorination of 1,2-dichloroethane using copper nanoparticles under reduction conditions of sodium borohydride

1,2-Dichloroethane (1,2-DCA) is a raw material used for the manufacture of vinyl chloride monomer (VCM) and therefore has very often been detected in the groundwater nearby the VCM manufacturing plant. Zero-valent iron (ZVI) is capable of degrading a wide array of highly chlorinated contaminants; however, the reactivity of ZVI towards 1,2-DCA is very low. In this study, zero-valent copper nanoparticles have been synthesized for effective dechlorination of 1,2-DCA under reduction conditions of sodium borohydride. Copper nanoparticles consisted of mainly metallic copper (Cu(0)) with small amounts of cuprous oxide (Cu(2)O). They have surface areas of about 19.0 m(2) g(-1) and an average diameter of 15 nm. Batch experiments were conducted to test the effectiveness of copper nanoparticles for 1,2-DCA degradation using sodium borohydride as electron donors where the ORP was measured as -1100 mV. More than 80% of 1,2-DCA (30 mg L(-1)) was rapidly degraded within 2 h in the presence of both copper nanoparticles (2.5 g L(-1)) and borohydride (25 mM). No reduction of 1,2-DCA was observed when the system contained either copper nanoparticles alone or borohydride alone. The degradation intermediates included ethane and ethylene accounting for 79% and ～1.5% of the 1,2-DCA lost, respectively. Potential environmental applications can be achieved by immobilizing copper nanoparticles onto the surface of reducing metals to form a reactive bimetallic structure.     

Bayesian treatment of a chemical mass balance receptor model with multiplicative error structure

The chemical mass balance (CMB) receptor model is commonly used in source apportionment studies as a means for attributing measured airborne particulate matter (PM) to its constituent emission sources. Traditionally, error terms (e.g., measurement and source profile uncertainty) associated with the model have been treated in an additive sense. In this work, however, arguments are made for the assumption of multiplicative errors, and the effects of this assumption are realized in a Bayesian probabilistic formulation which incorporates a ‘modified’ receptor model. One practical, beneficial effect of the multiplicative error assumption is that it automatically precludes the possibility of negative source contributions, without requiring additional constraints on the problem. The present Bayesian treatment further differs from traditional approaches in that the source profiles are inferred alongside the source contributions. Existing knowledge regarding the source profiles is incorporated as prior information to be updated through the Bayesian inferential scheme. Hundreds of parameters are therefore present in the expression for the joint probability of the source contributions and profiles (the posterior probability density function, or PDF), whose domain is explored efficiently using the Hamiltonian Markov chain Monte Carlo method. The overall methodology is evaluated and results compared to the US Environmental Protection Agency's standard CMB model using a test case based on PM data from Fresno, California.     

Effects of landscape protection on farm management and farmers' income in Norway

Protected Landscapes (PLs) are increasingly used in Norway to conserve cultural (human modified) landscapes. In many cases the maintenance of agricultural activities in PLs is required to preserve landscape character. Whilst research exists on land conservation policies in general, the particular effects of PL on management and adjustment of the farms involved have not received attention in the literature. We present results from a questionnaire sent to owners of agricultural land within PLs in Norway. Whilst landowners were divided upon the effects of PLs on farm management, the economic situation of the farm was little affected. Furthermore, changes in farm management after the establishment of a PL did not seem to have been driven by the establishment of the PLs per se. Most importantly, farm management changes were related to potential options to develop the farm and its land. A statistical model showed that PL-farms did not differ significantly from farms outside PL in the development of their land use or animal husbandry. Our findings thus indicate that the establishment of PL played a minor role as a driving force of changes in farm management and farm income.     

Using the soil and water assessment tool to estimate achievable water quality targets through implementation of beneficial management practices in an agricultural watershed

Runoff from crop production in agricultural watersheds can cause widespread soil loss and degradation of surface water quality. Beneficial management practices (BMPs) for soil conservation are often implemented as remedial measures because BMPs can reduce soil erosion and improve water quality. However, the efficacy of BMPs may be unknown because it can be affected by many factors, such as farming practices, land-use, soil type, topography, and climatic conditions. As such, it is difficult to estimate the impacts of BMPs on water quality through field experiments alone. In this research, the Soil and Water Assessment Tool was used to estimate achievable performance targets of water quality indicators (sediment and soluble P loadings) after implementation of combinations of selected BMPs in the Black Brook Watershed in northwestern New Brunswick, Canada. Four commonly used BMPs (flow diversion terraces [FDTs], fertilizer reductions, tillage methods, and crop rotations), were considered individually and in different combinations. At the watershed level, the best achievable sediment loading was 1.9 t ha(-1) yr(-1) (89% reduction compared with default scenario), with a BMP combination of crop rotation, FDT, and no-till. The best achievable soluble P loading was 0.5 kg ha(-1) yr(-1) (62% reduction), with a BMP combination of crop rotation and FDT and fertilizer reduction. Targets estimated through nonpoint source water quality modeling can be used to evaluate BMP implementation initiatives and provide milestones for the rehabilitation of streams and rivers in agricultural regions.     

Delineating landfill leachate discharge to an arsenic contaminated waterway

Discharge of contaminated ground water may serve as a primary and on-going source of contamination to surface water. A field investigation was conducted at a Superfund site in Massachusetts, USA to define the locus of contaminant flux and support source identification for arsenic contamination in a pond abutting a closed landfill. Subsurface hydrology and ground-water chemistry were evaluated in the aquifer between the landfill and the pond during the period 2005-2009 employing a network of wells to delineate the spatial and temporal variability in subsurface conditions. These observations were compared with concurrent measures of ground-water seepage and surface water chemistry within a shallow cove that had a historical visual record of hydrous ferric oxide precipitation along with elevated arsenic concentrations in shallow sediments. Barium, presumably derived from materials disposed in the landfill, served as an indicator of leachate-impacted ground water discharging into the cove. Evaluation of the spatial distributions of seepage flux and the concentrations of barium, calcium, and ammonium-nitrogen indicated that the identified plume primarily discharged into the central portion of the cove. Comparison of the spatial distribution of chemical signatures at depth within the water column demonstrated that direct discharge of leachate-impacted ground water was the source of highest arsenic concentrations observed within the cove. These observations demonstrate that restoration of the impacted surface water body will necessitate control of leachate-impacted ground water that continues to discharge into the cove.