Deposition of reactive nitrogen during the Rocky Mountain Airborne Nitrogen and Sulfur (RoMANS) study

Increases in reactive nitrogen deposition are a growing concern in the U.S. Rocky Mountain west. The Rocky Mountain Airborne Nitrogen and Sulfur (RoMANS) study was designed to improve understanding of the species and pathways that contribute to nitrogen deposition in Rocky Mountain National Park (RMNP). During two 5-week field campaigns in spring and summer of 2006, the largest contributor to reactive nitrogen deposition in RMNP was found to be wet deposition of ammonium (34% spring and summer), followed by wet deposition of nitrate (24% spring, 28% summer). The third and fourth most important reactive nitrogen deposition pathways were found to be wet deposition of organic nitrogen (17%, 12%) and dry deposition of ammonia (14%, 16%), neither of which is routinely measured by air quality/deposition networks operating in the region. Total reactive nitrogen deposition during the spring campaign was determined to be 0.45 kg ha⁻¹ and more than doubled to 0.95 kg ha⁻¹ during the summer campaign.     

Soil type affects migration pattern of airborne Pb and Cd under a spruce-beech forest of the UN-ECE integrated monitoring site Zöbelboden, Austria

Anthropogenic trace element emissions have declined. However, top soils all over the world remain enriched in trace elements. We investigated Pb and Cd migration in forest soils of a remote monitoring site in the Austrian limestone Alps between 1992 and 2004. Large spatial variability masked temporal changes in the mineral soil of Lithic Leptosols (Skeltic), whereas a significant reduction of Pb concentrations in their forest floors occurred. Reductions of concentrations in the less heterogeneous Cambisols (Chromic) were significant. In contrast, virtually no migration of Pb and Cd were found in Stagnosols due to their impeded drainage. Very low element concentrations (<1 μg l⁻¹) in field-collected soil solutions using tension lysimeters (0.2 μm nylon filters) imply that migration largely occurred by preferential flow as particulate-bound species during intensive rainfall events. Our results indicate that the extent of Pb and Cd migration in soils is largely influenced by soil type.     

Comparison among model estimates of critical loads of acidic deposition using different sources and scales of input data

The critical load (CL) of acidic atmospheric deposition represents the load of acidity deposited from the atmosphere to the earth’s surface at which harmful acidification effects on sensitive biological receptors are thought to occur. In this study, the CL for forest soils was estimated for 27 watersheds throughout the United States using a steady-state mass balance approach based on both national and site-specific data and using different approaches for estimating base cation weathering. Results suggested that the scale and source of input data can have large effects on the calculated CL and that the most important parameter in the steady-state model used to estimate CL is base cation weathering. These results suggest that the data and approach used to estimate weathering must be robust if the calculated CL is to be useful for its intended purpose.     

Elevated atmospheric deposition and dynamics of mercury in a remote upland forest of southwestern China

Mt. Gongga area in southwest China was impacted by Hg emissions from industrial activities and coal combustion, and annual means of atmospheric TGM and PHg concentrations at a regional background station were 3.98 ng m⁻³ and 30.7 pg m⁻³, respectively. This work presents a mass balance study of Hg in an upland forest in this area. Atmospheric deposition was highly elevated in the study area, with the annual mean THg deposition flux of 92.5 μg m⁻² yr⁻¹. Total deposition was dominated by dry deposition (71.8%), and wet deposition accounted for the remaining 28.2%. Forest was a large pool of atmospheric Hg, and nearly 76% of the atmospheric input was stored in forest soil. Volatilization and stream outflow were identified as the two major pathways for THg losses from the forest, which yielded mean output fluxes of 14.0 and 8.6 μg m⁻² yr⁻¹, respectively.     

INASUD project findings on integrated assessment of climate policies

This communication summarizes the main findings of INASUD, an European-wide research project on integrated assessment of climate policies. The project aimed at improving the framing of climate policy analysis through the parallel use of various existing integrated assessment models. It provides a comprehensive examination of the link between uncertainty regarding damages and inertia in economic systems. Results show that the Kyoto targets and timing are consistent with the precautionary principle but offers little insurance for longer-term climate protection. Flexibility mechanisms offer potentials for cooperation with developing countries, and are necessary to tap the environmental and economic benefits of joint carbon and sulfur emissions abatement.     

Electrocoagulation process for the treatment of metal-plating wastewater: Kinetic modeling and energy consumption

The wastewater from industrial area was treated by EC via Fe and Al electrodes. Cu, Ni, Cr and Zn were highly removed at the first minutes, simultaneously. Pseudo-2nd-order was found to be more suitable for kinetics. Adsorption capacities based on kinetic modeling were observed as Cr>Cu>Ni>Zn. The chemical cost in the case of pH adjustment after EC was less as 3.83 $/m³. It is known that wastewater produced by the metal-plating industry contains several heavy metals, which are acidic in nature and therefore toxic for the environment and for living creatures. In particular, heavy metals enter the food chain and accumulate in vital organs and cause serious illness. The precipitation of these metals is mostly achieved by pH adjustment, but as an alternative to this method, the electrocoagulation process has investigated in this study using iron and aluminum electrodes. The effects of the pH adjustment on removal before and after the electrocoagulation process were investigated, and cost analyses were also compared. It was observed that a high proportion of removal was obtained during the first minutes of the electrocoagulation process; thus, the current density did not have a great effect. In addition, the pH adjustment after the electrocoagulation process using iron electrodes, which are 10% more effective than aluminum electrodes, was found to be much more efficient than before the electrocoagulation process. In the process where kinetic modeling was applied, it was observed that the heavy metal removal mechanism was not solely due to the collapse of heavy metals at high pH values, and with this modeling, it was seen that this mechanism involved adsorption by iron and aluminum hydroxides formed during the electrocoagulation process. When comparing the ability of heavy metals to be adsorbed, the sequence was observed to be Cr>Cu>Ni>Zn, respectively.     

Investigating cumulative effects across ecological scales

Species, habitats, and ecosystems are increasingly exposed to multiple anthropogenic stressors, fueling a rapidly expanding research program to understand the cumulative impacts of these environmental modifications. Since the 1970s, a growing set of methods has been developed through two parallel, sometimes connected, streams of research within the applied and academic realms to assess cumulative effects. Past reviews of cumulative effects assessment (CEA) methods focused on approaches used by practitioners. Academic research has developed several distinct and novel approaches to conducting CEA. Understanding the suite of methods that exist will help practitioners and academics better address various ecological foci (physiological responses, population impacts, ecosystem impacts) and ecological complexities (synergistic effects, impacts across space and time). We reviewed 6 categories of methods (experimental, meta-analysis, single-species modeling, mapping, qualitative modeling, and multispecies modeling) and examined the ability of those methods to address different levels of complexity. We focused on research gaps and emerging priorities. We found that no single method assessed impacts across the 4 ecological foci and 6 ecological complexities considered. We propose that methods can be used in combination to improve understanding such that multimodel inference can provide a suite of comparable outputs, mapping methods can help prioritize localized models or experimental gaps, and future experiments can be paired from the outset with models they will inform.     

Population viability and harvest sustainability for Madagascar lemurs

Subsistence hunting presents a conservation challenge by which biodiversity preservation must be balanced with safeguarding of human livelihoods. Globally, subsistence hunting threatens primate populations, including Madagascar's endemic lemurs. We used population viability analysis to assess the sustainability of lemur hunting in Makira Natural Park, Madagascar. We identified trends in seasonal hunting of 11 Makira lemur species from household interview data, estimated local lemur densities in populations adjacent to focal villages via transect surveys, and quantified extinction vulnerability for these populations based on species-specific demographic parameters and empirically derived hunting rates. We compared stage-based Lefkovitch with periodic Leslie matrices to evaluate the impact of regional dispersal on persistence trajectories and explored the consequences of perturbations to the timing of peak hunting relative to the lemur birth pulse, under assumptions of density-dependent reproductive compensation. Lemur hunting peaked during the fruit-abundant wet season (March–June). Estimated local lemur densities were roughly inverse to body size across our study area. Life-history modeling indicated that hunting most severely threatened the species with the largest bodies (i.e., Hapalemur occidentalis, Avahi laniger, Daubentonia madagascariensis, and Indri indi), characterized by late-age reproductive onsets and long interbirth intervals. In model simulations, lemur dispersal within a regional metapopulation buffered extinction threats when a majority of local sites supported growth rates above the replacement level but drove regional extirpations when most local sites were overharvested. Hunt simulations were most detrimental when timed to overlap lemur births (a reality for D. madagascariensis and I. indri). In sum, Makira lemurs were overharvested. Regional extirpations, which may contribute to broad-scale extinctions, will be likely if current hunting rates persist. Cessation of anthropogenic lemur harvest is a conservation priority, and development programs are needed to help communities switch from wildlife consumption to domestic protein alternatives.     

A Bayesian strategy for combining predictions from empirical and process-based models

We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not.     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.