Variation in perception of environmental change in nine Solomon Islands communities: implications for securing fairness in community-based adaptation

Community-based approaches are pursued in recognition of the need for place-based responses to environmental change that integrate local understandings of risk and vulnerability. Yet the potential for fair adaptation is intimately linked to how variations in perceptions of environmental change and risk are treated. There is, however, little empirical evidence of the extent and nature of variations in risk perception in and between multiple community settings. Here, we rely on data from 231 semi-structured interviews conducted in nine communities in Western Province, Solomon Islands, to statistically model different perceptions of risk and change within and between communities. Overall, people were found to be less likely to perceive environmental changes in the marine environment than they were for terrestrial systems. The distance to the nearest market town (which may be a proxy for exposure to commercial logging and degree of involvement with the market economy), and gender had the greatest overall statistical effects on perceptions of risk. Yet, we also find that significant environmental change is underreported in communities, while variations in perception are not always easily related to commonly assumed fault lines of vulnerability. The findings suggest that there is an urgent need for methods that engage with the drivers of perceptions as part of community-based approaches. In particular, it is important to explicitly account for place, complexity and diversity of environmental risk perceptions, and we reinforce calls to engage seriously with underlying questions of power, culture, identity and practice that influence adaptive capacity and risk perception.

     

A review of regulations and guidelines related to winter manure application

Winter manure application elevates nutrient losses and impairment of water quality as compared to manure applications in other seasons. In conjunction with reviewing global distribution of animal densities, we reviewed worldwide mandatory regulations and voluntary guidelines on efforts to reduce off-site nutrient losses associated with winter manure applications. Most of the developed countries implement regulations or guidelines to restrict winter manure application, which range from a regulative ban to guidelines based upon weather and field management conditions. In contrast, developing countries lack such official directives, despite an increasing animal production industry and concern over water quality. An analysis of five case studies reveals that directives are derived from a common rationale to reduce off-site manure nutrient losses, but they are also affected by local socio-economic and biophysical considerations. Successful programs combine site-specific management strategies along with expansion of manure storage to offer farmers greater flexibility in winter manure management.     

Examining Trajectories of Change for Prosperous Forest Landscapes in Cambodia

Environmental Management - Tropical forest landscapes are undergoing rapid transition. Rural development aspirations are rising, and land use change is contributing to deforestation, degradation,...     

Assessing climate change adaptation strategies—the case of drought and heat wave in the French nuclear sector

Regional Environmental Change - Nuclear energy is a very important component of overall power supply in France. If the effects of future extreme weather events or climate shifts are not addressed,...     

Metal concentrations in used engine oils: Relevance to site assessments of soils

Used engine oils contain metals, which upon entering soils may pose risks to human health or the environment. In this study, previously published concentrations of 23 metals in 213 used engine oil samples from the early 1970s to the mid-1990s are statistically evaluated. Neat (100%) used engine oils were found to contain relatively high concentrations of lead, calcium, and zinc, attributable to piston blow-by of leaded gasoline, calcium salt detergent additives, and zinc-bearing anti-corrosion/anti-oxidation additives, respectively. Wear metal concentrations were lower. The lead concentration in used engine oils in the U.S. declined between the 1970s and early 1990s, potentially providing a basis to constrain the “age” of used engine oil(s) in soils. The concentrations of 23 metals in used engine oils were compared to soil risk benchmarks in 15 representative jurisdictions in the U.S., Canada, Australia, and Europe. The maximum concentrations in neat (100%) used engine oil of eight metals – Be, Co, Fe, Mn, Ni, Se, Ag, and Ti – were lower than their collective minimum benchmarks in soils for the jurisdictions surveyed, indicating their concentrations in soils could not be reasonably expected to exceed any soil benchmarks. Nine metals (As, Ba, Cd, Cr, Cu, Pb, Sn, V and Zn), but particularly arsenic, cadmium, lead, tin, and zinc, were identified as potential contaminants of concern (PCOC) for soils from locations impacted with used engine oils, owing to their higher median concentrations (i.e., 2.5, 1.4, 1038, 5.0, and 922 mg/kg in oil, respectively) relative to most soil benchmarks. Site-specific benchmarks and metal concentrations at reasonable oil in soil concentrations require consideration when developing the suite of PCOC metal analytes for conducting site assessments of soils impacted by used engine oil.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.     

Probability analyses of combining background concentrations with model-predicted concentrations

In order to calculate total concentrations for comparison to ambient air quality standards, monitored background concentrations are often combined with model predicted concentrations. Models have low skill in predicting the locations or time series of observed concentrations. Further, adding fixed points on the probability distributions of monitored and predicted concentrations is very conservative and not mathematically correct. Simply adding the 99th percentile predicted to the 99th percentile background will not yield the 99th percentile of the combined distributions. Instead, an appropriate distribution can be created by calculating all possible pairwise combinations of the 1-hr daily maximum observed background and daily maximum predicted concentration, from which a 99th percentile total value can be obtained. This paper reviews some techniques commonly used for determining background concentrations and combining modeled and background concentrations. The paper proposes an approach to determine the joint probabilities of occurrence of modeled and background concentrations. The pairwise combinations approach yields a more realistic prediction of total concentrations than the U.S. Environmental Protection Agency's (EPA) guidance approach and agrees with the probabilistic form of the National Ambient Air Quality Standards.

Implications: EPA's current approaches to determining background concentrations for compliance modeling purposes often lead to “double counting” of background concentrations and actual plume impacts and thus lead to overpredictions of total impacts. Further, the current Tier 1 approach of simply adding the top ends of the background and model predicted concentrations (e.g., adding the 99th percentiles of these distributions together) results in design value concentrations at probabilities in excess of the form of the National Ambient Air Quality Standards.