Pesticide concentrations in agricultural storm drainage inlets of a small Swiss catchment

Agricultural pesticides transported to surface waters pose a major risk for aquatic ecosystems. Modelling studies indicate that the inlets of agricultural storm drainage systems can considerably increase the connectivity of surface runoff and pesticides to surface waters. These model results have however not yet been validated with field measurements. In this study, we measured discharge and concentrations of 51 pesticides in four out of 158 storm drainage inlets of a small Swiss agricultural catchment (2.8 km²) and in the receiving stream. For this, we performed an event-triggered sampling during 19 rain events and collected plot-specific pesticide application data. Our results show that agricultural storm drainage inlets strongly influence surface runoff and pesticide transport in the study catchment. The concentrations of single pesticides in inlets amounted up to 62 µg/L. During some rain events, transport through single inlets caused more than 10% of the stream load of certain pesticides. An extrapolation to the entire catchment suggests that during selected events on average 30 to 70% of the load in the stream was transported through inlets. Pesticide applications on fields with surface runoff or spray drift potential to inlets led to increased concentrations in the corresponding inlets. Overall, this study corroborates the relevance of such inlets for pesticide transport by establishing a connectivity between fields and surface waters, and by their potential to deliver substantial pesticide loads to surface waters.

     

The interactive effects of conscientiousness,openness to experience,and political skill on job performance in complex jobs: The importance of context

Caveats concerning the ability of personality to predict job performance have been raised because of seemingly modest criterion‐related validity. The goal of the present research was to test whether narrowing the context via the type of job (i.e., jobs with complex task demands) and adding a social skill‐related moderator (i.e., political skill) would improve performance prediction. Further, along with political skill, the broad factor of personality demonstrated in prior research to have the strongest criterion validity (i.e., conscientiousness) was joined with a narrow construct closely related to openness to experience (i.e., learning approach) in a three‐way interactive prediction of supervisor‐rated task performance. With the employee–supervisor dyads among professionals, but not with the control group of non‐professional employees, task performance was predicted by the three‐way interaction, such that those high on all three received the highest performance ratings. Implications, strengths and limitations, and directions for future research are discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

Driver Workload Response to In-Vehicle Device Operations

A central concern of Intelligent Transportation Systems (ITS) is the effect of in-vehicle devices (e.g., cell phones, navigation systems, radios, etc.) on driver performance and safety. As diverse and innovative technologies are designed and implemented for in-vehicle use, questions regarding the presence and use of these devices assume progressively greater importance. Further concerns for advanced driver training require us to develop and validate reliable and effective procedures for assessing such effects. This work examines a number of candidate procedures, in particular the evaluation of change in cognitive workload as a strategy by which such goals might be achieved.     

Climate change in mountain regions: how local communities adapt to extreme events

This paper examines how local communities adapt to climate change and how governance structures can foster or undermine adaptive capacity. Climate change policies, in general, and disaster risk management in mountain regions, in particular, are characterised by their multi-level and multi-sectoral nature during formulation and implementation. The involvement of numerous state and non-state actors at local to national levels produces a variety of networks of interaction and communication. The paper argues that the structure of these relational patterns is critical for understanding adaptive capacity. It thus proposes an expanded concept of adaptive capacity that incorporates (horizontal and vertical) actor integration and communication flow between these actors. The paper further advocates the use of formal social network analysis to assess these relational patterns. Preliminary results from research on adaptation to climate change in a Swiss mountain region vulnerable to floods and other natural hazards illustrate the conceptual and empirical significance of the main arguments.     

Asymmetries in commitment in an avian communication network

Mobbing of predators occurs within a conspecific and heterospecific context but has not been quantified within the framework of a communication network and analysed with respect to heterospecific reciprocity. Here, we used playbacks of mobbing calls to show that mobbing is unequally distributed within a community of deciduous forest birds. Five species (great tit Parus major, blue tit Cyanistes caeruleus, marsh tit Poecile palustris, nuthatch Sitta europaea and chaffinch Fringilla coelebs) responded to each other’s playbacks of mobbing calls. Commitment to mob was measured by minimum distance, response latency and uttering of calls. Commitment was higher when conspecific calls were broadcast. Yet, responses to heterospecific calls were significantly different between the five species. Chaffinches had the lowest commitment, and blue tits tended to have the highest. The communication network is asymmetric. Some species invest more than they receive from other species. As mobbing might incur costs, these are unequally distributed across the community.     

Demonstrating Attainment of the Air Quality Standards: Integration of Observations and Model Predictions into the Probabilistic Framework

ABSTRACT

This paper introduces an integrated observational-modeling approach to transform the deterministic nature of attainment demonstrations of the National Ambient Air Quality Standard (NAAQS) into the probabilistic framework. While the methods presented here can be used to address any air quality standard that is based on extreme values, this paper focuses on the application to the 1-hr and 8-hr NAAQS for ozone. Extreme value statistics and resampling techniques are applied to estimate the probability of exceeding the NAAQS for both 1-hr and 8-hr ozone concentrations. Within the integrated observation-modeling analysis approach, we show that the model-to-model differences in the predicted responses to emission reductions are smaller than the model-to-model differences in predicted absolute ozone concentrations. We illustrate that the emission reductions stemming from a real-world emission control strategy would substantially reduce the probability of exceeding the NAAQS over a large portion of the eastern United States, especially for the 8-hr average ozone concentrations.     

An Assessment of the Emissions Inventory Processing Systems EMS-2001 and SMOKE in Grid-Based Air Quality Models

Abstract

In the United States, emission processing models such as Emissions Modeling System-2001 (EMS-2001), Emissions Preprocessor System-Version 2.5 (EPS2.5), and the Sparse Matrix Operator Kernel Emissions (SMOKE) model are currently being used to generate gridded, hourly, speciated emission inputs for urban and regional-scale photochemical models from aggregated pollutant inventories. In this study, two models, EMS-2001 and SMOKE, were applied with their default internal data sets to process a common inventory database for a high ozone (O₃) episode over the eastern United States using the Carbon Bond IV (CB4) chemical speciation mechanism. A comparison of the emissions processed by these systems shows differences in all three of the major processing steps performed by the two models (i.e., in temporal allocation, spatial allocation, and chemical speciation). Results from a simulation with a photochemical model using these two sets of emissions indicate differences on the order of ±20 ppb in the predicted 1-hr daily maximum O₃ concentrations. It is therefore critical to develop and implement more common and synchronized temporal, spatial, and speciation cross-reference systems such that the processes within each emissions model converge toward reasonably similar results. This would also help to increase confidence in the validity of photochemical grid model results by reducing one aspect of modeling uncertainty.     

Application and Evaluation of Two Air Quality Models for Particulate Matter for a Southeastern U.S. Episode

Abstract

The Models-3 Community Multiscale Air Quality (CMAQ) Modeling System and the Particulate Matter Comprehensive Air Quality Model with extensions (PMCAMx) were applied to simulate the period June 29–July 10, 1999, of the Southern Oxidants Study episode with two nested horizontal grid sizes: a coarse resolution of 32 km and a fine resolution of 8 km. The predicted spatial variations of ozone (O₃), particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM_2.5), and particulate matter with an aerodynamic diameter less than or equal to 10 μm (PM₁₀) by both models are similar in rural areas but differ from one another significantly over some urban/suburban areas in the eastern and southern United States, where PMCAMx tends to predict higher values of O₃ and PM than CMAQ. Both models tend to predict O₃ values that are higher than those observed. For observed O₃ values above 60 ppb, O₃ performance meets the U.S. Environmental Protection Agency's criteria for CMAQ with both grids and for PMCAMx with the fine grid only. It becomes unsatisfactory for PMCAMx and marginally satisfactory for CMAQ for observed O₃ values above 40 ppb.

Both models predict similar amounts of sulfate (SO₄ ^2?) and organic matter, and both predict SO₄ ^2? to be the largest contributor to PM_2.5. PMCAMx generally predicts higher amounts of ammonium (NH₄ ⁺), nitrate (NO₃ ^?), and black carbon (BC) than does CMAQ. PM performance for CMAQ is generally consistent with that of other PM models, whereas PMCAMx predicts higher concentrations of NO₃ ^?,NH₄ ⁺, and BC than observed, which degrades its performance. For PM₁₀ and PM_2.5 predictions over the southeastern U.S. domain, the ranges of mean normalized gross errors (MNGEs) and mean normalized bias are 37–43% and –33–4% for CMAQ and 50–59% and 7–30% for PMCAMx. Both models predict the largest MNGEs for NO₃ ^? (98–104% for CMAQ, 138–338% for PMCAMx). The inaccurate NO₃ ^? predictions by both models may be caused by the inaccuracies in the ammonia emission inventory and the uncertainties in the gas/particle partitioning under some conditions. In addition to these uncertainties, the significant PM overpredictions by PMCAMx may be attributed to the lack of wet removal for PM and a likely underprediction in the vertical mixing during the daytime.