Children's behavioral traits and risk of injury: Analyses from a case-control study of agricultural households

Problem

Children on family agricultural operations have high risk of injury. The association between children's behavioral traits and their risk of injury is not well understood.

Method

Data from the Regional Rural Injury Study-II were used to assess behavioral risk factors for injury to children ages six to < 20 years. A total of 379 injury events (cases) and 1,562 randomly selected controls were identified. Adjusted odds ratios (OR) and 95% confidence intervals (CI), calculated using logistic regression, were used to estimate injury risk in reference to behavioral traits.

Results

Injury risks were greater for children with high levels of depressive symptoms (OR = 1.9, CI = 1.0-3.7) and aggression (OR = 1.6, CI = 0.9-2.7), and low levels of careful/cautious behavior (OR = 1.8, CI = 1.1-2.9). Children with low levels of self-regulation had reduced risks (OR = 0.4, CI = 0.2-0.8).

Discussion

Results suggest that children's behaviors affect their risk of agricultural injury. Additional research could elucidate mechanisms and inform interventions.

Impact on industry

The development of multifaceted, sustainable approaches for prevention is necessary for this unique population. These findings suggest a need for interventions that incorporate specific behavior-related risk factors in the context of family farms and ranches.     

Nitrate Dynamics in Two Streams Impacted by Wastewater Treatment Plant Discharge: Point Sources or Sinks?

We examined nitrate processing in headwater stream reaches downstream of two wastewater treatment plant outfalls during low streamflow. Our objectives were to quantify nitrate mass flux before and after effluent discharge and to use field and laboratory techniques to assess the mechanism of nitrate uptake. Microcosm experiments were utilized to determine the location of nitrate processing, and molecular biomarkers were used to detect and quantify microbial denitrification. At one site, downstream nitrate mass flux was significantly (p = 0.01) lower than sum of upstream and wastewater effluent fluxes, indicating rapid stream assimilation of incoming nitrate in the vicinity of the point source. Microcosm experiments supported the theory that nitrate processing occurs in sediments. Molecular assays for denitrifcation‐associated functional genes nosZ, nirS, and nirK, provided evidence that effluent contained enriched denitrifying communities relative to ambient stream water. Nitrate loss at the site with greater uptake was correlated with sulfate loss (p < 0.01; r² = 0.86), suggesting a possible link between sulfate reducing bacteria and denitrifying bacterial communities. Results suggest there is an opportunity to better understand nitrate dynamics in cases where point sources may act as point sinks under specific sets of conditions.     

Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation1

Greenberg, Jonathan Asher, Erin L. Hestir, David Riano, George J. Scheer, and Susan L. Ustin, 2012. Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large‐Scale Riparian Deforestation. Journal of the American Water Resources Association (JAWRA) 48(5): 939‐948. DOI: 10.1111/j.1752‐1688.2012.00664.x Abstract: Riparian vegetation provides shade from insolation to stream channels. A consequence of removing vegetation may be an increase in insolation that can increase water temperatures and negatively impact ecosystem health. Although the mechanisms of riparian shading are well understood, spatially explicit, mechanistic models of shading have been limited by the data requirements of precisely describing the three‐dimensional structure of a riparian corridor. Remotely acquired, high spatial resolution LiDAR data provide detailed three‐dimensional vegetation structure and terrain topography over large regions. By parameterizing solar radiation models that incorporate terrain shadowing with LiDAR data, we can produce spatially explicit estimates of insolation. As a case study, we modeled the relative change in insolation on channels in the Sacramento‐San Joaquin River Delta under current conditions and under a hypothesized deforested Delta using classified LiDAR, rasterized at a 1‐m resolution. Our results suggest that the removal of levee vegetation could result in a 9% increase in solar radiation incident on Delta waters, and may lead to water temperature increases. General, coarse‐scale channel characteristics (reach width, azimuth, levee vegetation cover, and height) only accounted for 72% of the variation in the insolation. This indicates that the detailed information derived from LiDAR data has greater explanatory power than coarser reach‐scale metrics often used for insolation estimates.     

A Process-Based Hierarchical Framework for Monitoring Glaciated Alpine Headwaters

Recent studies have demonstrated the geomorphic complexity and wide range of hydrologic regimes found in alpine headwater channels that provide complex habitats for aquatic taxa. These geohydrologic elements are fundamental to better understand patterns in species assemblages and indicator taxa and are necessary to aquatic monitoring protocols that aim to track changes in physical conditions. Complex physical variables shape many biological and ecological traits, including life history strategies, but these mechanisms can only be understood if critical physical variables are adequately represented within the sampling framework. To better align sampling design protocols with current geohydrologic knowledge, we present a conceptual framework that incorporates regional-scale conditions, basin-scale longitudinal profiles, valley-scale glacial macroform structure, valley segment-scale (i.e., colluvial, alluvial, and bedrock), and reach-scale channel types. At the valley segment- and reach-scales, these hierarchical levels are associated with differences in streamflow and sediment regime, water source contribution and water temperature. Examples of linked physical-ecological hypotheses placed in a landscape context and a case study using the proposed framework are presented to demonstrate the usefulness of this approach for monitoring complex temporal and spatial patterns and processes in glaciated basins. This approach is meant to aid in comparisons between mountain regions on a global scale and to improve management of potentially endangered alpine species affected by climate change and other stressors.     

Medium-Sulfur Coal and Fly Ash Resistivity

Electric generating plants burning medium-sulfur coal need a way to predict when ESP performance will be limited by high electrical resistivity of the collected fly ash. The main uncertainty in mathematical predictions of fly ash resistivity lies in the marginal effect of naturally occurring SO₃ vapor in the flue gas. This paper results from a project to expand the data base of SO₃/SO₂ concentrations and fly ash resistivities measured in utility fly ash precipitators. Complete data sets are presented from three plants in the Southern Company electric system. In situ resistivity data are compared with laboratory measurements and with two different mathematical predictions of resistivity based on coal and ash analyses. The revised version of the resistivity predictor gives results in good agreement with resistivity values measured both in situ and in the laboratory.     

Predictions by the Odor From Feedlots,Setback Estimation Tool (OFFSET) Compared with Observations by Neighborhood Monitors

Abstract

A comparison between predicted and observed odor intensities at 20 neighborhood residences in the vicinity of seven various livestock farms in five different Minnesota counties was made to evaluate the Odor From Feedlots, Setback Estimation Tool (OFFSET) developed by the University of Minnesota. Observations by neighborhood monitors suggest that the OFFSET-predicted separation distances for annoyance-free frequencies of 99, 98, and 97% are large enough. The observations additionally indicate that predicted distances to obtain 94 and 91% annoyance-free frequencies may be large enough for some farms, but for other farms, greater distances may be needed. For two farms in the study, no significant difference between all of the observed and predicted intensities could be found. At four sites, a significant difference was found, and at three of these the difference was considerable. Odor emission rates used in the OFFSET model seem to describe the average emission fairly well for many odor sources, but improvement may be needed for some types of sources. Possible reasons for observations of annoying odor when not predicted include fluctuations in the odor emissions, wind speed fluctuations, topographic variation between sites, sensitivity differences by neighborhood monitors, and background emissions from other sources.