A tracer study of headspace ventilation in a collector sewer

A field-scale tracer test was conducted to evaluate in-situ ventilation rates in a major collector sewer. The sewer under study was approximately 11 km long and ranged from 0.61 to 2.1 m in diameter. For the purposes of the tracer testing, the collector was divided into four reaches, each of which was tested individually. The tracer test involved injecting a measured volume of CO gas into a manhole over a short time period. CO concentrations were then measured in the collector headspace at selected manholes along the length of the reach. The technique employed successfully measured average headspace velocities over extended lengths of the collector. In a section that had a relatively stagnant headspace, approximately 1.1 km of sewer could be evaluated, with substantial tracer loss attributed to losses to manholes. In a section of the sewer with elevated headspace velocities, a section approximately 7.0 km long was successfully tested with one injection of tracer gas. The velocities observed in the collector varied substantially with time and location in the collector. The lowest velocities measured were in the upstream sections, with a minimum observed value of 3.8 m/min. The highest velocities were observed in the downstream sections, with a maximum value of 31.5 m/min. The presence of a substantial drop structure appeared to reduce the headspace velocity in the upstream reach. In general, there was an increasing trend in gas-phase flows with distance along the length of the collector. Flows at the discharge end of the collector were almost 2 orders of magnitude greater than those at the beginning.     

Behaviour that influences dispersal and connectivity in the small,young larvae of a reef fish

Determining the scale of larval dispersal and population connectivity in demersal fishes is a major challenge in marine ecology. Historically, considerations of larval dispersal have ignored the possible contributions of larval behaviour, but we show here that even young, small larvae have swimming, orientation and vertical positioning capabilities that can strongly influence dispersal outcomes. Using young (11–15 days), relatively poorly developed (8–10 mm), larvae of the pomacentrid damselfish, Amblyglyphidodon curacao (identified using mitochondrial DNA), we studied behaviour relevant to dispersal in the laboratory and sea on windward and leeward sides of Lizard Island, Great Barrier Reef. Behaviour varied little with size over the narrow size range examined. Critical speed was 27.5 ± 1.0 cm s⁻¹ (30.9 BL s⁻¹), and in situ speed was 13.6 ± 0.6 cm s⁻¹. Fastest individuals were 44.6 and 25.0 cm s⁻¹, for critical and in situ speeds, respectively. In situ speed was about 50% of critical speed and equalled mean current speed. Unfed larvae swam 172 ± 29 h at 8–10 cm s⁻¹ (52.0 ± 8.6 km), and lost 25% wet weight over that time. Vertical distribution differed between locations: modal depth was 2.5–5.0 and 10.0–12.5 m at leeward and windward sites, respectively. Over 80% of 71 larvae observed in situ had directional swimming trajectories. Larvae avoided NW bearings, with an overall mean SE swimming direction, regardless of the direction to nearest settlement habitat. Larvae made smaller changes between sequential bearings of swimming direction when swimming SE than in other directions, making it more likely they would continue to swim SE. When swimming NW, 62% of turns were left (more than in other directions), which would quickly result in swimming direction changing away from NW. This demonstrates the larvae knew the direction in which they were swimming and provides insight into how they achieved SE swimming direction. Although the cues used for orientation are unclear, some possibilities seemingly can be eliminated. Thus, A. curacao larvae near Lizard Island, on average swam into the average current at a speed equivalent to it, could do this for many hours, and chose different depths in different locations. These behaviours will strongly influence dispersal, and are similar to behaviour of other settlement-stage pomacentrid larvae that are older and larger.     

Global warming contributions from wheat,sheep meat and wool production in Victoria,Australia – a life cycle assessment

This paper compares the life cycle global warming potential of three of Australia’s important agricultural production activities – the production of wheat, meat and wool in grazed subterranean clover (sub-clover) dominant pasture and mixed pasture (perennial ryegrass/phalaris/sub-clover/grass and cape weed) systems. Two major stages are presented in this life cycle assessment (LCA) analysis: pre-farm, and on-farm. The pre-farm stage includes greenhouse gas (GHG) emissions from agricultural machinery, fertilizer, and pesticide production and the emissions from the transportation of these inputs to paddock. The on-farm stage includes GHG emissions due to diesel use in on-farm transport and processing (e.g. seeding, spraying, harvesting, topdressing, sheep shearing), and non-CO₂ (nitrous oxide (N₂O), and methane (CH₄)) emissions from pastures and crop grazing of lambs.The functional unit of this life cycle analysis is the GHG emissions (carbon dioxide equivalents – CO₂ -e) from 1 kg of wheat, sheep meat and wool produced from sub-clover, wheat and mixed pasture plots. The GHG emissions (e.g. CO₂, N₂O and CH₄ emission) from the production, transportation and use of inputs (e.g. fertilizer, pesticide, farm machinery operation) during pre-farm and on-farm stages are also included. The life cycle GHG emissions of 1 kg of wool is significantly higher than that of wheat and sheep meat. The LCA analysis identified that the on-farm stage contributed the most significant portion of total GHG emissions from the production of wheat, sheep meat and wool. This LCA analysis also identified that CH₄ emissions from enteric methane production and from the decomposition of manure accounted for a significant portion of the total emissions from sub-clover and mixed pasture production, whilst N₂O emissions from the soil have been found to be the major source of GHG emissions from wheat production.     

Estimating Watershed Level Nonagricultural Pesticide Use From Golf Courses Using Geospatial Methods1

Abstract: Limited information exists on pesticide use for nonagricultural purposes, making it difficult to estimate pesticide loadings from nonagricultural sources to surface water and to conduct environmental risk assessments. A method was developed to estimate the amount of pesticide use on recreational turf grasses, specifically golf course turf grasses, for watersheds located throughout the conterminous United States (U.S.). The approach estimates pesticide use: (1) based on the area of recreational turf grasses (used as a surrogate for turf associated with golf courses) within the watershed, which was derived from maps of land cover, and (2) from data on the location and average treatable area of golf courses. The area of golf course turf grasses determined from these two methods was used to calculate the percentage of each watershed planted in golf course turf grass (percent crop area, or PCA). Turf‐grass PCAs derived from the two methods were used with recommended application rates provided on pesticide labels to estimate total pesticide use on recreational turf within 1,606 watersheds associated with surface‐water sources of drinking water. These pesticide use estimates made from label rates and PCAs were compared to use estimates from industry sales data on the amount of each pesticide sold for use within the watershed. The PCAs derived from the land‐cover data had an average value of 0.4% of a watershed with minimum of 0.01% and a maximum of 9.8%, whereas the PCA values that are based on the number of golf courses in a watershed had an average of 0.3% of a watershed with a minimum of <0.01% and a maximum of 14.2%. Both the land‐cover method and the number of golf courses method produced similar PCA distributions, suggesting that either technique may be used to provide a PCA estimate for recreational turf. The average and maximum PCAs generally correlated to watershed size, with the highest PCAs estimated for small watersheds. Using watershed specific PCAs, combined with label rates, resulted in greater than two orders of magnitude over‐estimation of the pesticide use compared to estimates from sales data.     

Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.