Effects of low concentrations of herbicides on full-season, field-grown potatoes

Current phytotoxicity plant test protocols for US pesticide registration require testing for effects on seedling emergence and early growth without regard to other important factors, such as plant reproduction. Yield and quality reduction can have significant economic and ecological effects. Therefore, field trials were conducted to determine if potato (Solanum tubersum L.) vegetative growth and tuber yield and quality were affected by herbicides at below recommended field rates. Potatoes were grown in fields at the Oregon State University Horticulture Farm with herbicides applied at below recommended field application rates 14 d after emergence (DAE) or at 28 DAE. Plant height was measured before and 14 d after application. Visual foliar injury was rated 14 d after application, and tuber yield and quality parameters were measured at harvest (120 DAE). Some tubers were grown in the greenhouse the following year to determine if there were carry-over effects. Potato vegetation and tuber yield quality were generally more affected by herbicides applied at 14 DAE than at 28 DAE. Tuber yield and quality parameters were more affected by lower herbicide rates than were plant height or injury. There were significant yield losses caused by low rates of sulfometuron methyl and imazapyr and, to a lesser extent, with glyphosate and cloransulam-methyl. Bromoxynil and MCPA ((4-chloro-2-methylphenoxy)acetic) acid had little effect on the plants. Vegetative responses did not accurately predict yield and quality responses of tubers; therefore, reproductive responses should be considered in phytotoxicity test protocols for pesticide registration in the USA.     

Carbon storage of different soil-size fractions in Florida silvopastoral systems

Compared with open (treeless) pasture systems, silvopastoral agroforestry systems that integrate trees into pasture production systems are likely to enhance soil carbon (C) sequestration in deeper soil layers. To test this hypothesis, total soil C contents at six soil depths (0-5, 5-15, 15-30, 30-50, 50-75, and 75-125 cm) were determined in silvopastoral systems with slash pine (Pinus elliottii) + bahiagrass (Paspalum notatum) and an adjacent open pasture (OP) with bahiagrass at four sites, representing Spodosols and Ultisols, in Florida. Soil samples from each layer were fractionated into three classes (250-2000, 53-250, and <53 microm), and the C contents in each were determined. Averaged across four sites and all depths, the total soil organic carbon (SOC) content was higher by 33% in silvopastures near trees (SP-T) and by 28% in the alleys between tree rows (SP-A) than in adjacent open pastures. It was higher by 39% in SP-A and 20% in SP-T than in open pastures in the largest fraction size (250-2000 microm) and by 12.3 and 18.8%, respectively, in the intermediate size fraction (53-250 microm). The highest SOC increase (up to 45 kg m(-2)) in whole soil of silvopasture compared with OP was at the 75- to 125-cm depth at the Spodosol sites. The results support the hypothesis that, compared with open pastures, silvopastures contain more C in deeper soil layers under similar ecological settings, possibly as a consequence of a major input to soil organic matter from decomposition of dead tree-roots.     

Trends in fine particle concentration and chemical composition in southern California

Airborne fine particle mass concentrations in Southern California have declined in recent years. Trends in sulfate and elemental carbon (EC) particle concentrations over the period 1982-1993 are consistent with this overall improvement in air quality and help to confirm some of the reasons for the changes that are seen. Fine particle sulfate concentrations have declined as a strict sulfur oxides (SOx) emission control program adopted in 1978 was implemented over time. Fine particle elemental (black) carbon concentrations have declined over a period when newer diesel engines and improved diesel fuels have been introduced into the vehicle fleet. Organic aerosol concentrations have not declined as rapidly as the EC particle concentrations, despite the fact that catalyst-equipped cars having lower particle emission rates were introduced into the vehicle fleet alongside the diesel engine improvements mentioned above. This situation is consistent with the growth in population and vehicle miles traveled in the air basin over time. Fine particle ammonium nitrate in the Los Angeles area atmosphere contributes more than half of the fine aerosol mass concentration on the highest concentration days of the year, emphasizing both the need for accurate aerosol nitrate measurements and the likely importance of deliberate control of aerosol nitrate as a part of any serious further fine particle control program for the Los Angeles area.     

Short-term temporal variation in PM2.5 mass and chemical composition during the Atlanta Supersite Experiment, 1999

Measurements in urban Atlanta of transient aerosol events in which PM2.5 mass concentrations rapidly rise and fall over a period of 3-6 hr are reported. The data are based on new measurement techniques demonstrated at the U.S. Environmental Protection Agency (EPA) Atlanta Supersite Experiment in August 1999. These independent instruments for aerosol chemical speciation of NO3-, SO4(2-), NH4+, and organic and elemental carbon (OC and EC), reconstructed the observed hourly dry PM2.5 mass to within 20% or better. Data from the experiment indicated that transient PM2.5 events were ubiquitous in Atlanta and were typically characterized by a sudden increase of EC (soot) and OC in the early morning or SO4(2-) in the late afternoon. The frequent temporal decoupling of these events provides insights into their origins, suggesting mobile sources in metro Atlanta as the main contributor to early morning PM2.5 and more regionally located point SO2 sources for afternoon PM2.5 events. The transient events may also have health implications. New data suggest that short-term PM2.5 exposures may lead to adverse health effects. Standard integrated filter-based techniques used in PM2.5 compliance monitoring networks and in most past PM2.5 epidemiologic studies collect samples over 24-hr periods and thus are unable to capture these transient events. Moreover, health-effects studies that focus on daily PM2.5 mass alone cannot evaluate the health implications of the unique and variable chemical properties of these episodes.     

Organochlorine contaminant and stable isotope profiles in Arctic fox (Alopex lagopus) from the Alaskan and Canadian Arctic

Arctic fox (Alopex lagopus) is a circumpolar species distributed across northern Canada and Alaska. Arctic fox muscle and liver were collected at Barrow, AK, USA (n=18), Holman, NT, Canada (n=20), and Arviat, NU, Canada (n=20) to elucidate the feeding ecology of this species and relate these findings to body residue patterns of organochlorine contaminants (OCs). Stable carbon (delta 13C) and nitrogen (delta 15N) isotope analyses of Arctic fox muscle indicated that trophic position (estimated by delta 15N) is positively correlated with increasing delta 13C values, suggesting that Arctic fox with a predominantly marine-based foraging strategy occupy a higher trophic level than individuals mostly feeding from a terrestrial-based carbon source. At all sites, the rank order for OC groups in muscle was polychlorinated biphenyls (Sigma PCB) > chlordane-related compounds (Sigma CHLOR) > hexachlorocyclohexane (Sigma HCH) > total toxaphene (TOX) > or = chlorobenzenes (Sigma ClBz) > DDT-related isomers (Sigma DDT). In liver, Sigma CHLOR was the most abundant OC group, followed by Sigma PCB > TOX > Sigma HCH > Sigma ClBz > Sigma DDT. The most abundant OC analytes detected from Arctic fox muscle and liver were oxychlordane, PCB-153, and PCB-180. The comparison of delta 15N with OC concentrations indicated that relative trophic position might not accurately predict OC bioaccumulation in Arctic fox. The bioaccumulation pattern of OCs in the Arctic fox is similar to the polar bear. While Sigma PCB concentrations were highly variable, concentrations in the Arctic fox were generally below those associated with the toxicological endpoints for adverse effects on mammalian reproduction. Further research is required to properly elucidate the potential health impacts to this species from exposure to OCs.     

Aquatic microcosm assessment of the effects of tylosin on Lemna gibba and Myriophyllum spicatum

Tylosin is a macrolide antibiotic commonly used for therapeutic treatment and prophylaxis in livestock. As part of a larger ecotoxicological study, the potential phytotoxic effects of tylosin on the rooted macrophyte Myriophyllum spicatum and the floating macrophyte Lemna gibba were assessed under semi-field conditions using 15 12 000-L microcosms. Concentrations of 0, 10, 30, 300 microg/L (n = 3), and 600, 1000, and 3000 microg/L (n = 1) were evaluated as part of separate ANOVA and regression analyses over an exposure period of 35 days. Fate of tylosin was monitored over time in the highest three treatments, where dissipation followed pseudo-first order kinetics with associated half-lives ranging from 9 to 10 days. For both M. spicatum and L. gibba, tylosin was found to cause no biologically significant changes to any endpoint assessed compared to controls at a Type I error rate of 0.1. However, subsequent power analyses revealed that there was generally insufficient power to declare that there were no significant differences at a Type II error rate of 0.2. Conclusions concerning biologically significant impacts were therefore further assessed based on other statistical criteria including comparisons of percent differences between replicated treatments and controls, minimum significant and minimum detectable differences, and coefficients of variation. Based on these criteria, at an ecological effect size of >20% change, tylosin was concluded to elicit no biologically or ecologically significant toxicity to M. spicatum or L. gibba. A hazard quotient assessment indicated that tylosin poses little risk to either species of macrophyte, with an HQ value calculated to be nearly three orders of magnitude below 1 (0.002).     

Epidemiology and patterns of facial fractures due to road traffic accidents in Taiwan: A 15-year retrospective study

Objective: The facial region is a commonly fractured site, but the etiology varies widely by country and geographic region. To date, there are no population-based studies of facial fractures in Taiwan.

Methods: We conducted a retrospective study of patients diagnosed with facial fracture and registered in the National Health Insurance Research Database of Taiwan between 1997 and 2011. The epidemiological characteristics of this cohort were analyzed, including the etiology, fracture site, associated injuries, and sex and age distributions.

Results: A total of 6,013 cases were identified that involved facial fractures. Most patients were male (69.8%), aged 18–29 years (35.8%), and had fractures caused by road traffic accidents (RTAs; 55.2%), particularly motorcycle accidents (31.5%). Falls increased in frequency with advancing age, reaching 23.9% among the elderly (age > 65 years). The most common sites of involvement were the malar and maxillary bones (54.0%), but nasal bone fractures were more common among those younger than 18 years.

Conclusion: Most facial injuries in Taiwan occur in young males and typically result from RTAs, particularly involving motorcycles. However, with increasing age, there is an increase in the proportion of facial injuries due to falls.     

Options for water storage and rainwater harvesting to improve health and resilience against climate change in Africa

West and East Africa experience high variability of rainfall that is expected to increase with climate change. This results in fluctuations in water availability for food production and other socioeconomic activities. Water harvesting and storage can mitigate the adverse effects of rainfall variability. But past studies have shown that when investments in water storage are not guided by environmental health considerations, the increased availability of open water surface may increase the transmission of water-related diseases. This is demonstrated for schistosomiasis associated with small reservoirs in Burkina Faso, and for malaria in Ethiopia around large dams, small dams, and water harvesting ponds. The concern is that the rush to develop water harvesting and storage for climate change adaptation may increase the risk for already vulnerable people, in some cases more than canceling out the benefits of greater water availability. Taking health issues into account in a participatory approach to planning, design, and management of rainwater harvesting and water storage, as well as considering the full range of water storage options would enable better opportunities for enhancing resilience against climate change in vulnerable populations in sub-Saharan Africa.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.