Point Sampling Digital Imagery with ‘Samplepoint’

Measuring percent occurrence of objects from digital images can save time and expense relative to conventional field measurements. However, the accuracy of image analysis had, until now, not reached the level of the best conventional field measurements. Additionally, most image-analysis software programs require advanced user training to successfully analyze images. Here we present a new software program, ‘SamplePoint,’ that provides the user a single-pixel sample point and the ability to view and identify the pixel context. We found SamplePoint to allow accuracy comparable with the most accurate field-methods for ground-cover measurements. Expert use of the program requires minimal training and its ease of use allows rapid measurements from image data. We recommend SamplePoint for calibrating the threshold-detection level of image-analysis software or for making direct measurements of percent occurrence from digital images.     

A Rapid Method for Assessing the Environmental Performance of Commercial Farms in the Pampas of Argentina

The generation of reliable updated information is critical to support the harmonization of socio-economic and environmental issues in a context of sustainable development. The agro-environmental assessment and management of agricultural systems often relies on indicators that are necessary to make sound decisions. This work aims to provide an approach to (a) assess the environmental performance of commercial farms in the Pampas of Argentina, and (b) propose a methodological framework to calculate environmental indicators that can rapidly be applied to practical farming. 120 commercial farms scattered across the Pampas were analyzed in this study during 2002 and 2003. Eleven basic indicators were identified and calculation methods described. Such indicators were fossil energy (FE) use, FE use efficiency, nitrogen (N) balance, phosphorus (P) balance, N contamination risk, P contamination risk, pesticide contamination risk, soil erosion risk, habitat intervention, changes in soil carbon stock, and balance of greenhouse gases. A model named Agro-Eco-Index was developed on a Microsoft-Excel support to incorporate on-farm collected data and facilitate the calculation of indicators by users. Different procedures were applied to validate the model and present the results to the users. Regression models (based on linear and non-linear models) were used to validate the comparative performance of the study farms across the Pampas. An environmental dashboard was provided to represent in a graphical way the behavior of farms. The method provides a tool to discriminate environmentally friendly farms from those that do not pay enough attention to environmental issues. Our procedure might be useful for implementing an ecological certification system to reward a good environmental behavior in society (e.g., through tax benefits) and generate a commercial advantage (e.g., through the allocation of green labels) for committed farmers.     

A Simulation Study to Assess the Sensitivity of a Forest Health Monitoring Network to Outbreaks of Defoliating Insects

The sensitivity of the United States Forest Health Monitoring network to outbreaks of defoliating insects was examined by means of a simulation study. A model constructed specifically for the study was used to generate a wide variety of defoliation patterns in forested landscapes. Forest configuration was that of Minnesota, USA, as expressed by the GAP land cover classification. Combinations of model parameters were based on a Latin Hypercube sample. The relationship between the average number of plots defoliated and outbreak characteristics was then examined via multiple regression. Both theoretical and model results pointed to a strong, linear relationship between the average number of plots defoliated and outbreak size. Model results provided additional insight, suggesting a significant relationship between the average number of plots defoliated and other outbreak characteristics after outbreak size was taken into account.     

Little Egrets (Egretta Garzetta) and Trace-Metal Contamination in Wetlands of China

The rapidly developing Asian countries may face threatening environmental contamination, that however remains largely unassessed. We studied persistent trace elements in three wetlands, Poyang Lake, relatively unpolluted, and Tai Lake, and Pearl River Delta, selected as hotspots of pollution in Central China. We adopted as indicator the Little Egret, a widespread colonial waterbird, and during 1999 and 2000 we collected and analysed the same samples as for a parallel study we conducted in Pakistan, e.g. eggs, body feathers of chicks, prey spontaneously regurgitated by the chicks, and sediments at the areas most used by foraging egrets. The levels of trace-metals at our three study areas were similar, or within the range, of those found in the few other studies for East Asia, with few exceptions. The concentrations of the various elements were below the threshold that may affect the survival or reproduction of the birds, and even the highest concentrations relative to background, i.e. Se in eggs, and Hg in feathers at Pearl Delta, do not pose toxic hazards. In sediments, the levels of trace elements were lower than the critical levels assumed for contaminated soil, except for alarming high levels of As at Poyang. These results do not confirm our expectation, that Poyang was relatively uncontaminated, while Tai and Pearl were polluted. Although trace metal concentration differed significantly among the three study areas, these differences were minor and were not consistent among elements and samples. The bioaccumulation ratios from sediments through prey, feather and egg, were consistent with our previous findings for Pakistan. Only Hg exhibited high bioaccumulation, while Se and Zn had low accumulation, and the other elements no accumulation. This reasserts that feathers of predators such as egrets, may be more sensitive indicators of environmental contamination for the elements subject to bioaccumulation, whereas the sediments or the organisms low in the food chain are better indicators for the other elements.     

Comparison of 24 h averaged VOC monitoring results for residential indoor and outdoor air using Carbopack X-filled diffusive samplers and active sampling--a pilot study

Analytical results obtained by thermal desorption GC/MS for 24 h diffusive sampling of 11 volatile organic compounds (VOCs) are compared with results of time-averaged active sampling at a known constant flow rate. Air samples were collected with co-located duplicate diffusive sampling tubes and one passivated canister. A total of eight multiple-component sampling events took place at fixed positions inside and outside three private homes. Subsequently, a known amount of sample air was transferred from the canister to an adsorbent tube for analysis by thermal desorption GC/MS. Results for the 11 most prevalent compounds--Freon 11, 1,3-butadiene, benzene, toluene, tetrachloroethene, ethylbenzene, m,p-xylene, o-xylene, 4-ethyltoluene, 1,3,5-trimethylbenzene, and p-dichlorobenzene--show that the ratio of average study values (diffusive sampling to active sampling) is 0.92 with 0.70 and 1.14 extreme ratios. Absolute percent difference for duplicate samples using diffusive sampling was <10% for the four most prevalent compounds. Agreement between the two sampling approaches indicates that the prediction of approximately constant diffusive sampling rates based on previous laboratory studies is valid under the field conditions.     

Development of an extraction method for perchlorate in soils

Perchlorate originates as a contaminant in the environment from its use in solid rocket fuels and munitions. The current US EPA methods for perchlorate determination via ion chromatography using conductivity detection do not include recommendations for the extraction of perchlorate from soil. This study evaluated and identified appropriate conditions for the extraction of perchlorate from clay loam, loamy sand, and sandy soils. Based on the results of this evaluation, soils should be extracted in a dry, ground (mortar and pestle) state with Milli-Q water in a 1 ratio 1 soil ratio water ratio and diluted no more than 5-fold before analysis. When sandy soils were extracted in this manner, the calculated method detection limit was 3.5 microg kg(-1). The findings of this study have aided in the establishment of a standardized extraction method for perchlorate in soil.     

Unintentional non-traffic injury and fatal events: Threats to children in and around vehicles

Objective: There have been substantial reductions in motor vehicle crash–related child fatalities due to advances in legislation, public safety campaigns, and engineering. Less is known about non-traffic injuries and fatalities to children in and around motor vehicles. The objective of this study was to describe the frequency of various non-traffic incidents, injuries, and fatalities to children using a unique surveillance system and database.

Methods: Instances of non-traffic injuries and fatalities in the United States to children 0–14 years were tracked from January 1990 to December 2014 using a compilation of sources including media reports, individual accounts from families of affected children, medical examiner reports, police reports, child death review teams, coroner reports, medical professionals, legal professionals, and other various modes of publication.

Results: Over the 25-year period, there were at least 11,759 events resulting in 3,396 deaths. The median age of the affected child was 3.7 years. The incident types included 3,115 children unattended in hot vehicles resulting in 729 deaths, 2,251 backovers resulting in 1,232 deaths, 1,439 frontovers resulting in 692 deaths, 777 vehicles knocked into motion resulting in 227 deaths, 415 underage drivers resulting in 203 deaths, 172 power window incidents resulting in 61 deaths, 134 falls resulting in 54 deaths, 79 fires resulting in 41 deaths, and 3,377 other incidents resulting in 157 deaths.

Conclusions: Non-traffic injuries and fatalities present an important threat to the safety and lives of very young children. Future efforts should consider complementary surveillance mechanisms to systematically and comprehensively capture all non-traffic incidents. Continued education, engineering modifications, advocacy, and legislation can help continue to prevent these incidents and must be incorporated in overall child vehicle safety initiatives.     

Before the crash: A systematic analysis of police narratives to determine the prevalence of pre-crash factors in fatality road transport crashes

Abstract

Objectives: The objectives of this study were to identify the prevalence of pre-crash factors that were present in fatal road transport crashes for the deceased and counterpart road user.

Methods: The study is a retrospective population-based case series study of transport-related deaths reported to coroners in Australia from 2013 to 2016. Data was extracted from the National Coronial Information System.

Results: In total, 6,137 fatality crashes occurred during the study period. Police reports were available for 5,523 crashes (89.9%). The most frequently reported pre-crash factors reported behaviour specifically drivers (e.g., driving without a license or while license was disqualified). Presence of intoxicating substances were also reported in the deceased and counterparts. Analyses of toxicology reports are continuing to determine if rates are comparable to level of use in community.

Conclusions: Coronial report provide detailed information that may be pertinent to understanding and potentially preventing crashes. Also emerging from the data is the extent of pre-crash factors that relate to illegal or deviant behavior and whether these are contextual or contributory factors.     

Cycle Aware Package: Using Haddon’s matrix to reduce novice driver crashes with cyclists

Abstract

Objectives: What are we teaching drivers about safely interacting with cyclists? This is the main objective of the Cycle Aware project. As part of the study, a teaching and learning package is being developed to fill this gap in Australia.

Methods: Cycle Aware is a major national project that takes a mixed methods approach to investigate how cyclists are taught to share the road with cyclists when learning to drive. The project has five stages: (1) a national review of the driver licensing documentation; (2) interviews with key stakeholders (n?=?35) involved with novice driver education; (3) analysis of cyclist-novice driver crash data to identify the most frequent crash types; (4) develop a teaching and learning package (Cycle Aware Package), and; trial and evaluate Cycle Aware Package.

Results: Overall, drivers are taught little about sharing the road with cyclists. Representation of cyclists in government documentation is mixed and mostly negative and interviews identified driver-cyclist tensions related to attitudes and awareness of sharing the road. Crash types were similar among novice and experienced drivers. The Cycle Aware Package is being finalised and will be trialled in South Australia and Northern Territory in early 2019.

Conclusions: Cycle Aware will provide new insights into the way drivers are taught to share the road with cyclists. The Cycle Aware Package with interactive online content and driver competencies will facilitate a new approach to addressing this gap among Australia drivers.     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.