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.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Chlorinated solvents in a petrochemical wastewater treatment plant: an assessment of their removal using self-organising maps

The self-organising map approach was used to assess the efficiency of chlorinated solvent removal from petrochemical wastewater in a refinery wastewater treatment plant. Chlorinated solvents and inorganic anions (11 variables) were determined in 72 wastewater samples, collected from three different purification streams. The classification of variables identified technical solvents, brine from oil desalting and runoff sulphates as pollution sources in the refinery, affecting the quality of wastewater treatment plant influent. The classification of samples revealed the formation of five clusters: the first three clusters contained samples collected from the drainage water, process water and oiled rainwater treatment streams. The fourth cluster consisted mainly of samples collected after biological treatment, and the fifth one of samples collected after an unusual event. SOM analysis showed that the biological treatment step significantly reduced concentrations of chlorinated solvents in wastewater.     

Effects of Urban Spatial Structure,Sociodemographics, and Climate on Residential Water Consumption in Hillsboro,Oregon1

House-Peters, Lily, Bethany Pratt, and Heejun Chang, 2010. Effects of Urban Spatial Structure, Sociodemographics, and Climate on Residential Water Consumption in Hillsboro, Oregon. Journal of the American Water Resources Association (JAWRA) 46(3):461-472. DOI: 10.1111/j.1752-1688.2009.00415.x Abstract: In the Portland metropolitan area, suburban growth in cities such as Hillsboro is projected to increase as people seek affordable housing near a burgeoning metropolis. The most significant determinants for increases in water demand are population growth, climate change, and the type of urban development that occurs. This study analyzes the spatial patterns of single family residential (SFR) water consumption in Hillsboro, Oregon, at the census block scale. The following research questions are addressed: (1) What are the significant determinants of SFR water consumption in Hillsboro, Oregon? (2) Is SFR water demand sensitive to drought conditions and interannual climate variation? (3) To what magnitude do particular census blocks react to drought conditions and interannual climate variation? Using ordinary least squares multiple regression and spatial regression methods, we found that base use, representing indoor water use, is dependent on household size and that seasonal use, representing external water use is dependent on both education level and the size of the property’s outdoor space. Spatial analysis techniques determined that although the water demand of the study area as a whole is not sensitive to drought conditions, certain individual census blocks do respond with a higher magnitude of water use. The most climate-sensitive census blocks tend to contain newer and larger homes, and have higher property values and more affluent and well-educated residents.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.     

ANALYSIS AND MODELING OF LONG-TERM STREAM TEMPERATURES ON THE STEAMBOAT CREEK BASIN,OREGON: IMPLICATIONS FOR LAND USE AND FISH HABITAT1

ABSTRACT: Steamboat Creek basin is an important source of timber and provides crucial spawning and rearing habitat for anadromous steelhead trout (Oncorhynchus mykiss). Because stream temperatures are near the upper limit of tolerance for the survival of juvenile steelhead, the possible long-term effect of clear-cut logging on stream temperatures was assessed. Twenty-year (1969–1989) records of summer stream temperature and flow from four tributaries and two reaches of Steamboat Creek and Boulder Creek (a nearby unlogged watershed) were analyzed. Logging records for the Steamboat Creek basin and air temperature records also were used in the analysis. A time-series model of the components of stream temperature (seasonal cycle of solar radiation, air temperature, streamflow, an autoregressive term of order 1, and a linear trend variable) was fitted to the water-temperature data. The linear trend variable was significant in all the fitted models except Bend Creek (a tributary fed by cool ground-water discharge) and Boulder Creek. Because no trends in either climate (i.e., air temperature) or streamflow were found in the data, the trend variable was associated with the pre-1969 loss and subsequent regrowth of riparian vegetation and shading canopies.     

CONFLICTING PERSPECTWES ABOUT DETECTION LIMITS AND ABOUT THE CENSORING OF ENVIRONMENTAL DATA1

ABSTRACT: Current conventions for reporting analytical results from environmental samples brings the objectives of laboratory scientists into conflict with those of environmental scientists. The objective of chemical analyses is to provide estimates of the true composition of samples. Reported results must reflect the analytical uncertainty. Current conventions require left-censoring of those results below the Limit of Detection. The objective of statistical interpretation of environmental data is to provide estimates of the characteristics of ecosystems. Such statistical analyses are often confounded by left-censoring of analytical results. We review the different points of view and propose a compromise which recognizes these conflicting perspectives.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

STATISTICAL METHODS AND SAMPLING DESIGN FOR ESTIMATING STEP TRENDS IN SURFACE-WATER QUALITY1

ABSTRACT: This paper addresses two components of the problem of estimating the magnitude of step trends in surface water quality. The first is finding a robust estimator appropriate to the data characteristics expected in water-quality time series. The Hodges-Lehmann class of estimators is found to be robust in comparison to other nonparametric and moment-based estimators. A seasonal Hodges-Lehmann estimator is developed and shown to have desirable properties. Second, the effectiveness of various sampling strategies are examined using Monte Carlo simulation coupled with application of this estimator. The simulation is based on a large set of total phosphorus data from the Potomac River. To assure that the simulated records have realistic properties, the data are modeled in a multiplicative fashion incorporating flow, hysteresis, seasonal, and noise components. The results demonstrate the importance of balancing the length of the two sampling periods and balancing the number of data values between the two periods. The inefficiency of sampling at frequencies much in excess of 12 samples per year is demonstrated. Rotational sampling designs are discussed, and efficient designs, at least for this river and constituent, are shown to involve more than one year of active sampling at frequencies of about 12 per year.     

A TAU-LIKE TEST FOR TREND IN THE PRESENCE OF MULTIPLE CENSORING POINTS1

ABSTRACT: A common problem arises in testing for trends in water quality when observations are reported as “less than detection limit.” If a single detection limit is used for the entire study, existing non-parametric statistical methods, modified for ties, are applicable. If, however, the detection limit varies during the course of the study, resulting in multiple detection limits, then the commonly used trend detection methods are not appropriate. A statistic similar to Kendall's tau, but based on expected ranks, is proposed. Monte Carlo simulations show that the normal approximation to the distribution of this statistic is quite good, even for small samples and a large proportion of censored observations. The statistic is also shown to have greater power than the ad-hoc method of treating all observations less than the target censored observation as tied.