Evaluation of geometrically personalized THUMS pedestrian model response against sedan–pedestrian PMHS impact test data

Objective: Evaluating the biofidelity of pedestrian finite element models (PFEM) using postmortem human subjects (PMHS) is a challenge because differences in anthropometry between PMHS and PFEM could limit a model's capability to accurately capture cadaveric responses. Geometrical personalization via morphing can modify the PFEM geometry to match the specific PMHS anthropometry, which could alleviate this issue. In this study, the Total Human Model for Safety (THUMS) PFEM (Ver 4.01) was compared to the cadaveric response in vehicle–pedestrian impacts using geometrically personalized models.

Methods: The AM50 THUMS PFEM was used as the baseline model, and 2 morphed PFEM were created to the anthropometric specifications of 2 obese PMHS used in a previous pedestrian impact study with a mid-size sedan. The same measurements as those obtained during the PMHS tests were calculated from the simulations (kinematics, accelerations, strains), and biofidelity metrics based on signals correlation (correlation and analysis, CORA) were established to compare the response of the models to the experiments. Injury outcomes were predicted deterministically (through strain-based threshold) and probabilistically (with injury risk functions) and compared with the injuries reported in the necropsy.

Results: The baseline model could not accurately capture all aspects of the PMHS kinematics, strain, and injury risks, whereas the morphed models reproduced biofidelic response in terms of trajectory (CORA score = 0.927 ± 0.092), velocities (0.975 ± 0.027), accelerations (0.862 ± 0.072), and strains (0.707 ± 0.143). The personalized THUMS models also generally predicted injuries consistent with those identified during posttest autopsy.

Conclusions: The study highlights the need to control for pedestrian anthropometry when validating pedestrian human body models against PMHS data. The information provided in the current study could be useful for improving model biofidelity for vehicle–pedestrian impact scenarios.     

A land use regression model for predicting ambient volatile organic compound concentrations in Toronto,Canada

More than 25 studies have employed land use regression (LUR) models to estimate nitrogen oxides and to a lesser extent particulate matter indicators, but these methods have been less commonly applied to ambient concentrations of volatile organic compounds (VOCs). Some VOCs have high plausibility as sources of health effects and others are specific indicators of motor vehicle exhaust. We used LUR models to estimate spatial variability of VOCs in Toronto, Canada. Benzene, n-hexane and total hydrocarbons (THC) were measured from July 25 to August 9, 2006 at 50 locations using the TraceAir organic vapor monitors. Nitrogen dioxide (NO₂) was also sampled to assess its spatial pattern agreement with VOC exposures. Buffers for land use, population density, traffic density, physical geography, and remote sensing measures of greenness and surface brightness were also tested. The remote sensing measures have the highest correlations with VOCs and NO₂ levels (i.e., explains >36% of the variance). Our regression models explain 66–68% of the variance in the spatial distribution of VOCs, compared to 81% for the NO₂ model. The ranks of agreement between various VOCs range from 48 to 63% and increases substantially – up to 75% – for the top and bottom quartile groups. Agreements between NO₂ and VOCs are much smaller with an average rank of 36%. Future epidemiologic studies may therefore benefit from using VOCs as potential toxic agents for traffic-related pollutants.     

Effects of particle size on chemical speciation and bioavailability of copper to earthworms (Eisenia fetida) exposed to copper nanoparticles

To investigate the role of particle size on the oxidation, bioavailability, and adverse effects of manufactured Cu nanoparticles (NPs) in soils, we exposed the earthworm Eisenia ferida to a series of concentrations of commercially produced NPs labeled as 20- to 40-nm or < 100-nm Cu in artificial soil media. Effects on growth, mortality, reproduction, and expression of a variety of genes associated with metal homeostasis, general stress, and oxidative stress were measured. We also used X-ray absorption spectroscopy and scanning X-ray fluorescence microscopy to characterize changes in chemical speciation and spatial distribution of the NPs in soil media and earthworm tissues. Exposure concentrations of Cu NPs up to 65 mg kg(-1) caused no adverse effects on ecologically relevant endpoints. Increases in metallothionein expression occurred at concentrations exceeding 20 mg kg(-1) of Cu NPs and concentrations exceeding 10 mg kg(-1) of CuSO4. Based on the relationship of Cu tissue concentration to metallothionein expression level and the spatial distribution and chemical speciation of Cu in the tissues, we conclude that Cu ions and oxidized Cu NPs were taken up by the earthworms. This study suggests that oxidized Cu NPs may enter food chains from soil but that adverse effects in earthworms are likely to occur only at relatively high concentrations (> 65 mg Cu kg(-1) soil).     

An information–motivation–behavioral skills assessment of curbside recycling behavior

Despite annual increases in the amount of material solid waste (MSW) that is recycled in the United States, the overall amount of MSW generated continues to increase. In an effort to identify and test specific predictors of curbside recycling behavior, the current study adapted the empirically validated Information–Motivation–Behavioral Skills model (IMB), traditionally used to predict personal health behavior, to explain curbside recycling behavior. Using structured computer assisted telephone surveys with two random community samples; structural equation models indicated that the IMB model significantly predicted curbside recycling. Data from the current study provides specific information about critical psychosocial determinants of curbside recycling behavior and public policy implications.     

Determination of specific gravity of municipal solid waste

This investigation was conducted to evaluate experimental determination of specific gravity (G_s) of municipal solid waste (MSW). Water pycnometry, typically used for testing soils was adapted for testing MSW using a large flask with 2000 mL capacity and specimens with 100–350 g masses. Tests were conducted on manufactured waste samples prepared using US waste constituent components; fresh wastes obtained prior and subsequent to compaction at an MSW landfill; and wastes obtained from various depths at the same landfill. Factors that influence specific gravity were investigated including waste particle size, compaction, and combined decomposition and stress history. The measured average specific gravities were 1.377 and 1.530 for as-prepared/uncompacted and compacted manufactured wastes, respectively; 1.072 and 1.258 for uncompacted and compacted fresh wastes, respectively; and 2.201 for old wastes. The average organic content and degree of decomposition were 77.2% and 0%, respectively for fresh wastes and 22.8% and 88.3%, respectively for old wastes. The G_s increased with decreasing particle size, compaction, and increasing waste age. For fresh wastes, reductions in particle size and compaction caused occluded intraparticle pores to be exposed and waste particles to be deformed resulting in increases in specific gravity. For old wastes, the high G_s resulted from loss of biodegradable components that have low G_s as well as potential access to previously occluded pores and deformation of particles due to both degradation processes and applied mechanical stresses. The G_s was correlated to the degree of decomposition with a linear relationship. Unlike soils, the G_s for MSW was not unique, but varied in a landfill environment due both to physical/mechanical processes and biochemical processes. Specific gravity testing is recommended to be conducted not only using representative waste composition, but also using representative compaction, stress, and degradation states.     

Modeling Inorganic Aerosols and Their Response to Changes in Precursor Concentration in Mexico City

Abstract

Based on data from the 1997 Investigación sobre Materia Particulada y Deterioro Atmosférico-Aerosol and Visibility Evaluation Research (IMADA-EVER) campaign and the inorganic aerosol model ISORROPIA, the response of inorganic aerosols to changes in precursor concentrations was calculated. The aerosol behavior is dominated by the abundance of ammonia and thus, changes in ammonia concentration are expected to have a small effect on particle concentrations. Changes in sulfate and nitrate are expected to lead to proportional reductions in inorganic fine particulate matter (PM_2.5). Comparing the predictions of ISORROPIA with the observations, the lowest bias and error are achieved when the aerosols are assumed to be in the efflorescence branch. Including crustal species reduces the bias and error for nitrate but does not improve overall model performance. The estimated response of inorganic PM_2.5 to changes in precursor concentrations is affected by the inclusion of crustal species in some cases, although average responses are comparable with and without crustal species. Observed concentrations of particle chloride suggest that gas phase concentrations of hydrogen chloride may not be negligible, and future measurement campaigns should include observations to test this hypothesis. Our ability to model aerosol behavior in Mexico City and, thus, design control strategies, is constrained primarily by a lack of observations of gas phase precursors. Future campaigns should focus in particular on better understanding the temporal and spatial distribution of ammonia concentrations. In addition, gas phase observations of nitric acid are needed, and a measure of particle water content will allow stable versus metastable aerosol behavior to be distinguished.     

Ultraviolet absorbance as a proxy for total dissolved mercury in streams

Stream water samples were collected over a range of hydrologic and seasonal conditions at three forested watersheds in the northeastern USA. Samples were analyzed for dissolved total mercury (THg_d), DOC concentration and DOC composition, and UV₂₅₄ absorbance across the three sites over different seasons and flow conditions. Pooling data from all sites, we found a strong positive correlation of THg_d to DOC (r² = 0.87), but progressively stronger correlations of THg_d with the hydrophobic acid fraction (HPOA) of DOC (r² = 0.91) and with UV₂₅₄ absorbance (r² = 0.92). The strength of the UV₂₅₄ absorbance-THg_d relationship suggests that optical properties associated with dissolved organic matter may be excellent proxies for THg_d concentration in these streams. Ease of sample collection and analysis, the potential application of in-situ optical sensors, and the possibility for intensive monitoring over the hydrograph make this an effective, inexpensive approach to estimate THg_d flux in drainage waters.     

An analysis of assessment outcomes from eight years' operation of the Australian border weed risk assessment system

The majority of Australian weeds are exotic plant species that were intentionally introduced for a variety of horticultural and agricultural purposes. A border weed risk assessment system (WRA) was implemented in 1997 in order to reduce the high economic costs and massive environmental damage associated with introducing serious weeds. We review the behaviour of this system with regard to eight years of data collected from the assessment of species proposed for importation or held within genetic resource centres in Australia. From a taxonomic perspective, species from the Chenopodiaceae and Poaceae were most likely to be rejected and those from the Arecaceae and Flacourtiaceae were most likely to be accepted. Dendrogram analysis and classification and regression tree (TREE) models were also used to analyse the data. The latter revealed that a small subset of the 35 variables assessed was highly associated with the outcome of the original assessment. The TREE model examining all of the data contained just five variables: unintentional human dispersal, congeneric weed, weed elsewhere, tolerates or benefits from mutilation, cultivation or fire, and reproduction by vegetative propagation. It gave the same outcome as the full WRA model for 71% of species. Weed elsewhere was not the first splitting variable in this model, indicating that the WRA has a capacity for capturing species that have no history of weediness. A reduced TREE model (in which human-mediated variables had been removed) contained four variables: broad climate suitability, reproduction in less or than equal to 1year, self-fertilisation, and tolerates and benefits from mutilation, cultivation or fire. It yielded the same outcome as the full WRA model for 65% of species. Data inconsistencies and the relative importance of questions are discussed, with some recommendations made for improving the use of the system.     

Elite cues,media coverage,and public concern: an integrated path analysis of public opinion on climate change, 2001–2013

To analyze the factors affecting US public concern about the threat of climate change between January 2002 and December 2013, data from 74 separate surveys are used to construct quarterly measures of public concern over global climate change. Five factors should account for changes in levels of concern: extreme weather events, public access to accurate scientific information, media coverage, elite cues, and movement/countermovement advocacy. Structural equation modeling indicates that elite cues, movement advocacy efforts, weather, and structural economic factors influence the level of public concern about climate change. While media coverage exerts an important influence, it is itself largely a function of elite cues and economic factors. Promulgation to the public of scientific information on climate change has no effect. Information-based science advocacy has had only a minor effect on public concern, while political mobilization by elites and advocacy groups is critical in influencing climate change concern.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.