Biodegradable Compatibilized Poly(<Emphasis Type="SmallCaps">l</Emphasis>-lactide)/Thermoplastic Polyurethane Blends: Design,Preparation and Property Testing

Biodegradable blends of poly(l-lactide) (PLL) toughened with a polycaprolactone-based thermoplastic polyurethane (TPU) elastomer and compatibilized with a purpose-designed poly(l-lactide-co-caprolactone) (PLLCL) copolymer were prepared. Both 2-component (PLL/TPU) and 3-component (PLL/TPU/PLLCL) blends of various compositions were prepared by melt mixing, hot-pressed into thin films and their properties tested. The results showed that, although the TPU could toughen the PLL, the blends were immiscible leading to phase separation with the TPU domains distributed in the PLL matrix. However, addition of the PLLCL copolymer could partially compatibilize the blend by improving the interfacial adhesion between the two phases. Biodegradability testing showed that the blends were biodegradable and that the PLLCL copolymer could increase the rate of biodegradation under controlled composting conditions. The 3-component blend of composition PLL/TPU/PLLCL?=?90/10/10 parts by weight was found to exhibit the best all-round properties.     

Adaptive response criteria in road hazard detection among older drivers

Objectives: The majority of existing investigations on attention, aging, and driving have focused on the negative impacts of age-related declines in attention on hazard detection and driver performance. However, driving skills and behavioral compensation may accommodate for the negative effects that age-related attentional decline places on driving performance. In this study, we examined an important question that had been largely neglected in the literature linking attention, aging, and driving: can top-down factors such as behavioral compensation, specifically adaptive response criteria, accommodate the negative impacts from age-related attention declines on hazard detection during driving?

Methods: In the experiment, we used the Drive Aware Task, a task combining the driving context with well-controlled laboratory procedures measuring attention. We compared younger (n = 16, age 21–30) and older (n = 21, age 65–79) drivers on their attentional processing of hazards in driving scenes, indexed by percentage of correct responses and reaction time of hazard detection, as well as sensitivity and response criteria using signal detection analysis.

Results: Older drivers, in general, were less accurate and slower on the task than younger drivers. However, results from this experiment revealed that older, but not younger, drivers adapted their response criteria when the traffic condition changed in the driving scenes. When there was more traffic in the driving scene, older drivers became more liberal in their responses, meaning that they were more likely to report that a driving hazard was detected.

Conclusions: Older drivers adopt compensatory strategies for hazard detection during driving. Our findings showed that, in the driving context, even at an older age our attentional functions are still adaptive according to environmental conditions. This leads to considerations on potential training methods to promote adaptive strategies that may help older drivers maintain performance in road hazard detection.     

Driving under the influence of alcohol and drugs in the eastern part of Denmark in 2015 and 2016: Abuse patterns and trends

Objectives: The objective of this study was to examine the frequency of psychoactive drugs and alcohol in drivers under suspicion of driving under the influence of drugs and alcohol in 2015 and 2016 in the eastern part of Denmark. The trends in the number of traffic cases sent for drug analysis since 2000 and alcohol analysis since 2011 are also discussed.

Methods: Blood samples from drivers suspected of being under the influence of alcohol and/or medication and/or illicit drugs in 2015 and 2016 were investigated as requested by the police. The blood samples were screened for alcohol and/or tetrahydrocannabinol (THC) alone, for other drugs (covering all drugs, except THC, listed in the Danish list of narcotic drugs), or for THC and other drugs. Age and gender were also recorded. The number of drug traffic cases since 2000 and the number of alcohol cases since 2011 were extracted from our Laboratory Information Management System (LIMS).

Results: In total, 11,493 traffic cases were investigated. Alcohol and/or drugs exceeded the legal limit in 9,657 (84%) cases. Men constituted 95% of the drivers investigated for drugs and 88% of the alcohol cases. The drivers investigated for drugs consisted primarily of young men, whereas drivers investigated for alcohol were older.

The frequency was higher for positive alcohol cases above the legal limit (87%) than for drug cases (76%) above the fixed concentration limit. THC (67–69%) was the most frequently detected drug above the legal limit, followed by cocaine (27–28.5%), amphetamine (17%), and clonazepam (6–7%) in both years. Morphine (5.4%), included among the 5 most frequent drugs in 2015, was replaced by methadone (4.6%) in 2016. Few new psychoactive drugs (NPS) were detected.

The number of traffic cases sent for drug analysis has increased more than 30-fold since 2000–2006, and the number of traffic cases submitted in 2016 for drug analysis was higher than the number for alcohol analysis; the latter has decreased since 2011.

Conclusion: Overall, alcohol was the most frequent compound detected above the legal limit in both years, followed by the well-known illicit drugs THC, cocaine, and amphetamine. NPS were seldom seen. One consequence of the increased focus on drugs in traffic has been an immense increase in drug traffic cases sent for analysis since 2006 in the eastern part of Denmark. Although this survey revealed only minimal changes compared to earlier investigations, surveys like this are invaluable for monitoring abuse patterns and trends in drugged and drunken driving.     

Optimizing the flow adjustment of constituent concentrations via LOESS for trend analysis

Trend analysis of stream constituent concentrations requires adjustment for exogenous variables like discharge because concentrations often have variable relations with flow. To remove the influence of flow on stream water quality data, an accurate characterization of the relationship between the constituent and streamflow is needed. One popular method, locally weighted regression (LOESS), provides an effective means for flow-adjusting concentrations. The LOESS fit can be tailored to the data via the smoothing parameter (f), so that the user can avoid overfitting or oversmoothing the data. However, it is a common practice to use a single f value when flow-adjusting water quality data for trend analysis. This study provides a robust, automated method for determining the optimal f value (f_opt) for each dataset via an iterative K-fold cross-validation procedure that minimizes prediction error in LOESS. The method is developed by analyzing datasets of seven different constituents across 17 sites (119 datasets total) from a stream monitoring program in northwest Arkansas (USA). We recommend using 10 iterations of 10-fold cross-validation (10?×?10 CV) in order to select f_opt when flow-adjusting water quality data with LOESS. The use of a default f value did not produce different trend interpretations for the data used here; however, the proposed approach may be helpful in other water quality studies which employ similar statistical fitting methods. Additionally, we provide an implementation of the method in the R statistical computing environment.     

Erratum to: Estimating urban water demand under conditions of rapid growth: the case of Shanghai

Regional Environmental Change - Many of the world’s major cities are expected to face significant water shortages in coming decades, largely due to increased demand arising from economic and...     

Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

Protected areas and deforestation: new results from high‐resolution panel data

This paper investigates the effectiveness of protected areas in slowing tropical forest clearing in 64 countries in Asia/Pacific, Africa and Latin America during the period of 2001–2012 by comparing deforestation rates inside and within 10 km outside of the boundaries of protected areas. Annual time series of these deforestation rates were constructed from recently published high‐resolution data on forest clearing from Hansen et al. (2013). For 4,028 parks, panel estimation based on a variety of park characteristics was conducted to test if deforestation was lower in protected areas because of their protected status, or if other factors explained the difference. From a sample of 726 parks established since 2002, a test was also conducted to investigate the effect of park establishment on protection. Findings suggest park size, national park status and management by indigenous people all are significantly associated with effective protection across regions. For the Asia/Pacific region, the test offers compelling evidence that park establishment has a near‐immediate and powerful effect.     

Effects of Urbanization on Flow Duration and Stream Flashiness: A Case Study of Puget Sound Streams,Western Washington,USA

The overall influence of urbanization on how flows of different frequency might change over time, while important in hydrologic design, remains imprecisely known. In this study, we investigate the effects of urbanization on flow duration curves (FDCs) and flow variability through a case study of eight watersheds that underwent different amounts of growth, in the Puget Sound region in Western Washington State, United States. We computed annual FDCs from flow records spanning 1960‐2010 and, after accounting for the effects of precipitation, we conducted statistical trend analyses on flow metrics to quantify how key FDC percentiles changed with time in response to urbanization. In the urban watersheds, the entire FDC tended to increase in magnitude of flow, especially the 95th‐99th percentile of the daily mean flow series, which increased by an average of 43%. Stream flashiness in urban watersheds was found to increase by an average of 70%. The increases in FDC magnitude and flashiness in urbanizing watersheds are most likely a result of increasing watershed imperviousness and altered hydrologic routing. Rural watersheds were found to have decreasing FDC magnitude over the same time period, which is possibly due to anthropogenic extractions of groundwater, and increasing stream flashiness, which is likely a result of reductions in base flow and increasing precipitation intensity and variability.     

Analysis of macroinvertebrate assemblages in relation to environmental gradients among lotic habitats of California's Central Valley

We assessed relationships between environmental characteristicsand macroinvertebrate assemblages in lotic habitats of California's Central Valley with community metric and multivariate statistical approaches. Using canonical ordinationanalyses, we contrasted results when assemblage structure was assessed with macroinvertebrate metrics, as suggested for use inindices of biotic integrity, or with genera abundances. Our objectives were to identify metrics or taxa diagnostic of loticenvironmental stressors and compare the capacity of theseapproaches to detect stressors in order to suggest how they mightbe used to diagnose stressors. For macroinvertebrate metrics,redundancy analysis (RDA) extracted three axes correlated withchannel morphology and substrates. For genera abundances, canonical correspondence analysis (CCA) extracted two axescorrelated with soluble salts and with channel morphology andsubstrates but did not separate these gradients onto differentaxes. Cluster analyses identified five RDA and five CCA sitegroups, which exhibited differences for environmental variables,metrics, or genera abundances, and agreement between the analysesin partitioning of sites was greater than if sites were partitioned randomly. These approaches differ in their abilityto detect environmental stressors, because they measure differentaspects of assemblages and would be complementary in design ofnew metrics diagnostic of stressors.