Techniques for High-Quality Ambient Coarse Particle Mass Measurements

ABSTRACT

Several recent studies have shown associations between ambient concentrations of particle mass (PM) and rates of morbidity and mortality in the general population. These studies have raised the issue of quality of coarse mass (CM, PM between 2.5 and 10 µm) data used for these purposes. CM data may have precision three or more times worse than the associated PM _2.5 or PM₁₀ data, depending on the measurement method, PM _2.5 to PM ₁₀ ratios, and CM concentrations. CM is measured either as the difference between collocated _PM10 and _PM2.5 samplers or more directly with a dichotomous (virtual impactor) sampler. CM precision for the difference method is degraded due to the increased errors inherent with using the difference between two independent measurements, as well as the high PM_2.5 to PM₁₀ ratios (and low CM concentrations) typical of the eastern United States. The dichotomous sampler (dichot) makes a more direct measurement of CM, but there is a potential for significant postexposure loss of particles from unoiled CM dichot filters, as well as uncertainties in the dichot’s CM channel enrichment factor. Compared to the dichot, low-volume inertial impactor samplers such as the Harvard Impactor (HI) or PM2.5 Federal Reference Method (FRM) are simpler to operate and maintain, provide sharper cut points, and do not require oiled filters to prevent loss of CM from the filter during transport. With the recent interest in CM spatial and temporal variability with respect to PM health effects, we have developed modifications to the HI PM method to provide measurements of 24-hour PM with estimated CM precision of better than 5% CV and r² higher than 0.95, primarily by lowering field blank variability and increasing gravimetric analytical precision. These high-precision PM techniques are not limited to the HI sampler; they can also be applied to the _PM2.5 FRM sampler. The measurement methods described here can be applied to future PM studies to avoid the potential problems with exposure assessment caused by CM measurements that have poor precision.     

The effects of driver factors and sign design features on the comprehensibility of traffic signs

PROBLEM: This paper addresses the effects of driver factors and sign design features on the comprehensibility of traffic signs. METHODS: A survey was designed to capture subjects' personal particulars, ratings on sign features, and comprehension scores, and then administered to 109 Hong Kong full driving license holders. RESULTS: Years with driving license and education level were significant predictors of sign comprehensibility. Contrary to expectation, the driver factors of age group, years of active driving, hours of driving, last time driving, driving frequency, and non-local driving experience had no effect on comprehension performance. Sign familiarity was correlated with comprehension score for licensed drivers, whereas sign concreteness, simplicity, and meaningfulness were not. IMPACT ON INDUSTRY: The results of this study provide useful guidelines for designing more user-friendly traffic signs in the future. It identified particular driver groups who lacked good understanding of traffic signs, and this information may assist the relevant organizations to better allocate traffic training resources, and better target future studies of traffic sign comprehension.     

Meta-analysis of environmental contamination by phthalates

Phthalate acid esters (PAE), commonly named phthalates, are toxics classified as endocrine-disrupting compounds; they are primarily used as additives to improve the flexibility in polyvinyl chloride. Many studies have reported the occurrence of phthalates in different environmental matrices; however, none of these studies has yet established a complete overview for those compounds in the water cycle within an urban environment. This review summarizes PAE concentrations for all environmental media throughout the water cycle, from atmosphere to receiving waters. Once the occurrences of compounds have been evaluated for each environmental compartment (urban wastewater, wastewater treatment plants, atmosphere, and the natural environment), we reviewed data in order to identify the fate of PAE in the environment and establish whether geographical and historical trends exist. Indeed, geographical and historical trends appear between Europe and other countries such as USA/Canada and China, however they remain location dependent. This study aimed at identifying both the correlations existing between environmental compartments and the processes influencing the fate and transport of these contaminants into the environment. In Europe, the concentrations measured in waterways today represent the background level of contamination, which provides evidence of a past diffuse pollution. In contrast, an increasing trend has actually been observed for developing countries, especially for China.     

Neural Network Input Selection for Hydrological Forecasting Affected by Snowmelt1

Abstract: Snowmelt largely affects runoff in watersheds in Nordic countries. Neural networks (NN) are particularly attractive for streamflow forecasting whereas they rely at least on daily streamflow and precipitation observations. The selection of pertinent model inputs is a major concern in NNs implementation. This study investigates performance of auxiliary NN inputs that allow short‐term streamflow forecasting without resorting to a deterministic snowmelt routine. A case study is presented for the Rivière des Anglais watershed (700 km²) located in Southern Québec, Canada. Streamflow (Q), precipitations (rain R and snow S, or total P), temperature (T) and snow lying (A) observations, combined with climatic and snowmelt proxy data, including snowmelt flow (Q_SM) obtained from a deterministic model, were tested. NN implemented with antecedent Q and R produced the largest gains in performance. Introducing increments of A and T to the NNs further improved the performance. Long‐term averages, seasonal data, and Q_SM failed to improve the networks.     

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.