PubMed · 14940632
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W HELLPACH. 1951-12-01. [Variolability and statotonicity].. https://pubmed.ncbi.nlm.nih.gov/14940632/
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Variations in pyrene concentrations in motor-vehicle emissions were assessed on the basis of aerodynamic particle size and by the type of vehicle (i.e., car, truck, or bus) that passed through a Taipei, Taiwan, highway toll station. Airborne particles were collected with 8-stage cascade samplers equipped with 34-mm polyvinyl chloride filters and located in the breathing zones of toll-station workers. The authors used gas chromatography/mass spectrometry to analyze 22 polycyclic aromatic hydrocarbons that were collected. The absolute concentration of pyrene was highest in the fine-particle emission fraction for trucks, buses, and passenger cars; however, fine particles in truck and bus exhausts contained higher pyrene concentrations than the corresponding size fraction of particles emitted from passenger cars. Truck and bus emissions contained a higher concentration of pyrene than car emissions because trucks and buses produced greater amounts of fine and coarse particles, and their fine particles contained higher concentrations of pyrene.
Environmental fate modeling results are often used in risk assessment without adequately considering uncertainty in exposure predictions. Sensitivity analysis is fundamental to model validation and error prediction since sensitive model input parameters account for the largest variance in model prediction. Once identified, sensitive model input parameters can be used to propagate parametric uncertainty in numerical predictions. Output sensitivity to variation in input code sequences was investigated for the pesticide root zone model (PRZM 3) using Plackett-Burman analysis for six runoff and leaching data sets. The analysis utilized an incomplete block factorial design with even parameter weighting and uniform proportional input perturbation. Timing and duration of key period rainfall were assumed a priori to be dominant sensitive inputs. Thus, meteorological data were fixed, allowing identification of additional input components contributing to model sensitivity. Results validated expert modeler assumptions concerning parameters most critical for model validation. For leaching data sets, the application rate, soil bulk density (an indicator of available water-holding capacity), chemical partition coefficient, and pesticide degradation rates were commonly the most sensitive inputs. For runoff data sets, the in-crop runoff curve number was the most significant input governing pesticide loss in runoff and erosion flux. The chemical partition coefficient, soil and foliar decay rates, and soil bulk density were also common sensitive components for runoff predictions. These commonly observed sensitive components for runoff and leaching prediction need to be carefully considered in the design and conduct of relevant field studies, modeling assessment of such studies, and future improvements in algorithms for environmental transport modeling.
An automatic method for measuring the radon flux at soil surfaces is described. The proposed experimental design minimises the disturbance induced by the accumulator technique widely used to measure radon exhalation from the ground. By exposing the ground surface to the normal weather conditions between two consecutive measurements, this method can be used to study, in the long term, the effects of meteorological factors on the radon flux density at the soil-atmosphere interface.