Road Dust from Pavement Wear and Traction Sanding

Vehicles affect the concentrations of ambient airborne particles through exhaust emissions, but particles are also formed in the mechanical processes in the tire-road interface, brakes, and engine. Particles deposited on or in the vicinity of the road may be re-entrained, or resuspended, into air through vehicle-induced turbulence and shearing stress of the tires. A commonly used term for these particles is road dust . The processes affecting road dust emissions are complex and currently not well known. Road dust has been acknowledged as a dominant source of PM10 especially during spring in the sub-arctic urban areas, e.g. in Scandinavia, Finland, North America and Japan. The high proportion of road dust in sub-arctic regions of the world has been linked to the snowy winter conditions that make it necessary to use traction control methods. Traction control methods include dispersion of traction sand, melting of ice with brine solutions, and equipping the tires with either metal studs (studded winter tires), snow chains, or special tire design (friction tires). Several of these methods enhance the formation of mineral particles from pavement wear and/or from traction sand that accumulate in the road environment during winter. When snow and ice melt and surfaces dry out, traffic-induced turbulence makes some of the particles airborne. A general aim of this study was to study processes and factors underlying and affecting the formation and emissions of road dust from paved road surfaces. Special emphasis was placed on studying particle formation and sources during tire road interaction, especially when different applications of traction control, namely traction sanding and/or winter tires were in use. Respirable particles with aerodynamic diameter below 10 micrometers (PM10) have been the main concern, but other size ranges and particle size distributions were also studied. The following specific research questions were addressed: i) How do traction sanding and physical properties of the traction sand aggregate affect formation of road dust? ii) How do studded tires affect the formation of road dust when compared with friction tires? iii) What are the composition and sources of airborne road dust in a road simulator and during a springtime road dust episode in Finland? iv) What is the size distribution of abrasion particles from tire-road interaction? The studies were conducted both in a road simulator and in field conditions. The test results from the road simulator showed that traction sanding increased road dust emissions, and that the effect became more dominant with increasing sand load. A high percentage of fine-grained anti-skid aggregate of overall grading increased the PM10 concentrations. Anti-skid aggregate with poor resistance to fragmentation resulted in higher PM levels compared with the other aggregates, and the effect became more significant with higher aggregate loads. Glaciofluvial aggregates tended to cause higher particle concentrations than crushed rocks with good fragmentation resistance. Comparison of tire types showed that studded tires result in higher formation of PM emissions compared with friction tires. The same trend between the tires was present in the tests with and without anti-skid aggregate. This finding applies to test conditions of the road simulator with negligible resuspension. Source and composition analysis showed that the particles in the road simulator were mainly minerals and originated from both traction sand and pavement aggregates. A clear contribution of particles from anti-skid aggregate to ambient PM and dust deposition was also observed in urban conditions. The road simulator results showed that the interaction between tires, anti-skid aggregate and road surface is important in dust production and the relative contributions of these sources depend on their properties. Traction sand grains are fragmented into smaller particles under the tires, but they also wear the pavement aggregate. Therefore particles from both aggregates are observed. The mass size distribution of traction sand and pavement wear particles was mainly coarse, but fine and submicron particles were also present.

Periglasiaalisten ilmiöiden alueellinen mallintaminen subarktisessa ympäristössä

Periglacial processes act on cold, non-glacial regions where the landscape deveploment is mainly controlled by frost activity. Circa 25 percent of Earth's surface can be considered as periglacial. Geographical Information System combined with advanced statistical modeling methods, provides an efficient tool and new theoretical perspective for study of cold environments. The aim of this study was to: 1) model and predict the abundance of periglacial phenomena in subarctic environment with statistical modeling, 2) investigate the most import factors affecting the occurence of these phenomena with hierarchical partitioning, 3) compare two widely used statistical modeling methods: Generalized Linear Models and Generalized Additive Models, 4) study modeling resolution's effect on prediction and 5) study how spatially continous prediction can be obtained from point data. The observational data of this study consist of 369 points that were collected during the summers of 2009 and 2010 at the study area in Kilpisjärvi northern Lapland. The periglacial phenomena of interest were cryoturbations, slope processes, weathering, deflation, nivation and fluvial processes. The features were modeled using Generalized Linear Models (GLM) and Generalized Additive Models (GAM) based on Poisson-errors. The abundance of periglacial features were predicted based on these models to a spatial grid with a resolution of one hectare. The most important environmental factors were examined with hierarchical partitioning. The effect of modeling resolution was investigated with in a small independent study area with a spatial resolution of 0,01 hectare. The models explained 45-70 % of the occurence of periglacial phenomena. When spatial variables were added to the models the amount of explained deviance was considerably higher, which signalled a geographical trend structure. The ability of the models to predict periglacial phenomena were assessed with independent evaluation data. Spearman's correlation varied 0,258 - 0,754 between the observed and predicted values. Based on explained deviance, and the results of hierarchical partitioning, the most important environmental variables were mean altitude, vegetation and mean slope angle. The effect of modeling resolution was clear, too coarse resolution caused a loss of information, while finer resolution brought out more localized variation. The models ability to explain and predict periglacial phenomena in the study area were mostly good and moderate respectively. Differences between modeling methods were small, although the explained deviance was higher with GLM-models than GAMs. In turn, GAMs produced more realistic spatial predictions. The single most important environmental variable controlling the occurence of periglacial phenomena was mean altitude, which had strong correlations with many other explanatory variables. The ongoing global warming will have great impact especially in cold environments on high latitudes, and for this reason, an important research topic in the near future will be the response of periglacial environments to a warming climate.