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.

Acidification of lakes in Finland : regional estimates of lake chemistry and critical loads

Yhteenveto: Järvien happamoituminen Suomessa: Alueellinen vedenlaatu ja kriittinen kuormitus

Concentrations and loads of some metals and fluorine in the River Kokemäenjoki in 1975 and 1977

Tiivistelmä: Metallien ja fluorin pitoisuuksista ja määristä Kokemäenjoessa vuosina 1975—1977

Modern diagnosis of Epstein-Barr virus infections and post-transplant lymphoproliferative disease

Infection by Epstein-Barr virus (EBV) occurs in approximately 95% of the world s population. EBV was the first human virus implicated in oncogenesis. Characteristic for EBV primary infection are detectable IgM and IgG antibodies against viral capsid antigen (VCA). During convalescence the VCA IgM disappears while the VCA IgG persists for life. Reactivations of EBV occur both among immunocompromised and immunocompetent individuals. In serological diagnosis, measurement of avidity of VCA IgG separates primary from secondary infections. However, in serodiagnosis of mononucleosis it is quite common to encounter, paradoxically, VCA IgM together with high-avidity VCA IgG, indicating past immunity. We determined the etiology of this phenomenon and found that, among patients with cytomegalovirus (CMV) primary infection a large proportion (23%) showed antibody profiles of EBV reactivation. In contrast, EBV primary infection did not appear to induce immunoreactivation of CMV. EBV-associated post-transplant lymphoproliferative disease (PTLD) is a life threatening complication of allogeneic stem cell or solid organ transplantation. PTLD may present with a diverse spectrum of clinical symptoms and signs. Due to rapidity of PTLD progression especially after stem cell transplantation, the diagnosis must be obtained quickly. Pending timely detection, the evolution of the fatal disease may be halted by reduction of immunosuppression. A promising new PTLD treatment (also in Finland) is based on anti-CD-20 monoclonal antibodies. Diagnosis of PTLD has been demanding because of immunosuppression, blood transfusions and the latent nature of the virus. We set up in 1999 to our knowledge first in Finland for any microbial pathogen a real-time quantitative PCR (qPCR) for detection of EBV DNA in blood serum/plasma. In addition, we set up an in situ hybridisation assay for EBV RNA in tissue sections. In collaboration with a group of haematologists at Helsinki University Central Hospital we retrospectively determined the incidence of PTLD among 257 allogenic stem cell transplantations (SCT) performed during 1994-1999. Post-mortem analysis revealed 18 cases of PTLD. From a subset of PTLD cases (12/18) and a series of corresponding controls (36), consecutive samples of serum were studied by the new EBV-qPCR. All the PTLD patients were positive for EBV-DNA with progressively rising copy numbers. In most PTLD patients EBV DNA became detectable within 70 days of SCT. Of note, the appearance of EBV DNA preceded the PTLD symptoms (fever, lymphadenopathy, atypical lymphocytes). Among the SCT controls, EBV DNA occurred only sporadically, and the EBV-DNA levels remained relatively low. We concluded that EBV qPCR is a highly sensitive (100%) and specific (96%) new diagnostic approach. We also looked for and found risk factors for the development of PTLD. Together with a liver transplantation group at the Transplantation and Liver Surgery Clinic we wanted to clarify how often and how severely do EBV infections occur after liver transplantation. We studied by the EBV qPCR 1284 plasma samples obtained from 105 adult liver transplant recipients. EBV DNA was detected in 14 patients (13%) during the first 12 months. The peak viral loads of 13 asymptomatic patients were relatively low (<6600/ml), and EBV DNA subsided quickly from circulation. Fatal PTLD was diagnosed in one patient. Finally, we wanted to determine the number and clinical significance of EBV infections of various types occurring among a large, retrospective, nonselected cohort of allogenic SCT recipients. We analysed by EBV qPCR 5479 serum samples of 406 SCT recipients obtained during 1988-1999. EBV DNA was seen in 57 (14%) patients, of whom 22 (5%) showed progressively rising and ultimately high levels of EBV DNA (median 54 million /ml). Among the SCT survivors, EBV DNA was transiently detectable in 19 (5%) asymptomatic patients. Thereby, low-level EBV-DNA positivity in serum occurs relatively often after SCT and may subside without specific treatment. However, high molecular copy numbers (>50 000) are diagnostic for life-threatening EBV infection. We furthermore developed a mathematical algorithm for the prediction of development of life-threatening EBV infection.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.