Land use regression model (LUR) for ultrafine particles in Brisbane
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Traffic-related air pollution has been associated with a wide range of adverse health effects. One component of traffic emissions that has been receiving increasing attention is ultrafine particles(UFP, < 100 nm), which are of concern to human health due to their small diameters. Vehicles are the dominant source of UFP in urban environments. Small-scale variation in ultrafine particle number concentration (PNC) can be attributed to local changes in land use and road abundance. UFPs are also formed as a result of particle formation events. Modelling the spatial patterns in PNC is integral to understanding human UFP exposure and also provides insight into particle formation mechanisms that contribute to air pollution in urban environments. Land-use regression (LUR) is a technique that can use to improve the prediction of air pollution.Keywords:
Ultrafine particle
Particle (ecology)
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Ultrafine particles (UFPs) (˂0.1 µm) contribute approximately 90% to the ambient total particle number concentration (PNC) in an urban area, which has a negative impact on humans. The development of predictive statistical models for PNC is critical for epidemiological studies. Many studies applied linear regression models to land use data (e.g., road length, areas of industrial, openspace and residential, and population density) in order to develop predictive land use regression model. However, none of them applied advanced machine learning (ML) algorithms to predict urban PNC and assessed their performance...
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People living in urban environments are often exposed to particle concentration levels exceeding the PM10 standards established by the European directive (Directive 2008/50/EC). Airborne particle concentration levels in cities are mostly related to anthropic urban activities/sources (such as transportation and heating).These sources are characterized by combustion processes mainly producing high levels of sub-micrometric and ultrafine particles (UFPs, particles with diameter smaller than 100 nm). Clinical and toxicological studies have shown a link between the exposure to high particle concentration levels and adverse health effects. That is a reason why is crucial a characterization of the exposure to the different metrics of the airborne particles (number, surface area, mass, black carbon content, carcinogenic compound content) in cities. Recent studies have demonstrated that measurements performed using fixed monitoring stations do not provide adequate input into the assessment of population exposure to airborne particles. To overcome this limitation, the use of mobile monitoring platforms (bikes, buses, etc.), equipped with cutting edge instruments which allow real-time collection of air quality data, is getting a foothold in the scientific field. In particular, these systems (platforms with onboard instruments) allow the spatial and temporal characterization of air quality in urban microenvironments. In the present study the mobile monitoring approach was applied to investigate the spatial variability of all the key airborne particle metrics (number, alveolar-deposited surface area, mass concentrations) in an Italian urban area. Streets characterized by exposure levels statistically higher than the background levels for all the particle metrics were identified for different seasons, meteo-climatic and traffic conditions. A higher number of hot spots was measured for metrics affected by ultrafine particles (number and alveolar-deposited surface area concentrations) with respect to PM10. The effect of metrological requirements of the instrumentation on the proposed method was also discussed.
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There is clear evidence of the adverse health impacts of traffic-related ultrafine particulate matter. As more commuters are spending a significant portion of their daily routine inside vehicles it is increasingly relevant to study exposure levels to harmful pollutants. This study is the first research effort to simultaneously link detailed traffic data, traffic video analysis, and in-vehicle ultrafine particulate (UFP) exposure data. The objective is to empirically test relationships between traffic characteristics and UFP exposure concentrations. We also study the impact of vehicle shell effects including windows, ventilation, and air conditioning on UFP levels. The results of statistical tests and analysis show that the vehicle shell is the most important factor for in-vehicle UFP exposure concentrations. Closing the external air intake vent is more than twice as effective as rolling up the windows alone – showing that there are steps individual travelers can take to reduce their exposure. Surprisingly, traffic variables have little significant impact on UFP exposure concentrations. Traffic density is the most significant traffic variable, suggesting that inter-vehicle spacing is more important than changing emissions rates in congestion. Finally, qualitative analysis suggests that heterogeneity in the vehicle fleet is the other major factor influencing variations in exposure concentrations. The results of this research have important implications for exposure modeling and potential exposure mitigation strategies.
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Exposure Assessment
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