Intra-Urban Mobility in the Estimation of Risk Scenarios by BTEX Emissions (original) (raw)
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Movement of People and Air Pollutants: Exposure Assessment using Dynamic Population Data
Proceedings of the 20th International Clean Air and Environment Conference, Auckland, 2011
Calculating population exposure to air pollutants requires a knowledge of the spatial and temporal patterns of air pollution in relation to the exposed population. Population data are often treated as if everyone remained at their residences all day; this is clearly not the case. Furthermore, peak air pollutant concentrations do not always occur where people are most concentrated; this is especially true for secondary pollutants which may develop many kilometres downwind of source areas. In order to build improved estimates of population exposure in Melbourne, the 2007 Victorian Integrated Survey of Travel and Activity (VISTA) was used to construct twenty-four maps of the urban population, one for each hour of the day. This data was combined with air pollutant concentrations from a CSIRO grid-based chemical transport model to estimate exposure of the population, accounting for typical daily movements on weekdays and weekends. A further refinement was to separate the population data into age groups, since younger and older residents travel less than those of working age; with the aim of improving exposure estimates for those with increased vulnerability to air pollution. The results are compared with the more conventional approaches of assuming either a single population total for the city, or a map of fixed residential population. It was found that average dynamic exposure estimates were slightly higher than estimates based on residential population, whilst peak dynamic exposure estimates were significantly higher.
Atmospheric Environment, 2012
h i g h l i g h t s < Population movement was integrated in the evaluation of a policy on EC exposure. < Using residential exposure, decrease in concentrations was limited to major roads. < Including population movement, a larger decrease in EC exposure was seen. < Largest differences between exposure approaches were seen for non-urban areas. < Concentrations at non-home based activities and in transport affect exposure.
Journal of the Air & Waste Management Association, 2011
Environmental epidemiology and more specifically timeseries analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM 2.5 (particulate matter [PM] Յ 10 m in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.
Spatially differentiated and source-specific population exposure to ambient urban air pollution
Models assessing exposure to air pollution often focus on macro-scale estimates of exposure to all types of sources for a particular pollutant across an urban study area. While results based on these models may aid policy makers in identifying larger areas of elevated exposure risk, they often do not differentiate the proportion of population exposure attributable to different polluting sources (e.g. traffic or industrial). In this paper, we introduce a population exposure modeling system that integrates air dispersion modeling, Geographic Information Systems (GIS), and population exposure techniques to spatially characterize a source-specific exposure to ambient air pollution for an entire urban population at a fine geographical scale. By area, total population exposure in Dallas County in 2000 was more attributable to vehicle polluting sources than industrial polluting sources at all levels of exposure. Population exposure was moderately correlated with vehicle sources (r = 0.440, p < 0.001) and weakly with industrial sources (r = 0.069, p = 0.004). Population density was strongly correlated with total exposure (r = 0.896, p < 0.001) but was not significantly correlated with individual or combined sources. The results of this study indicate that air quality assessments must incorporate more than industrial or vehicle polluting sources-based population exposure values alone, but should consider multiple sources. The population exposure modeling system proposed in this study shows promise for use by municipal authorities, policy makers, and epidemiologists in evaluating and controlling the quality of the air in the process of urban planning and mitigation measures.
A model for evaluating the population exposure to ambient air pollution in an urban area
Atmospheric Environment, 2002
A mathematical model is presented for the determination of human exposure to ambient air pollution in an urban area. The main objective was to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments with reasonable accuracy, instead of analysing in detail personal exposures for specific individuals. We have utilised a previously developed modelling system for predicting the traffic flows and emissions, emissions originating from stationary sources, and atmospheric dispersion of pollution in an urban area. A model was developed for combining the predicted concentrations, information on people's activities (such as the time spent at home, in the workplace and at other places of activity during the day) and location of the population. Time-microenvironment activity data for the working-age population was obtained from the EXPOLIS study (air pollution distributions within adult urban populations in Europe). Information on the location of homes and workplaces was obtained from local municipalities. The concentrations of NO 2 were modelled over the Helsinki Metropolitan Area for 1996 and 1997. The computed results were processed and visualised using the geographical information system (GIS) MapInfo. The utilisation of the modelling system has been illustrated by presenting numerical results for the Helsinki Metropolitan Area. The results show the spatial and temporal (diurnal) variation of the ambient air NO 2 concentrations, the population density and the corresponding average exposure. The model developed has been designed to be utilised by municipal authorities in urban planning, e.g., for evaluating the impacts of traffic planning and land use scenarios.
This study aims to empirically demonstrate the necessity to consider both the spatiotemporal variability of air pollution and individual daily movement patterns in exposure and health risk assessment. It compares four different types of exposure estimates generated by using (1) individual movement data and hourly air pollution concentrations; (2) individual movement data and daily average air pollution data; (3) residential location and hourly pollution levels; and (4) residential location and daily average pollution data. These four estimates are significantly different, which supports the argument that ignoring the spatiotemporal variability of environmental risk factors and human mobility may lead to misleading results in exposure assessment. Additionally, three-dimensional (3D) geovisualization presented in the paper shows how person-specific space-time context is generated by the interactions between air pollution and an individual, and how the different individualized contexts place individuals at different levels of health risk.
The traditional approach of using static maps of residential population and annual average concentrations to determine population exposure levels is not capable of taking into account the spatial heterogeneity and the temporal variability of both ambient air pollutant concentrations, and the fact that populations are highly mobile. People spend substantial amounts of time at work places, schools, universities, often far away from their residence. In the United Kingdom, the 2011 census revealed that for some local authorities in the city of London, the population during a working day was tens of times larger than outside of working hours. This is, to a varying degree, the case in all urban areas. As pollution levels vary due to the temporal profile of emissions (driven by human activities), meteorology, physical transport and chemical transformation as well, applying state-of-the-art atmospheric chemistry transport models (ACTMs), integrated with the latest information on population distribution, offer the capability of quantifying human exposure in a dynamic fashion and with high spatial resolution. However, spatial and temporal resolution are related to at times substantial costs, in computing time, in the amount and degree of detail of input data required, and output data generated. For this reason, applying a nested approach with urban scale dispersion models (e.g. ADMS-Urban) within regional ACTMs (e.g. EMEP4UK) provides a suitable balance by providing the necessary resolution where it matters, while being efficient with regard to computing time and data needs overall. In this paper, we focus on two aspects, first, we introduce the state of work on integrating data from the 2011 census to generate a consistent, detailed population data product for ingestion in our air pollution models. Secondly, we demonstrate the approach taken for a one-way nesting of the ADMS-Urban model within EMEP4UK. Finally, we illustrate the direct relevance and application of this approach for the development of national air pollution control policies on the example of identifying options for reducing population exposure to fine particulate matter (PM 2.5 ) in the United Kingdom. The research described here is work in progress, as the census 2011 data have only recently been made available. Data processing is currently being completed with the results being computed in time for both the submission of the final version of this paper, as well as for presentation at iEMSs in San Diego. This paper will be revised accordingly for final submission to include these results.
Allocation of onroad mobile emissions to road segments for air toxics modeling in an urban area
Transportation Research Part D: Transport and Environment, 2004
Dispersion models are useful tools for setting emission control priorities and developing strategies for reducing air toxics emissions. Previous methodologies for modeling hazardous air pollutant emissions for onroad mobile sources are based on using spatial surrogates to allocate county level emissions to grid cells. A disadvantage of this process is that it spreads onroad emissions throughout a grid cell instead of along actual road locations. High local concentrations may be underestimated near major roadways, which are often clustered in urban centers. Here, we describe a methodology which utilizes a Geographic Information System to allocate benzene emissions to major road segments in an urban area and model the segments as elongated area sources. The Industrial Source Complex Short Term dispersion model is run using both gridded and link-based emissions to evaluate the effect of improved spatial allocation of emissions on ambient modeled benzene concentrations. Allocating onroad mobile emissions to road segments improves the agreement between modeled concentrations when compared with monitor observations, and also results in higher estimated concentrations in the urban center.