An air pollution model for use in epidemiological studies: evaluation with measured levels of nitrogen dioxide and benzene (original) (raw)
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Modelling concentrations of and human exposure to air pollution in Danish cities
Mathematical transport-chemistry models are strong tools for evaluation of emission reduction strategies, for providing information to the public, and as the central part of models for human exposure to air pollution. NERI's recently developed AirGIS is an example of such a system. An air pollution forecasting system THOR has been established and provides 72-h air pollution forecasts two to four times a day. The THOR system generates forecasts for three different scales: rural areas, urban background and street level. Various human exposure studies have been carried out in Denmark and serve the basis for model development and validation. Recently monitoring of particulate matter has been included in these studies and preliminary results indicate that exposure to indoor air pollution plays an important role for the personal exposure to PM 2.5 .
High resolution multi-scale air quality modelling for all streets in Denmark
Transportation Research Part D: Transport and Environment
The annual concentrations of NO 2 , PM 2.5 and PM 10 in 2012 have for the first time been modelled for all 2.4 million addresses in Denmark based on a multi-scale air quality modelling approach. All addresses include residential, industrial, institutional, shop, school, restaurant addresses etc. The approach is based on a suite of chemistry-transport models all developed at Aarhus University and includes regional modelling, urban background modelling and street modelling. Information about traffic volumes is based on a newly developed national Danish Transport Model, and national travel speed data have been obtained from a recent dataset based on GPS readings of vehicles. Air quality model results are validated by comparisons with measurements obtained from the fixed site monitoring stations under the Danish Air Quality Monitoring Programme. The validation showed that calculated street concentrations of NO 2 for the five available street monitoring stations are within À27% to +12%. The model results were also verified with comparisons with previous model results for NO 2 at 98 selected streets in Copenhagen and 31 streets in Aalborg. The verification showed good correlation in Copenhagen (r 2 = 0.70) and fairly good agreement in Aalborg (r 2 = 0.60). The target groups for the air quality mapping of all Danish addresses are the general public for information and awareness about air quality, and local and national authorities whom may use the information as a screening tool for air quality assessment. The air quality map has been provided on a WebGIS platform on the internet in September 2016 (http://luftenpaadinvej.au.dk). The air quality map is named AirStreet for Air Quality at Your Street.
Environment International
Background: Health impact assessment (HIA) of exposure to air pollution is commonly based on city level (fine) particle concentration and may underestimate health consequences of changing local traffic. Exposure to trafficrelated air pollution can be assessed at a high resolution by modelling levels of nitrogen dioxide (NO 2), which together with ultrafine particles mainly originate from diesel-powered vehicles in urban areas. The purpose of this study was to estimate the health benefits of reduced exposure to vehicle emissions assessed as NO 2 at the residence among the citizens of Copenhagen Municipality, Denmark. Methods: We utilized residential NO 2 concentrations modelled by use of chemistry transport models to calculate contributions from emission sources to air pollution. The DYNAMO-HIA model was applied to the population of Copenhagen Municipality by using NO 2 concentration estimates combined with demographic data and data from nationwide registers on incidence and prevalence of selected diseases, cause specific mortality, and total mortality of the population of Copenhagen. We used exposure-response functions linking NO 2 concentration estimates at the residential address with the risk of diabetes, cardiovascular diseases, and respiratory diseases derived from a large Danish cohort study with the majority of subjects residing in Copenhagen between 1971 and 2010. Different scenarios were modelled to estimate the dynamic impact of NO 2 exposure on related diseases and the potential health benefits of lowering the NO 2 level in the Copenhagen Municipality. Results: The annual mean NO 2 concentration was 19.6 μg/m 3 and for 70% of the population the range of exposure was between 15 and 21 μg/m 3. If NO 2 exposure was reduced to the annual mean rural level of 6 μg/m 3 , life expectancy in 2040 would increase by one year. The greatest gain in disease-free life expectancy would be lifetime without ischemic heart disease (1.4 years), chronic obstructive pulmonary disease (1.5 years for men and 1.6 years for women), and asthma (1.3 years for men and 1.5 years for women). Lowering NO 2 exposure by 20% would increase disease-free life expectancy for the different diseases by 0.3-0.5 years. Using gender specific relative risks affected the results. Conclusions: Reducing the NO 2 exposure by controlling traffic-related air pollution reduces the occurrence of some of the most prevalent chronic diseases and increases life expectancy. Such health benefits can be quantified by DYNAMO-HIA in a high resolution exposure modelling. This paper demonstrates how traffic planners can assess health benefits from reduced levels of traffic-related air pollution.
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.
Evaluation of the Operational Street Pollution Model Using Data from European Cities
Environmental Monitoring and Assessment, 2004
This paper presents a sensitivity analysis and an evaluation of the semi-empirical model known as Operational Street Pollution Model (OSPM). The model is capable of calculating airborne concentrations of exhaust gases emitted by vehicles, within a street canyon. OSPM has been extensively evaluated using data collected over a two year period (1994–1995), during a monitoring campaign carried out in Jagtvej, Denmark. Further evaluation of the model was carried out using data collected in Göttinger Strasse, Hannover (1994) and Schildhorn Strasse, Berlin (1995), both in Germany. In all cases, model runs were carried out for carbon monoxide.Two sets of emission factors were used for the two street canyons in Germany; namely that available within OSPM and another separate set of emission factors derived from data collected in Germany. In the calculation of the latter set, the urban driving patterns and variations in the vehicle fleet composition according to the engine capacity were assumed accordingly. A correlation coefficient of 0.90 between the modelled and measured concentrations was obtained for all the cases considered when using the emission factors of OSPM. A correlation coefficient of about 0.85 was obtained with the newly proposed emission factors when applied to Göttinger and Schildhorn Strasse.
Science of The Total Environment, 1996
Measurements of urban air pollution are usually confined to a few locations within a city area. Monitoring stations are often situated in streets with significant traffic levels or in places where severe pollution problems are expected. Such measurements are naturally influenced by very local conditions and care must be taken in interpreting the results. This is especially important when the measurements are used for estimating urban air pollution levels or comparing air quality in different cities. Another frequent application of street measurements is for public information or warning the population of elevated pollution levels. Estimating the dangers of long-term exposure to air pollution means careful consideration must be given to how representative these measurements actually are. In this paper the influence of local conditions on air pollution concentrations is discussed, regarding especially the dependency of pollution levels on street configuration and meteorolgical parameters. The examples used are based on measurements from locations in Copenhagen and on model calculations using the Danish Operational Street Pollution Model (OSPM). It is shown that large concentration gradients can occur in street canyons with leeward concentrations far higher than windward concentrations. Thus, street measurements are site-dependent and not representative for urban areas. Model calculations with OSPM agree well with measurements.
Modeling the intra-urban variability of outdoor traffic pollution in Oslo, Norway—A GA2LEN project
Atmospheric Environment, 2007
Traffic is a major source of air pollutants in urban environments, and exposure to these pollutants may be associated with adverse health effects. However, inconsistencies in observational epidemiological studies may be caused by differential measurement errors in various approaches in assessing exposure. We aimed to evaluate a simple method for assessing outdoor air pollutant concentrations in Oslo, Norway, through a land-use regression method. Samples of nitrogen oxides (NO x) were collected in two different weeks using Ogawa passive diffusion samplers simultaneously at 80 locations across Oslo. Independent variables used in subsequent regression models as predictors of the pollutants were derived using the Arc 9 geographic information system (GIS) software. Indicators of land use, traffic, population density, and physical geography were tested. The final regression model yielded an adjusted coefficient of determination (R 2) of 0.77 for nitrogen dioxide (NO 2), 0.66 for nitric oxide (NO), and 0.73 for NO x. The results suggest that a good predictive exposure model can be derived from this approach, which can be used to estimate long-term small-area variation in concentrations for individual exposure assessment in epidemiological studies in a highly cost-effective way. These small-area variations in traffic pollution are important since they may have associations with health effects.
Human exposure to traffic pollution. Experience from Danish studies
Pure and Applied Chemistry, 2001
Air pollution may have severe long-term as well as short-term health effects. The determination of possible links between pollution levels and impact on human health is, however, not a straightforward task. A key problem is the assessment of human exposure to ambient pollution levels. In later years, the possible role of particulate pollution as a health hazard has drawn major attention and is, therefore, the subject of research projects in many countries including Denmark. The present paper gives a review of recent and ongoing/planned Danish air pollution exposure studies. Furthermore, key results from Danish studies of ultrafine particles from urban traffic are outlined. The exposure studies show that air pollution models may be strong tools in impact assessment studies, especially when used in combination with personal exposure monitoring and application of biomarkers. Personal exposure measurements in Copenhagen indicate that indoor pollution levels may be very important for the personal exposure to fine fraction particles (PM 2.5 ). Measurements with a differential mobility analyzer (DMA) in Danish urban areas show that number concentrations of ultrafine particles (<100 nm) in busy streets are strongly correlated with classic traffic pollutants such as nitrogen oxides and carbon monoxide. The number concentrations in urban Danish streets have decreased considerably between two campaigns in 1999 and 2000, apparently as a result of reductions in sulfur contents in Danish diesel fuels that took place in July 1999.
Environmental research, 2015
In two earlier case-control studies conducted in Montreal, nitrogen dioxide (NO2), a marker for traffic-related air pollution was found to be associated with the incidence of postmenopausal breast cancer and prostate cancer. These studies relied on a land use regression model (LUR) for NO2 that is commonly used in epidemiologic studies for deriving estimates of traffic-related air pollution. Here, we investigate the use of a transportation model developed during the summer season to generate a measure of traffic emissions as an alternative to the LUR model. Our traffic model provides estimates of emissions of nitrogen oxides (NOx) at the level of individual roads, as does the LUR model. Our main objective was to compare the distribution of the spatial estimates of NOx computed from our transportation model to the distribution obtained from the LUR model. A secondary objective was to compare estimates of risk using these two exposure estimates. We observed that the correlation (spear...
Journal of Exposure Science and Environmental Epidemiology, 2010
In large epidemiological studies, many researchers use surrogates of air pollution exposure such as geographic information system (GIS)-based characterizations of traffic or simple housing characteristics. It is important to evaluate quantitatively these surrogates against measured pollutant concentrations to determine how their use affects the interpretation of epidemiological study results. In this study, we quantified the implications of using exposure models derived from validation studies, and other alternative surrogate models with varying amounts of measurement error on epidemiological study findings. We compared previously developed multiple regression models characterizing residential indoor nitrogen dioxide (NO 2), fine particulate matter (PM 2.5), and elemental carbon (EC) concentrations to models with less explanatory power that may be applied in the absence of validation studies. We constructed a hypothetical epidemiological study, under a range of odds ratios, and determined the bias and uncertainty caused by the use of various exposure models predicting residential indoor exposure levels. Our simulations illustrated that exposure models with fairly modest R 2 (0.3 to 0.4 for the previously developed multiple regression models for PM 2.5 and NO 2) yielded substantial improvements in epidemiological study performance, relative to the application of regression models created in the absence of validation studies or poorer-performing validation study models (e.g., EC). In many studies, models based on validation data may not be possible, so it may be necessary to use a surrogate model with more measurement error. This analysis provides a technique to quantify the implications of applying various exposure models with different degrees of measurement error in epidemiological research.