%0 Journal Article %1 Hoek2008 %A Hoek, Gerard %A Beelen, Rob %A de Hoogh, Kees %A Vienneau, Danielle %A Gulliver, John %A Fischer, Paul %A Briggs, David %D 2008 %J Atmospheric Environment %K LURsurvey lur pm pullution regression %N 33 %P 7561-7578 %T A review of land-use regression models to assess spatial variation of outdoor air pollution %U http://ac.els-cdn.com/S1352231008005748/1-s2.0-S1352231008005748-main.pdf?_tid=73a34bd4-f1e9-11e6-a107-00000aab0f27&acdnat=1486989745_523b7c2aa16610d6bf4d404a2f1adc33 %V 42 %X Studies on the health effects of long-term average exposure to outdoor air pollution have played an important role in recent health impact assess\ ments. Exposure assessment for epidemiological studies of long-term exposure to ambient air pollution remains a difficult challenge because of substantial sm\ all-scale spatial variation. Current approaches for assessing intra-urban air pollution contrasts include the use of exposure indicator variables, interpolat\ ion methods, dispersion models and land-use regression (LUR) models. ŁUR\ models have been increasingly used in the past few years. This paper provides a \ critical review of the different components of ŁUR\ models. We identified 25 land-use regression studies. Land-use regression combines monitoring of air p\ ollution at typically 20–100 locations, spread over the study area, and development of stochastic models using predictor variables usually obtained through g\ eographic information systems (GIS). Monitoring is usually temporally limited: one to four surveys of typically one or two weeks duration. Significant predic\ tor variables include various traffic representations, population density, land use, physical geography (e.g. altitude) and climate. Land-use regression meth\ ods have generally been applied successfully to model annual mean concentrations of NO2, NOx, PM2.5, the soot content of PM2.5 and \VOCs\ in different sett\ ings, including European and North-American cities. The performance of the method in urban areas is typically better or equivalent to geo-statistical methods\ , such as kriging, and dispersion models. Further developments of the land-use regression method include more focus on developing models that can be transfer\ red to other areas, inclusion of additional predictor variables such as wind direction or emission data and further exploration of focalsum methods. Models t\ hat include a spatial and a temporal component are of interest for (e.g. birth cohort) studies that need exposure variables on a finer temporal scale. There is a strong need for validation of ŁUR\ models with personal exposure monitoring.

@article{Hoek2008, abstract = {Studies on the health effects of long-term average exposure to outdoor air pollution have played an important role in recent health impact assess\ ments. Exposure assessment for epidemiological studies of long-term exposure to ambient air pollution remains a difficult challenge because of substantial sm\ all-scale spatial variation. Current approaches for assessing intra-urban air pollution contrasts include the use of exposure indicator variables, interpolat\ ion methods, dispersion models and land-use regression (LUR) models. \{LUR\} models have been increasingly used in the past few years. This paper provides a \ critical review of the different components of \{LUR\} models. We identified 25 land-use regression studies. Land-use regression combines monitoring of air p\ ollution at typically 20–100 locations, spread over the study area, and development of stochastic models using predictor variables usually obtained through g\ eographic information systems (GIS). Monitoring is usually temporally limited: one to four surveys of typically one or two weeks duration. Significant predic\ tor variables include various traffic representations, population density, land use, physical geography (e.g. altitude) and climate. Land-use regression meth\ ods have generally been applied successfully to model annual mean concentrations of NO2, NOx, PM2.5, the soot content of PM2.5 and \{VOCs\} in different sett\ ings, including European and North-American cities. The performance of the method in urban areas is typically better or equivalent to geo-statistical methods\ , such as kriging, and dispersion models. Further developments of the land-use regression method include more focus on developing models that can be transfer\ red to other areas, inclusion of additional predictor variables such as wind direction or emission data and further exploration of focalsum methods. Models t\ hat include a spatial and a temporal component are of interest for (e.g. birth cohort) studies that need exposure variables on a finer temporal scale. There is a strong need for validation of \{LUR\} models with personal exposure monitoring.}, added-at = {2017-02-13T17:35:48.000+0100}, author = {Hoek, Gerard and Beelen, Rob and de Hoogh, Kees and Vienneau, Danielle and Gulliver, John and Fischer, Paul and Briggs, David}, biburl = {https://www.bibsonomy.org/bibtex/2b4332a8405a35468f793a1565f643408/lautenschlager}, description = {A review of land-use regression models to assess spatial variation of outdoor air pollution}, interhash = {75a6e4ed77a0e829ee483a59c4133689}, intrahash = {b4332a8405a35468f793a1565f643408}, journal = {Atmospheric Environment}, keywords = {LURsurvey lur pm pullution regression}, number = 33, pages = {7561-7578}, timestamp = {2017-12-31T14:10:08.000+0100}, title = {A review of land-use regression models to assess spatial variation of outdoor air pollution}, url = {http://ac.els-cdn.com/S1352231008005748/1-s2.0-S1352231008005748-main.pdf?_tid=73a34bd4-f1e9-11e6-a107-00000aab0f27&acdnat=1486989745_523b7c2aa16610d6bf4d404a2f1adc33}, volume = 42, year = 2008 }