Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Spatio-temporal modeling of chronic PM10 exposure for the Nurses’ Health Study
AbstractChronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R2=0.62), with little bias (−0.4μgm−3) and high precision (6.4μgm−3). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R2=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R2=0.51) or inverse distance weighted interpolation (cross-validation R2=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM10 exposures for large populations.
Spatio-temporal modeling of chronic PM10 exposure for the Nurses’ Health Study
AbstractChronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R2=0.62), with little bias (−0.4μgm−3) and high precision (6.4μgm−3). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R2=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R2=0.51) or inverse distance weighted interpolation (cross-validation R2=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM10 exposures for large populations.
Spatio-temporal modeling of chronic PM10 exposure for the Nurses’ Health Study
Yanosky, Jeff D. (Autor:in) / Paciorek, Christopher J. (Autor:in) / Schwartz, Joel (Autor:in) / Laden, Francine (Autor:in) / Puett, Robin (Autor:in) / Suh, Helen H. (Autor:in)
Atmospheric Environment ; 42 ; 4047-4062
19.01.2008
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Air pollution , Particulate matter , Geographic Information System , Spatial smoothing , Generalized additive models , AQS , air quality system , CFCC , Census Feature Class Code , ESRI , Environmental Systems Research Institute , GAM , generalized additive model , GIS , IDW , inverse distance weighting , IMPROVE , Interagency Monitoring of Protected Visual Environments , km , kilometer , NCDC , National Climatic Data Center , NLCD , National Land Cover Data Set , NEI , National Emissions Inventory , NED , National Elevation Data Set , PM , particulate matter , PM<inf>10</inf> , mass concentration of airborne inhalable particles (aerodynamic diameter<10<hsp></hsp>μm) , RMSPE , square root of the mean of the squared prediction errors , SLBP , sea-level adjusted barometric pressure , US , United States , USEPA , US Environmental Protection Agency , USGS , US Geological Survey , VIEWS , Visibility Information Exchange Web System
Temporal aggregation and spatio-temporal traffic modeling
| Elsevier | 2015
Temporal aggregation and spatio-temporal traffic modeling
| Elsevier | 2015
Semiparametric spatio‐temporal frailty modeling
| Wiley | 2003
Semiparametric spatio-temporal frailty modeling
| Online Contents | 2003