The models also incorporated information on several geospatial predictors including distance to road, population density, point sources (e.g., power-generating utilities, waste combustors), elevation, and meteorology.
Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides emitting power plant were all statistically significant predictors of measured NO2 (model R = 0.88, CV R = 0.90).
The covariates we considered included distance to road, population density, elevation, surrounding land use, distance to and emission from power plants, and variables for census region of the country (northeast, west, south, and midwest) to adjust for regional patterns.
Interestingly, the Newnes population showed no evidence of admixture in contrast to the geographically close Bells Line of Road population in which 35% of individuals had a PP of >80% of being admixed.
Vineland begins in a small northern Californian city of the same name, fictional but incorporating elements – misty weather, marijuana production, a freewheeling, end-of-the-road population – of existing local settlements.
The document was the product of another on-the-road population, a small army of photographers who, under the auspices of the Farm Security Administration in the 1930's and 40's, fanned out across the country to capture a volatile American era in pictures.
The remote sensing analysis focuses on the spatial distribution and evolution of human infrastructure (i.e. roads, population centers), which is used as a proxy for newly degraded areas [35, 36].
A GIS was developed with a range of predictor variables including information on land cover, roads, population, altitude, and X-Y coordinates of each monitoring site.
The remote sensing analysis focuses on the spatial distribution and evolution of human infrastructure (i.e. roads, population centers), which is used as a proxy for newly degraded areas [ 35, 36].
The unknown pollution process on the grid is represented as where μ is an overall mean and h k(w k, s) are smooth regression functions of grid-scale covariates: the density of A1, A2, and A3 roads, population density, elevation, and nonpoint-source area emissions.
We assessed PM2.5, PM2.5 absorbance (which depends on the blackness of PM2.5, a marker of traffic-related air pollution), and nitrogen dioxide levels using a land-use regression model, taking into account the type and length of roads, population density, land coverage around the home address, and temporal variations in pollution during pregnancy.
