Sentence examples for residential variability from inspiring English sources

These observations broadly apply to four areas: (1) urban residential variability in traffic densities and pollution concentrations, (2) fraction of urban residential pollution that is attributable to traffic, (3) selection of traffic indicators for residential exposure estimation, and (4) modification of traffic-concentration relationships by site characteristics and meteorology.

The first principal component accounted for 39% of the total county-level non-residential ambient environmental variability.

There is substantial variability in residential energy use, partly driven by heterogeneous behavioral patterns.

Exposure to nitrogen dioxide (NO2) was estimated from city-specific land use regression models accounting for residential mobility and temporal variability in ambient concentrations.

Using refined exposure estimates that incorporated temporal variability and residential mobility, we found that traffic-related air pollution during the first year of life was associated with atopy.

We used a locally weighted regression smoother (loess) which adapts to changes in the data density that are likely to occur in analyses of residential locations due to variability in population density.

However, the assumption that small scale spatial contrasts in NO2 and NOx concentration remained stable throughout the study period (i.e., the assumption that at each residential address, the temporal variability of ambient concentrations was identical to that observed at the nearest monitoring site) might have been inappropriate [ 13].

These neighborhood-level covariates explained a large portion of the regional variability in residential-dust levels of PAHs.

In model 3, including census tract rankings of ambient air PAH concentration estimates (i.e., neighborhood covariates) explained 14 100% of the regional variability in residential-dust PAH concentrations.

In model 5, including the date of dust collection and the sequence of dust analysis (i.e., temporal covariates) explained –1 to 15% of the within-household variability in residential-dust PAH concentrations over time (where negative values indicate that within-household variability was smaller in model 1 compared with model 5; i.e., the covariates were not informative).

This study presents a multi-year (2008 2011) assessment of the composition and spatial variability of the residential waste stream, by both weight and volume, at Furman University, a small private Liberal Arts Institution in Greenville, SC, United States.