Sentence examples for mobile emissions using from inspiring English sources

Our LUR model estimated PM2.5 coming from on road mobile emissions using TT, AC, and EE.

The LUR we present fills that knowledge gap, with a specific focus on using annual PM2.5 explained by on road mobile emissions and stationary emissions as its predictors.

To our knowledge this is one of the first LUR models to capture secondary PM2.5 using easily obtainable explanatory variables describing on road mobile emissions and stationary emissions.

For our process-based method we select LUR over CTMs because of its ability to use readily available information about on road mobile emissions and stationary emissions to predict annual PM2.5.

We hypothesized that EE using population density corrects for over prediction of on road mobile emissions coming from TT and AC.

We then used eq 5 with ẑmobile(p ) (PM2.5 explained by on road mobile emissions) and ẑstationary(p ) (PM2.5 explained by stationary emissions).

For l = mobile we use the LUR in a relative manner to estimate the ratio αLUR mobile(p ) = ((Î mobile ·× β̂mobile) V mobile, p )/ ẑLUR, p ) corresponding to the proportion of PM2.5 that the LUR model explains from on road mobile emissions.

Finally we use our LUR/BME model to perform a risk assessment that differentiates the number of annual PM2.5 predicted deaths that can be explained by on road mobile emissions and stationary emissions.

We focus on on road mobile emissions and stationary emissions because they are two major contributors to anthropogenic pollution.

Because exposure to mobile emissions can be variable across short distances and depends on personal activity patterns, assessing such exposures requires methods that go beyond the use of government monitoring data alone.

Because exposure to mobile emissions can vary across short distances and depends on personal activity patterns, assessing such exposures requires methods that go beyond the use of government monitoring data alone.