Sentence examples for meteorology variables from inspiring English sources

Data on particulate pollutants and meteorology variables.

PP and SBP were generally associated with individual-level covariates in the expected direction, and meteorology variables (temperature and atmospheric pressure) were positively associated with BP (data not shown).

Note that the integrated model was driven by daily meteorology variables from REMO, and the parameter setting and model structure (see Ermert et al. 2011a, 2011b) do not include other factors such as malaria control measures.

Adjusted for age, sex, race/ethnicity, income, education, BMI, diabetes, cigarette smoking, alcohol use, physical activity, BP medication, and meteorology variables, a 10-μg/m increase in PM2.5 was associated with 1.12 mmHg higher PP [95% confidence interval (CI), 0.28 1.97] and 0.99 mmHg higher SBP [although CIs included the null value (95% CI, –0.15 to 2.13), model 2].

Parameter values for other variables in the model (time and meteorology variables) were estimated from the observed data using models as specified above, except that models for generation of parameter estimates for Scenario Set 2 (which had a priori specification of pollutant risk ratios) did not include the pollution variable.

Toward the end, we mention other possible applications of this technique to recognize stable and unstable regimes in the evolution of variables in meteorology (such as pollution content or atmospheric pressure), biology (blood pressure) and econophysics (prices of the stock market).

Pearson bivariate correlations were performed for particle measurements (PNC, PD) with independent variables of meteorology, symptoms and age.

These maps were created as a visual tool to assess average spatial patterns before adjusting for meteorology or other predictive variables.

To that end, we applied a chemistry transport model, using input data from detailed emission inventories, meteorology, and land use variables, to estimate the surface concentration of air pollutants.

These models included data on several variables including traffic, meteorology, roadway geometry, vehicle emission, air quality, and land use.

To help explain variability in the DMIRET and iF, we summarized several independent variables to represent local meteorology, population near the airport, and distance from the airport to the nearest receptor.