The phrase "air pollution scale" is correct and usable in written English.
It can be used when discussing the measurement or assessment of air pollution levels in a specific area or context.
Example: "The city implemented an air pollution scale to inform residents about the current air quality and its potential health impacts."
Alternatives: "air quality index" or "pollution measurement scale".
The Department for Environment Food and Rural Affairs (Defra) grades air pollution on a scale of 1-10, from low to very high, based on concentrations of ground level ozone, nitrogen dioxide, sulpher dioxide and tiny particles of oily soot called PM10s and PM2.5s.
In light of the complexity of measurement error in air pollution, the time scale of our validation data, and the uncertainty in our estimated calibration coefficients, our aim was to estimate and characterize calibration coefficients for PM2.5, but not to recommend their use to adjust health effect estimates in epidemiology studies directly.
To date, few population exposure assessments have incorporated multiple sources of data, specifically satellite pollutant estimates, LUR modeling of geographic characteristics, and information on proximity and pollution gradients, to estimate local-scale air pollution concentrations at a national scale.
Incorporating traffic-related air pollution into large-scale epidemiologic studies requires models linking traffic and ambient concentrations.
Of particular interest are recent studies that suggest that myocardial infarctions risk (Peters et al. 2001a, 2004), inflammation (Ruckerl et al. 2006), and cardiac repolarization changes (Henneberger et al. 2005; Yue et al. 2007) may be associated with exposures to air pollution on time scales of less than a day.
The NYCCAS fine-scale air pollution data enabled examination of spatial correlations across pollutants – and between pollution and social stressors – and provided fine-scale surfaces for validation of areal re-aggregations.
The paper suggests an approach to prioritize policy maker objectives when planning air pollution policies at regional scale.
The results indicate the possibility of using the model as a decisional support in assessing the applications and strategies for air pollution control on local scale.
The OOP methodology of patterns and pattern languages was applied to construct the object oriented version of the large scale air pollution model known as the Danish Eulerian Model (DEM).
"There are effective ways to reduce air pollution and, given the scale of the exposure affecting people worldwide, this report should send a strong signal to the international community to take action without further delay".
