Many studies show that there exists a positive linear correlation between AOD (Arial Optical Distortion) and PM2.5 (Particulate Matter <2.5mm), after correcting for weather variables such as wind speed, cloud cover, etc.
I was wondering what "correcting for weather conditions" means. Suppose now I have the following variables: PM2.5, AOD, wind speed, cloud cover, and sea level pressure. How should I correct for weather conditions to find the correlation between PM2.5 and AOD?
It depends greatly upon the context, but in many statistical methods this means that a regression modeling approach was used to "average out" differences which are attributable to aspects of weather. That means the regression model is a multivariable regression model.
In your example, satellite imaging is used to estimate based on some level of "blurriness" (aerial optical distortion) how much ambient air pollution (particulate matter smaller than 2.5 mm in diameter) there is. Pm2.5 has been associated with several negative health conditions. The problem is that benign contributors to AOD can be measured and "controlled for" by subtracting their contributions to AOD. This is achieved with multivariable modeling.
