Correlation Between Pm2.5 And Aerosol Optical Depth Essay

Correlation Between Pm2.5 And Aerosol Optical Depth Essay

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The following studies have compared PM2.5 to meteorological variables, identified a relationship between PM2.5 and aerosol optical depth, looked at PM2.5 source apportionment, and used the multiple linear regression to predict PM concentration respectively.
In 2010, Tai et al. applied a multiple linear regression model to study the correlation of PM2.5 and its components with meteorological variables from 1998-2008 across the contiguous United States (US). The study obtained daily mean meteorological data from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and daily precipitation from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center and the PM2.5 data was obtained from the Environmental Protection Agency-Air Quality System (EPA-AQS). The PM2.5 monitoring varied from every day, every third day (most common) or every sixth day. Typically, speciation data including sulfate, nitrate, ammonium, organic carbon (OC), and elemental carbon (EC) and were collected every sixth day as well.

The study found that using nine meteorological predictor variables (temperature, relative humidity (RH), precipitation, cloud cover, 850-hPa geospatial height, humidity, sea-level pressure tendency, wind speed and wind direction) could explain as much as 50% of daily PM2.5 variability in the US using a multiple linear regression (MLR) model. However, correlations differed depending on PM2.5 component and meteorological variable (i.e. temperature is positively correlated to sulfate, OC and EC, whereas precipitation negatively correlates with all PM2.5 components).

My study used temperature, wind speed, and relativ...

... middle of paper ... variables (temperature, wind speed, visibility and relative humidity). Meteorological data were obtained from the national climatic data center (NCDC) and limited to 26 weather stations that provided continuous daily data from 2000 to 2008. Grid cells were then matched to the closest weather station for meteorological variables. The results demonstrated how AOD could be used to predict daily concentrations of PM2.5 in the Mid-Atlantic region and used to assess short and long-term exposures.

My study did not incorporate the GAM due to limited time and will only use the data available. Many of the data used in my study were limited to dates when PM2.5 samples and AOD were collected. Multiple data gaps were observed when PM2.5 samples were collected and AOD was not and vice versa, however I did not use the GAM and only compared the data available.

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