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Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data.

Song YZ, Yang HL, Peng JH, Song YR, Sun Q, Li Y - PLoS ONE (2015)

Bottom Line: In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3.The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%.The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance.

View Article: PubMed Central - PubMed

Affiliation: School of Land Science and Technology, China University of Geosciences, Beijing, China.

ABSTRACT
Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.

No MeSH data available.


Related in: MedlinePlus

Schematic overview of the estimation experiment.
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pone.0142149.g006: Schematic overview of the estimation experiment.

Mentions: The flowchart of the experiment process is illustrated in Fig 6. The three major steps of the experiment are the exploratory analysis, variable selection and estimation model. The first step exploratory analysis aims at charactering PM2.5 concentration data, testing its statistical features, and removing the outliers. The target of the second step is to select the variables that could be statistically related to PM2.5 concentration for statistical models. Finally, the last step is to find potential non-linear relationships, to estimate model parameters and to quantify the variances of the potential variables.


Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data.

Song YZ, Yang HL, Peng JH, Song YR, Sun Q, Li Y - PLoS ONE (2015)

Schematic overview of the estimation experiment.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4634950&req=5

pone.0142149.g006: Schematic overview of the estimation experiment.
Mentions: The flowchart of the experiment process is illustrated in Fig 6. The three major steps of the experiment are the exploratory analysis, variable selection and estimation model. The first step exploratory analysis aims at charactering PM2.5 concentration data, testing its statistical features, and removing the outliers. The target of the second step is to select the variables that could be statistically related to PM2.5 concentration for statistical models. Finally, the last step is to find potential non-linear relationships, to estimate model parameters and to quantify the variances of the potential variables.

Bottom Line: In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3.The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%.The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance.

View Article: PubMed Central - PubMed

Affiliation: School of Land Science and Technology, China University of Geosciences, Beijing, China.

ABSTRACT
Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.

No MeSH data available.


Related in: MedlinePlus