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. ABSTRACTParticulate 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 © Copyright Policy Related In: Results  -  Collection License getmorefigures.php?uid=PMC4634950&req=5 .flowplayer { width: px; height: px; } pone.0142149.g011: Evaluations of the model: (a) histogram of residuals and (b) fitted PM2.5 concentrations vs. observations. Mentions: The R2 value of the generalized additive model is 0.691, and the fitted residuals are normally distributed and not skewed (Fig 11). The model explains 69.50% of the total deviance in the PM2.5 concentrations data. To evaluate the model, a comparison between the estimated results and the results of a stepwise linear regression is considered, and evaluations of the fitted residuals and fitted values in this model are calculated. The fitting R2 value is 0.582 for the stepwise linear regression with the same variables and processed data after the exploratory analysis as those used in the generalized additive model. Therefore, the model in this paper is better than the stepwise linear model with the fitness improvement of 18.73%.

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)

Related In: Results  -  Collection

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pone.0142149.g011: Evaluations of the model: (a) histogram of residuals and (b) fitted PM2.5 concentrations vs. observations.
Mentions: The R2 value of the generalized additive model is 0.691, and the fitted residuals are normally distributed and not skewed (Fig 11). The model explains 69.50% of the total deviance in the PM2.5 concentrations data. To evaluate the model, a comparison between the estimated results and the results of a stepwise linear regression is considered, and evaluations of the fitted residuals and fitted values in this model are calculated. The fitting R2 value is 0.582 for the stepwise linear regression with the same variables and processed data after the exploratory analysis as those used in the generalized additive model. Therefore, the model in this paper is better than the stepwise linear model with the fitness improvement of 18.73%.

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