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.g005: Spatial distribution of annual mean AOT in 2013 for Xi'an and its surrounding cities. Mentions: AOT, which is the integral of aerosol extinction coefficients in the vertical direction from the ground to the top of atmosphere, is an alternative satellite product to effectively predict PM2.5 concentrations. The strong correlation between AOT and PM2.5 concentrations has been documented by a series of studies in recent years [21, 23, 28]. The daily AOT (550 nm) data is a MODIS Terra Atmosphere level 3 product, downloaded from the Global Space Flight Center MODIS Level 1 and Atmosphere Archive and Distribution System Web [67]. The AOT values for the 13 observation stations are generated using the Ordinary Kriging method and the MODIS Terra Atmosphere level 3 product with a spatial resolution of 1°. Fig 5 shows the spatial distribution of the annual mean AOT (550 nm) obtained from the MODIS satellite data product for 2013 in Xi'an and its surrounding cities. This figure also shows that Xi'an City is one of the areas with relatively high AOT (550 nm) values.

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.g005: Spatial distribution of annual mean AOT in 2013 for Xi'an and its surrounding cities.
Mentions: AOT, which is the integral of aerosol extinction coefficients in the vertical direction from the ground to the top of atmosphere, is an alternative satellite product to effectively predict PM2.5 concentrations. The strong correlation between AOT and PM2.5 concentrations has been documented by a series of studies in recent years [21, 23, 28]. The daily AOT (550 nm) data is a MODIS Terra Atmosphere level 3 product, downloaded from the Global Space Flight Center MODIS Level 1 and Atmosphere Archive and Distribution System Web [67]. The AOT values for the 13 observation stations are generated using the Ordinary Kriging method and the MODIS Terra Atmosphere level 3 product with a spatial resolution of 1°. Fig 5 shows the spatial distribution of the annual mean AOT (550 nm) obtained from the MODIS satellite data product for 2013 in Xi'an and its surrounding cities. This figure also shows that Xi'an City is one of the areas with relatively high AOT (550 nm) values.

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