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.g002: Time series of daily PM2.5 concentrations at 13 monitoring stations of Xi'an in 2013.The gray vertical lines indicate the range of daily PM2.5 concentrations at the 13 monitoring stations (from maximum to minimum), the colored dots are the average concentrations of each day (according to the defined colors in Table 1), and the black line illustrates the continuous 7-day average concentration. Mentions: In Table 1, the PM2.5 concentrations and air quality are classified according to the definitions in Technical Regulation on Ambient Air Quality Index (under review) (HJ 633–2012, China's environment protection standard). The air quality conditions can be presented with different colors. The time series of Fig 2 show the variation and seasonality of PM2.5 concentrations in Xi'an in 2013. We can see from the time series that excellent and good air qualities are primarily from May to August, and seriously polluted and severely polluted air qualities are from January to March and form October to December. Especially, severely polluted air qualities are in the whole winter (December, January, and February). Table 2 lists the summary statistics of the PM2.5 monitoring data (raw dataset is S1 Appendix). Missing PM2.5 concentrations data accounts for 10.7% (509/4745 data points). In other words, we use 89.3% of the raw data (4236 data points) to conduct the following experiment.

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.g002: Time series of daily PM2.5 concentrations at 13 monitoring stations of Xi'an in 2013.The gray vertical lines indicate the range of daily PM2.5 concentrations at the 13 monitoring stations (from maximum to minimum), the colored dots are the average concentrations of each day (according to the defined colors in Table 1), and the black line illustrates the continuous 7-day average concentration.
Mentions: In Table 1, the PM2.5 concentrations and air quality are classified according to the definitions in Technical Regulation on Ambient Air Quality Index (under review) (HJ 633–2012, China's environment protection standard). The air quality conditions can be presented with different colors. The time series of Fig 2 show the variation and seasonality of PM2.5 concentrations in Xi'an in 2013. We can see from the time series that excellent and good air qualities are primarily from May to August, and seriously polluted and severely polluted air qualities are from January to March and form October to December. Especially, severely polluted air qualities are in the whole winter (December, January, and February). Table 2 lists the summary statistics of the PM2.5 monitoring data (raw dataset is S1 Appendix). Missing PM2.5 concentrations data accounts for 10.7% (509/4745 data points). In other words, we use 89.3% of the raw data (4236 data points) to conduct the following experiment.

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