Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data
© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diamet...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | English |
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PUBLIC LIBRARY SCIENCE
2015
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/77046 |
| _version_ | 1848763806657806336 |
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| author | Song, Yongze Yang, H.L. Peng, J.H. Song, Y. Sun, Q. Li, Y. |
| author_facet | Song, Yongze Yang, H.L. Peng, J.H. Song, Y. Sun, Q. Li, Y. |
| author_sort | Song, Yongze |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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. |
| first_indexed | 2025-11-14T11:09:19Z |
| format | Journal Article |
| id | curtin-20.500.11937-77046 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:09:19Z |
| publishDate | 2015 |
| publisher | PUBLIC LIBRARY SCIENCE |
| recordtype | eprints |
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| spelling | curtin-20.500.11937-770462019-11-29T08:00:00Z Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data Song, Yongze Yang, H.L. Peng, J.H. Song, Y. Sun, Q. Li, Y. Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics AEROSOL OPTICAL DEPTH GROUND-LEVEL PM2.5 AIR-POLLUTANT CONCENTRATIONS PARTICULATE MATTER PM2.5 UNITED-STATES SOURCE APPORTIONMENT EMPIRICAL RELATIONSHIP SEASONAL-VARIATIONS ELEMENTAL CARBON THICKNESS © 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/77046 10.1371/journal.pone.0142149 English http://creativecommons.org/licenses/by/4.0/ PUBLIC LIBRARY SCIENCE fulltext |
| spellingShingle | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics AEROSOL OPTICAL DEPTH GROUND-LEVEL PM2.5 AIR-POLLUTANT CONCENTRATIONS PARTICULATE MATTER PM2.5 UNITED-STATES SOURCE APPORTIONMENT EMPIRICAL RELATIONSHIP SEASONAL-VARIATIONS ELEMENTAL CARBON THICKNESS Song, Yongze Yang, H.L. Peng, J.H. Song, Y. Sun, Q. Li, Y. Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data |
| title | Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data |
| title_full | Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data |
| title_fullStr | Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data |
| title_full_unstemmed | Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data |
| title_short | Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data |
| title_sort | estimating pm 2.5 concentrations in xi'an city using a generalized additive model with multi-source monitoring data |
| topic | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics AEROSOL OPTICAL DEPTH GROUND-LEVEL PM2.5 AIR-POLLUTANT CONCENTRATIONS PARTICULATE MATTER PM2.5 UNITED-STATES SOURCE APPORTIONMENT EMPIRICAL RELATIONSHIP SEASONAL-VARIATIONS ELEMENTAL CARBON THICKNESS |
| url | http://hdl.handle.net/20.500.11937/77046 |