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Aerosol Science and Technology Volume 44, 2010 - Issue 9
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Regular Articles

Using Statistical Regressions to Identify Factors Influencing PM2.5 Concentrations: The Pittsburgh Supersite as a Case Study

Nanjun Chu Statistics Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
,
Joseph B. Kadane Statistics Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
&
Cliff I. Davidson Department of Civil & Environmental Engineering and Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
Pages 766-774 | Received 15 Sep 2009, Accepted 18 Apr 2010, Published online: 27 Jul 2010

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Paul H. Garthwaite, Shafeeqah A. Al-Awadhi, Fadlalla G. Elfadaly & David J. Jenkinson. (2013) Prior distribution elicitation for generalized linear and piecewise-linear models. Journal of Applied Statistics 40:1, pages 59-75.
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Articles from other publishers (13)

Han Yue, Lian Duan, Mingshen Lu, Hongsheng Huang, Xinyin Zhang & Huilin Liu. (2022) Modeling the Determinants of PM2.5 in China Considering the Localized Spatiotemporal Effects: A Multiscale Geographically Weighted Regression Method. Atmosphere 13:4, pages 627.
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Mehdi Ashayeri, Narjes Abbasabadi, Mohammad Heidarinejad & Brent Stephens. (2021) Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns. Environmental Research 196, pages 110423.
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Alexander Buevich, Alexander Sergeev, Andrey Shichkin & Elena Baglaeva. (2020) A two-step combined algorithm based on NARX neural network and the subsequent prediction of the residues improves prediction accuracy of the greenhouse gases concentrations. Neural Computing and Applications 33:5, pages 1547-1557.
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Alexander Sergeev, Andrey Shichkin, Alexander Buevich, Elena Baglaeva, Irina Subbotina, Anna Rakhmatova, Alexandra Kosachenko, Anastasia Moskaleva, Alexander Medvedev & Marina Sergeeva. Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model. Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model.
Andrey Shichkin, Alexander Buevich, Alexander Sergeev, Aleksandr Medvedev, Alexandra Kosachenko, Anastasia Moskaleva & Marina Sergeeva. Training algorithms for artificial neural networks for time series forecasting of greenhouse gas concentrations. Training algorithms for artificial neural networks for time series forecasting of greenhouse gas concentrations.
Jingyi Zhang, Bin Li, Yumin Chen, Meijie Chen, Tao Fang & Yongfeng Liu. (2018) Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data. International Journal of Environmental Research and Public Health 15:6, pages 1228.
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Alexander Sergeev, Andrey Shichkin & Alexander Buevich. Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region. Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region.
Jingyi Zhang, Yumin Chen, Xiangfei Li, Qianjiao Wu, Jiang Zhou, Yan Lu & Mo Cheng. (2017) Estimating ground PM2.5 concentration using eigenvector spatial filtering regression. Estimating ground PM2.5 concentration using eigenvector spatial filtering regression.
Brett J Tunno, Drew R Michanowicz, Jessie L C Shmool, Ellen Kinnee, Leah Cambal, Sheila Tripathy, Sara Gillooly, Courtney Roper, Lauren Chubb & Jane E Clougherty. (2015) Spatial variation in inversion-focused vs 24-h integrated samples of PM2.5 and black carbon across Pittsburgh, PA. Journal of Exposure Science & Environmental Epidemiology 26:4, pages 365-376.
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Patricio Perez & Ernesto Gramsch. (2016) Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes. Atmospheric Environment 124, pages 22-27.
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Gabriela lorga, Cristina Balaceanu Raicu & Sabina Stefan. (2015) Annual air pollution level of major primary pollutants in Greater Area of Bucharest. Atmospheric Pollution Research 6:5, pages 824-834.
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Wu Chen, Fa Qin Dong, Yue Quan Deng, Qun Wei Dai, Yun Bi Huang & Shi Ping Zhou. (2013) Concentration Variation and Morphological Types of PM<sub>2.5</sub> in Suburban of Yinchuan City. Key Engineering Materials 562-565, pages 1428-1433.
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Yi-Ming Kuo, Sheng-Wei Wang, Cheng-Shin Jang, Naichia Yeh & Hwa-Lung Yu. (2011) Identifying the factors influencing PM2.5 in southern Taiwan using dynamic factor analysis. Atmospheric Environment 45:39, pages 7276-7285.
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