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| Comprehensive integration approaches for PM2.5 statistical interpretation in shanghai |
| LI Ya-yun1, SHU Jiong1, SHEN Yu2 |
1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China;
2. Shanghai Climate Center, Shanghai 200030, China |
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Abstract The comprehensive integration approaches for statistical interpretation were employed to predict PM2.5 concentration in Shanghai. Three kinds of model output statistics (MOS) models were built up by combining WRF model output with kalman filtering (KALMAN), partial least squares regression (PLS) and real-time iterative model (RTIM) respectively. And then three types of integrated model, based on MOS model above Multi-model average integration, recursive positive weight synthesis and multivariate linear regression integration, were separately apply to three days prediction for daily average concentration of PM 2.5 from Dec. 2 to 31, 2014 (light pollution process) and Dec. 15, 2015 to Jan. 13, 2016 (heavy pollution process). The prediction ability of pollution weather process for these integration models was improved by providing reasonable information and reducing the systematic errors in comparison with a single MOS models, which reduced the risk of decision-making in the process of pollution. The multivariate linear regression integration model presented higher precision and stability by comparative analysis of light and heavy pollution prediction processes. In all, the comprehensive integration approaches for statistical interpretation have great potential to be applied to regional air pollution prediction in operational model.
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Received: 18 June 2016
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