4.7 Article

Identifying the impacts of climate on the regional transport of haze pollution and inter-cities correspondence within the Yangtze River Delta

期刊

ENVIRONMENTAL POLLUTION
卷 228, 期 -, 页码 26-34

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.envpol.2017.05.002

关键词

PM2.5; Granger causality test; VAR model; Variance decomposition; Carbon and nitrogen isotope

资金

  1. National Natural Science Foundation of China [U1405235]
  2. Chinese Academy of Sciences [KJZD-EW-TZ-G06-02]

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Regional haze pollution has become an important environmental issue in the Yangtze River Delta (YRD) region. Regional transport and inter-influence of PM2.5 among cities occurs frequently as a result of the subtropical monsoon climate. Backward trajectory statistics indicated that a north wind prevailed from October to March, while a southeast wind predominated from May to September. The temporal relationships of carbon and nitrogen isotopes among cities were dependent on the prevailing wind direction. Regional PM2.5 pollution was confirmed in the YRD region by means of significant correlations and similar cyclical characteristics of PM2.5 among Lin'an, Ningbo, Nanjing and Shanghai. Granger causality tests of the time series of PM2.5 values indicate that the regional transport of haze pollutants is governed by prevailing wind direction, as the PM2.5 concentrations from upwind area cities generally influence that of the downwind cities. Furthermore, stronger correlation coefficients were identified according to monsoon pathways. To clarify the impacts of the monsoon climate, a vector autoregressive (VAR) model was introduced. Variance decomposition in the VAR model also indicated that the upwind area cities contributed significantly to PM2.5 in the downwind area cities. Finally, we attempted to predict daily PM2.5 concentrations in each city based on the VAR model using data from all cities and obtained fairly reasonable predictions. These indicate that statistical methods of the Granger causality test and VAR model have the potential to evaluate inter-influence and the relative contribution of PM2.5 among cities, and to predict PM2.5 concentrations as well. (C) 2017 Elsevier Ltd. All rights reserved.

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