中国PM_(2.5)序列惯性特征及政策含义
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摘要
基于北京、成都、广州、上海、沈阳5大城市2009～2017年的小时PM_(2.5)质量浓度监测数据,利用AR模型、未知内生结构断点检验和TAR模型等方法实证分析了PM_(2.5)序列惯性特征、结构变化和放大效应.研究发现,5大城市PM_(2.5)序列惯性均较强,其中成都PM_(2.5)序列惯性最强,北京、上海和广州在2009～2017年之间发生了结构性变化.5大城市的PM_(2.5)序列存在明显的非线性,各大城市PM_(2.5)总体在低区制运行,而且惯性特征明显,但不存在明显的放大效应,这就意味着雾霾防治不需要考虑PM_(2.5)序列的放大效应,未来政策应当更加聚焦于低区制下PM_(2.5)序列惯性特征.
This paper empirically analyzed the inertial characteristics, structural changes and amplification effects of PM_(2.5) sequences using AR model and TAR model, using hourly PM_(2.5) mass concentration monitoring data from 2009 to 2017 in Beijing, Chengdu, Guangzhou, Shanghai and Shenyang. There existed relatively strong inertia of PM_(2.5) sequences in the five cities, especially in Chengdu. It was also found that there were structural changes from 2009 to 2017 in Beijing, Shanghai, and Guangzhou. The PM_(2.5) sequences of the five cities are obviously nonlinear. The PM_(2.5) in the five cities were running at a low level, and the inertial characteristics were obvious. In addition, there was no obvious amplification effect, suggesting that the haze pollution prevention was not necessary to consider the amplification effect of the PM_(2.5) sequence. The inertial characteristics of the PM_(2.5) sequences at the low regime required more attention in policy-making in the future.
This paper empirically analyzed the inertial characteristics, structural changes and amplification effects of PM_(2.5) sequences using AR model and TAR model, using hourly PM_(2.5) mass concentration monitoring data from 2009 to 2017 in Beijing, Chengdu, Guangzhou, Shanghai and Shenyang. There existed relatively strong inertia of PM_(2.5) sequences in the five cities, especially in Chengdu. It was also found that there were structural changes from 2009 to 2017 in Beijing, Shanghai, and Guangzhou. The PM_(2.5) sequences of the five cities are obviously nonlinear. The PM_(2.5) in the five cities were running at a low level, and the inertial characteristics were obvious. In addition, there was no obvious amplification effect, suggesting that the haze pollution prevention was not necessary to consider the amplification effect of the PM_(2.5) sequence. The inertial characteristics of the PM_(2.5) sequences at the low regime required more attention in policy-making in the future.
引文
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[23]杨欣,陈义珍,赵妤希,等.2014~2017年北京城区霾污染态势及潜在来源[J].中国环境科学,2018,38(9):3232-3239.Yang X,Chen Y Z,Zhao Y X,et al.Analysis of haze pollution situation and potential sources in Beijing urban area from 2014 to2017[J].China Environmental Science,2018,38(9):3232-3239.
[24]Hansen B E.Approximate asymptotic P values for structural-change tests[J].Journal of Business and Economic Statistics,1997,15(1):60-67.
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[26]Liang X,Zou T,Guo B,et al.Assessing Beijing's PM2.5 pollution:Severity,weather impact,APEC and winter heating[J].Proceedings of the Royal Society A Mathematical Physical&Engineering Sciences,2015,471(2182):20150257.
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[2]翟华,朱彬,赵雪婷,等.长江三角洲初冬一次重污染天气成因分析[J].中国环境科学,2018,38(11):4001-4009.Zhai H,Zhu B,ZHAO X T,et al.Analysis of a heavy air pollution event in early winter in the Yangtze River Delta[J].China Environmental Science,2018,38(11):4001-4009.
[3]王申博,余雪,赵庆炎,等.郑州市两次典型大气重污染过程成因分析[J].中国环境科学,2018,38(7):2425-2431.Wang S B,Yu X,Zhao Q Y,et al.Analysis of the formation of two typical atmospheric heavy pollution episodes in Zhengzhou[J].China Environmental Science,2018,38(7):2425-2431.
[4]李令军,王英,李金香,等.2000~2010北京大气重污染研究[J].中国环境科学,2012,32(1):23-30.Li L J,Wang Y,Li J X,et al.The analysis of heavy air pollution in Beijing during 2000~2010[J].China Environmental Science,2012,32(1):23-30.
[5]王琳琳,王淑兰,王新锋,等.北京市2009年8月大气颗粒物污染特征[J].中国环境科学,2011,31(4):553-560.Wang L L,Wang S L,Wang X F,et al.Characteristics of particulate matter pollution during August 2009 in Beijing[J].China Environmental Science,2011,31(4):553-560.
[6]田莎莎,张显,卞思思,等.沈阳市PM2.5污染组分特征及其来源解析[J].中国环境科学,2019,39(2):487-496.Tian S S,Zhang X,Bian S,et al.Characteristics of PM2.5 pollution components and their sources in Shenyang[J].China Environmental Science,2019,39(2):487-496.
[7]孙天乐,邹北冰,黄晓锋,等.深圳市大气PM2.5来源解析[J].中国环境科学,2019,39(1):13-20.Sun T L,Zou B B,Huang X F,et al.Source apportionment of PM2.5pollution in Shenzhen[J].China Environmental Science,2019,39(1):13-20.
[8]杨欣,陈义珍,刘厚凤,等.北京2013年1月连续强霾过程的污染特征及成因分析[J].中国环境科学,2014,34(2):282-288.Yang X,Chen Y Z,Liu H F,et al.Characteristics and formation mechanism of a serious haze event in January 2013 in Beijing[J].China Environmental Science,2014,34(2):282-288.
[9]Tie X,Zhang Q,He H,et al.A budget analysis of the formation of haze in Beijing[J].Atmospheric Environment,2015,100(1),25-36.
[10]周彬,刘端阳,魏建苏,等.降水对气溶胶颗粒物清除作用的初步分析[J].长江流域资源与环境,2015,24(S1):160-170.Zhou B,Liu D Y,Wei J S,et al.A preliminary analysis on scavenging effect of precipitation on aerosol particles[J].Resources and Environment in the Yangtze Basin,2015,24(S1):160-170.
[11]Liang X,Zou T,Guo B,et al.Assessing Beijing's PM2.5 pollution:Severity,weather impact,APEC and winter heating[J].Proceedings:Mathematical,Physical and Engineering Sciences,2015,471(2182):1-20.
[12]顾为东.中国雾霾特殊形成机理研究[J].宏观经济研究,2014,(6):3-7+123.Gu W D.Study on the special formation mechanism of haze in China[J].Macroeconomic Research,2014,(6):3-7+123.
[13]Cao F,Zhang Y L.Tightening nonfossil emissions control:A potential opportunity for PM2.5 mitigation in China[J].Proceedings of the National Academy of Sciences of the United States of America,2015,112(12):1402-1402.
[14]Guo S,Hu M,Zamora M,et al.Elucidating severe urban haze formation in China[J].Proceedings of the National Academy of Sciences of the United States of America,2014,111(49):17373-17378.
[15]李云燕,殷晨曦.京津冀地区PM2.5减排实效与影响因素的门限效应[J].中国环境科学,2017,37(4):1223-1230.Li Y Y,Yin C X.Threshold effect of socio-economic factors influencing PM2.5 in Beijing-Tianjin-Hebei[J].China Environmental Science,2017,37(4):1223-1230.
[16]Fuhrer J,Moore G.Inflation Persistence[J].Quarterly Journal of Economics,1995,110(1):127-159.
[17]Pivetta F,Reis R.The persistence of inflation in the United States[J].Journal of Economic Dynamics&Control,2007,31(4):1326-1358.
[18]Andrews D K,Hong-Yuan Chen.Approximately Median-Unbiased Estimation of Autoregressive Models[J].Journal of Business&Economic Statistics,1994,12(2):187-204.
[19]Ploberger W.Optimal tests when a nuisance parameter is present only under the alternative[J].Econometrica,1994,62(6):1383-1414.
[20]张成思.中国通胀惯性特征与货币政策启示[J].经济研究,2008,(2):33-43.Zhang C S.The nature of inflation inertia in China and its implications on monetary policy[J].Economic Research Journal,2008,(2):33-43.
[21]Brown R L,Durbin J,Evans J M.Techniques for testing the constancy of regression relationships over time[J].Journal of the Royal Statistical Society,1975,37(2):149-163.
[22]Ploberger W,Kramer W.The CUSUM test with OLS residuals[J].Econometrica,1992,60(2):271-285.
[23]杨欣,陈义珍,赵妤希,等.2014~2017年北京城区霾污染态势及潜在来源[J].中国环境科学,2018,38(9):3232-3239.Yang X,Chen Y Z,Zhao Y X,et al.Analysis of haze pollution situation and potential sources in Beijing urban area from 2014 to2017[J].China Environmental Science,2018,38(9):3232-3239.
[24]Hansen B E.Approximate asymptotic P values for structural-change tests[J].Journal of Business and Economic Statistics,1997,15(1):60-67.
[25]唐宜西,张小玲,徐敬,等.北京城区和郊区本底站大气污染物浓度的多时间尺度变化特征[J].环境科学学报,2016,36(8):2783-2793.Tang Y X,Zhang X L,Xu J,et al.Multi-temporal scale variations of atmospheric pollutants concentrations in rural and urban areas of Beijing[J].Acta Scientiae Circumstantiae,2016,36(8):2783-2793.
[26]Liang X,Zou T,Guo B,et al.Assessing Beijing's PM2.5 pollution:Severity,weather impact,APEC and winter heating[J].Proceedings of the Royal Society A Mathematical Physical&Engineering Sciences,2015,471(2182):20150257.
[27]Keenan M R.A Tukey nonadditivity-type test for time series nonlinearity[J].Biometrika,1985,72(1):39-44.
[28]Tsay R S.Nonlinearity tests for time series[J].Biometrika,1986,73(2):461-466.
[29]Cryer J D,Chan K S.Time series analysis with applications in R(Second Edition)[M].New York:Springer-Verlag,2008:400.