广州市空气污染的变化特征及预报

纪忠萍; 罗森波; 邝建新; 罗秋红; 陈敏升; 梁桂雄

广州市空气污染的变化特征及预报

1.
广州中心气象台, 广东, 广州, 510080
2.
广州市环境监测中心站, 广东, 广州, 510030

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出版历程
- 收稿日期: 2005-11-15
- 修回日期: 2006-04-18

VARIATION CHARACTERISTICS AND PREDICTION OF AIR POLLUTION IN GUANGZHOU

1.
Guangzhou central meteorological observatory, Guangzhou 510080, China
2.
Guangzhou Environmental Monitoring Centre, Guangzhou 510030, China

摘要

摘要: 利用2002年11月-2004年9月广州市空气污染指数(API)和PM₁₀、NO₂、SO₂等污染物逐日浓度资料,采用小波分析、相关分析等方法对广州市空气污染的变化特征及与同期地面气象要素的关系进行了分析,并采用最优子集回归方法分别建立冬、夏季API指数及污染物浓度的预报方程。结果表明,PM₁₀是广州市的主要污染物,其次为NO₂、SO₂。除SO₂外,广州市API指数、NO₂、PM₁₀等污染物浓度具有冬半年(11-4月)偏高,夏半年(5-10月)偏低的变化规律。API指数及各种污染物浓度均具有明显的年周期振荡及5-7天的准单周、10-20天准双周、30-60天左右的季节内振荡,且30-60天的季节内振荡在冬半年较强而在夏半年较弱。冬半年API指数和PM₁₀、NO₂、SO₂浓度与气压、风速、降水呈稳定负相关,与温度、相对湿度等呈稳定的正相关,而夏半年主要与风速、降水具有较好且稳定的负相关。增加前一天的污染物浓度作为预报因子后,所建的最优子集回归方程比单选用气象因子要稳定,具有较强的预测能力。
- 空气污染 /
- 变化特征 /
- 低频振荡 /
- 小波分析 /
- 最优子集回归
Abstract: Using the methods of wavelet and correlation analysis, variation characteristics of Guangzhou daily air pollution index(API) and SO₂, NO₂ and PM₁₀ concentration and relationship between them and surface meteorological elements are analyzed for the time from November 2002 through September 2004. The winter(Nov.-Apr.) and summer(May-Oct.) predictive equations for API, SO₂, NO₂ and PM₁₀ concentration are respectively built based on surface meteorological elements by optimum subset regression analysis. The results show that PM₁₀ is the main pollutant of Guangzhou, followed by NO₂, SO₂, API. NO₂ and PM₁₀ concentration are significantly higher during winter and lower during summer with the exception of SO₂. API, SO₂, NO₂ and PM₁₀ concentration exhibits significant annual cycles and oscillations of 5-7 days, 10-20 days and 30-60 days, and the 30-60 days oscillation is stronger in winter and weaker in summer. In winter all of the API, SO₂, NO₂ and PM₁₀ concentration are significant and stable negative correlation with pressure, wind speed and precipitation, and stable positive correlation with temperature and relative humidity. In summer, however, all of the API, SO₂, NO₂ and PM₁₀ concentration are only significant and stable negative correlation with wind speed and precipitation. Adding pollutant concentration of the previous day as predictor, the regression equations built by optimum subset analysis has stable and better prediction of pollutant concentrations than that built only by meteorological factors.
- air pollution /
- variation characteristics, low-frequency oscillation /
- wavelet analysis /
- stepwise regression. /

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