| 基于增强回归树的城市PM2.5日均值变化分析:以常州为例 |
| 摘要点击 2621 全文点击 967 投稿时间：2016-07-09 修订日期：2016-09-12 |
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| 中文关键词 常州市区 PM2.5 季节变化 增强回归树 模拟 验证 贡献率 |
| 英文关键词 urban area in Changzhou PM2.5 seasonal changes boosted regression tree(BRT) simulation verification contribution percentage |
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| 中文摘要 |
| 利用2014年12月至2015年11月常州市区6个国控监测站空气污染物浓度逐时数据，分析了PM2.5浓度季节变化特征，采用增强回归树模拟分析了PM10、4种气态污染物和7个气象因子对ρ（PM2.5）日变化的贡献.结果表明，常州市区PM2.5污染季节差异明显，冬季污染严重且持续时间长，夏季污染较轻.四季ρ（PM2.5）空间分布特征存在一定差异，但各季内不同监测站差异较小.增强回归树对ρ（PM2.5）日均值进行模拟和验证得到，训练数据的相关性为0.981，交叉验证的相关性为0.957.此外，模拟值与实测值的标准化平均偏差为1.80%，标准化平均误差为10.41%，可见模型拟合效果较好.PM10、气态污染物、气象因子和区域输送及扩散这4种影响类型对全年ρ（PM2.5）日均值差异的贡献率分别为23.4%、28%、36.2%和12.6%，表明在对ρ（PM2.5）日均值差异的影响上，气象因子 > 二次形成 > 一次源 > 区域输送及扩散.在对ρ（PM2.5）日均值差异贡献率大于5%的因子中，ρ（PM2.5）日均值与PM10、相对湿度、CO和O3正相关，与温度、SO2和混合层高度负相关，与大气压和NO2关系较复杂.区域输送及扩散方面，东南风向、偏西风向和偏北风向等上风向周边城市的污染物输送对常州市区PM2.5污染存在较大的负面影响. |
| 英文摘要 |
| Based on hourly concentration data from six state-controlled air quality monitoring stations in urban area of Changzhou from December 2014 to November 2015, the seasonal variation of PM2.5 pollution was analyzed, and the contributions of PM10, four kinds of gaseous pollutants and seven meteorological factors to daily changes of ρ(PM2.5) were quantified by boosted regression tree (BRT). The results showed that:the seasonal differences of PM2.5 pollution were significant, the pollution was serious in winter and the pollution duration was long, while the pollution was light in summer. The spatial distribution of ρ(PM2.5) in four seasons was different, but the six monitoring stations showed similar trends in each season. Daily average ρ(PM2.5) was simulated and verified by BRT. The correlation coefficient of the training data was 0.981, and the cross-validation correlation coefficient was 0.957. In addition, the mean deviation between the simulated values and the measured values was 1.80%, and the standardized mean error was 10.41%, which showed that the model fitted well. The contribution percentages of four kinds of impact types (PM10, gaseous pollutants, meteorological factors and regional transport and diffusion) to daily average ρ(PM2.5) changes of four seasons were 23.4%, 28%, 36.2% and 12.6%, respectively. So, the most significant affecting factor was meteorological condition, followed by secondary formation, primary emission, and regional transport and diffusion. In the factors with contribution percentages of more than 5%, the daily average ρ(PM2.5) was positively associated with PM10, relative humidity, CO and O3, and was negatively correlated with temperature, SO2 and mixed layer high. In addition, the daily average ρ(PM2.5) had complex relationships with atmospheric pressure and NO2. For regional transport and diffusion, the polluted air flow from southeast, west and north had a relatively great negative impact on PM2.5 pollution of urban area in Changzhou. |
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