| 京津冀地区城市臭氧污染趋势及原因探讨 |
| 摘要点击 3428 全文点击 1155 投稿时间：2019-05-29 修订日期：2019-07-16 |
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| 中文关键词 KZ滤波法 气象要素 臭氧 京津冀地区 细颗粒物 |
| 英文关键词 KZ filter meteorological conditions surface ozone Beijing-Tianjin-Hebei Region PM2.5 |
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| 中文摘要 |
| 采用Kolmogorov-Zurbenko（KZ）滤波分析了2013~2018年京津冀地区13个城市的臭氧最大日8h滑动平均（O3-8h）序列，评估污染趋势并探讨原因.KZ滤波分离出的O3-8h短期、季节和长期等3个分量分别占原始序列总方差的32.7%、63.9%和3.4%，各分量之间相互独立；以滤除了中短期过程影响的长期分量进行比较，京津冀地区远高于柏林、巴黎和伦敦等欧洲城市，与美国洛杉矶20世纪90年代初和最近4年状况相当，普遍低于上海和南京等长三角主要城市.但是，2013~2018年京津冀地区各城市臭氧污染加剧显著，长期分量升高速率达2.31~7.12 μg·（m3·a）-1，均值为4.97 μg·（m3·a）-1，快于长三角地区.拟合结果提示，臭氧浓度升高受气象条件影响不大（贡献比均值约为9.6%），主要由大气污染排放变化造成（90.4%），将该排放变化拆分为PM2.5下降和臭氧前体物排放变化两项，其贡献比均值分别是27.3%和63.1%，其中北京-廊坊-天津城市带PM2.5下降对臭氧升高贡献较大，分别为50.8%、32.5%和36.7%，衡水则达到48.6%.PM2.5浓度降低已成为北京、衡水等地臭氧升高的最关键因素，这意味着需进一步减少前体物排放，以抵消PM2.5降低导致臭氧增加的反作用.需要指出，该结果尚待实验和模型模拟验证. |
| 英文摘要 |
| Photochemical pollution, which is believed to be influenced by emission changes and meteorological factors, is presently quite serious in the Beijing-Tianjin-Hebei (BTH) region, China. There is a need to ascertain the effectiveness of air quality management in the region based on long-term air quality trends independent from meteorological influences. We apply Kolmogorov-Zurbenko (KZ) filtering, a technique used to separate different scales of motion in a time series, to analyze the time series of the maximum daily 8-hour running average for ozone (O3-8h) from 13 cities in the BTH region during 2013-2018, and also discuss trends and driving factors. Results of the KZ filtering revealed that the short-term, seasonal, and long-term components of the O3-8h accounted for 32.7%, 63.9%, and 3.4% of the total variance, respectively. The long-term component of the BTH region was much higher than of those reported by others for Berlin, Paris, and London, and was comparable to that of Los Angeles in the early 1990s and in the 4 years previous to our study. Although we found a lower long-term component than of those reported for Shanghai and Nanjing in the Yangtze River Delta, China, the BTH region had higher rates of increase that ranged from 2.31 to 7.12 μg·(m3·a)-1[mean 4.97 μg·(m3·a)-1]. Based on the linear fitting results-that had not been verified by experiments or model simulations-the average increase rates could be mainly attributed to emission changes (90.4%), which may be distinguished into two parts, the decrease of particulate matter (PM) (27.3%) and the emission of O3 precursors (63.1%). Decreases of PM2.5in Beijing, Langfang, Tianjin, and Hengshui were considered to be responsible for the increase at the levels of 50.8%, 32.5%, 36.7%, and 48.6%, respectively. This suggests that the rapid decrease in PM2.5 could be the most important factor in the increasing trend of O3 in some cities. We conclude that further decreases in the emission of O3 precursors are required to overcome the effect of decreasing PM2.5 causing an increase in O3. |
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