| 摘要: |
| 为了揭示城市近地面臭氧浓度的变化特征及其相关气象因素,尝试进行近地面臭氧浓度预报。通过对2005年夏季(6～9月上旬)上海徐家汇地区近地面臭氧的观测与分析,建立了用于夏季臭氧浓度预报和高浓度臭氧污染事件预警的一种简便、实用的统计回归方法。结果表明:天气条件对臭氧形成具有明显的作用,臭氧浓度晴天最大、多云天次之、阴雨天最小;臭氧具有明显的日变化特征,12:00～14:00之间为最大值,凌晨3:00～5:00之间有一很小的次峰,5:00～6:00之间为最小值。产生高浓度臭氧污染是多项因子的综合结果,一般在高压系统的影响下,晴天少云,紫外辐射较强,相对湿度较低,气温较高,地面和高空吹偏北风,且风速较小的情形时容易产生高浓度臭氧污染。引进高浓度臭氧潜势指数和风向影响指数两个指标,并综合考虑多种气象要素,通过逐步回归建立的臭氧浓度预报方程,对逐日最大臭氧浓度具有较好的拟合效果和可预报性。 |
| 关键词: 臭氧浓度 气象因子 变化特征 臭氧预报 |
| DOI： |
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| 基金项目:中国气象局气候变化专项(项目编号CCSF2005-3-DH11);上海市气象局研究型业务专项共同资助 |
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| ANALYSIS AND PREDICTION OF SURFACE O3 CONCENTRATION AND RELATED METEOROLOGICAL FACTORS IN SUMMERTIME IN URBAN AREA OF SHANGHAI |
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TAN Jian-guo1, LU Guo-liang2, GENG Fu-hai1,2, ZHEN Xin-rong1
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1.Shanghai Urban Environmental Meteorological Research Center, Shanghai 200135, China;2.Laboratory of Atmospheric Chemistry, Shanghai Meteorological Bureau, Shanghai 200030, China
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| Abstract: |
| Observation and analysis of surface ozone concentration was performed in Xujiahui,Shanghai in the summer of 2005.By studying the relationship of ozone concentration with meteorological factors,a simple and practical regressive statistical method was used to forecast hourly maximum ozone concentration.The following conclusions were drawn.(1) Weather condition affects the formation of ozone obviously,which means that the average ozone concentration in sunny days is much higher than that in overcast days and rainy days.(2) The ozone concentration has an obvious diurnal variation with its main peak at 12:00～14:00 and valley before sunrise(05:00～06:00) and another very small peak between 03:00～5:00.All the factors,such as high-pressure system,cloud cover,high radiation,low relative humidity,wind speed,wind direction and high temperature,lead to high concentration of ozone pollution.When the surface and upper-level winds blow from the northwest with low velocity,it is prone to have high concentration of ozone pollution.By using high-ozone-concentration-potentiality-index(HPPI) and wind-direction-index(WDI) and other meteorological factors,a stepwise regression equation was built to give daily forecast of hourly maximum ozone concentrations. |
| Key words: ozone concentration meteorological factors variation characteristics ozone forecast |
