| 佛山臭氧浓度预报方程的建立与应用 |
| 摘要点击 3362 全文点击 1687 投稿时间：2021-12-02 修订日期：2022-02-18 |
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| 中文关键词 臭氧(O3) 臭氧预报 气象因子 风向指数 多元回归 |
| 英文关键词 ozone (O3) ozone forecast meteorological factors wind direction index multiple regression |
| 作者 | 单位 | E-mail | | 陈辰 | 佛山市气象局, 佛山 528000
广东省环珠江口气候环境与空气质量变化野外科学观测研究站, 广州 510275 | chenchen1991nuist@163.com | | 洪莹莹 | 珠三角环境气象预报预警中心, 广东省生态气象中心, 广州 510640 | hongyy3@mail2.sysu.edu.cn | | 谭浩波 | 佛山市气象局, 佛山 528000 | hbtan@gd121.cn | | 司徒淑娉 | 广东省佛山生态环境监测站, 佛山 528000 | | | 程银琳 | 佛山市气象局, 佛山 528000 | | | 步巧利 | 佛山市气象局, 佛山 528000 | | | 吴蒙 | 佛山市气象局, 佛山 528000
广东省环珠江口气候环境与空气质量变化野外科学观测研究站, 广州 510275 | | | 潘巧英 | 佛山市气象局, 佛山 528000 | |
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| 中文摘要 |
| 臭氧作为大气中的二次污染物，其形成和变化复杂，臭氧预报更是当下空气污染治理的难题之一.通过分析2014~2017年佛山地区近地面O3浓度与高低层气象要素的关系，建立了佛山O3浓度预报方程，并进行了检验和应用.结果表明，佛山近地面O3的变化与高低层气象要素关系密切，气温和日照时数等气象要素与O3浓度呈显著正相关，相对湿度、总（低）云量和风速等与其呈负相关；高浓度O3污染发生的气象条件为小风速、晴间少云、低相对湿度、较长的日照时间和较高的温度，高浓度O3潜势指数（HOPI）和风向指数（WDI）的定义可以较好地衡量O3污染气象条件的好坏；综合考虑HOPI和不同高度WDI等13种气象要素，采用多指标叠套和多元逐步回归建立了佛山地区臭氧浓度预报方程；利用2018年资料检验发现，模拟值与实测值二者的相关系数R可达0.82，预报方程具有良好的拟合效果和可预报性. |
| 英文摘要 |
| The formation and changes of ozone (O3), a secondary pollutant in the atmosphere, are complex, and ozone forecasting has become one of the current problems in air pollution prevention and control. In this study, the relationships between the near-surface O3 concentration and meteorological elements (high- and low-level) in Foshan from 2014 to 2017 were analyzed, and the concentration forecasting equation was established, tested, and applied. The results showed that the near-surface O3 changed closely related to high- and low-level meteorological elements. Meteorological elements such as temperature and sunshine hours were significantly positively correlated with O3 concentration, whereas relative humidity, total (low) cloud cover, and wind speed were negatively correlated with O3. Heavy O3 pollution often occurred with meteorological conditions of low wind speed, sunny days and few clouds, low relative humidity, longer sunshine time, and higher temperature. The definitions of high-concentration O3 potential index (HOPI) and wind direction index (WDI) in the Foshan area could better characterize the meteorological conditions of O3 pollution. Considering 13 meteorological elements, such as HOPI and WDI at different heights, the O3 concentration forecasting equation in the Foshan area was established using multi-indicator stacking and multiple stepwise regression methods. Using the 2018 data, it was found that the correlation coefficient R between the simulated values and the measured values reached 0.82, and the forecast equation had a good fitting effect and predictability. |
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