| 京津冀臭氧污染特征、气象影响及基于神经网络的预报效果评估 |
| 摘要点击 5200 全文点击 698 投稿时间：2021-11-15 修订日期：2021-12-21 |
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| 中文关键词 京津冀 臭氧(O3) 污染特征 预报评估 气象影响 神经网络 |
| 英文关键词 Beijing-Tianjin-Hebei region ozone (O3) pollution characteristics assessment of forecast results meteorological impacts neural network |
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| 中文摘要 |
| 基于生态环境监测和气象观测数据，分析了2016~2020年京津冀13个城市臭氧（O3）浓度特征，讨论了O3污染高发月份日最高温度（Tmax）、日均地面气压（p）、日均地面相对湿度（RH）和日均地面风速（v）等气象要素对O3-8h浓度和O3-8h超标情况的影响规律，并采用AQI级别预报准确率、O3浓度范围预报准确率和O3级别预报准确率等方法，评估了基于神经网络的O3统计预报效果.结果表明，2016~2020年期间京津冀13城市ρ（O3-8h-90per）分别为157.4、177.2、177.3、190.6和175.6μg·m-3，区域臭氧浓度5a上升了11.6%，2016~2019年期间总体呈波动上升趋势，2020年环比下降；2020年与2016年相比，除北京、张家口和承德略有下降外，其他10个城市ρ（O3-8h-90per）上升了6~45.5μg·m-3.O3-8h月均值呈现"两头低，中间高"现象，ρ（O3-8h）在4~9月的月均值超过了100 μg·m-3，在6月最高，为158.10 μg·m-3.城市O3-8h超标率范围为8.6%~19.2%，97.8%的O3-8h超标情况发生在4~9月.区域尺度上O3-8h浓度与日最高温度相关性最强，当Tmax在25~28℃区间时，所有城市开始出现O3-8h超标.O3-8h浓度与日均地面气压呈负相关关系；当RH在60%以下时，大部分城市O3-8h浓度随相对湿度上升缓慢增长；当RH在61%~70%以上时，大部分城市O3-8h浓度随日均相对湿度上升而下降.O3-8h超标时的地面主导风向主要为偏南风，大部城市O3-8h浓度高值易集中出现在2~3m·s-1及以下低风速区间.OPAQ统计模式提前1~9 d预报相关系数范围为0.72~0.86，AQI级别预报平均准确率为67%~86%，O3-8h浓度范围预报平均准确率为63%~84%.在O3-8h超标情况多发的4~9月，模式对O3轻度污染和O3-8h超标情况提前3 d预报准确率分别为69%和66%，可为O3-8h超标管控提供参考依据. |
| 英文摘要 |
| The ozone concentration characteristics of 13 cities in Beijing-Tianjin-Hebei regions from 2016 to 2020 were analyzed based on ecological environment monitoring and meteorological observation data. The influence of meteorological elements such as daily maximum temperature (Tmax), daily average ground pressure (p), daily average ground relative humidity (RH), and daily average ground wind speed (v) on ozone concentration[ρ(O3-8h)] and the exceeding standard rate of O3-8h were discussed. The AQI, ozone concentration range, and ozone pollution level forecast accuracy rates were evaluated using the neural network statistical model. The results showed that the concentrations of O3-8h-90per[ρ(O3-8h-90per)] of 13 cities in the Beijing-Tianjin-Hebei region from 2016 to 2020 were 157.4, 177.2, 177.3, 190.6, and 175.6 μg·m-3, respectively. The regional ozone concentration increased by 11.6% over the five years from 2016 to 2019. From 2016 to 2019, there was an overall upward trend in volatility, followed by a decline in 2020. Compared with that in 2016, the concentration of O3-8h-90per in the other 10 cities increased by 6-45.5 μg·m-3, except for in Beijing, Zhangjiakou, and Chengde, where it decreased slightly. The average value of ρ(O3-8h) from April to September was higher than 100 μg·m-3, and the highest monthly average concentration of O3-8h was 158.10 μg·m-3 in June. The range of the over standard rate of O3-8h was 8.6%-19.2% in the 13 cities, and 97.8% of ozone concentrations exceeded the standard in the period from April to September. At the regional scale, the concentration of O3-8h had the strongest correlation with the daily maximum temperature. Furthermore, when Tmax was in the range of 25-28℃, the concentration of O3-8h in the 13 cities began to exceed the standard concentration of 160 μg·m-3. Additionally, the concentration of O3-8h negatively correlated with p. When RH was below 60%, ozone concentration increased slowly with relative humidity in most cities. When RH was above 61%-70%, ozone concentration decreased with the increase in daily relative humidity in most cities. When ozone exceeded the standard concentration of 160 μg·m-3, the dominant wind was mainly southerly wind, and the high ozone concentration in most cities tended to be concentrated in the low wind speed range of 2-3 m·s-1 and below. Moreover, the correlation coefficient range of the statistical model of OPAQ 1-9 days in advance was 0.72-0.86, the average accuracy of AQI level forecasts was 67%-86%, and the average accuracy of O3-8h concentration forecasts was 63%-84%. In April to September, when ozone exceeded the standard of 160 μg·m-3, the accuracy rates of the model forecast of light ozone pollution and ozone exceeding the standard concentration of 160 μg·m-3three days in advance were 69% and 66%, which can provide a reference for the management and control of ozone pollution. |
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