Influence of meteorological conditions on $ O_{3} $ mass concentration variation in Hefei City

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Aug.2024, Vol.47 No.8: 1009-1014

DOI: 10.3969/j.issn.1003-5060.2024.08.001

Authors

DENG Peng $ ^{1,2} $, LIN Baoguo $ ^{2} $, XIA Xiaoyu $ ^{2} $, CHEN Gongming $ ^{2} $, ZHU Chengzhu $ ^{1} $

(1. School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China; 2. Anhui Wanxin Environmental Technology Co., Ltd., Hefei 230088, China)

Abstract and Keywords

Abstract: Based on the hourly monitoring data of atmospheric pollutants in Hefei City from 2016 to 2020 and the daily meteorological data of the same period, this paper used Kolmogorov-Zurbenko(KZ) filtering to decompose the $ O_{3} $ mass concentration and daily series of meteorological elements in Hefei City into long-term, seasonal and short-term components, and a meteorological factor regression model was established according to the decomposed mass concentration series. The variation trend of $ O_{3} $ mass concentration and the influence of meteorological conditions on $ O_{3} $ mass concentration in Hefei City were comprehensively evaluated. The results show that seasonal variation is the most important factor that causes the fluctuation of the original sequence of $ O_{3} $ mass concentration. The long-term trend of $ O_{3} $ mass concentration in Hefei City is increasing first and then decreasing. Among the long-term components, relative humidity may be the most important meteorological element that affects $ O_{3} $ mass concentration, and exhibits a significant negative correlation $ (r = -0.581) $. Among the original sequence and other components, solar net radiation is the most important factor affecting $ O_{3} $ mass concentration, with a significant positive correlation $ (r = 0.911) $. Cluster analysis finds that the average daily maximum temperature and relative humidity corresponding to heavy pollution of $ O_{3} $ are 30.9 ℃ and 71%, respectively. Meteorological conditions promoted $ O_{3} $ pollution before the middle of 2019, with an average increase of 2.19 $ \mu $g/m $ ^{3} $ for $ \rho(\mathrm{O}_{3}) $, while inhibited $ O_{3} $ pollution after the middle of 2019, with an average decrease of 3.91 $ \mu $g/m $ ^{3} $ for $ \rho(\mathrm{O}_{3}) $.

Keywords: Hefei City; ozone $ O_{3} $ pollution; Kolmogorov-Zurbenko(KZ) filtering; meteorological element

作者信息

邓鹏 $ ^{1,2} $，林保国 $ ^{2} $，夏晓宇 $ ^{2} $，陈公名 $ ^{2} $，朱承驻 $ ^{1} $

(1. 合肥工业大学资源与环境工程学院，安徽合肥 230009；2. 安徽皖欣环境科技有限公司，安徽合肥 230088)

摘要和关键词

摘要: 文章基于2016－2020年合肥市大气污染物逐时监测数据及同期逐日气象数据, 利用 KZ(Kolmogorov-Zurbenko) 滤波法将合肥市 $ O_{3} $ 质量浓度及各气象要素逐日序列分解为长期、季节以及短期分量, 并针对分解浓度序列建立气象因子回归模型, 综合评估合肥市 $ O_{3} $ 质量浓度变化趋势及气象条件对 $ O_{3} $ 质量浓度的影响。结果表明: 季节变化是引起 $ O_{3} $ 质量浓度原始序列波动的最主要因素, 合肥市 $ O_{3} $ 质量浓度的长期变化趋势为先增长后下降; 在长期分量中, 相对湿度可能是影响 $ O_{3} $ 质量浓度的最主要气象要素, 且呈现显著负相关 $ (r = -0.581) $, 而在原始序列以及其余分量中, 太阳净辐射对 $ O_{3} $ 质量浓度的影响最为重要, 呈现为显著正相关 $ (r $ 最高为 0.911 $; 聚类分析发现 $ O_{3} $ 在较重污染时对应的日最高温度和相对湿度的均值分别为 30.9 ℃ 和 71\%; 气象条件在 2019 年中期以前对 $ O_{3} $ 污染起促进作用, 使 $ \rho(O_{3}) $ 平均上升了 2.19 $ \mu g/m^{3} $, 而在 2019 年中期以后对 $ O_{3} $ 污染起抑制作用, 使 $ \rho(O_{3}) $ 平均下降了 3.91 $ \mu g/m^{3} $。

关键词: 合肥市； $ O_{3} $ 污染；Kolmogorov-Zurbenko 滤波法；气象要素

Funding

国家自然科学基金资助项目（21876038）；合肥市生态环境保护局资助项目（2018FFCZ3692）

合肥工业大学学报(自然科学版)

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