Abstract:To address the issue of biases in the representational capabilities of existing assessment methods for ozone pollution meteorological conditions, stemming from a lack of boundary layer indicators, this study utilized meteorological and environmental observation data collected from 2019 to 2023. By integrating ozone numerical simulations and incorporating source tracking along with process rate analysis techniques within the model framework, we developed a joint model and observation-based Tianjin Ozone Pollution Meteorological Condition Assessment Index (OWI). This index aims to accurately assess ozone pollution meteorological conditions in Tianjin. The research findings reveal a strong correlation between ozone concentrations and various meteorological factors. The OWI index was constructed based on parameters such as average temperature, maximum temperature, relative humidity, daily precipitation, daytime ultraviolet radiation, midday ultraviolet radiation, sunshine duration, average wind speed, and wind direction. It effectively characterizes the impact of these meteorological conditions on ozone levels. Notably, this index exhibits a correlation coefficient of 0.82 with O3 concentration and demonstrates an ability to identify 82% of mild or more severe ozone pollution incidents. Furthermore, by analyzing the effects of daytime and nighttime boundary layer heights on precursor diffusion processes—such as near-surface nitrogen oxide titration and vertical exchange of ozone—the study addresses potential overestimations in O3 concentrations by the OWI index under favorable vertical diffusion conditions. To optimize the OWI index further, we incorporated indicators for both daytime and nighttime boundary layer heights. Through ozone numerical simulations, the study calculated the effects of horizontal and vertical transport, convection, chemical generation, turbulent mixing, and regional transport on ozone levels. By combining simulation results with observations, the OWI index was oized under specific conditions, such as adjusting upwards when daytime vertical transport exceeds 15μg/(m3×h) or daytime ozone chemical generation exceeds 20μg/(m3×h); and considering surrounding meteorological conditions and ozone transport impacts when regional transport was too strong.
蔡子颖, 郝囝, 张敏, 樊文雁, 韩素芹, 邱晓滨, 唐颖潇, 杨旭, 姚青. 基于数值模拟天津臭氧污染特征及其气象影响评估分析方法研究[J]. 中国环境科学, 2025, 45(4): 1810-1819.
CAI Zi-ying, HAO Jian, ZHANG-min, FAN Wen-yan, HAN Su-qin, QIU Xiao-bin, TANG Yin-xiao, YANG Xu, YAO Qing. Research on the characteristics of ozone pollution and meteorological impact assessment method in Tianjin based on numerical simulation. CHINA ENVIRONMENTAL SCIENCECE, 2025, 45(4): 1810-1819.
[1] 臭氧污染控制专业委员会.中国大气臭氧污染防治蓝皮书(2020年)[M].北京:科学出版社, 2020. Ozone Pollution Control Committee. China atmospheric ozone pollution prevention and control blue book (2020)[M]. Beijing:Science Press, 2020. [2] 刘超,张碧辉,花丛,等.风廓线雷达在北京地区一次强沙尘天气分析中的应用[J].中国环境科学, 2018,38(5):1663-1669. Liu C, Zhang B H, Hua C, et al. Application of wind profiler radar in a strong sand dust weather analysis in Beijing[J]. China Environmental Science, 2018,38(5):1663-1669. [3] 常炉予,许建明,瞿元昊,等.上海市臭氧污染的大气环流客观分型研究[J].环境科学学报, 2019,39(1).169-179. Chang L Y, Xu J M, Chui Y H, et al. Study on objective synoptic classification on ozone pollution in Shanghai[J]. Acta Scientiae Circumstantiae, 2019,39(1):169-179. [4] 王磊,刘端阳,韩桂荣,等.南京地区近地面臭氧浓度与气象条件关系研究[J].环境科学学报, 2018,38(4).1285-1296. Wang L, Liu D Y, Han G R,et al. Study on the relationship between surface ozone concentrations and meteorological conditions in Nanjing, China[J]. Acta Scientiae Circumstantiae, 2018,38(4):1285-1296. [5] 李颖若,韩婷婷,汪君霞,等.ARIMA时间序列分析模型在臭氧浓度中长期预报中的应用[J].环境科学, 2021,42(7):3118-3126. Li Y R, Han T T, Wang J X, et al. Application of ARIMA model for mid-and long-term forecasting of ozone concentration[J]. Environmental Science, 2021,42(7):3118-3126. [6] 王馨陆,黄冉,张雯娴,等.基于机器学习方法的臭氧和PM2.5污染潜势预报模型-以成都市为例[J].北京大学学报(自然科学版), 2021,57(5):938-950. Wang X L, Huang R, Zhang W X, et al. Forecasting ozone and PM2.5 pollution potentials using machine learning algorithms:A case study in Chengdu[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2021,57(5).938-950. [7] 张恒德,张庭玉,李涛,等.基于BP神经网络的污染物浓度多模式集成预报[J].中国环境科学, 2018,38(4):1243-1256. Zhang H D, Zhang T Y, Li T, et al. Forecast of air quality pollutants'concentrations based on BP neural network multi-model ensemble method[J]. China Environmental Science, 2018,38(4):1243-1256. [8] Sayeed A, Choi Y, Eslami E, et al. A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14days in advance[J]. Scientific Reports, 2021,11(1):10891. [9] 张恒德,张庭玉,李涛,等.基于BP神经网络的污染物浓度多模式集成预报[J].中国环境科学, 2018,38(4):1243-1256. Zhang H D, Zhang T Y, Li T, et al. Forecast of air quality pollutants'concentrations based on BP neural network multi-model ensemble method[J]. China Environmental Science, 2018,38(4):1243-1256. [10] 庞杨,韩志伟,朱彬,等.利用WRF-Chem模拟研究京津冀地区夏季大气污染物的分布和演变[J].大气科学学报, 2013,36(6):674-682. Pang Y, Han Z W, Zhu B, et al. A model study on distribution and evolution of atmospheric pollutants over Beijing-Tianjin-Hebei region in summertime with WRF-Chem[J]. Journal of the Atmospheric Sciences, 2013,36(6):674-682. [11] 张亮,朱彬,高晋徽,等.长江三角洲夏季一次典型臭氧污染过程的模拟[J].环境科学, 2015,36:3981-3988. Zhang L, Zhu B, Gao J H, et al. Modeling study of a typical summer ozone pollution event over Yangtze River Delta[J]. Environmental Science, 2015,36:3981-3988. [12] 黄丛吾,陈报章,马超群,等.基于极端随机树方法的WRF-CMAQ-MOS模型研究[J].气象学报, 2018,76(5):779-789. Huang C Y, Chen B Z, Ma C Q, et al. WRF-CMAQ-MOS studies based on extremely randomized trees[J]. Acta Meteorologica Sinica, 2018,76(5):779-789. [13] 吴莹,王玉祥.NAQPMS和CMAQ模式在臭氧预报应用中的效果检验[J].四川环境, 2019,38(1):81-84. Wu Y, Wang Y X, The effects of NAQPMS model and CMAQ model in ozone forecasting applications[J].Sichuan Environment, 2019,38(1):81-84. [14] Wang T, Xue L, Brimblecombe P, et al. Ozone pollution in China:A review of concentrations, meteorological influences, chemical precursors, and effects[J]. Science of the Total Environment, 2017, 575:1582-1596. [15] Porter W C, Heald C L. The mechanisms and meteorological drivers of the summertime O3-temperature relationship[J]. Atmospheric Chemistry and Physics, 2019,19:13367-13381. [16] Liu Y, Wang T. Worsening urban O3 pollution in China from 2013 to 2017-Part 1:The complex and varying roles of meteorology[J]. Atmospheric Chemistry and Physics, 2020,20:6305-6321. [17] Seinfeld J H, Pandis S N. Atmospheric chemistry and physics:From air pollution to climate change, 2nd ed[M]. John Wiley& Sons, Inc, New York, USA, 2016:383-705. [18] Orter W C, Heald C L. The mechanisms and meteorological drivers of the summertime O3-temperature relationship[J]. Atmospheric Chemistry and Physics, 2019,9:13367-13381. [19] Dang R J, Liao H, Fu Y, et al. Quantifying the anthropogenic and meteorological influences on summertime surface O3 in China over 2012~2017[J]. Science of the Total Environment, 2021,754,142394. [20] YANG W, Chen H, Wang W, et al.Modeling study of ozone source apportionment over the Pearl River Delta in 2015[J]. Environmental Pollution., 2019,253. [21] 樊文雁,蔡子颖,姚青,等.区域输送对天津臭氧污染的影响[J].中国环境科学, 2022,42(11):4991-4999. Fan W Y, Cai Z Y, Yao Q,et al. Effect of regional transport on ozone pollution in Tianjin[J]. China Environmental Science, 2022,42(11):4991-4999. [22] 黄俊,廖碧婷,王春林,等.广州逐时臭氧污染气象条件指数研究及应用[J].环境科学学报, 2023,43(1):63-75. Huang J, Liao B T, Wang C L, et al. Meteorological condition index (MCI) for hourly ozone pollution in Guangzhou:Development and application[J]. 2023,43(1):63-75. [23] 潘巧英,李婷苑,陈靖扬,等.2023.基于GRAPES模式佛山市臭氧污染气象指数的构建和预报[J].环境科学学报, 2023,43(1):140-151. Pan Q Y, Li T Y, Chen J Y, et al. Construction and prediction of ozone weather index in Foshan based on GRAPES model[J]. Acta Scientiae Circumstantiae, 2023,43(1):140-151. [24] 肖致美,李鹏,孔君,等.天津市持续高温强光照天气下臭氧污染差异性[J].中国环境科学, 2023,43(7):3322-3330. Xiao Z M, Li P, Kong J, et al.Difference of ozone pollution under the continuous high temperature and strong sunlight weather in Tianjin[J]. China Environmental Science, 2023,43(7):3322-3330. [25] Tang G, Liu Y, Wang Y et al,. Aggravated ozone pollution in the strong free convection boundary layer[J]. Science of the Total Environment, 2021,788,147740. [26] GB3095-2012环境空气质量标准[S]. GB3095-2012 Ambient air quality standards[S]. [27] Gao J, Zhu B, Xiao H, et al. A case study of surface ozone source apportionment during a high concentration episode, under frequent shifting wind conditions over the Yangtze River Delta, China[J]. Science of the Total Environment, 2016.544:853-863. [28] Gao J, Zhu B, Xiao H, et al. Diurnal variations and source apportionment of ozone at the summit of Mount Huang, a rural site in Eastern China[J]. Environmental Pollution, 2017,222:513-522. [29] 姚青,马志强,郝天依,等.京津冀区域臭氧时空分布特征及其背景浓度估算[J].中国环境科学, 2021,41(11):4999-5008. Yao Q, Ma Z Q, Hao T Y, et al. Temporal and spatial distribution characteristics and background concentration estimation of ozone in Beijing-Tianjin-Hebei region[J]. China Environmental Science, 2021, 41(11):4999-5008.