基于观测与模拟的北京光解速率长期变化特征研究

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  • 1. 成都信息工程大学电子工程学院,四川 成都 610225
    2. 中国科学院大气物理研究所 大气环境与极端气象全国重点实验室,北京 100029
    3. 德州学院化学化工学院,山东 德州 253000
    4. 东北农业大学资源与环境学院,黑龙江 哈尔滨 150030

网络出版日期: 2025-06-04

基金资助

国家自然科学基金面上项目(42075184);山东省自然科学基金青年项目(ZR2022QD140

Characterisation of Long-term Changes in Photolysis Rate in Beijing based on Observation and Simulation

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  • 1. College of Electronic EngineeringChengdu University of Information TechnologyChengdu 610225SichuanChina
    2. State Key Laboratory of Atmospheric Environment and Extreme MeteorologyInstitute of Atmospheric Physics
    Chinese Academy of SciencesBeijing 100029China
    3. School of Chemistry and Chemical EngineeringDezhou UniversityDezhou 253000ShandongChina
    4. School of Resources and EnvironmentNortheast Agricultural UniversityHarbin 150030HeilongjiangChina

Online published: 2025-06-04

摘要

为探究北京地区大气光化学基础参数的演变规律及气溶胶的影响机制,本研究基于20189月至20198月期间北京地区近地面观测的二氧化氮光解速率 JNO2)和紫外辐射数据,结合辐射传输模型TUV 模拟与气溶胶光学参数分析,分析了 JNO2)的时空变化特征,并探讨了气溶胶对 JNO2)的影响。此外,本研究还重构了2013-2023年的长期光解速率数据集,进一步揭示了长期变化趋势与气溶胶特征的相关性。研究结果表明,JNO2)日变化呈现典型的单峰型分布,峰值通常出现在正午时段[12:00(地方时,下同)-13:00],受太阳天顶角变化的直接影响。夏季 JNO2)的极值为冬季的 1. 9 倍,分别为5. 65×10-3 s-12. 95×10-3 s-1,表明夏季由于太阳辐射强度更高,光解速率显著增加。季节变化特征表现为夏季(3. 77×10-3 s-1>春季(3. 51×10-3 s-1>秋季(2. 97×10-3 s-1>冬季(2. 25×10-3 s-1),主要受到太阳辐射强度季节性变化及夏季多雨天气影响的双重作用。此外,利用紫外辐射晴空指数(KUV)与太阳天顶角余弦值构建的 JNO2)估算模型,计算值与观测值之间的线性拟合相关系数达 0. 99,且平均相对误差为8. 8%,均方根误差为0. 00036。结果验证了该模型在复杂大气条件下的较高适用性,提供了有效的预测手段。通过该模型重构的长期数据集表明,北京地区JNO22013-2023年总体呈显著上升趋势,年均增幅达2. 73%2023年均值为4. 20×10⁻³ s⁻¹,比2013年(3. 20×10⁻³ s⁻¹)增长了31. 3%。这一上升趋势与同期 PM2. 5浓度和气溶胶光学厚度的持续下降密切相关,PM2. 5浓度年均降幅为 5. 51%AOD 年均降幅为5. 74%。这些变化表明,随着大气清洁化措施的推进,颗粒物的减少降低了对紫外辐射的衰减作用,间接促进了光解速率的提升。研究发现,在污染条件下,JNO2)显著降低。当 PM2. 5浓度高于 75 μg·m-3时,JNO2)极值比无污染条件下降了 22. 8%。这一发现表明,PM2. 5的浓度变化是影响光解速率的一个重要因素。通过对气溶胶光学特性的分析发现,JNO2)与AOD及波长指数(AE)呈负相关,而与单次散射反照率(SSA)呈正相关。敏感性试验表明,当 AOD 0. 5 增加到 2. 5 时,JNO2)日极值降低了45. 6%。尤其是在 AOD 2. 5时,正午时段的衰减率达 49. 8%。相反,当 SSA 0. 2增至 1. 0时,气溶胶的散射能力增强,使得 JNO2)日极值增加了 43%。当 AE 0. 5 增加至 2. 0 时,JNO2)极值仅降低3. 0%,相较 AOD SSAAE 的变化对 JNO2)的影响较小。气溶胶对光化学过程的影响排序为:AOD > SSA > PM2. 5 > AE,其中 AOD JNO2)变化的影响最为显著,揭示了气溶胶的消光作用对光化学过程的抑制作用占据主导地位。此外,本研究还通过对TUV模型的验证,确认了其在模拟JNO2)时空变化中的可靠性,模拟值与观测值的相关性达0. 93。该验证结果为量化气溶胶-辐射-光化学耦合机制提供了重要的方法支撑。

本文引用格式

李春燕, 赵舒曼, 武淑敏, 刘 昆, 胡 波 . 基于观测与模拟的北京光解速率长期变化特征研究[J]. 高原气象, 0 : 1 . DOI: 10.7522/j.issn.1000-0534.2025.00037

Abstract

To investigate the evolutionary patterns of atmospheric photochemical parameters and the influence mechanisms of aerosols in Beijingthis study analyzed the spatiotemporal characteristics of nitrogen dioxide photolysis rate JNO2and explored the impacts of aerosols on JNO2based on near-surface observations of J NO2and ultravioletUVradiation in Beijing from September 2018 to August 2019combined with radiative transfer model TUV simulations and aerosol optical parameter analysis. Additionallythis study reconstructed a long-term photolysis rate dataset from 2013 to 2023 to further reveal the correlation between long-term trends and aerosol characteristics. The results show that the diurnal variation of JNO2exhibits a typical unimodal pat‐ ternwith the peak generally occurring during the noon period12:00-13:00,(Local Time the same as after)],directly influenced by changes in solar zenith angle. The daytime maxima of JNO2in summer are 1. 9 times those in wintermeasuring 5. 65×10-3 s-1 and 2. 95×10-3 s-1respectivelyindicating significantly enhanced photolysis rates in summer due to higher solar radiation intensity. Seasonal variations follow the ordersummer 3. 77×10-3 s-1> spring3. 51×10-3 s-1> autumn2. 97×10-3 s-1> winter2. 25×10-3 s-1),driven by seasonal changes in solar radiation intensity and the combined effects of increased summer precipitation. Furthermorean estimation model for JNO2constructed using the UV clear-sky indexKUVand the cosine of solar zenith angle demonstrated a linear correlation coefficient of 0. 99 between calculated and observed valueswith a mean relative error of 8. 8% and a root mean square error of 0. 00036. These results validate the model’s high applicability under complex atmospheric conditionsproviding an effective predictive tool. The reconstructed long-term dataset reveals a significant upward trend in JNO2in Beijing from 2013 to 2023with an annual increase rate of 2. 73%. The 2023 annual mean4. 20×10-3 s-1increased by 31. 3% compared to 20133. 20×10-3 s-1),closely linked to the continuous decline in PM 2. 5 concentrationsannual reduction rate5. 51%and aerosol optical depthAODannual reduction rate5. 74%. These changes suggest that reduced particulate matter due to air quality improvement measures has diminished UV radiation attenuationindirectly promoting photolysis rate enhancement. The study found that JNO2significantly decreases under polluted conditions. When PM2. 5 concentrations exceed 75 μg·m-³the JNO2maximum decreases by 22. 8% compared to clean conditionsindicating that PM 2. 5 concentration is a critical factor influencing photolysis rates. Analysis of aerosol optical properties revealed that JNO2is negatively correlated with AOD and Ångström exponentAE),but positively correlated with single-scattering albedoSSA. Sensitivity tests demonstrated that increasing AOD from 0. 5 to 2. 5 reduces the daily maximum JNO2by 45. 6%with a noon attenuation rate of 49. 8% at AOD = 2. 5. Converselyin‐ creasing SSA from 0. 2 to 1. 0 enhances aerosol scattering capacityraising the daily maximum JNO2by 43%. Increasing AE from 0. 5 to 2. 0 results in only a 3. 0% reduction in JNO2maximaindicating that AE has a weaker influence compared to AOD and SSA. The hierarchy of aerosol impacts on photochemical processes is ranked as AOD > SSA > PM 2. 5 > AEwith AOD exerting the most significant influence on JNO2variationshighlighting the dominant role of aerosol extinction in suppressing photochemical processes. Additionallyvali‐ dation of the TUV model confirmed its reliability in simulating JNO2spatiotemporal variationswith a correlation coefficient of 0. 93 between simulated and observed values. This validation provides crucial methodological support for quantifying aerosol-radiation-photochemistry coupling mechanisms.

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