【作者】 李敏；

【导师】 王新锋；

【作者基本信息】 山东大学 ， 环境科学， 2025， 博士

【摘要】 大气硝基酚类化合物是含氮有机气溶胶和棕色碳的重要组成部分,能够改变大气辐射平衡,影响区域乃至全球气候和空气质量,并对生态系统和人体健康带来危害,其污染特征和源汇机制是当前大气化学领域的研究热点之一。过去十余年,日臻完善的监测分析手段使得大气硝基酚类化合物的污染水平和组成特征等有了较为细致的表征,然而硝基酚类化合物在实际大气环境中的相态分布、来源贡献及形成机理仍不明确。因此,开展多相态同步观测以及模型模拟研究,深入解析硝基酚类化合物的生成机制及其影响因素,对于推进大气污染防治和改善区域空气质量具有重要的科学意义和现实需求。本研究首先在泰山高山站点典型云雾事件期间,开展了颗粒态、气态和云雾水样品的同步采集与大气强化观测实验。此外还在我国东部其他典型高山、城市和农村站点的不同季节开展大气观测实验,系统采集了大气细颗粒物样品并分析了硝基酚类化合物的浓度水平与变化特征。基于上述观测数据,结合多相化学盒子模型以及可解释性集成机器学习模型等,系统解析了硝基酚类化合物的相态分布特征,明确了其主要来源及生成机制,并深入探讨了硝基酚类化合物的浓度变化与关键驱动因素之间的复杂非线性关系。针对泰山不同季节的典型云雾事件,明确了实际大气环境中硝基酚类化合物在颗粒相、气相和云雾水中的分布特征,证实了液相生成对其相态分布及生成的显著影响。颗粒态硝基酚类化合物总平均浓度略低于其在气相中的浓度,且呈现冬季高于春季的变化特征,而春季云雾水中硝基酚类化合物的浓度最高。4-硝基酚和硝基水杨酸是颗粒相硝基酚类化合物的主要成分,而气相和云雾水样品则以4-硝基酚和2,4-二硝基酚为主。多数硝基酚类化合物主要分布于颗粒相,同时实测亨利系数值显著高于理论值,且颗粒态硝基酚类化合物的实测浓度显著高于理论预测值,表明液相反应在高湿环境下显著促进硝基酚类化合物的生成。此外,云雾水中2,4-二硝基酚与4-硝基酚和5-硝基水杨酸总和的比值整体高于颗粒相,进一步证明液相过程对硝基酚类化合物生成和分布的重要作用。基于泰山冬季大气强化观测数据,利用多相化学盒子模型揭示了大气传输、气-粒分配以及非均相过程对细颗粒态硝基酚类化合物形成不可忽视的贡献。泰山冬季细颗粒态硝基酚类化合物总平均浓度处于较高污染水平,其中硝基苯酚类和二硝基酚类是含量最丰富的化合物。多数硝基酚类化合物表现出显著的昼夜变化特征,白天正午前后浓度达到峰值,这主要归因于日间山谷风将城市大气污染物输送至高山采样站点以及增强的光氧化反应。相关性分析表明,细颗粒态硝基酚类化合物浓度受温度、湿度以及气溶胶表面积多种等因素调控。利用改进的耦合了气-粒分配、非均相反应、排放、沉降等过程的多相化学盒子模型对细颗粒态硝基酚类化合物生成事件进行模拟,结果表明细颗粒态硝基酚类化合物的生成机制具有显著的昼夜差异,即在白天以一次排放源的传输过程为主,而在夜间由气-粒分配以及气溶胶表面的非均相反应主导。利用我国东部多站点、不同季节大气细颗粒物中硝基酚类化合物的综合观测数据,构建了融合一次排放、二次生成与气象条件的可解释性多目标变量集成机器学习模型,探索了硝基酚类化合物与关键环境驱动因素的非线性关系,量化了不同季节、不同官能团类型以及站点类型下硝基酚类化合物对驱动因素的响应及贡献。集成机器学习模型结果确认了人为排放是最重要的驱动因素,同时气象因素和二次生成对硝基酚类化合物也有显著影响。其中,一次排放和二次生成对硝基酚类化合物呈正相关影响,而温度和边界层高度(<600m)则呈现负相关关系。季节性分析表明,一次排放在春、夏和秋季对硝基酚类化合物的贡献较大,而冬季温度则成为主导驱动因素。此外,不同官能团类型的硝基酚类化合物的主要驱动因素也存在显著差异。冬季人为排放对城市和农村地区细颗粒态硝基酚类化合物生成起主导作用,而高山地区则主要归因于低温条件气相氧化反应导致的二次生成。本研究通过多站点野外观测与多模型模拟相结合的研究手段,系统揭示了我国东部典型地区大气硝基酚类化合物的污染特征和生成机制。相关结果为进一步认识大气活性氮的环境行为和污染成因提供了新视角,同时也为应用多相化学盒子模型及机器学习模型解析大气活性氮的生成机制及相关化学过程提供了参考,并为制定精细化污染防控措施和改善区域空气质量提供了有力的科学支撑。更多还原

【Abstract】 Nitroaromatic compounds(NACs)are key constituents of atmospheric nitrogencontaining organic aerosols and brown carbon.They can alter the atmospheric radiative balance,influence regional and even global climate and air quality,and pose potential threats to ecosystems and human health.The pollution characteristics and source-sink mechanisms of NACs turn to be a hotspot in the research community of atmospheric chemistry.Over the past decade,the concentration levels,compositions,and pollution characteristics of NACs have been comprehensively investigated based on sophisticated monitoring and analytical techniques.However,their phase distribution,sources,and formation mechanisms in real atmosphere still remain unclear.Therefore,conducting multi-phase simultaneous observations and model simulations to elucidate the formation mechanisms and influencing factors of NACs is of significant scientific importance and practical necessity for promoting the prevention and control of atmospheric pollution and improving regional air quality.This study first conducted simultaneous sampling of particle-,gaseous-,aqueous-phase samples during typical cloud events at Mount Tai,along with intensive atmospheric observation experiments.Additionally,observation campaigns were also carried out across different seasons at various representative mountain,urban,and rural sites in eastern China,where fine particulate matter samples were collected and the concentrations and variation characteristics of NACs were analyzed.Based on the observational data,combined with a multiphase chemical box model and an interpretable ensemble machine learning model,etc.,this study systematically elucidated the phase distribution characteristics of NACs,identified their sources and formation mechanisms,and deeply explored the complex nonlinear relationships between NACs concentrations and key driving factors.For cloud events across different seasons at Mount Tai,this study investigated the distribution characteristics of NACs in the particles,gas phase,and cloud water in the actual atmospheric environment,and confirmed the significant influence of aqueous-phase reactions on their distribution and formation.The average total concentrations of particle-phase NACs were a little lower than those measured in the gas-phase,exhibiting a seasonal pattern of higher concentrations in winter than in spring,while the highest concentrations in cloud water were observed in spring.4-Nitrophenol and nitrosalicylic acids were the most dominant compounds in particles,whereas 4-nitrophenol and 2,4-dinitrophenol were the most abundant species in the gas phase and cloud water samples.Most NACs were predominantly distributed in the particle phase.The field-derived effective Henry’s law coefficients were significantly higher than their theoretical values,and the measured concentrations of particulate NACs were substantially greater than the theoretical predictions.The above findings indicate that aqueous-phase reactions significantly promoted the formation of NACs under high-humidity conditions.Furthermore,the generally higher ratios of 2,4-dinitrophenol to the sum of 4-nitrophenol and 5-nitrosalicylic acid in cloud water than those in particles further confirm the significance role of aqueous processes in the formation and distribution on NACs.The significant contributions of atmospheric transport,gas-particle partitioning,and heterogenous processes to the formation of fine particulate NACs were revealed with application of multiphase chemical box model combined with intensive wintertime observational data at Mount Tai.The average total concentration of particulate NACs in winter was at a relatively high pollution level,with nitrophenols and dinitrophenols being the most abundant species.Most of NACs exhibited distinct diurnal pattern with a high concentration peak during daytime around noon.The daytime enhancement of NACs was mainly attributed to pollutant transport by the frequent daytime mountain-valley breeze and the facilitated photooxidation at the mountaintop site.Correlation analysis indicated that the concentrations of fine particulate NACs were regulated by various factors,including temperature,humidity,and aerosol surface area.Innovatively,a multiphase chemical box model for particulate NACs incorporating gas-particle partitioning,heterogeneous reactions,emissions,and deposition processes was applied to elucidate the formation pathways and source contribution,and the results demonstrated distinct diurnal differences in their formations.Specifically,air mass transport from primary emission sources played a crucial role during daytime,whereas at nighttime secondary formation via gas-particle partitioning and heterogenous processes dominated the observed particulate NACs.By integrating multi-seasonal and multi-site observational data of fine particulate NACs across eastern China,this study constructed an interpretable multi-target variable ensemble machine learning model that incorporates primary emissions,secondary formation,and meteorological conditions.This framework enabled exploration the nonlinear relationships between NACs and key environmental driving factors,and quantified the responses and contributions of NACs to these factors under various seasons,functional group types,and sampling site types.The ensemble machine learning model recognized that anthropogenic emissions(i.e.,coal combustion,traffic emission,and biomass burning)were the most important driving factors,while significant influences from meteorology and secondary formation were also confirmed.Among the driving factors,primary emissions and secondary formation contributed positively to the enhancement of NACs,while temperature and boundary layer height(<600 m)displayed negative impacts.Seasonal variations analysis showed that direct emissions presented positive responses to NACs concentrations in spring,summer,and autumn,while temperature had the largest impact in winter.Furthermore,the main drivers of NACs differed significantly among functional groups.In winter,anthropogenic emissions were the dominant factor for NACs formation in urban and rural areas,while in the mountain region,reduced ambient temperature along with secondary formation from gas-phase oxidation was the main driver for relatively high particulate NACs levels.This study systematically reveals the pollution characteristics and formation mechanisms of atmospheric NACs in typical regions of eastern China based on multi-site field observations and multi-model simulations.The relevant results provide new insights into the environmental behaviors and pollution causes of reactive nitrogen in the atmosphere.Meanwhile,this study also provides a reference for the application of multiphase chemical box model and machine learning model to analyze the formation mechanisms and related chemical processes of reactive nitrogen.Moreover,these results provide strong scientific support for formulating refined control strategies and improving regional air quality.更多还原