阳泉市区夏季挥发性有机物污染特征、来源解析及其环境影响

采集阳泉市区夏季3个监测点的环境空气样品,利用气相色谱-质谱/氢火焰离子化检测器(GC-MSD/FID)测定了挥发性有机物(VOCs)的组成,研究了其浓度特征,运用特征比值法和正定矩阵因子分析模型(PMF)解析了VOCs来源,评估了VOCs对O_3和二次有机气溶胶(SOA)生成的影响.结果表明,阳泉市区VOCs平均总浓度为(82.1±22.7)μg·m~(-3),其中烷烃浓度占比最大(51.8%),其次是芳香烃(17.8%)和烯烃(8.0%),炔烃浓度占比最小(3.8%). VOCs呈现双峰的变化特征,分别于08:00～10:00和18:00～20:00出现峰值,在12:00～14:00出现谷值.苯/甲苯和异戊烷/正戊烷的均值分别为2.1±1.3和1.7±0.6,表明环境空气VOCs可能受燃煤排放和机动车排放的双重影响. PMF解析出VOCs来源分别为燃煤源(34.9%)、机动车排放源(18.2%)、汽油挥发源(15.2%)、工业排放源(13.6%)、植物排放源(9.2%)和溶剂使用源(9.0%). VOCs臭氧生成潜势(OFP)均值为156.6μg·m~(-3),烯烃贡献最大,二次有机气溶胶生成潜势(SOA_p)均值为68.7μg·m~(-3),芳香烃的贡献达到93.4%.总之,燃煤排放对VOCs的贡献较高,因此,控制燃煤源排放是阳泉市区VOCs管控重点,需加快矸石山治理和能源结构调整,同时机动车排放源、汽油挥发源和工业排放源的管控也不容忽视.     

南北极大气气溶胶单颗粒成分特点研究

运用定量电子探针微区分析技术(EPMA)分别测定了采自北极新奥尔松地区(78?55′N、11?56′E)和南极乔治王岛(62?13′S、58?47′W)极昼天气下PM10大气颗粒物样品.结果表明,北极和南极大气颗粒物化学成分存在很大差异,表现出各自不同的特点.北极的颗粒类型中,“反应的海盐”和“矿物尘”分别占颗粒总数的44%和27%,“新鲜海盐”所占的比例不到10%,“反应的海盐”中以含硝酸盐的颗粒为主,反映了外来物质或人为污染对该地气溶胶影响较大;南极的颗粒类型中,“新鲜海盐”占总数的74%左右,“反应的海盐”占19%,反应的海盐全部含硫酸盐、未发现含硝酸盐的颗粒,推测与海盐反应的含硫物质来源于海洋浮游生物代谢过程产生的二甲硫醚(DMS)及其降解产物,而与人为污染无关.     

西安市春季大气细粒子的质量浓度及其水溶性组分的特征

为了探讨西安市春季大气细粒污染物的污染水平及水溶性组分的特征及来源,2005年3—5月对西安大气PM2.5进行了观测,并应用离子色谱对其中的水溶性组分进行了分析。结果显示,西安市春季大气PM2.5的质量浓度为159.9μg·m-3。分析的11种阴阳离子(Na 、NH4 、K 、Mg2 、Ca2 、F-、Cl-、Br-、NO2-、NO3-和SO42-)质量浓度占PM2.5的30%,表明水溶性组分是大气细粒子的主要组成之一。NH4 、SO42-和NO3-为水溶性离子的主要组分,其平均质量浓度分别为6.6、20.1和7.6μg·m-3,在总水溶性离子中的百分比分别为12.4%、47.4%和16.9%,SO42-和NO3-质量浓度与能见度有较好的负相关性,表明细粒子中二次气溶胶组分对能见度有显著的影响。阴阳离子的平衡和pH值的测定结果显示,西安市大气细粒子污染物为弱酸性。离子间的相关性分析揭示水溶性离子在颗粒物中主要结合方式为(NH4)2SO4、NH4HSO4、NH4NO3、KHSO4和K2SO4。Mg2 和Ca2 的相关性也较好,其摩尔比率为0.07,小于中国北方沙漠和黄土的平均值(0.15),揭示二次扬尘和建筑扬尘等过程对Ca2 的质量浓度影响较大。计算的NO3-/SO42-质量浓度比值的均值为0.38,说明固定排放源(燃煤)对细粒子中水溶性组分的贡献大于移动排放源(机动车)。     

北京森林BVOCs排放现状及动态变化特征分析

以北京地区森林植被为研究对象,基于森林资源清查蓄积资料和逐小时气象数据,采用光温影响模型对2000~2020年北京森林BVOCs排放量进行估算,并分析其对空气质量的影响.结果显示,2020年北京森林BVOCs排放量为39.57×10⁹g C,异戊二烯、单萜烯和OVOCs分别占72.19%、17.48%和10.32%,杨树、栎树等阔叶树是主要的异戊二烯排放源,油松等针叶树是主要的单萜烯排放源.2000~2020年森林BVOCs排放量从20.30×10⁹g C/a增加到39.57×10⁹g C/a,年平均增长率4.75%;BVOCs排放量的变化表现出明显阶段性特征,2000~2010年增长缓慢,2010~2020年出现大幅上升.20年间异戊二烯所占比重呈下降趋势,单萜烯和OVOCs所占比重则呈上升趋势;杨树对BVOCs排放量的贡献逐渐降低,栎树和其他阔叶树的贡献明显增加,北京新增森林更加注重物种多样化.2000~2020年,BVOCs的O₃生成潜势从181.76×10⁹g增加到331.07×10⁹g,异戊二烯占92.70%,是主要的贡献者;SOA生成潜势从1.11×10⁹g增加到2.65×10⁹g,单萜烯和异戊二烯分别占75.40%和24.60%.O₃生成潜势最大的树种是杨树,SOA生成潜势最大的树种是油松.森林BVOCs排放在夏季对O₃污染的贡献最大,未来绿化中应考虑优化树种组成.     

Nitrogenous and carbonaceous aerosols in PM2.5 and TSP during pre-monsoon: Characteristics and sources in the highly polluted mountain valley

This study reports for the first time a comprehensive analysis of nitrogenous and carbonaceous aerosols in simultaneously collected PM_2.5 and TSP during pre-monsoon (March–May 2018) from a highly polluted urban Kathmandu Valley (KV) of the Himalayan foothills. The mean mass concentration of PM_2.5 (129.8 µg/m³) was only ~25% of TSP mass (558.7 µg/ m³) indicating the dominance of coarser mode aerosols. However, the mean concentration as well as fractional contributions of water-soluble total nitrogen (WSTN) and carbonaceous species reveal their predominance in find-mode aerosols. The mean mass concentration of WSTN was 17.43±4.70 µg/m³ (14%) in PM_2.5 and 24.64±8.07 µg/m³ (5%) in TSP. Moreover, the fractional contribution of total carbonaceous aerosols (TCA) is much higher in PM_2.5 (~34%) than that in TSP (~20%). The relatively low OC/EC ratio in PM_2.5 (3.03 ± 1.47) and TSP (4.64 ± 1.73) suggests fossil fuel combustion as the major sources of carbonaceous aerosols with contributions from secondary organic aerosols. Five-day air mass back trajectories simulated with the HYSPLIT model, together with MODIS fire counts indicate the influence of local emissions as well as transported pollutants from the Indo-Gangetic Plain region to the south of the Himalayan foothills. Principal component analysis (PCA) also suggests a mixed contribution from other local anthropogenic, biomass burning, and crustal sources. Our results highlight that it is necessary to control local emissions as well as regional transport while designing mitigation measures to reduce the KV's air pollution.     

南京雾过程对大气气溶胶谱分布及化学组成的影响

为探究南京地区雾过程对气溶胶粒子化学组成和尺度分布的影响,在2017年冬季的雾观测中平行收集了3级分档雾水和分粒径气溶胶样品,并对雾微物理量与气溶胶谱分布、3级分档雾水与雾前、雾中、雾后分粒径气溶胶化学组成对比分析。结果表明,2017年冬季南京第1次雾过程的雾滴液态水含量随粒径分布为不对称“V”型,最低值位于7μm处,第2次雾过程的雾滴液态水含量随粒径分布为3峰型,峰值分别位于5,15,21.5μm处。在雾形成、发展阶段,粒径<0.33μm的气溶胶质量浓度降低,粒径0.38μm气溶胶质量浓度升高,雾成熟阶段,气溶胶粒子质量浓度在全粒径段均达到最低,粒径0.38μm的气溶胶质量浓度大幅降低,与雾前相比,雾后气溶胶质量浓度峰值向大粒径方向移动。雾前,气溶胶水溶性离子组分富集在粒径<0.43μm的小粒子中,随着雾过程进行,成核作用和吸湿增长使得水溶性离子向较大粒径段富集。雾中新生成的气溶胶随着雾滴的蒸发被释放,导致雾后NO₃^-、SO₄^2-和NH₄⁺浓度升高。较小粒径的气溶胶中和率更高,雾形成初期的新生雾滴酸性较强,随着雾过程的进行逐渐中和,雾水pH值逐渐升高。     

Assessing the influence of environmental conditions on secondary organic aerosol formation from a typical biomass burning compound

The atmospheric chemistry in complex air pollution remains poorly understood. In order to probe how environmental conditions can impact the secondary organic aerosol (SOA) formation from biomass burning emissions, we investigated the photooxidation of 2,5-dimethylfuran (DMF) under different environmental conditions in a smog chamber. It was found that SO₂ could promote the formation of SOA and increase the amounts of inorganic salts produced during the photooxidation. The formation rate of SOA and the corresponding SOA mass concentration increased gradually with the increasing DMF/OH ratio. The addition of (NH₄)₂SO₄ seed aerosol accelerated the SOA formation rate and significantly shortened the time for the reaction to reach equilibrium. Additionally, a relatively high illumination intensity promoted the formation of OH radicals and, correspondingly, enhanced the photooxidation of DMF. However, the enhancement of light intensity accelerated the aging of SOA, which led to a gradual decrease of the SOA mass concentration. This work shows that by having varying influence on atmospheric chemical reactions, the same environmental factor can affect SOA formation in different ways. The present study is helpful for us to better understand atmospheric complex pollution.     

Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds

Fine particulate matter (PM_2.5) and ozone (O₃) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O₃ and secondary organic aerosols (SOA) contributing PM_2.5. Herein, we investigated 54 VOCs, O₃ and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O₃, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O₃ was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O₃ and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O₃ formation; alkanes (C_n, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O₃ and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O₃ formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O₃.     

超大城市新粒子生成事件对云凝结核贡献分析

高浓度气溶胶在受人类活动影响的污染地区种类和组成非常复杂,因此,其环境和气候效应引起了广泛关注,但在超大城市背景下气溶胶粒子活化成为云凝结核的过程与边界层的相互作用尚不完全清楚.本研究基于北京（BJ）、上海（SH）、广州（GZ）的观测数据,选取3个城市的春季和冬季（北京冬季11月和广州冬季12月的连续观测,以及上海春季4月的加强观测）集成观测气溶胶数据和云凝结核同期观测的变化并结合其他污染物演化规律,对比分析了新粒子生成事件和环境变量演化对3个超大城市云凝结浓度形成的影响.结果表明,以用云凝结核（CCN）浓度与气溶胶（CN）数浓度的比值作为3个城市的活化率,北京CCN数浓度约为（500±200） #·cm^-3,CN最大浓度小于（1.0×10⁴±0.3×10⁴） #·cm^-3,活化率约为0.07%.上海CCN数浓度为（1500±500） #·cm^-3,CN最大浓度小于（98.0×10⁴±0.3×10⁴） #·cm^-3,最大活化率为0.05%.广州CCN数浓度为（150±30） #·cm^-3,CN最大浓度为（24.0×10³±0.3×10³） #·cm^-3,最大活化率为0.03%.本文旨在阐明气溶胶物理化学性质（粒子谱、化学成分、排放源等的时空演变特征）在不同排放源和大气边界层条件的影响下,造成不同类型的排放和输送过程对气溶胶活化率的影响,对比发现新粒子生成（NPF）期间CCN数浓度明显高于非新粒子生成（Non-NPF）时期,证明NPF发生时对CCN的活化率（AR）有显著的提升,在过饱和度SS=0.1%下,北京达到峰值在4×10^-3附近,广州的最大值约为17×10^-4,上海的最大值为3×10^-3.且在NPF期间气溶胶活化率显著增加,3个超大城市在NPF和Non-NPF期间,CCN数浓度与活化率的关系在上海尤其明显,化学成分包括有机物、硝酸盐和硫酸盐等物质也会影响超大城市地区气溶胶粒子的活化率,对比发现3个城市在NPF期间CCN数浓度和活化率都明显区别于其他时段.发现气溶胶的物理和化学性质以及与活化率的直接关系,可用以评估新粒子生成对区域大气环境（如霾）的影响,并估计气溶胶贡献为CCN的间接气候影响.     

2003~2018年四川盆地气溶胶光学厚度空间分异及驱动因子

为揭示四川盆地气溶胶光学厚度（AOD）的空间分布格局并定量评估影响其时空分异的驱动因子,基于2003~2018年（16a） MODIS气溶胶产品数据,采用Mann-Kendall突变检验法,空间自相关,空间热点探测分析和地理探测器等地统计方法分析研究.结果表明：2003~2018年四川盆地AOD总体呈现下降趋势,且突变年为2015年,并依据趋势变化将2003~2018年分为6个时段.四川盆地气溶胶区域性污染特征明显,AOD高值区主要聚集在盆地中部低海拔地区,而AOD低值区则多聚集在盆地边缘高海拔地区.AOD空间分布具有显著的聚集性规律（空间正相关,Moran's I指数>0）,自2012年以来高-高值聚集区面积不断减小,且不同时段聚集区AOD年际变化与AOD值分布变化态势一致.利用主成分分析法优选出8个因子,经地理探测器分析表明,16a来盆地区域AOD时空分异主要是由于城市化和工业化发展水平不均衡引起的.2014~2015年所有驱动因子的驱动力较之前时间段出现11.2%~59.2%的减小,且社会经济因子尤为明显,与2015年为突变年的结论相一致.