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.     

在线表征萘二次有机气溶胶硝基多环芳烃

硝基多环芳烃(NPAHs)广泛存在于大气气溶胶中,是棕色碳的重要组成部分.萘和其他多环芳烃是NPAHs的重要前体物.为研究NO₂对NPAHs形成的影响,本文利用气溶胶激光飞行时间质谱仪(ALTOFMS)在线测定不同NO₂浓度下萘光氧化形成的二次有机气溶胶(SOA)的NPAHs组分.实验结果表明,NO₂对NPAHs的产生和萘SOA的形成有促进作用.通过ALTOFMS在线检测、模糊C均值(FCM)聚类分析,结合离线电喷雾电离质谱验证,测得萘酚和羰基化合物是不存在NO₂时萘SOA粒子主要成分,而通过OH-萘加合物和萘酚硝化产生的硝基萘、二硝基萘、硝基萘酚和二硝基萘酚及其衍生物是NO₂存在时萘SOA粒子的主要组分.这为城市大气高浓度NO_x背景下,研究NPAHs的化学组分和形成机理提供了实验依据.     

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.     

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。     

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.     

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.     

贵阳市环境空气挥发性有机物浓度组成特征及其大气化学作用

为深入了解挥发性有机物(VOCs)的大气化学作用,基于贵阳市2022年5月VOCs离线观测数据,系统性分析VOCs的浓度水平、化学组成、OH活性、NO₃活性、O₃活性、臭氧生成潜势(OFP)和二次有机气溶胶生成潜势(SOAP)。结果表明,观测期间,VOCs的浓度、OH活性、NO₃活性、O₃活性、OFP和SOAP平均值分别为71.86±12.86μg/m³、3.52±1.28s^-1、1.65×10^-3±1.57×10^-3s^-1、3.87×10^-7±4.31×10^-7s^-1、36.08±35.44μg/m³和369.41±231.42μg/m³,均呈现晚上高白天低的日变化。烷烃是VOCs浓度的主要贡献组分,占比为38.66%,需重点关注丙酮、二氯甲烷、正丁醛、一溴二氯甲烷和氯仿等高浓度物种;OVOCs是OH活性贡献最大的组分,占比为46.50%,烯烃是NO₃活性和O₃活性主要贡献种类,贡献占比分别为68.07%和97.85%,需重点关注乙烯、丙烯、正丁醛、异戊二烯、丁烯、反-2-丁烯和顺-2-丁烯等活性物种;OVOCs和芳香烃分别是OFP和SOAP主要贡献种类,占比分别为48.18%和78.92%,需重点关注正丁醛、乙烯、乙醛和丁烯等主要的OFP贡献组分,和芳香烃类物种、苯甲醛和正十二烷等主要的SOAP贡献组分。后向轨迹研究发现,为进一步削减贵阳市O₃和颗粒物污染,VOCs控制政策应该重点关注贵阳市东部和北部地区。     

2008～2018年武汉市BC气溶胶时间演变特征及其来源分析

黑碳(BC)作为最重要的吸收性气溶胶,其辐射强迫显著地改变大气边界层结构和近地面大气污染物的累积。基于2008～2018年武汉市BC和气象要素的观测数据,结合CWT潜在来源模型,分析了BC的时间演变特征和潜在来源分布。结果表明武汉BC平均质量浓度为6 926.4±4 090.6 ng/m³,Ångström指数(AAE)和液体燃料源对BC贡献占比(P)的平均值分别为0.98±0.44和76.6%,BC主要来自液体燃料的燃烧。2014～2017年BC质量浓度呈现显著的下降趋势,液体燃料对BC的贡献逐年增加。BC的季节分布为冬季(8 537.3 ng/m³)>春季(7 513.2 ng/m³)>秋季(6 820.2 ng/m³)>夏季(6 161.9 ng/m³),BC_liquid占比为秋季(80.0%)>冬季(77.3%)>春季(76.2%) >夏季(72.9%)。不同季节BC日变化特征不同。四个季节BC日变化在2008～2013年均以单峰型分布为主,而在2016～2017年则为双峰型分布。不同季节BC的潜在来源分布存在显著区别。潜在来源高值区在2008～2010年主要分布于武汉市的西南部,范围较小;而2016～2017年主要集中在武汉市周边地区,范围变大。潜在源区的演变反映了周边城市群对武汉市BC的影响逐渐变大,这可能是造成武汉市BC质量浓度日变化的年际差异的原因。     

基于卫星观测的中三角地区吸收性气溶胶的时空演化解析

基于臭氧检测仪（Ozone Monitoring Instrument,OMI）的遥感数据,利用ArcGIS10.2对2005—2020年中三角地区（湖北省、湖南省、江西省）紫外吸收性气溶胶指数（Ultraviolet Aerosol Index,UVAI）的时空变化进行分析,结合气溶胶颗粒物（PM_2.5、PM₁₀）和气态污染物（CO）数据,利用HYSPLIT（Hybrid Single-Particle Lagrangian Integrated Trajectory model）方法研究主要污染城市气溶胶颗粒物的来源与传输路径,通过核密度估计法、相关性分析、聚类分析,研究其影响因素.结果表明：①在空间分布上,中三角地区吸收性气溶胶的高值区集中在襄阳市北部、孝感市 东部、武汉市西部;在时间分布上,2008年UVAI最低,2014年达到最大值;季节分布具有明显变化,2005—2020年吸收性气溶胶指数季均值为冬季＞春季＞秋季＞夏季.②UVAI与人口增长率、第二产业产值占总产值的比重呈正相关性,与节能环保预算支出呈显著负相关（p<0.05）. 不同的土地利用方式也是影响吸收性气溶胶空间分布的原因：UVAI低值分布地区的林地密度较高,而城乡、工矿、居民用地密度高的地区 吸收性气溶胶指数均较高.③结合气流输送情况与PM_2.5、PM₁₀、CO数据可知,来自湖北省武汉市与麻城市交界的近距离气流携带的气溶胶 颗粒物（PM_2.5、PM₁₀）及远距离气流所携带的碳质污染物（CO）,会对中三角地区吸收性气溶胶产生影响.④以襄阳市、武汉市为受点城市,对其进行潜在源贡献因子分析（PSCF）：2020年襄阳市冬季UVAI的潜在源区主要集中在河南省南部与湖北省东北部地区;2020年武汉市冬季UVAI的潜在源贡献因子（WPSCF）高值区主要分布在湖北省东部、江西省西北部、湖南省东北部.