杭州市大气VOCs浓度特征及其化学活性研究

2018年4月至2019年3月对杭州市城区大气中117种挥发性有机物（VOCs）开展了为期一年的手工采样观测,分析了VOCs各组分的浓度特征、臭氧生成潜势（OFP）和二次有机气溶胶（SOA）生成潜势。结果显示,观测期杭州市大气VOCs体积分数均值为（56.72±29.56）×10^-9,含氧挥发性有机物（OVOCs）、烷烃和卤代烃是其主要组分,分别占33.86%、30.70%、15.73%。VOCs体积分数前10位的物种为丙烷、甲醛、异丁烷、乙烷、乙酸乙酯、二氯甲烷、正丁烷、丙酮、甲苯和1,2-二氯乙烷。杭州市VOCs的OFP为135.18×10^-9,各VOCs组分的OFP贡献为OVOCs（45%） > 芳香烃（22%） > 烯烃和炔烃（21%） > 烷烃（11%） > 卤代烃（1%）,其中甲醛、乙烯和乙醛是OFP主要贡献者。SOA生成潜势为1.64 μg/m³,芳香烃是最重要的SOA前体物。SOA生成潜势最大的5种VOCs物种为甲苯、对/间二甲苯、乙苯、邻二甲苯和苯,因此控制来自机动车尾气和溶剂使用过程中产生的VOCs可有效降低SOA的生成。通过甲苯与苯体积分数比分析发现,杭州市城区芳香烃除了来自机动车尾气以外,在春、夏季和秋、冬季还分别受到生物质燃烧和涂料溶剂的影响;分析了乙烷与乙炔体积分数比、乙炔与CO体积分数比,发现杭州市气团的老化程度呈现整体较高的特点。     

基于高分辨率MARGA分析成都市PM2.5中水溶性离子污染特征

利用高分辨率MARGA对2019年成都市PM_2.5中水溶性离子展开连续监测,结合气象参数分析水溶性离子污染特征.结果表明,MARGA监测的8种水溶性离子质量浓度与PM_2.5变化趋势一致,水溶性离子年均浓度为(23.1±13.6)μg·m^-3,在PM_2.5中占比为48.6%,表明水溶性离子为PM_2.5重要组分.各离子质量浓度大小顺序依次为:NO^-₃、 SO₄^2-、 NH⁺₄、 Cl^-、 Ca²⁺、 K⁺、 Mg²⁺和Na⁺,其中二次离子(NO^-₃、 SO₄^2-和NH⁺₄)年均质量浓度为(20.2±2.7)μg·m...     

基于稳定碳同位素的济南市二元羧酸类SOA的污染特征与形成机制

为研究济南市冬季PM2.5中二元羧酸类二次有机气溶胶(SOA)的来源、液相形成机制及影响因素,于2016年1-2月昼夜共采集46个PM2.5样品,并对二元羧酸类SOA(包括二元羧酸、酮羧酸与α-二羰基化合物)与左旋葡聚糖的昼夜变化特征进行分析.研究结果表明,二元羧酸、α-二羰基化合物与左旋葡聚糖均呈昼低夜高的变化特征,...     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.     

成都碳质气溶胶变化特征及二次有机碳的估算

利用2020年6月~2021年5月在成都市观测的碳质气溶胶小时分辨率数据,分析了气溶胶中总碳(TC)、有机碳(OC)、元素碳(EC)和二次有机碳(SOC)的变化特征.结果表明:观测期间m(TC)、m(OC)、m(EC)和r(OC/EC)的年均值分别为(9.5±4.4)μg/m³,(6.4±3.2)μg/m³,(3.2±1.1)μg/m³,2.2±0.5.成都m(TC)、m (OC)、m (EC)均表现冬为季最高((15.8±8.2),(11.1±5.8),(4.6±2.5)μg/m³),春秋次之,夏季最低((6.1±0.9),(4.5±2.0),(2.7±1.4)μg/m³)的特征.r(OC/EC)季节均值(1.9~2.6)以及四个季节的m(TC)、m(OC)、m(EC)呈现早(07:00~09:00)晚(22:00~01:00)“双峰”的日变化特征,表明机动车排放源对成都碳质气溶胶的影响较大.春夏季OC与EC的相关性小于秋冬季,表明春夏季OC、EC来源差异较大.由EC示踪法和最小相关性法得到m(SOC),r(SOC/OC)在夏季最大(40.4%),冬季最小(27.3%).春、夏季SOC与O₃呈显著正相关,表明较强的光化学反应对SOC生成有重要贡献.选取各季节连续高m(TC)时段与季节平均作对比,发现碳质气溶胶有明显夜间积累过程,夏季高浓度时段二次生成使得m(OC)增长显著高于m(EC),r(OC/EC)也迅速上升.