长三角沙尘中气溶胶粒径分布及化学组分特征

基于PM、10nm~10μm气溶胶数谱、水溶性离子和气象要素数据,分析了2017年5月3日~8日一次沙尘远距离输送过程中长三角地区气溶胶粒径分布及其化学组成的污染特征.结果表明,此次沙尘伴随天气系统由北往南的传输过程中,PM的浓度逐渐降低,但是高浓度PM持续时间逐渐增加.沙尘在呼和浩特市影响时间为38h,而在南京的影响时间超过60h.沙尘期间气溶胶数浓度谱的峰值向大粒径段偏移,沙尘和非沙尘期间峰值分别位于33和26nm.表面积浓度谱在非沙尘期间为三峰型分布,但是在沙尘期间为四峰型分布.在沙尘期间PM_2.5和PM₁₀中水溶性离子的排序为Ca²⁺ > NH₄⁺ > SO₄^2- > NO₃^- > Mg²⁺ > Na⁺ > Cl^- > NO₂^- > K⁺ > F^-,非沙尘期间为NH₄⁺ > SO₄^2- > NO₃^- > Mg²⁺ > Ca²⁺ > Cl^- > NO₂^- > K⁺ > Na⁺ > F^-.沙尘期间不同水溶性离子的浓度变化不同,沙尘天PM_2.5和PM₁₀中Ca²⁺浓度分别是非沙尘天的9.5和13.7倍,Na⁺分别是非沙尘天的4.4倍和4.6倍.沙尘天PM_2.5和PM₁₀中Ca²⁺占总离子的比例分别为24.7%和24.9%,是非沙尘天的4.9和5.7倍.NO₃^-在PM₁₀中的占总离子的比例为18.7%,高于非沙尘天（13.9%）,但是在PM_2.5中占总离子的比例仅为7.9%,低于非沙尘天（13.2%）.沙尘天F^-、Cl^-、SO₄^2-、NH₄⁺和K⁺离子在PM_2.5和PM₁₀中所占总离子的比例均低于非沙尘天.     

原位载铁中孔活性炭吸附As和天然有机物效能

采用配煤、原位浸渍和两步活化法制备了4种原位载铁活性炭（FGL1/2/3/4）,并以空白炭C-GL为基础的表面铁浸渍后改性炭（Fe-GL-2/3/4）为对照,研究了原位载铁炭对水中As和腐植酸（HA）的同步吸附效能.结果表明,炭化料原位载铁促进了比表面积（S_BET）和中孔结构的发育.其中,原位载铁炭FCL4（载铁量6.51%）在45Å~480Å的范围内的中孔容积（V_mes）比C-GL增加了0.1146cm³/g;而后改性载铁则造成S_BET和V_mes的显著降低.原位载铁同时促进了表面碱度的增加,保证了中性条件下更好的As离子吸附能力;FCL4对As（Ⅲ）和As（V）的Langmuir最大吸附量（L-Q_max）分别达到2.566和2.825mg/g.原位载铁炭进一步发育的中孔结构促进了对HA（<10mg DOC/L）的吸附效能,FGL4对HA的Langmuir最大吸附量（Q_HA）达到46.25mg DOC/g.As-HA共存体系内FGL4对各组分的吸附容量有所降低,但As（Ⅲ）和As（V）的吸附容量仍达到2.325和2.675mg/g.     

巫山地区三类黄土沉积物的粒度特征及物源启示

长江三峡地区的巫山黄土位于润湿地区,其来源及成因具有重要的古气候意义。黄土的粒度常用于指示其成因及源区,目前关于巫山黄土成因和来源还是存在许多争议,并且过去对巫山黄土粒度的研究主要集中于单一剖面,缺乏空间上的系统对比研究。基于此,对长江三峡巫山地区三个海拔梯度变化明显的黄土剖面进行系统的粒度组成分析,结果表明:顶部望天坪、中部圣泉和底部江东嘴剖面的粒度频率曲线大多表现为不对称的多峰特征。圣泉剖面以粉砂级粒径含量为主的特点说明它的风成成因,物源主要来自近缘河谷;江东嘴剖面呈现出两种完全不一样的峰型,可能由两种不同成因混合而成;位于望天坪剖面沉积物粒径要细得多,推测为远源风成物质。     

典型集雨人饮地区窖水微生物群落多样性及差异解析

为阐释我国典型集雨饮水地区窖水水质自然变化机制,应用16S rRNA基因-Illumina Mi Seq高通量测序技术,对以庭院为组织单元、不同类型集流面和水窖组合模式构建的集雨水源地水体中微生物群落及功能多样性差异进行了研究,表明窖水微生物群落结构及其功能存在显著的差异,造成这种差异的主要因素是窖水内部环境因子、外部环境因子等的异质性.窖水微生物群落结构多样性差异呈现大量的稀有种和少数常见优势种的分布模式,不同类型集流面和水窖组合模式构建的集雨水源地水体中含有不同的显著差异性微生物类群;微生物群落功能多样性差异体现在:编码新陈代谢功能的基因序列与其他功能基因序列相比具有显著的优势性,氨基酸、碳水化物、能量、脂类、辅助因子和维生素、萜类和酮类化合物、核苷酸、多糖的合成与代谢、外来物质的降解与代谢是构成新陈代谢功能的主要基因序列类群,表明窖水中存在大量、具有相对特定生态功能的微生物进行着诸多活跃的与新陈代谢相关活动.研究认为混凝土集流面与混凝土水窖组合模式用于收集和贮存雨水,水质更好、更安全.研究结果加深了对窖水微生物群落结构和多样性的认识,为开展集雨窖水水源地优选及改善窖水水质提供了借鉴.     

唐山市大气颗粒物OC/EC浓度谱分布观测研究

华北重工业城市唐山大气颗粒物污染严重,2009～2011年PM1.1、PM2.1、PM9.0及TSP年均值分别为(75±43)、(106±63)、(221±100)和(272±113)μg.m-3;碳质气溶胶在各粒径段均占较大比重,其中,元素碳(EC)在PM1.1、PM2.1、PM9.0及TSP各粒径段的年均比重分别约为9%、9%、6%和4%,有机碳(OC)年均比重分别为25%、24%、16%和14%.颗粒物浓度谱分布及碳质气溶胶富集量呈显著季节变化,秋冬季节细颗粒物中EC和OC浓度可高达(9±4)、(11±5)和(19±7)、(28±10)μg.m-3,分别占PM2.1的11%、10%和26%、25%;春夏季节EC和OC在粗细粒子中的富集量基本相当,分别为(5±2)、(5±1)和(15±3)、(15±1)μg.m-3,分别约占颗粒物总量的7%、6%和26%、18%.     

三醋酸甘油酯在活性炭上的吸附特性

三醋酸甘油酯在卷烟中主要作为醋酸纤维过滤嘴的增塑剂使用。在不同的温度和相对湿度条件下,研究活性炭对三醋酸甘油酯的吸附机理,探讨了活性炭的孔结构对其吸附性能的影响。结果表明:活性炭吸附三醋酸甘油酯符合Langmuir模型,其饱和吸附量为333 mg/g;高温有利于吸附三醋酸甘油酯,而增加湿度则抑制了三醋酸甘油酯的吸附;在一定范围内,当活性炭孔面积大于三醋酸甘油酯最小截面积的2.8倍时,活性炭对三醋酸甘油酯的吸附量增多,反之减少。因此,较大的微孔孔径有利于活性炭吸附三醋酸甘油酯。     

不同城市功能区地表灰尘Pb水平及粒级行为——以贵阳市为例

本文以我国西南贵州省会城市贵阳为例,对地表按城市功能分区采集灰尘样品,从不同功能区灰尘Pb总量和不同粒级灰尘Pb含量及贡献两个角度探析城市功能对地表灰尘Pb的影响。研究表明,贵阳市地表灰尘Pb平均含量为93.2mg/kg。商贸区和交通区地表灰尘Pb离散度较小,工业区和垃圾站地表灰尘Pb分异较大,住宅区、广场和学校灰尘Pb数据离散度处于中间水平。不同功能区地表灰尘Pb水平存在较大差异,工业区地表灰尘Pb含量显著高于商贸区和垃圾站;广场和学校地表灰尘Pb含量显著高于商贸区和交通区;住宅区Pb含量显著高于交通区。城市功能区从功能和地理位置两个方面影响城区地表灰尘Pb水平。各功能区地表灰尘不同粒级颗粒物中Pb含量存在较大差异,但所有区域地表灰尘中<250μm粒级颗粒物对地表灰尘Pb的贡献基本达到80%。     

上海市郊春节期间大气颗粒物及其组分的粒径分布

利用电称低压冲击仪(ELPI)在线监测上海市嘉定区2009年春节前后不同粒径(50%切割粒径分别为:0.03、0.06、0.11、0.17、0.26、0.40、0.65、1.00、1.60、2.50、4.40、6.80μm)大气颗粒物的粒子数浓度变化.对比春节与非节日期间该地区大气颗粒物(<0.49、0.49—0.95、0.95—1.50、1.50—3.00、3.00—7.20、>7.20μm)中Na、Mg、Al、K、Ca、Ti、V、Cr、Mn、Fe、Co、Ni、Cu、Zn、As、Se、Pb 17种元素,F-、Cl-、NO 2-、SO24-、NO 3-、Na+、NH 4+、K+、Mg2+、Ca2+10种离子与有机碳(OC)、元素碳(EC)质量浓度的粒径分布.结果表明,春节对于大气颗粒物粒子数浓度的影响主要集中在0.11—1.60μm.如果以3.00μm为界将大气颗粒物划分成细(≤3.00μm)和粗(>3.00μm)颗粒物时,春节影响体现在细颗粒的元素为:Na、As、Pb,且主要集中于<0.49μm颗粒中;春节影响集中于大颗粒(>7.20μm)的元素为:Ni、Co;没有受到显著影响的为:Ca、V、Mn、Fe、Z...     

Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications - including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for some variables. Model performance varied greatly according to the level of regularization. Intermediate regularization consistently led to the best models, with decreased performance at low and generally at high regularization. Optimal levels of regularization differed between sample-size-dependent and sample-size-independent approaches, but both reached similar levels of maximal performance. In several cases, the optimal regularization value was different from (usually higher than) the default one. Models calibrated with both linear and quadratic features outperformed those made with just linear features. Results were remarkably consistent across the examined sample sizes. Models made with few and biased localities achieved high predictive ability when appropriate regularization was employed and optimal model complexity was identified. Species-specific tuning of model settings can have great benefits over the use of default settings.     

Predictors of occupancy trend across spatial scale

Many explorations of extinction probability have had a global focus, yet it is unclear whether variables that explain the probability of extinction at large spatial extents are the same as those at small spatial extents. Thus, we used nearly annual presence-absence records for the most recent 40 years of a 110-year data set from Palenque, Mexico, an area with ongoing deforestation, to explore which of >200 species of birds have probabilities of extirpation that are likely to increase. We assessed associations between long-term trends in species presence (i.e., detection in a given year) and body size, geographic range size, diet, dependence on forest cover, taxonomy, and ecological specialization. Our response variable was the estimated slope of a weighted logistic regression for each species. We assessed the relative strength of each predictor by means of a model ranking scheme. Several variables associated with high extinction probability at global extents, such as large body size or small geographic range size, were not associated with occurrence of birds over time at our site. Body size was associated with species loss at Palenque, but occurrence trends of both very large and very small species, particularly the latter, have declined, or the species have been extirpated. We found no association between declining occurrence trend and geographic range size, yet decline correlated with whether a species depends on forest (mean occupancy trend =-0.0380, 0.0263, and 0.0186 for, respectively, species with high, intermediate, or low dependence on forest) and with complex combinations of diet and foraging strata (e.g., occurrence of canopy insectivores and terrestrial omnivores has increased, whereas occurrence of mid-level frugivores and terrestrial granivores has decreased). Our findings emphasize that analyses of local areas are necessary to explicate extirpation risk at various spatial extents.