大气中痕量挥发性有机物分析方法研究

利用自行研制的累积式大气采样装置、大气痕量有机物二步冷冻浓缩进样系统和气相色谱/质谱(GC/MS)联用技术对大气中的痕量挥发性有机物进行分析(CCS-GC/MS),并对分析方法进行了评估.结果表明,CCS-GC/MS系统将单纯GC/MS分析大气中痕量挥发性有机物检测下限体积分数值从10^-6量级扩展到10^-12,39种目标化合物平均响应因子为2.9×10^-12A^-1,1000cm³进样量最低检出限体积分数值为7×10^-12～40×10^-12;流出峰保留时间定性分析相对平均偏差<2.5s,对目标化合物定性分析准确率达到100%;0～400×10^-9浓度范围内外标定量曲线r²平均值大于0.99;回收率为88%～111%,平均值100.8%±5.6%;精密度误差2%～14%,平均6.6%.     

上海市光化学污染期间挥发性有机物的组成特征及其对臭氧生成的影响研究

光化学污染导致的高浓度臭氧(O3)是上海面临的重要大气污染问题.本研究分别选取了市区(徐汇)、城郊(青浦)和郊区(南汇)3个典型地区在夏季光化学污染易发季节开展了O3及其前体物挥发性有机物(VOCs)和氮氧化物(NOx)的观测,结合光化学箱模型研究探讨了O3生成的主控污染物.研究表明,不同地区O3污染呈现较强的同步性,日最大浓度也比较接近;但南汇郊区由于受机动车排放影响较小,NOx浓度显著低于其他两个地区,导致该地区O3浓度日变化曲线相对平缓,夜间O3浓度也维持在较高水平.大气VOCs浓度较高时,往往伴随高浓度的O3;3个地区VOCs浓度和组成差异明显,就VOCs浓度而言,徐汇青浦南汇;浓度贡献最主要的物种为甲苯、C2~C3的烷烃和烯烃、丙酮以及辛烷;而C7~C10芳香烃、C3~C4的烯烃、异戊二烯以及乙醛是上海大气臭氧生成潜势贡献最大的VOCs类物质.3个地区O3的生成主要受人为排放的二甲苯类和C3~C4烯烃类物质控制;对于徐汇,只控制NOx会导致O3浓度升高,而南汇郊区O3的生成对NOx排放不敏感.     

基于垂直加密观测的北京地区霾和沙尘复合污染形成条件分析

为了弄清蒙古气旋外围出现的霾和沙尘复合污染特征及其形成的关键气象条件,本研究利用多种遥感设备（增强型云高仪、风廓线雷达和微波辐射计等）垂直加密观测数据,结合大气主要污染物（PM₁₀、PM_2.5、SO₂、NO₂）监测数据、加密自动气象站观测数据,以及常规地面和高空气象观测数据、NCEP再分析数据等,分析发生在北京春季的2次霾和沙尘重污染过程.结果表明,2017年5月4—5日为一次PM₁₀和PM_2.5混合污染过程,与上游地区强烈发展的蒙古气旋后部风沙区的输送有关.上游地区因受中-低空西来槽影响上升气流加强,使沙尘细颗粒物（粒径≤10 μm）悬浮于空中,由中-低空偏西风输送至下游地区,被北京及附近的弱下沉气流带至地面造成严重的PM₁₀、PM_2.5混合污染.其中,地面偏西风对上游地区的PM₁₀、PM_2.5的水平输送作用明显;2018年3月27—28日凌晨是受蒙古气旋底部低压区辐合作用和偏南气流输送作用形成的积累型霾（PM_2.5）污染.28日凌晨2：00开始蒙古气旋后部沙尘区随东-西向冷高压南压而向南扩散.随后冷高压不断东移形成回流偏东风,偏东风使北京及西北部地区的低层大气产生辐合上升运动,导致本地尘土扬起,造成PM_2.5重污染和PM₁₀极严重污染;浮尘天气引发的大气污染具有突发性特征,且持续时间较长.边界层高度低、低层大气存在逆温层（或等温层）并长时间维持是霾和沙尘复合污染形成和持续的重要条件.霾和沙尘复合重污染的形成是人为污染物、沙尘细颗粒物水平和垂直输送,以及大气层结稳定共同作用的结果.     

观测数据权重对地下水有机污染物生物降解模型参数估计的影响

地下水多组分反应运移模型是刻画解释微生物降解地下水有机污染物的水文地质、生物地球化学耦合过程并对各组分分布特征进行预测的重要工具.反应运移模型往往包含多个参数,并含有多种不同生物化学组分的观测数据,高维参数和观测数据类型的多样性给模型参数的反演带来了极大的困难.因此,本文以反应运移软件PHT3D模拟某污染场地地下水中有机污染物生物降解的理想正问题为例,对比讨论了不同观测数据权重对反应运移模型参数估计的影响.结果表明:未考虑观测数据权重时参数估计结果与真实值相差较大,而采用反映观测误差的权重时参数估计值与真实值更接近.敏感性分析表明,参数对于不同观测组数据的敏感度有明显差异.对于本例,生物降解模型中的降解反应参数最敏感,其次是溶解速率迁移常数,多相溶解度摩尔分数敏感度较小.     

“精密度偏性分析”是环境监测质量保证的重要手段

“精密度偏性分析”是环境监测质量保证的重要手段。通过它可以了解其方法的误差和检出限是否达到要求,同时也了解实验室和操作人员对该方法的适应性。     

大气气溶胶地基光学遥感物联网系统研究

研究了大气气溶胶地基光学遥感物联网系统的设计问题.根据其应用特点从传感层、传输层、数据层、应用层这几个组成部分对系统进行了设计,并初步实现.测试表明:设计的大气气溶胶地基光学遥感物联网系统具有支持数据实时共享、无人值守运行、快速数据融合、环境参数在线显示、历史数据快速查询和分析、仪器远程管理维护等优点,可实现大气气溶胶特性的网络化和自动化观测,对研究气溶胶对环境和气候的影响具有重要应用价值.     

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10–20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.     

小集水区酸化过程观测研究

在贵阳市近郊一封闭的小集水区设置了环境酸化过程试验场.本文介绍了试验场中观测点的布置、各项观测的方法和要求、经过长时间的观测和试验,取得了从酸沉降开始,穿过树冠,渗入土壤,最后进入地面水的全过程中各个环节化学行为和互相影响的可靠数据,由此得知贵州省酸性黄壤对酸化的缓冲能力不强,即容易发生酸化,并且有毒铝离子容易释放等重要信息.     

Estimation of similarity measure for multivariate normal distributions

Measures of niche overlap are used to assess the similarity or dissimilarity of two populations. Matusita's measure is one of commonly used niche overlap measures. We consider the problem of estimating Matusita's measure when samples are from multivariate normal distributions with unknown mean vectors and covariance matrices. Asymptotic variances and biases of Matusita's measure estimates are derived and bias reduction methods are proposed. Simulation results are shown to illustrate characteristics of the estimates and bias reduction methods.     

Sentiment analysis of the Twitter response to Netflix's Our Planet documentary

The role of nature documentaries in shaping public attitudes and behavior toward conservation and wildlife issues is unclear. We analyzed the emotional content of over 2 million tweets related to Our Planet, a major nature documentary released on Netflix, with dictionary and rule-based automatic sentiment analysis. We also compared the sentiment associated with species mentioned in Our Planet and a set of control species with similar features but not mentioned in the documentary. Tweets were largely negative in sentiment at the time of release of the series. This effect was primarily linked to the highly skewed distributions of retweets and, in particular, to a single negatively valenced and massively retweeted tweet (>150,000 retweets). Species mentioned in Our Planet were associated with more negative sentiment than the control species, and this effect coincided with a short period following the airing of the series. Our results are consistent with a general negativity bias in cultural transmission and document the difficulty of evoking positive sentiment, on social media and elsewhere, in response to environmental problems.