生物质燃烧源排放烟尘罩稀释采样系统设计与应用

为研究生物质燃烧源颗粒物与气态污染物的排放特征,设计了可用于生物质燃烧排放实验室模拟研究的烟尘罩稀释采样系统.该系统可模拟生物质燃烧释放的高温高浓烟气排放到大气中的冷却、稀释、生长等理化过程,捕集燃烧排放的细颗粒物和多种痕量气态污染物.通过更换不同内径采样嘴等方法,系统稀释倍数可在10~70倍之间调节.详细介绍了系统的设计原理及结构,并对系统可靠性进行了检验.整个采样系统通过了气密性测试,分级采样系统平行性良好,细颗粒物损失在可接受范围,对燃烧状态扰动低,燃烧状态可实时评估.应用该系统测定了我国南方地区典型生物质燃烧源含碳污染物种的排放特征,结果表明其适于模拟各种类型的生物质燃烧.     

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.     

Conservation decision-making in large state spaces

When looking for the best course of management decisions to efficiently conserve metapopulation systems, a classic approach in the ecology literature is to model the optimisation problem as a Markov decision process and find an optimal control policy using exact stochastic dynamic programming techniques. Stochastic dynamic programming is an iterative procedure that seeks to optimise a value function at each timestep by evaluating the benefits of each of the actions in each state of the system defined in the Markov decision process.Although stochastic dynamic programming methods provide an optimal solution to conservation management questions in a stochastic world, their applicability in metapopulation problems has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. The high computational requirements of stochastic dynamic programming methods mean that only simple metapopulation management problems can be analysed. In this paper we overcome the complexity burden of exact stochastic dynamic programming methods and present the benefits of an on-line sparse sampling algorithm proposed by Kearns, Mansour and Ng (2002). The algorithm is particularly attractive for problems with large state spaces as the running time is independent of the size of the state space of the problem. This appealing improvement is achieved at a cost: the solutions found are no longer guaranteed to be optimal.We apply the algorithm of Kearns et al. (2002) to a hypothetical fish metapopulation problem where the management objective is to maximise the number of occupied patches over the management time horizon. Our model has multiple management options to combat the threats of water abstraction and waterhole sedimentation. We compare the performance of the optimal solution to the results of the on-line sparse sampling algorithm for a simple 3-waterhole case. We find that three look-ahead steps minimises the error between the optimal solution and the approximation algorithm. This paper introduces a new algorithm to conservation management that provides a way to avoid the effects of the curse of dimensionality. The work has the potential to allow us to approximate solutions to much more complex metapopulation management problems in the future.     

搅拌子吸附-热脱附-气相色谱-质谱法分析北京大气中33种多氯联苯

用包覆聚二甲基硅氧烷(PDMS)吸附层的搅拌子作被动大气采样器,建立了搅拌子吸附-热脱附-气相色谱-质谱法(SBSE-TD-GC-MS)分析大气中多氯联苯(PCBs)的方法。优化后的热脱附温度为280℃,热脱附时间为6 min,聚焦冷阱的温度为-40℃,33种PCB单体的热脱附残留比例低于7%。验证了方法的线性、精密度和重复性。标准曲线5个点线性关系良好,33种PCB单体的相关系数r>0.998。方法精密度验证的相对标准偏差(RSD)为0.79%~20.2%(n=8),日内重复性实验的RSD为0.43%~29.8%(n=10),日间重复性实验的RSD为3.20%~29.6%(n=20),方法检出限(LOD)为0.01~2.51 pg,回收率为98%±18%。将建立的方法用于北京大气中PCBs的分析。检出8种PCB单体,质量为2.94~129 pg。在32 d的采样周期内PCB-52、PCB-123、PCB-155一直处于线性吸附阶段,而PCB-1和PCB-3经过了线性吸附阶段、曲线吸附阶段和平衡阶段。研究结果表明,SBSE-TD-GC-MS可以用于大气中PCBs的样品采集和快速分析。     

Ranked set sample design for environmental investigations

Ranked set sampling can provide an efficient basis for estimating parameters of environmental variables, particularly when sampling costs are intrinsically high. Various ranked set estimators are considered for the population mean and contrasted in terms of their efficiencies and useful- ness, with special concern for sample design considerations. Specifically, we consider the effects of the form of the underlying random variable, optimisation of efficiency and how to allocate sampling effort for best effect (e.g. one large sample or several smaller ones of the same total size). The various prospects are explored for two important positively skew random variables (lognormal and extreme value) and explicit results are given for these cases. Whilst it turns out that the best approach is to use the largest possible single sample and the optimal ranked set best linear estimator (ranked set BLUE), we find some interesting qualitatively different conclusions for the two skew distributions     

罐采样-正压进样-气相色谱法测定环境空气中温室气体

建立了罐采样-正压进样-气相色谱法测定环境空气中温室气体的分析方法。用正压采样器采集环境空气样品至罐中呈正压状态,用气相色谱仪进行分析,并对氮气空白、除烃空气、不同湿度、不同环境温度、不同罐压及精确控制进样压力等因素进行研究。在最佳实验条件下,N₂O、CO₂、CH₄、SF₆和CO的方法检出限分别为7 nmol/mol、1.5μmol/mol、0.1μmol/mol、9 pmol/mol、0.2μmol/mol,标准曲线相关系数均大于0.995,低、中、高浓度水平测定结果相对标准偏差均小于2.0%。利用该法对环境空气样品进行了检测分析,与该站点现有的高精度在线监测仪器同时测定结果进行比对,比对结果较好,该方法适用于城市环境空气和无组织废气中温室气体的准确测定。     

利用主动和被动采样技术和发光菌毒性测试评价水中有机污染物的毒性

提出利用能够模拟生物吸收过程的半渗透膜采样技术(SPMD)富集疏水性有机污染物,然后进行生物毒性测试．这一方法在原理上不同于传统主动富集．生物毒性测试技术体系,能够模拟生物富集和产生毒性的过程．用三油酸脂半渗透膜采样技术对洋河和北京市高碑店污水处理厂水样进行富集,并采用海洋发光菌P．phosphoreum T3和青海弧菌V．qinghainesis Q67发光菌对原水样和经过富集的水中有机污染物进行毒性测试．研究结果表明,洋河水中存在导致急性毒性的有机物,主要来自宣化工业区,以取代苯类化合物(如：硝基芳烃)为主,相比于此类物质,持久性有机污染物对急性毒性的贡献不大．对高碑店污水厂进行的实验表明,用发光菌方法直接测定污水,很难解释急性毒性变化规律．而采用SPMD技术富集水中有机污染物后进行毒性测试,结果和化学分析数据相当吻合．结合SPMD的优点,推荐用该方法作为环境有机有毒污染物毒性监测和生态风险评价的样品前处理手段．     

Pairing Modern and Paleolimnological Approaches to Evaluate the Nutrient Status of Lakes in Upper Midwest National Parks

Understanding what constitutes a reference (background) nutrient condition for lakes is important for National Park Service managers responsible for preserving and protecting aquatic resources. For this study we characterize water quality conditions in 29 lakes across four national parks, and compare their nutrient status to U.S. Environmental Protection Agency (USEPA) nutrient reference criteria and alternative criteria recently proposed by others. Where appropriate we also compare the nutrient status of these 29 lakes to state or tribal nutrient reference criteria or standards. For lakes that exceed reference criteria we investigate physical and chemical patterns, and for a subset of lakes compare modern nutrient conditions to paleolimnological (i.e., diatom‐inferred [DI]) nutrient reconstructions. Many lakes exceeded USEPA nutrient reference criteria, but met alternative less restrictive criteria. Modern nutrient conditions were also largely consistent with DI historic (pre‐1900) nutrient conditions. Lakes exceeding alternative nutrient criteria and with elevated nutrient levels relative to DI historic conditions were mostly small, shallow, and dystrophic; continued attention to their nutrient dynamics and biological response is warranted. Coupling modern and paleolimnological data offer an innovative and scientifically defensible approach to understand long‐term nutrient trends and provide greater context for comparison with reference conditions.     

典型挥发性有机化合物在气体采样罐中的存储稳定性

选取了6种有代表性的挥发性有机化合物（VOCs）,异丙醇、二氯甲烷、乙酸乙烯酯、正己烷、苯和四氯化碳,实测了这些VOCs在气体采样罐（SUMMA罐）中的存储稳定性。结果表明：苯、正己烷和二氯甲烷在采样罐中均较稳定,其含量在84 d的存储期内基本无变化;乙酸乙烯酯在6个VOCs中最不稳定,在普通和惰性采样罐中含量均明显下降;异丙醇和四氯化碳的稳定性与采样罐的类型有关,在普通采样罐内含量下降明显,而在惰性采样罐内则相对稳定。实际监测工作中,为提高VOCs分析的准确性,如目标分析物有含氧类（醇,酮,酯等）或含卤素类VOCs,则采样后需尽快分析,同时尽量选择惰性采样罐为采样容器。     

鄱阳湖出流水质2004~2014年变化及其对水位变化的响应:对水质监测频率的启示

通过对鄱阳湖湖口2004~2014年以周为单位的水质指标,包括溶解氧（DO）,氨氮（NH₄⁺-N）和高锰酸盐指数（COD_Mn）的变化特征及其与水位响应关系进行分析,并对合理的监测频率进行了探讨。结果表明：（1）就DO,NH₄⁺-N和COD_Mn而言,鄱阳湖出湖水质在2004~2014年没有显著恶化的趋势,然而在年内呈现明显的周期性变化,其浓度与湖泊水位波动有较显著的负相关性（p<0.01）,相关系数分别达到-0.63,-0.67和-0.36;（2）考虑水质指标在湖相状态与河相状态存在显著的差异（p<0.01）,概率密度分布曲线进一步表明,在鄱阳湖呈湖相时,湖口NH₄⁺-N浓度小于0.25 mg/L的概率为93%,而在河相时仅为32.8%。DO与COD_Mn浓度在河湖相的特征与NH₄⁺-N相似。因此,在湖相状态下,鄱阳湖出流水质良好的概率更大,而高水位下的稀释作用可能是影响湖泊年内变化的主要控制因素;（3）时间序列分析表明DO,NH₄⁺-N和COD_Mn存在明显的自相关性,1~2月一次的监测频率基本能够准确的描述NH₄⁺-N和DO的动态变化特征,而COD_Mn仍需要1~2周一次的监测,从而避免过多的损失动态信息。能够为将来更深入的研究湖泊水情与水质定量关系提供基础和思路,从而为湖泊水环境管理和调控提供对策和建议。