液相化学发光分析在环境监测中的应用

介绍液相化学发光分析法的原理和特点以及十年来在我国环境监测中的应用。收录1983年～1991年国内的应用论文50余篇,作了较为系统全面的评述。     

彗星试验技术在水生与土壤环境中的毒理学应用

彗星试验是一种检测真核细胞脱氧核糖核酸(DNA)损伤的技术,快速、低成本、适用于几乎所有真核细胞,因此常应用于遗传毒理学、辐射生物学、肿瘤细胞学、毒性检测等方面。尤其以灵敏度高、检测联合毒性的特点大量运用于环境研究中。主要介绍彗星试验方法学发展历程,彗星试验的原理及其在环境领域中的应用,并指出应用中的受限因素以及未来的发展前景,为今后进一步推广该试验技术提供理论上的指导与参考。     

南京市溧水区大气挥发性有机物污染特征及来源解析

为了解南京市溧水区大气挥发性有机物(VOCs)的组分、来源及其对臭氧的贡献,2021年对区域内VOCs开展了为期1 a的走航监测,进行数据分析.结果表明,溧水区ρ(TVOC)年均值为223.45μg·m^-3,其中ρ(烷烃)为49.45μg·m^-3(占比22.13%),ρ[含氧(氮)VOCs]为50.63μg·m^-3(占比22.66%),ρ(卤代烃)为64.73μg·m^-3(占比28.95%),ρ(芳香烃)为35.46μg·m^-3(占比15.87%),ρ(烯烃)为18.26μg·m^-3(占比8.19%),其他为4.9μg·m^-3(占比2.2%).夏季的ρ(TVOC)平均值较高,为263.75μg·m^-3,冬季较低,为187.2μg·m^-3,春季为246.11μg·m^-3,秋季为204.77μg·m^-3.日均TVOC浓度,在09:00～10:00和14...     

获取日均值的大气监测时段选择

利用数理统计方法，以污染物瞬时监测数据为基础数据，选用控制在一定置信度下最大允许误差的统计公式确定出样本容量，并采用模糊集合运算—优化组合选择法，确定出获取日均值的最佳监测时段．     

建设项目竣工验收现场平行样控制指标研究

依据建设项目竣工环境保护验收中污水监测的要求,通过积累大量验收监测质量检查的实验数据,运用数理统计的方法,以化学需氧量和氨氮两个项目为例,建立了质控数据的评价方法,并确定了现场平行样偏差指标的限値。     

免疫传感器用于环境中痕量有害物质检测的研究进展

随着免疫测定技术与传感技术的发展,人们开发了以固定化的抗体(抗原)为识别元件,基于特异性的免疫反应原理的免疫传感器.该传感器灵敏度高、选择性好,可实现对农药分子、工业有机污染物、重金属、生物毒素等有害物质的实时、在体、在线测定,在环境污染监测中具有巨大的应用前景.本文对免疫传感器的原理、制备、分类作了简要介绍,综述了近年来用于痕量有害物质检测的免疫传感器在固定化方法和传感技术等方面的最新研究成果,并探讨了免疫传感器在环境监测中的应用和发展方向.     

动态稀释校准仪对VOCs自动监测系统的影响

基于环境空气VOCs自动监测系统中动态稀释校准仪在相同稀释比和不同稀释流量条件下,对仪器测试结果误差的影响进行研究,指出了当前环境空气VOCs自动监测普遍使用的校准仪的不足之处。选取臭氧前体物(PAMS) 57种有机化合物,以不同稀释总流量,分别测试0. 5和1. 0 nmol/mol的平均检出限以及平均回测偏差,结果显示,在输出流量1 000 m L/min时,平均检出限分别在0. 02～0. 17和0. 01～0. 10 nmol/mol;平均回测偏差在-12%～26%和-28%～10%。当总输出流量 1 000 m L/min时,多点曲线各浓度点重复性5%。     

兰州市区大气中TSP及其成分含量的测定与变化规律的研究

报导了兰州市一年内大气中TSP及其成分含量的监测结果、污染程度和变化规律,为兰州市进一步治理大气污染,加强环境管理,提高环境质量提供了科学依据。     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Optimization of capture–recapture monitoring of elusive species illustrated with a threatened grasshopper

Information on population sizes and trends of threatened species is essential for their conservation, but obtaining reliable estimates can be challenging. We devised a method to improve the precision of estimates of population size obtained from capture–recapture studies for species with low capture and recapture probabilities and short seasonal activity, illustrated with population data of an elusive grasshopper (Prionotropis rhodanica). We used data from 5 capture–recapture studies to identify methodological and environmental factors affecting capture and recapture probabilities and estimates of population size. In a simulation, we used the population size and capture and recapture probability estimates obtained from the field studies to identify the minimum number of sampling occasions needed to obtain unbiased and robust estimates of population size. Based on these results we optimized the capture–recapture design, implemented it in 2 additional studies, and compared their precision with those of the nonoptimized studies. Additionally, we simulated scenarios based on thresholds of population size in criteria C and D of the International Union for Conservation of Nature (IUCN) Red List to investigate whether estimates of population size for elusive species can reliably inform red-list assessments. Identifying parameters that affect capture and recapture probabilities (for the grasshopper time since emergence of first adults) and optimizing field protocols based on this information reduced study effort (−6% to −27% sampling occasions) and provided more precise estimates of population size (reduced coefficient of variation) compared with nonoptimized studies. Estimates of population size from the scenarios based on the IUCN thresholds were mostly unbiased and robust (only the combination of very small populations and little study effort produced unreliable estimates), suggesting capture–recapture can be considered reliable for informing red-list assessments. Although capture–recapture remains difficult and costly for elusive species, our optimization procedure can help determine efficient protocols to increase data quality and minimize monitoring effort.