Evaluation of ogawa passive sampling devices as an alternative measurement method for the nitrogen dioxide annual standard in El Paso, Texas

Nitrogen Dioxide (NO₂) is a common urban air pollutant that results from the combustion of fossil fuels. It causes serious human health effects, is a precursor to the formation of ground level ozone, another serious air pollutant, and is one of the six criteria air pollutants established by the United States (U.S.) Clean Air Act (CAA). Ogawa Passive Sampling Devices (PSDs) for NO₂ were collocated and operated at six NO₂ Federal Reference Method (FRM) monitor locations in the El Paso, Texas area for the 2004 calendar year. Passive samples were taken at 2-week, 3-week, and 4-week intervals and compared against the continuously operating FRM monitors. Results showed that the collective NO₂ annual arithmetic mean for all passive monitors was identical to the NO₂ mean for all FRM monitors. Of the individual locations, three passive annual NO₂ means were identical to their corresponding FRM means, and three passive annual NO₂ means differed from their corresponding FRM means by only one part per billion (ppb). Linear correlation analysis between all readings of the individual NO₂ PSDs and FRM values showed an average absolute difference of 1.2 ppb with an r ² of 0.95. Paired comparison between high and low concentration annual NO₂ sites, seasonal considerations, and interlab quality control comparisons all showed excellent results. The ease of deployment, reliability, and the cost-savings that can be realized with NO₂ PSDs could make them an attractive alternative to FRM monitors for screening purposes, and even possibly an equivalent method for annual NO₂ monitoring. More tests of the Ogawa NO₂ PSD are recommended for different ecosystem and climate regimes.     

生态恢复过程中的种群遗传学考虑

生态恢复是生态科学的最终实验,从种群角度看,恢复的目标是使种群（尤其是优势种或建群种）到具有生长、繁殖和适应进化变化的能力。要达到这个目标,种群遗传学知识必不可少。遗传变异是物种适应变化环境的基础,而局部适应则是种群适应局部环境遗传分化的结果,它们在生态恢复中起着重要的作用。分析了生态恢复过程中影响种群遗传变异因素,主要有取样误差和小种群效应（主要是瓶颈或建立者事件及其以后的近交和漂变等）,在进行     

“一带一路”战略背景下的中国省域绿色发展:现状、问题与对策

以绿色发展推动生态文明建设将会是我国长期面临的重大课题。"一带一路"是我国新时期的对外开放国家战略,涵盖范围广泛,涉及国内18个省域和沿线65个国家,研究"一带一路"战略背景下的中国省域绿色发展,意义重大。本文通过对"一带一路"战略的基本回顾认为,建设绿色"一带一路"是促进我国经济绿色转型、消除国际社会担忧、推进战略顺利实施的必然选择。"一带一路"战略涉及国内18个省份之间的环境污染状况和绿色发展水平差异较大,东部沿海省份的经济发展水平远高于中西部省份,绿色发展水平相对领先,但同时污染物排放量也明显较大。本文进一步探讨了我国省域绿色发展过程中存在的四个问题,即中国绿色经济发展整体水平不高、地区间同质化竞争严重、污染性产业向中西部省份梯度转移、生态环境脆弱,并分别给出了相应的对策建议。     

欧美经验对我国“十三五”大气污染防治战略的启示

当前,我国大气污染形势严峻,复合型污染特征突出。新修订的《中华人民共和国环境保护法》、《中华人民共和国大气污染防治法》、《环境空气质量标准》以及《大气污染防治行动计划》的发布和实施,对我国大气污染防治工作提出了更高要求。"十三五"时期是我国环境保护负重前行困难期和大有作为关键期,也是我国改善大气环境质量的攻坚期,需要系统谋划大气污染防治战略。欧盟和美国在制定大气污染防治战略时,以改善空气质量为核心,构建了涵盖一次污染物和二次污染物的多污染物目标体系,规定了环境浓度、排放量、直观感受(能见度)、健康以及管理等约束性指标,并且针对大气污染防治政策分析实施的成本与收益,开发相应的模型工具,基于收益成本比进行科学决策。本文对欧美环境规划和战略中的多污染物协同控制目标体系以及大气污染防治政策成本效益评估经验进行梳理,分析我国大气污染防治战略的现状和问题,并提出相关政策建议,以期为解决"心肺之患"、实现环保"十三五"总体目标提供参考。     

基于分布型电力系统的 PSO 优化设计

优化了传统粒子群算法,运用了混沌算法对粒子做初始化,优化了传统粒子群算法的收敛特性,并在算法的优化过程中,增加了混沌干扰元素对整体最优化极值做混沌干扰,将符合杂交几率的优化粒子做杂交处理,提高了粒子群的种类,增强了粒子群算法的搜寻水平。并通过IEEE33节点的分布型电力系统做无功优化仿真验证,通过比较,验证了优化算法的优越性与准确性。     

加拿大推动可持续发展战略的策略及实践

文章介绍了加拿大的简况,讨论了加拿大可持续发展的历程以及促使各政府部门将可持续发展战略落实到工作中的环境审计制度。探讨了加拿大可持续发展战略实施方面的部分经验,如向国会提交年度环境与可持续发展审计报告、工业可持续发展战略的制定及加拿大-中国的可持续发展合作等。     

重庆市特大旱灾的自然与社会机制分析

文章针对2006年的特大旱灾探讨了2006年的特大旱灾的特征,分析了2006年特大旱灾的形成自然机制和社会机制,并提出了今后重庆市抗御旱灾的策略。认为2006年的特大旱灾是自然因素与社会因素叠加的结果,是人类社会经济系统自身易损性的体现。     

Sustainable method towards magnetic ordered mesoporous polymers for efficient Methylene Blue removal

The difficulty in achieving high removal efficiency for contaminants in textile wastewater over a wide range of pH impedes the progress of its treatment technique greatly. Herein, a facile and sustainable strategy was adopted for constructing magnetic ordered mesoporous polymers (M-OMPs) without the assistance of organic solvent and catalyst. The prepared M-OMPs were endowed with high special surface area and good superparamagnetism simultaneously, and exhibited high removal efficiency (>99%) for Methylene Blue (MB) within a short time (10 min) at a concentration of 50 mg/L. What's more, high removal efficiency was achieved over a wide range of pH 2-12 and the adsorption capacity for MB on M-OMPs was substantially retained even after 5 adsorption-desorption cycles, further demonstrating the application potential of M-OMPs in the decontamination of textile wastewater.     

Multi-stage sampling for large scale natural resources surveys: a case study of rice and waterfowl

Large-scale sample surveys to estimate abundance and distribution of organisms and their habitats are increasingly important in ecological studies. Multi-stage sampling (MSS) is especially suited to large-scale surveys because of the natural clustering of resources. To illustrate an application, we: (1) designed a stratified MSS to estimate late autumn abundance (kg/ha) of rice seeds in harvested fields as food for waterfowl wintering in the Mississippi Alluvial Valley (MAV); (2) investigated options for improving the MSS design; and (3) compared statistical and cost efficiency of MSS to simulated simple random sampling (SRS). During 2000-2002, we sampled 25-35 landowners per year, 1 or 2 fields per landowner per year, and measured seed mass in 10 soil cores collected within each field. Analysis of variance components and costs for each stage of the survey design indicated that collecting 10 soil cores per field was near the optimum of 11-15, whereas sampling >1 field per landowner provided few benefits because data from fields within landowners were highly correlated. Coefficients of variation (CV) of annual estimates of rice abundance ranged from 0.23 to 0.31 and were limited by variation among landowners and the number of landowners sampled. Design effects representing the statistical efficiency of MSS relative to SRS ranged from 3.2 to 9.0, and simulations indicated SRS would cost, on average, 1.4 times more than MSS because clustering of sample units in MSS decreased travel costs. We recommend MSS as a potential sampling strategy for large-scale natural resource surveys and specifically for future surveys of the availability of rice as food for waterfowl in the MAV and similar areas.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.