博物馆室内微环境中碱性气体的被动采样方法研究

针对博物馆微环境中的碱性污染气体-氨气,采用自行研制的被动采样器,建立了一套适合博物馆馆藏文物保存环境中氨气含量分析的被动采样-离子色谱检测方法,应用于博物馆的实地采样分析,通过与主动法采样对照,获得了被动采样器在博物馆微环境中氨的吸收系数,从而获得采样点空气中氨气的浓度,为博物馆文物保存环境监测提供了可行方法。     

Identifying the largest individual sample value from a two-way composite sampling design

Composite sampling techniques for identifying the largest individual sample value seem to be cost effective when the composite samples are internally homogeneous. However, since it is not always possible to form homogeneous composite samples, these methods can lead to higher costs than expected. In this paper we propose a two-way composite sampling design as a way to improve on the cost effectiveness of the methods available to identify the largest individual sample value.     

碱片-离子色谱法测定硫酸盐化速率影响因素

采用碱片-离子色谱法测定空气的硫酸盐化速率,方法在硫酸盐质量浓度为1.00~20.0 mg/L范围内线性良好(r≥0.999 0);碱片剪碎或不剪碎对空白碱片中硫酸根离子质量浓度的测定无明显影响,且高低两种加标浓度的回收率均在80.0%~120%之间;对于碱片实际样品,碱片剪碎更有利于硫酸根离子的快速溶出;随浸泡时间的延长,碱片中溶出的硫酸根离子质量浓度呈上升趋势,7 h硫酸根离子基本溶出完全,从保证结果质量和节省时间的角度考虑,确定最佳浸泡时间为3.5 h,建议碱片全部采取剪碎处理。     

排放源烟道气二噁英空间分布对监测结果的影响

针对生活垃圾焚烧企业烟气排放烟囱6个断面单点采样的试验结果,分析按照《固定污染源排气中颗粒物测定与气态污染物采样方法》(GB/T 16157—1996)多断面点采样与单一固定断面点采样对监测结果的影响。试验表明,烟道气在不同断面点质量浓度分布差异较大,相对于样品实际监测值,各断面点二噁英毒性当量浓度最大正偏差达到+306%,最大负偏差达到-96%,测定的18个断面点样品,只有2个样品与实际样品监测值相近。     

室内空气中的气态多环芳烃的被动采样监测

利用自制被动采样装置,在2011年秋冬季对南京市部分地区室内空气中5种气态多环芳烃(PAHs)(萘、苊烯、苊、芴、菲)进行了为期100d的连续采样检测,被动采样器的采样速率为0.012m3/d,5种PAHs的回收率在63%～105%之间,方法检出限在1.1～2.4ng范围内。结果表明,南京市5处不同室内环境空气中萘的浓度最高,占总量的90%以上。室内环境空气中5种PAHs的总浓度为230～1564ng/m3。住宅内人体对5种PAHs的暴露速率为479～560ng/h。     

决策树模型在水环境监测网络中选取代表性样点的应用

地表水体中的硝酸盐污染已经成为全球关注的热点环境问题之一。现今,国内外均建立了相关的监测网络对地表水体的水质实施长期监测,但是却导致大量的监测数据累积,给后续的科学研究工作带来了不便,尤其是在庞大的监测网络中如何选取有代表性样点的研究点则成为急需解决的问题之一。以比利时弗拉芒地区地表水的长期监测物理化学指标为例,利用决策树模型评估地表水样点的硝酸盐污染来源专家分类的有效性,为点位优化提供理论依据。原有监测点位的污染源专家分类和模型输出的可匹配率为80%,优化后监测点位从原有47个点降低到30个点,提高了监测工作效率。     

挥发性有机物污染土壤样品采样方法比较

以苯系物污染土壤样品的采集为例,比较了4种不同采样方法导致样品检测结果的差异。其中,方法 1将样品装填至广口瓶内并压实密封,方法2采用非扰动采样器采集10 g样品后转移至加有10 mL甲醇保护剂的Vial瓶中密封,方法 3用非扰动采样器采集10 g样品后直接将其密封于采样器内,方法 4用Encore采样器采样后将其密封于采样器内。结果表明,方法 2采集样品的检出率最高,其余3种方法的差异不明显,方法 2采集样品的检出结果 71%以上都大于其余3种方法。而且,对于挥发性较强的苯与甲苯,以方法 2采集的样品91%以上都大于其余3种方法,最大及平均检出浓度高出2~3个数量级。5种不同土质样品检测结果表明,对于有机质含量较低的细砂,方法2的最高及平均检出浓度均高于其余3种方法 1~3个数量级,差异随土壤有机质含量的升高而降低。可见,对于苯系物及挥发性强于苯系物的其他挥发性有机物污染土壤样品的采集,方法 2效果最优,可指定为VOCs污染场地土壤样品的采样方法。     

Estimation of sampling uncertainty for determination of pesticide residues in plant commodities

In order to provide residue data for refining the estimated sampling uncertainty, a coordinated research program was initiated for performing field studies on residues in individual items of leafy vegetables, small and large crops. The trials were carried out in 13 countries with 3 small fruits, 5 large crops, 2 medium/large crops and 3 leafy vegetables. The 25 pesticide active ingredients applied represented the dicarboximide (3), organophosphorus (8), synthetic pyrethroids (5), phthalimides (2), organochlorine (1) and other types of pesticides (6). In addition, 11 supervised field trials were performed in grapes and lettuce by the pesticide manufacturers, and their results were provided for evaluation. The studies represented actual agriculture practice around the world, and provide reliable data for estimation of sampling uncertainty. Based on the 12346 residue data, the best estimate for the relative sampling uncertainty for composite samples, assuming sample size of 10 for small crops and leafy vegetables and 5 for large crops, with 95% confidence limits in brackets are: small commodities: 0.25 (0.20–0.29); Brassica leafy vegetables: 0.20 (0.16–0.24); large commodities: 0.33 (0.29–0.38).     

Characterization of distribution of pesticide residues in crop units

The characteristic features of distribution of pesticide residues in crop units and single sample increments were studied based on more than 19,000 residue concentrations measured in root vegetables, leafy vegetables, small-, medium- and large-size fruits representing 20 different crops and 46 pesticides. Log-normal, gamma and Weibull distributions were found to provide the best fit for the relative frequency distributions of individual residue data sets. The overall best fit was provided by lognormal distribution. The relative standard deviation of residues (CV) in various crops ranged from 15–170%. The 100–120 residue values being in one data set was too small to identify potential effects of various factors such as the chemical and physical properties of pesticides and the nature of crops. Therefore, the average of CV values, obtained from individual data sets, were calculated and considered to be the best estimate for the likely variability of unit crop residues for treated field (CV = 0.8) and market samples (CV = 1.1), respectively. The larger variation of residues in market samples was attributed to the potential mixing of lots and varying proportion of non-detects. The expectable average variability of residues in composited samples can be calculated from the typical values taking into account the sample size.     

Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery1

Claggett, Peter R., Judy A. Okay, and Stephen V. Stehman, 2010. Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery. Journal of the American Water Resources Association (JAWRA) 46(2):334-343. DOI: 10.1111/j.1752-1688.2010.00424.x Abstract: The Chesapeake Bay watershed encompasses 165,760 km² of land area with 464,098 km of rivers and streams. As part of the Chesapeake Bay restoration effort, state and federal partners have committed to restoring 26,000 miles (41,843 km) of riparian forest buffers. Monitoring trends in riparian forest buffers over large areas is necessary to evaluate the efficacy of these restoration efforts. A sampling approach for estimating change in riparian forest cover from 1993/1994 to 2005 was developed and implemented in Anne Arundel County, Maryland, to exemplify a method that could be applied throughout the Bay watershed. All stream reaches in the county were stratified using forest cover change derived from Landsat imagery. A stratified random sample of 219 reaches was selected and forest cover change within the riparian buffer of each sampled reach was interpreted from high-resolution aerial photography. The estimated footprint of gross change in riparian forest cover (i.e., the sum of gross gain and gross loss) for the county was 1.83% (SE = 0.22%). Stratified sampling taking advantage of a priori knowledge of locations of change proved to be a practical and efficient protocol for estimating riparian forest buffer change at the county scale and the protocol would readily extend to much broader scale monitoring.