2002—2012年京津唐PM10变化规律及差异

对2002年6月—2012年5月京津唐城市群大气可吸入颗粒物(PM10)质量浓度的长期监测数据进行分析,结合3市的地理、气候气象条件,分析了京津唐城市群大气颗粒物质量浓度的变化特征;根据3市PM10相互之间的发散系数,定量分析了3市PM10变化的差异。结果表明,2002—2012年北京市的PM10质量浓度变化范围为0.012~0.600 mg/m3,天津、唐山2市的PM10质量浓度变化范围分别为0.014~0.600、0.019~0.452 mg/m3。2008、2011年天津市PM10质量浓度年平均值达到二级标准,唐山市从2008年后PM10质量浓度年平均值达到二级标准;北京市PM10质量浓度总体变化趋势为春季秋季冬季夏季,天津、唐山市均为冬季春季秋季夏季,但不同年份的变化趋势略不同;北京-唐山、北京-天津、天津-唐山之间PM10的月度发散系数范围分别为0.402 7~0.159 2、0.406 8~0.142 9、0.323 1~0.107 8,说明空间距离最近的天津-唐山之间大气污染的相互影响较北京-天津、北京-唐山之间大。     

Factors affecting the quantitative uncertainty of the estimated short-term intake. Part I—Calculation methods

ABSTRACT

The calculation of the combined uncertainty of the international estimated short-term intake (IESTI) of ethephon residues in apples is shown as an example. The ethephon residues in apples were reported by the Joint FAO (Food and Agriculture Organization of the United Nations)/WHO (World Health Organization) Meeting on Pesticide Residues (JMPR). The apple consumption data were taken from the IESTI (international short-term intake) calculation template used by the JMPR. The IESTI was calculated with the currently used method (case 2a) and a proposed one recommended by the EFSA (European Food Safety Authority)/RIVM (Dutch National Institute for Public Health) Scientific Workshop co-sponsored by FAO and WHO. In this example, the ratio of IESTI_proposed/IESTI_current and their combined relative uncertainty are about 2.8, and 1.7, respectively. The larger IESTI and uncertainty obtained with the proposed equation are the consequence of calculation only with the large portion (LP) instead of its combination with unit mass, and the MRL instead of the highest residue (HR). The LP is the major contributor to the combined uncertainty. Both the calculated IESTI and its combined uncertainty depend on the actual food – pesticide residue combination, and should be calculated for each case.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.     

广东省雾霾天气能见度的时空特征分析Ⅰ:季节变化

雾霾已经成为一种新的气象及环境灾害性现象,分析雾霾天气下低能见度的时空分布特征,对掌握雾霾的发生规律和减灾预报具有重要意义。文章利用1980—2003年广东省沿海地区26个地面气象观测站23年的气象观测资料,分析了广东省雾霾天气下能见度的时空分布特征。分析结果发现,1980—2003年广东地区雾霾天气下能见度的季节变化规律与雾天气时能见度的季节变化规律大体一致,即广东地区低能见度天气主要受雾天气的影响;另一方面,雾、霾天气时能见度的季节变化规律有很大不同,雾天气时能见度观测值在冬、春两季相对较高,最低值出现在3月份,最高值出现在9月份,其年变化表现为峰—谷型、稳定型和不规则波动型三种变化;霾天气时能见度的值在秋、冬两季相对较高,最低值出现在6月份,而且在5、6、7三个月中能见度值都很低;受广东沿海地形特征影响,雾天能见度的空间分布从西到东呈低—高—低—高的波列状分布,而霾天气时能见度的空间分布则没有明显的区域差异。     

地统计学在土壤重金属研究中的应用及展望

从采矿学与地质学研究中发展起来的地统计学是应用数理统计学的一个分支。与传统的统计学相比，地统计学可应用于土壤重金属研究中，能探索土壤重金属的空间分布特征及其变异规律。地统计学的基础理论与方法主要包括：区域化变量、半方差函数、克立格空间插值技术。半方差函数可以用来描述研究土壤重金属分布的空间相关性；而克立格插值可以对未采样区土壤重金属的含量进行无偏最优估计。在对地统计学理论进行简要阐述的基础上，回顾了近些年在土壤重金属研究的采样设计、空间结构分析、空间插值等方面的应用，并就其应用前景作了展望。     

A nonparametric procedure for analyzing repeated measures of spatially correlated data

Many agricultural, biological, and environmental studies involve detecting temporal changes of a response variable, based on data observed at sampling sites in a spatial region and repeatedly over several time points. That is, data are repeated measures over time and are potentially correlated across space. The traditional repeated-measures analysis allows for time dependence but assumes that the observations at different sampling sites are mutually independent, which may not be suitable for field data that are correlated across space. In this paper, a nonparametric large-sample inference procedure is developed to assess the time effects while accounting for the spatial dependence using a block bootstrap. For illustration, the methodology is applied to describe the population changes of root-lesion nematodes over time in a production field in Wisconsin.     

A genetic component to size in queens of the ant,<Emphasis Type="Italic"> Formica truncorum</Emphasis>

The genetic basis of morphological traits in social insects remains largely unexplored. This is even true for individual body size, a key life-history trait. In the social insects, the size of reproductive individuals affects dispersal decisions, so that small size in queens is often associated with reduced dispersal, and large size with long-range dispersal and independent colony founding. Worker size is connected to division of labour when workers specialize in certain tasks according to their size. In many species, variation in worker size has been shown to increase colony performance. In this study, we present the first evidence of an additive genetic component to queen size in ants, using maternal half sib analysis. We also compared intra-colony size variation in colonies with high (queen doubly mated) versus low (queen singly mated) genetic variability. We found a high and significant heritability (h²=0.51) for queen size in one of the two study years, but not in the other. Size variation among queens was greater in colonies headed by a doubly mated queen in one of the study years, but not in the other. This indicates that genetic factors can influence queen size, but that environmental factors may override these under some circumstances. The heritability for worker size was low (h²=0.09) and non-significant. Increased genetic diversity did not increase worker size variation in the colonies. Worker size appeared largely environmentally determined, potentially allowing colonies to adjust worker size ratios to current conditions.Communicated by J. Heinze     

Intra- and inter- laboratory variability in paper industry PCDD/PCDF analysis

Studies were conducted to quantify variability for pulp industry wastewater effluent, pulp and sludge analyses. Intralaboratory variability studies indicated that there is a potential for greater between batch variability in results than for within a batch. Intralaboratory relative standard deviations for replicate analyses ranged from 6% to 60%. Interlaboratory studies, using standard reference materials showed relative standard deviations from 4% to 135%. There appeared to be little dependance of variability on concentration, suggesting matrix effects were very important. In general, effluent variability was greater than observed for pulps or sludges. Analysis of reference standards indicated that 13% to 17% variability can be attributed to differences in calibration standards. A limited study of full congener PCDD/PCDF analysis variability showed it to be greater than observed for TCDD/TCDF.     

长三角地区2015年大气重污染特征及其影响因素

基于2015年长三角地区129个环境空气质量监测站的空气质量指数（AQI）及主要大气污染物浓度数据,结合气象资料和HYSPLIT后向轨迹模式,探究长三角地区大气重污染的时间变化和空间集聚特征,并深入分析气象条件和区域传输对重污染过程发生和维持的影响.结果表明,2015年长三角地区各城市平均出现AQI超过200的重污染天气共8 d,重污染频率为2.01%,PM_2.5作为首要污染物出现频次最多.从时间变化看,重污染主要分布在1月和12月;从空间分布看,北部地区重污染相比南部地区更为严重,徐州和常州市出现频率最高.选取典型重污染过程1月9—11日（纬向扩散型）、1月24—26日（经向扩散型）和12月20—26日（两种模式相结合的重污染天气）进行成因分析,发现长三角地区重污染天气主要受到西北风向、低风速、高湿度和逆温层的影响,导致大气污染物积累且不易扩散.基于HYSPLIT的大气传输轨迹及频率分布表明,来自西北方向的气流对江苏北部地区的污染输送特征有着显著影响.     

中国城市PM2.5时空动态变化特征分析:2015-2017年

近年来我国雾霾事件频发.采用2015-2017年全国329个地级及以上城市PM_2.5浓度每小时监测数据,利用全域空间自相关法、自然正交函数和空间描述统计分析的方法,从时空视角来揭示PM_2.5浓度的时间动态变化规律以及空间分布特征.研究发现：①从全国范围内来看,PM_2.5浓度均值逐年降低,降幅最高为夏季,最低为冬季,PM_2.5浓度位于40~60 μg·m^-3之间的城市降幅较大.PM_2.5浓度年内表现为"冬高夏低,春秋居中"的时间动态变化规律,且各年PM_2.5浓度达优良率不断提高.②细颗粒物污染改善程度最大的为德州,京津冀城市群和长三角城市群改善程度居中.全国PM_2.5污染范围逐年缩小,但新疆西部和冀鲁豫仍为高污染区,西南和东南沿海地区为低污染区.各区域污染的空间集聚逐年缩小.优良达标率在空间分布特征上无显著变化.③"大气十条"部分指标已完成,未来细颗粒物污染治理重点区域仍以京津冀地区为核心.在防治空气污染方面,必须加强区域联防联控机制.