不同水期洞庭湖水体中磷分布特征及影响因素

为研究不同水期洞庭湖水体中磷的分布特征及影响因素,分别于2016年1～12月对洞庭湖湖区及其入湖河流共18个代表性监测断面采集水样,分析了样品中各形态磷质量浓度.结果表明,总体上,洞庭湖水体中总磷(TP)、溶解性总磷(DTP)和磷酸盐(DPO)均表现为平水期枯水期丰水期,具有明显的季节性变化特征.空间分布上,枯、平水期湖区水体磷质量浓度由西向东增加,丰水期由西向东减少.时间分布上,东、西洞庭湖和出湖口水体中各形态磷质量浓度季节性分布差异明显,西洞庭湖表现为丰水期大于枯、平水期,东洞庭湖和出湖口表现为枯、平水期大于丰水期.形态组成上,与三峡水库蓄水前不同,洞庭湖湖区及其入湖河流水体中磷以溶解态为主,DTP是TP的主要形态,DPO是DTP的主要形态.洞庭湖水体中悬浮颗粒(SS)和磷的分布有一定的协同性,各水期SS和磷质量浓度呈显著或极显著相关关系,因此认为SS是洞庭湖水体中磷季节性变化的重要影响因素.另外,南嘴断面、大小西湖断面、东洞庭湖湖区等区域受周边人类活动影响造成的水体磷污染较重,应引起高度重视.     

北京市大气PM10环境背景值的计算方法探讨

为了消除或降低PM10环境背景值计算中城市局地污染的影响,并且提高背景值在污染过程模拟中的准确程度,紧密联系天气过程、通过监测和统计途径探讨了确定环境背景值的方法.北京市空气质量背景监测站点在地方性天气过程中明显受到城市污染的影响,文中根据天气形势场、垂直方向上风向一致性、系统影响持续时间等判别标准区分了系统性和地方性过程,筛选出受系统性过程影响的、"背景时段"的监测数据.背景时段监测数据统计计算时,将背景值视为变量,用百分位数、剪裁均值、最大概率密度值、50%概率区间等特征统计量来表达其概率分布.文中还将背景值细化,根据天气系统特征和区域自然环境、人类活动的差异将背景值区分为4类,以代表不同污染过程的背景水平.使用2003年11月至2004年12月北京市北部和南部共5个清洁站点的监测数据,计算了117个中尺度天气系统影响时,PM10浓度概率密度分布以及背景值特征统计量.结果表明,4类背景值差别很大,从小到大排序依次是:北风＞东风＞西风＞南风,剪裁均值依次为:24～34、58、89～110和105μg·m-3.对4类背景值综合运算,得到2004年PM10年尺度上的综合背景值为58～67μg·7-3,较好地扣除了局地污染的影响.     

Hazards, spatial transmission and timing of outbreaks in epidemic metapopulations

Highly infectious, immunizing pathogens can cause violent local outbreaks that are followed by the agent’s extinction as it runs out of susceptible hosts. For these pathogens, regional persistence can only be secured through spatial transmission and geographically asynchronous epidemics. In this paper we develop a hazard model for the waiting time between epidemics. We use the model, first, to discuss the predictability in timing of epidemics, and, second, to estimate the strength of spatial transmission. Based on the hazard model, we conclude that highly epidemic pathogens can at times be predictable in the sense that the waiting-time distribution between outbreaks is probabilistically bounded; The greater the spatial transmission the more periodic the outbreak dynamics. When we analyze the historical records of measles outbreaks in England and Wales between 1944 and 1965, we find the waiting-time between epidemics to depend inversely on community size. This is because large communities are much more tightly coupled to the regional metapopulation. The model further help identify the most important areas for spatial transmission. We conclude that the data on absence of these pathogens is the key to understanding spatial spread.     

Soil enzymatic activities and microbial community structure with di erent application rates of Cd and Pb

This study focused on the changes of soil microbial diversity and potential inhibitory effects of heavy metals on soil enzymatic activities at different application rates of Cd andor Pb. The soil used for experiments was collected from Beijing and classified as endoaquepts. Pots containing 500 g of the soil with different Cd andor Pb application rates were incubated for a period of 0, 2, 9, 12 weeks in a glasshouse and the soil samples were analyzed for individual enzymes, including catalase, alkaline phosphatase and dehydrogenase, and the changes of microbial community structure. Results showed that heavy metals slightly inhibited the enzymatic activities in all the samples spiked with heavy metals. The extent of inhibition increased significantly with increasing level of heavy metals, and varied with the incubation periods. The soil bacterial community structure, as determined by polymerase chain reaction-denaturing gradient gel electrophoresis techniques, was different in the contaminated samples as compared to the control. The highest community change was observed in the samples amended with high level of Cd. Positive correlations were observed among the three enzymatic activities, but negative correlations were found between the amounts of the heavy metals and the enzymatic activities.     

A framework for investigating the air quality variation characteristics based on the monitoring data: Case study for Beijing during 2013–2016

In this study, an analysis framework based on the regular monitoring data was proposed for investigating the annual/inter-annual air quality variation and the contributions from different factors(i.e., seasons, pollution periods and airflow directions), through a case study in Beijing from 2013 to 2016. The results showed that the annual mean concentrations(MC) of PM_(2.5), SO_2, NO_2 and CO had decreased with annual mean ratios of 7.5%, 28.6%, 4.6%and 15.5% from 2013 to 2016, respectively. Among seasons, the MC in winter contributed the largest fractions(25.8%～46.4%) to the annual MC, and the change of MC in summer contributed most to the inter-annual MC variation(IMCV) of PM_(2.5) and NO2. For different pollution periods, gradually increase of frequency of S-1(PM_(2.5), 0～ 75 μg/m~3) made S-1 become the largest contributor(28.8%) to the MC of PM_(2.5) in 2016, it had a negative contribution(-13.1%) to the IMCV of PM_(2.5); obvious decreases of frequencies of heavily polluted and severely polluted dominated(44.7% and 39.5%) the IMCV of PM_(2.5). For different airflow directions, the MC of pollutants under the south airflow had the most significant decrease(22.5%～62.5%), and those decrease contributed most to the IMCV of PM_(2.5)(143.3%),SO2(72.0%), NO_2(55.5%) and CO(190.3%); the west airflow had negative influences to the IMCV of PM_(2.5), NO_2 and CO. The framework is helpful for further analysis and utilization of the large amounts of monitoring data; and the analysis results can provide scientific supports for the formulation or adjustment of further air pollution mitigation policy.     

华北与闽粤沿海及银川地震区(带)地震周期的划分与研究

按地震在空间上的相似活动过程排列的华北与闽粤沿海及银川地震区 (带 )地震活动周期表 ,以华北地震活动为主线 ,将公元前 2 80年咸阳 5 级地震至 1 988年 2月 2 5日彰武 4 8级地震间 2 2 6 7年的华北地震活动划分为 4个时间层次。其组合关系是 ,一个超长地震周期由初、前、主、余震期 4个地震期组成 ;除前震期只有一个地震周期外 ,初、主、余震期都包含前弱后强两个地震周期 ;每个地震周期多分为初、前、主、余震段 4个地震段。闽粤沿海地震区和银川地震带的主震段分别与华北初、余震段同期活动。地震周期是介于前人划分的地震幕与地震活动期之间一个新的更为重要的时间层次。华北 7个地震周期的起止时间是 :-2 80 .5 0～ 8.2 2～ 880 .1 2～ 1 337.6 9～ 1 5 6 8.37～ 1 730 .75～ 1 85 5 .95～ 1 988.1 5年。并从时空强 3方面阐述了华北地震周期性活动规律 ,分析了 3个地震区、带地震周期的相互关系。华北地震周期可作为标准地震周期与不同地区地震周期相对比 ,具有重要的预报意义