Dynamics of growth and seed production in the Siberian stone pine: The level and pattern of variation in characters

A statistical analysis of interannual variation in a set of vegetative and generative characters over 13 years has been performed to characterize the growth and seed production dynamics of the Siberian stone pine (Pinus sibirica Du Tour) in the southeast of the forest zone in western Siberia. The results have shown that the range of fluctuations in most of vegetative characters is ordinary and their distribution is close to normal. The range of fluctuations in many generative characters is enormous (from 0 to +∞), and the distribution of their values is usually skewed, with low values being recorded several times more frequently than high values. Most variable are the proportion of abortive cones and other characters that are determined mainly in the spring of the pollination year. These characters account for a very high level of variation in the total seed production.     

Coat color polymorphism in populations of the northern mole vole in the Ural Region

Collections made in the course of long-term field studies on ecology of the northern mole vole Ellobius talpinus Pall. in the Ural Region and neighboring areas (more than 2000 individuals from 24 points of the species range) were used to analyze geographic variation in its coat color (color morphs). On the basis of long-term observations (1985–1999) on marked animals from a polymorphic population (Kurtamyshskii raion, Kurgan oblast), the life spans of males and females and the dependence of life span on population density and structure were estimated in animals of different color morphs. Each color morph of E. talpinus was shown to have specific features of the seasonal dynamics of age structure and migrations.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

基于时序NDVI数据的鄱阳湖流域常绿覆被季节性变化特征

选择位于红壤丘陵区的鄱阳湖流域作为研究对象，利用1 km×1 km分辨率的时序SPOT4 VEGETATION数据，对流域内典型土地覆被--常绿覆被的绿度值、峰值、谷值、年均NDVI(NDVI-I)和NDVI年内极差(NDVI MM)等特征值进行了提取。在此基础上，探讨了不同常绿覆被类型的NDVI指数年内季节变化规律。结果表明：时序NDVI指数基本上能够较好地刻画不同常绿覆被类型之间的差异性，植被指数NDVI特征值随覆被的类型及其生长状态有规律地变化，即NDVI年均值和最小值基本上按“常绿阔叶林＞常绿针阔叶混交林＞常绿针叶林＞常绿针叶-落叶混交林”的顺序变化；典型常绿阔叶林的NDVI指数年内变化曲线基本上没有大的起伏波动；常绿针叶林以及常绿针阔叶混交林占主导地位的常绿混交林NDVI指数年内变化比较和缓，但常在8月和11月有所波动；以常绿针叶林为主、但有较多落〖JP2〗叶林混杂其中的常绿混交林，其NDVI指数年内变化曲线基本上呈和缓的单峰型波动。     

Variation of leaf gland volatile oil within a population of sweet gale (Myrica gale) (Myricaceae)

Summary The leaf gland volatile oils of ten sweet gale plants from a Scottish population were extracted in early summer. The results differed notably from reports of other populations in respect of the sesquiterpenes, -elemenone and germacrone, which were major components of the volatile oil. Three dihydrochalcones were also detected in the volatile oil. Variation within the population existed, particularly with respect to the relative importance of germacrone. Five plants were resampled in late summer and exhibited a marked reduction in -elemenone, a lesser reduction in germacrone and changes in the proportions of some monoterpenes.     

北京南部城区PM2.5中碳质组分特征

为了解《大气污染防治行动计划》实施后北京市大气PM_2.5中碳质组分特征,于2017年12月至2018年12月在北京污染较重的南部城区进行了PM_2.5连续采样,对其中的有机碳（OC）和元素碳（EC）进行了全面研究.结果表明,北京大气PM_2.5、OC和EC浓度变化范围分别为4.2~366.3、0.9~74.5和0.0~5.5 μg ·m^-3,平均浓度分别为（77.1±52.1）、（11.2±7.8）和（1.2±0.8）μg ·m^-3,碳质组分（OC和EC）整体占PM_2.5的16.1%.OC质量浓度季节特征表现为：冬季[（13.8±8.7）μg ·m^-3] > 春季[（12.7±9.6）μg ·m^-3] > 秋季[（11.8±6.2）μg ·m^-3] > 夏季[（6.5±2.1）μg ·m^-3],EC四季质量浓度水平均较低,范围为0.8~1.5 μg ·m^-3.二次有机碳（SOC）年均质量浓度为（5.4±5.8）μg ·m^-3,四季贡献比例范围为45.7%~52.3%,年均贡献为48.2%,凸显了二次形成的重要贡献.随污染加重,尽管OC和EC贡献比例均降低,但浓度水平却成倍升高,OC和EC浓度在严重污染天分别是空气质量为优天的6.3和3.2倍.与非供暖时段相比,供暖时段PM_2.5、OC和SOC浓度分别增加了14.4%、47.9%和72.1%,体现了OC对供暖季PM_2.5污染的重要贡献.PSCF分析表明,位于北京西南的山西省和河南省部分区域是PM_2.5和OC的主要潜在源区,且PM_2.5潜在源区更为集中;EC的PSCF高值（>0.7）区域较少,主要位于北京南部,如山东省和河南省部分地区,且北京市及周边地区贡献明显.     

典型西南工业城市柳州市核心区大气污染物时空分布与气象因素研究

为研究柳州市核心区大气污染物浓度时空变化规律与气象因素之间的关系,统计分析了2018年全年研究区内6个自动监测站点PM_(2.5)、PM_(10)、SO_2、NO_2、O_3和CO的浓度监测数据和气象站气象数据,并对28次超标日污染物来源进行了解析.结果显示:①核心区颗粒污染物污染较为严重,且以PM_(2.5)为主的细颗粒污染物仍为柳州市主要的大气污染物;各污染物月均浓度季节差异显著,除NO_2外柳州大气污染物浓度下降明显,指示柳州市多项节能减排综合整治措施成效显著;PM_(2.5)、PM_(10)、CO受早晚高峰期影响,浓度日变化均呈双峰型;NO_2在不同季节峰型不同,作为O_3前体物其浓度日变化与O_3相反,呈现"早峰午谷"的变化趋势.②通过对污染物浓度插值发现,由于核心区主要工商业区位于西部且处于主导风向下风向,故PM_(2.5)和SO_2浓度西北高、东南低,PM_(10)、NO_2和CO浓度西南高、东北低;核心区东部的山区为O_3生成带来大量前体物,使O_3浓度东南高、西北低.③由于气候特征,核心区春、夏季主要气象因素均为降水量;秋季的主要气象因素是风速,风速与污染物的负相关关系表明了风的扩散效应;冬季大部分污染物与气象因素的相关性不显著,表明人为因素对污染物的影响大于气象因素;核心区大气污染物主要来源于局地排放和区域传输,且南北主导风向对大气污染影响最大.④HYSPLIT模型结果指示柳州超标日大气污染物主要来自于珠三角地区,且陆源颗粒物浓度普遍比海洋源高,来自南部的远距离输送气流颗粒物含量最低,表明远距离输送为影响颗粒物传播的主要原因.     

O3-BAC深度处理工艺中细菌群落时空分布及动态变化规律

为弄清饮用水O₃-BAC深度处理工艺过程中细菌群落的时空分布和动态变化规律,本研究以我国南方某O₃-BAC深度处理工艺水厂为研究对象,采用NovaSeq6000高通量测序技术对夏季和冬季各工艺单元出水及滤砂和活性炭生物膜等细菌群落进行解析.结果表明,出厂水pH、浊度、COD_Mn、菌落总数等指标均满足《生活饮用水卫生标准》（GB5749-2006）的要求.夏季细菌群落多样性明显高于冬季,活性炭生物膜的细菌群落多样性高于滤砂生物膜;混凝沉淀、臭氧化和消毒是影响细菌群落多样性的主要工艺单元.水样和生物膜样品绝对优势菌门均为变形菌门（Proteobacteria）,且主要菌门组成大体相同,但细菌群落门水平相对丰度存在一定的时空差异,属水平上差异则更为明显.此外,检测到的条件致病菌属主要包括芽孢杆菌属（Bacillus）、不动杆菌属（Acinetobacter）、假单胞菌属（Pseudomonas）和分支杆菌属（Mycobacterium）,且其所占核心微生物OTUs数目不受季节性影响.水温和生物可降解溶解性有机碳（BDOC）是显著影响细菌群落分布的主要水质参数.以上研究结果表明,O₃-BAC深度处理工艺过程中细菌群落具有时空变化特性,并可为饮用水微生物安全保障提供支撑.     

南京和北京气溶胶光学厚度与PM2.5质量浓度关系对比分析

利用南京与北京地区2014年5月1日—2019年10月31日的PM_2.5监测数据、气溶胶光学厚度观测资料以及同期MICAPS地面气象要素的观测资料,对两地PM_2.5浓度的变化规律及其与气溶胶光学厚度、气象要素的关系进行了分析和讨论,结果表明：南京与北京均呈现PM_2.5浓度冬季显著高于夏季,AOD冬季小于夏季的特征;对比而言,北京PM_2.5月均浓度高于南京地区;南京与北京的PM_2.5浓度与AOD均为正相关关系,PM_2.5浓度与AOD间相关性存在显著的季节差异,主要表现为夏季相关性大于冬季相关性;两地AOD与PM_2.5浓度均为正相关关系,在同一AOD水平下,相对湿度越大,PM_2.5浓度越大,气溶胶吸湿增长易造成污染物积累;南京PM_2.5浓度与能见度的r为0.57,而北京的r为0.83,两地的PM_2.5浓度与能见度的冬季相关性较夏季好,在高相对湿度下,同一PM_2.5浓度水平时,南京能见度较北京好.     

粤港澳大湾区NO2污染的时空特征及影响因素分析

利用Aura卫星搭载的臭氧观测仪（OMI）反演的对流层NO₂柱密度数据,分析了自2005年以来粤港澳大湾区（GBA）对流层NO₂柱密度的空间分布特征、时间变化趋势及其影响因素.研究结果表明GBA对流层NO₂柱密度从2005~2018年呈减少的趋势,每年递减约为2.8%.小波系数显示时间演化过程中存在9个月的主振荡周期,冬季浓度较高,夏季较低.人为排放和各种自然因素,导致了GBA对流层NO₂柱浓度月变化在时间和空间上存在明显差异,最小值和最大值分别出现在6和12月,多年平均值分别为3.9665×10¹⁵和12.3423×10¹⁵molec/cm².NO₂在空间分布上呈现明显的空间分异特征,冬季12月最明显.NO₂污染严重的高值区主要出现在中部地区,如广州市、佛山市和中山市,最大的对流层NO₂柱密度可达18.8306×10¹⁵molec/cm²,大约是周边地区的3 倍,且高污染区域向四周逐渐扩散,连成一片.低值区主要在北部的肇庆市和东部的惠州市,多年平均的对流层NO₂柱密度约为7.1400×10¹⁵molec/cm².对流层NO₂柱密度的增长率在不同区域的变化趋势呈现明显的差异,变化范围为-15×10¹⁵~6×10¹⁵molec/cm²,增长率百分比范围为-65%~65%.出现增长的地区主要是肇庆市北部和惠州市东部的低值区;对流层NO₂出现明显减少的区域集中在中部的高值区,减少量最大的地区为广州市、佛山市和中山市交界处.