CO2加富对塔玛亚历山大藻叶绿素荧光参数及产毒的影响

由大气中CO_2浓度升高引起的海洋酸化,是全球性的重大环境问题之一。本研究采用实验生态学的方法,以塔玛亚历山大藻(Alexandrium tamarense)为研究对象,分析其在CO_2加富条件下叶绿素荧光动力学参数及产毒特征的变化。调制叶绿素荧光结果显示,CO_2加富对塔玛亚历山大藻的PSⅡ最大光化学量子产量(Fv/Fm)、最大相对电子传递效率(r ETRmax)有显著影响(P0.05),且随着培养时间的增长Fv/Fm、r ETRmax均降低,对半饱和光强(Ik)、快速光曲线初始斜率(α)却无显著影响(P0.05)。结果说明CO_2加富能促进塔玛亚历山大藻的PSⅡ最大光化学量子产量,提高其最大光能转换效率和相对最大电子传递效率。高效液相色谱法分析结果显示,该株塔玛亚历山大藻主要产漆沟藻毒素1(GTX1)、漆沟藻毒素4(GTX4)、N-磺酰氨甲酰基毒素(C1)及N-磺酰氨甲酰基毒素(C2)四种PSTs毒素,CO_2加富不改变主要麻痹性贝毒(PSTs)的种类组成,但能显著提高氨基甲酸酯类毒素(GTX1、GTX4)产量(P0.05),而降低N-磺酰氨甲酰基类毒素(C1、C2)产量(P0.05),说明加富能使塔玛亚历山大藻所产毒素发生转化,进而影响藻细胞的整体毒性。     

粉末压片-X射线荧光光谱法测定盐湖样品中的主次元素

采用粉末样品压片制样,利用波长色散X射线荧光光谱仪对盐湖样品中K、Ca、Na、Mg、Cl、SO_4~(2-)等6个主次元素(组分)含量进行测定。使用化学定值得到的样品作为人工标样,其值作为标准样品参考值,建立了X射线荧光光谱法测定盐湖样品中主次元素的方法。经验证,该分析方法的准确度完全能够满足化学分析的要求,其相对标准偏差RSD7%,分析元素结果与化学法结果的相对误差均小于5%。     

2010—2020年黄河下游河南典型灌区浅层地下水中砷和氟的演化特征及变化机制

黄河下游典型灌区河南段是豫北平原重要的农业种植区。该地区浅层水质整体较差,因常用于作物灌溉或家畜饮用,会对人体健康产生风险,因此对该地区地下水中砷与氟浓度变化特征和机制的研究将有助于提高对该地区地下水污染的认识水平。本文基于2010年和2020年在灌区范围内采集的327组浅层地下水样品,研究区内地下水砷和氟分布情况,并在此基础上对比研究十年间灌区浅层地下水中砷、氟的演化特征,探索分析砷与氟浓度及空间变化机制。研究结果表明：该地区浅层地下水中存在砷与氟超标问题,2020年浅层地下水中高砷（砷浓度大于10μg/L）和高氟（氟浓度大于1mg/L）的样品数量分别占总数的26.1%和26.06%。高砷水分布在太行山前洼地与黄河冲积平原等泥沙互层结构的沉积环境中,还原性较强,同时地下水径流不畅,较强的阳离子交换作用使得其所处环境中Ca²⁺浓度较高。近十年间砷浓度增加的水样占总数31.8%,砷浓度减少的水样占36.7%。砷浓度的增长（减少）是地下水还原性增强（减弱）使得锰氧化物溶解释放（吸附）导致。近十年间不同地区农业灌溉和水源置换等用水方式导致水位变化是引起砷浓度变化的潜在因素。高氟水主要分布在河南新乡与濮阳的黄河沿线,氟离子浓度受到沉积物中萤石等钙质矿物溶解影响,使得高氟地下水出现在低钙环境中。近十年间研究区中氟离子浓度减少的占总数60.2%,氟离子浓度增加的占32.1%,整体变化趋势向好,但是高氟区中氟离子浓度继续增加。氟浓度的变化同样受到Ca²⁺变化影响,在Ca²⁺浓度降低（升高）时氟浓度进一步升高（降低）。地下水中氟升高地区分布在黄河沿线,因此受到黄河水补给影响较大,地下水径流条件较好,阳离子交换作用减弱,使得Ca²⁺浓度降低,此时地下水中砷浓度受到环境影响而降低,因此研究区氟增加地区中砷与氟的分布和演化呈现反向关系。     

基于现场数据的叶绿素荧光量子产量算法研究

自然水体中太阳光激发的叶绿素a荧光的最早观测记录可以追溯到1967年.Tyler et al.发现,在自然水体的反射光谱中685 nm附近存在一个比较明显的反射峰[1],当时解释为由于670nm处的叶绿素吸收而导致的“异常色散”[2].随着Morel et al.[3]和Neville et al.[4]进一步的研究,人们认识到这应该是太阳光激发的叶绿素荧光峰,但这一荧光的量子产量非常低,Gordon估算了它的值在0.002到0.020之间[5].     

Mesozooplankton biomass and grazing responses to Cyclone Opal, a subtropical mesoscale eddy

As part of E-Flux III cruise studies in March 2005, plankton net collections were made to assess the effects of a cyclonic cold-core eddy (Cyclone Opal) on the biomass and grazing of mesozooplankton. Mesozooplankton biomass in the central region of Cyclone Opal, an area of uplifted nutricline and a subsurface diatom bloom, averaged 0.80±0.24 and 1.51±0.59 g DW m⁻², for day and night tows, respectively. These biomass estimates were about 80% higher than control (OUT) stations, with increases more or less proportionately distributed among size classes from 0.2 to >5 mm. Though elevated relative to surrounding waters south of the Hawaiian Islands (Hawai’i lee), total biomass and size distribution in Cyclone Opal were almost exactly the same as contemporary measurements made at Stn. ALOHA, 100 km north of the islands, by the HOT (Hawaii Ocean Time-series) Program. Mesozooplankton biomass and community composition at the OUT stations were also similar to ALOHA values from 1994 to 1996, preceding a recent decadal increase. These comparisons may therefore provide insight into production characteristics or biomass gradients associated with decadal changes at Stn. ALOHA. Gut fluorescence estimates were higher in Opal than in ambient waters, translating to grazing impacts of 0.11±0.02 d⁻¹ (IN) versus 0.03±0.01 d⁻¹ (OUT). Over the depth-integrated euphotic zone, mesozooplankton accounted for 30% of the combined grazing losses of phytoplankton to micro- and meso-herbivores in Opal, as compared to 13% at control stations. Estimates of active export flux by migrating zooplankton averaged 0.81 mmol C m⁻² d⁻¹ in Cyclone Opal and 0.37 mmol C m⁻² d⁻¹ at OUT stations, 53% and 24%, respectively, of the carbon export measured by passive sediment traps. Migrants also exported 0.18 mmol N m⁻² d⁻¹ (117% of trap N flux) in Cyclone Opal compared to 0.08 mmol N m⁻² d⁻¹ (51% of trap flux) at control stations. Overall, the food-web importance of mesozooplankton increased in Cyclone Opal both in absolute and relative terms. Diel migrants provided evidence for enhanced export flux in the eddy that was missed by sediment trap and ²³⁴Th techniques, and migrant-mediated flux was the major export term in the observed bloom-perturbation response and N mass balance of the eddy.     

Field detection of chlorophyll-a concentration in the sea surface layer by an airborne oceanographic lidar

An airborne oceanographic lidar, with a frequency-tripled Q-switched Nd： YAG laser of 355 nm, has been designed to measure chlorophyll-a （Chl-a） concentration in the sea surface layer by the Ocean Remote Sensing Institute, OUC. The field experiment was carried out in the bay which is located south of the Liaodong Peninsula on the 10th of September 2005. After the flight, the raw data were processed and analyzed by the fluorescence-to-Raman ratio method with seawater attenuation coefficients calculated from signal profiles. The results of Chl-a concentration sea water were also compared with those of Chl-a concentration by measurements by lidar are shown. The measurements in clear a Moderate Resolution Imaging Spectroradiometer （MODIS）.     

Field Detection of Chlorophyll-a Concentration in the Sea Surface Layer by an Airborne Oceanographic Lidar

An airborne oceanographic lidar, with a frequency-tripled Q-switched Nd: YAG laser of 355 nm, has been designed to measure chlorophyll-a (Chl-a) concentration in the sea surface layer by the Ocean Remote Sensing Institute, OUC. The field experiment was carried out in the bay which is located south of the Liaodong Peninsula on the 10th of September 2005. After the flight, the raw data were processed and analyzed by the fluorescence-to-Raman ratio method with seawater attenuation coefficients calculated from signal profiles. The results of Chl-a concentration measurements by lidar are shown. The measurements in clear sea water were also compared with those of Chl-a concentration by a Moderate Resolution Imaging Spectroradiometer (MODIS).     

主成分分析法在油荧光光谱波段选择中的应用

355nm激光器发射激光入射到海水表面,激发海表面溢油的荧光光谱,运用高光谱图像降维中应用广泛的分段主成分分析算法对油荧光光谱进行波段选择。该算法把每个分段被映射到主成分的信息量的大小作为是否被选择的标准,保证了选择波段的信息丰富;通过分段分析消除了传统主成分分析的全局性引起的波段忽略问题,获得较为满意的降维效果。     

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology.