莆田地区11月份暴雨成因初析

本文讨论１９６１￣１９９６年１１月份福建省莆田地区出现暴雨过程的成因,分析表明暴雨的发生与南海东部沿海热带气旋（或低压）的例槽和北方冷空气共同影响密切相关,并讨论其形势特点、分析其物理量特征,同时进行对比分析。     

中国对虾(Penaeus chinensis)幼体血液循环的活体观察

溞对中国对虾早期幼体的循环系统及血液流向进行了活体观察 ,描述了中国对虾早期幼体发育阶段中幼体循环系统血管的走向与分布、血液的流向及循环路径等 ,比较各期幼体循环系统的形态 ,并探讨对虾早期幼体循环系统的发育过程。     

胶州湾海水微表层粘度及其在海-气通量计算中的作用

物质海 -气通量计算新建议中将物质海 -气通量计算公式 F=K(CL- b Cg)中的 CL 用CL ( SML ) 代替。本文着重于对公式中质量迁移系数 K的讨论。在测定了海水微表层、次表层水粘度并同时测定了其它一些化学参量基础上 ,得出如下结论 :海水粘度与盐度、碱度有一定相关性 ;微表层与次表层海水的粘度变化小于 3%。因此 ,海水微表层效应影响 K值 ,与海水微表层效应影响物质浓度相比 ,可以不考虑。     

雨生红球藻细胞形态转换和藻落形成差异研究

基于雨生红球藻(Haematococcus pluvialis)培养过程中游动细胞和不动细胞的形态差异与转换,对比研究了两种细胞类型在离心收集与藻落形成效率上的差异。结果表明,雨生红球藻SCCAP K-0084在BYA培养基中培养4~6 d和9~12 d分别以绿色游动细胞与绿色不动细胞为主;早期游动细胞离心收集需要更高的离心力,但500~1 000 g离心5 min可以在不影响细胞存活率的情况下有效收获所有类型细胞;相比于传统直接涂布法,双层琼脂平板法可有效提高平板藻落形成效率,且游动细胞藻落形成效率比不动细胞藻落形成效率更高。TAP培养基由于藻落形成效率较低并不适合于雨生红球藻平板培养与筛选,自养型培养基虽然具有较好的藻落形成效率但是藻落形成速度较慢,相比而言,BYA培养基藻落形成效率较高且生长速度快,更适合用于雨生红球藻细胞的后期平板培养与筛选过程。     

南海北部表层颗粒有机碳的季节和年际变化遥感分析

海洋颗粒有机碳（POC）是海洋固碳的一个关键参数。为了研究南海北部陆架及海盆表层POC浓度的时空分布特征以及变化趋势,本文利用2009-2011年4个季节的实测数据,对NASA发布的MODIS/AQUA卫星月平均POC遥感产品,进行了验证和校正;并利用校正后的遥感数据分析了2003-2014年POC的时空分布特征和变化趋势。发现POC遥感产品与南海北部实测数据具有较好的线性关系（R2=0.72）,但存在系统性偏高,需利用实测数据对遥感数据进行区域性校正。分析校正后的遥感数据发现,南海北部陆架POC浓度较高,平均为（33.34±8.02）mg/m3;吕宋海峡西南海域浓度较低,平均为（29.25±6.20）mg/m3;中央海盆区浓度最低,平均为（27.02±4.84）mg/m3。春夏季POC浓度较低,最低值一般出现在5月,冬季（12月至翌年1月）POC浓度达到最高。利用2003-2014年的长时间序列遥感叶绿素（Chl a）和海表温度（SST）、混合层深度（MLD）模式数据,以及实测数据对南海北部POC浓度的影响机制进行了分析。发现POC与Chl a在秋冬呈现较好的相关关系（R2=0.51）,但在春夏季较离散,表明秋冬季生物作用对POC影响较大。2003-2014年期间,POC与Chl a、MLD及SST存在明显的年际变化,但并没有显著的上升或下降趋势。     

Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutants Ⅰ. Application of incomplete adjoint operator

The ultimate solution to anthropogenic air pollution depends on an adjustment and upgrade of industrial and energy structures. Before this process can be completed, reducing the anthropogenic pollutant emissions is an effective measure. This is a problem belonging to “Natural Cybernetics”, i.e., the problem of air pollution control should be solved together with the weather prediction; however, this is very complicated. Considering that heavy air pollution usually occurs in stable weather conditions and that the feedbacks between air pollutants and meteorological changes are insufficient, we propose a simplified natural cybernetics method. Here, an off-line air pollution evolution equation is first solved with data from a given anthropogenic emission inventory under the predicted weather conditions, and then, a related “incomplete adjoint problem” is solved to obtain the optimal reduction of anthropogenic emissions. Usually, such solution is sufficient for satisfying the air quality and economical/ social requirements. However, a better solution can be obtained by iteration after updating the emission inventory with the reduced anthropogenic emissions. Then, this paper discusses the retrieval of the pollutant emission source with a known spatio-temporal distribution of the pollutant concentrations, and a feasible mathematical method to achieve this is proposed. The retrieval of emission source would also help control air pollution.     

Carbon pools and fluxes in the China Seas and adjacent oceans

The China Seas include the South China Sea, East China Sea, Yellow Sea, and Bohai Sea. Located off the Northwestern Pacific margin, covering 4700000 km~2 from tropical to northern temperate zones, and including a variety of continental margins/basins and depths, the China Seas provide typical cases for carbon budget studies. The South China Sea being a deep basin and part of the Western Pacific Warm Pool is characterized by oceanic features; the East China Sea with a wide continental shelf, enormous terrestrial discharges and open margins to the West Pacific, is featured by strong cross-shelf materials transport; the Yellow Sea is featured by the confluence of cold and warm waters; and the Bohai Sea is a shallow semiclosed gulf with strong impacts of human activities. Three large rivers, the Yangtze River, Yellow River, and Pearl River, flow into the East China Sea, the Bohai Sea, and the South China Sea, respectively. The Kuroshio Current at the outer margin of the Chinese continental shelf is one of the two major western boundary currents of the world oceans and its strength and position directly affect the regional climate of China. These characteristics make the China Seas a typical case of marginal seas to study carbon storage and fluxes. This paper systematically analyzes the literature data on the carbon pools and fluxes of the Bohai Sea,Yellow Sea, East China Sea, and South China Sea, including different interfaces(land-sea, sea-air, sediment-water, and marginal sea-open ocean) and different ecosystems(mangroves, wetland, seagrass beds, macroalgae mariculture, coral reefs, euphotic zones, and water column). Among the four seas, the Bohai Sea and South China Sea are acting as CO_2 sources, releasing about0.22 and 13.86–33.60 Tg C yr~(-1) into the atmosphere, respectively, whereas the Yellow Sea and East China Sea are acting as carbon sinks, absorbing about 1.15 and 6.92–23.30 Tg C yr~(-1) of atmospheric CO_2, respectively. Overall, if only the CO_2 exchange at the sea-air interface is considered, the Chinese marginal seas appear to be a source of atmospheric CO_2, with a net release of 6.01–9.33 Tg C yr~(-1), mainly from the inputs of rivers and adjacent oceans. The riverine dissolved inorganic carbon (DIC) input into the Bohai Sea and Yellow Sea, East China Sea, and South China Sea are 5.04, 14.60, and 40.14 Tg C yr~(-1),respectively. The DIC input from adjacent oceans is as high as 144.81 Tg C yr~(-1), significantly exceeding the carbon released from the seas to the atmosphere. In terms of output, the depositional fluxes of organic carbon in the Bohai Sea, Yellow Sea, East China Sea, and South China Sea are 2.00, 3.60, 7.40, and 5.92 Tg C yr~(-1), respectively. The fluxes of organic carbon from the East China Sea and South China Sea to the adjacent oceans are 15.25–36.70 and 43.93 Tg C yr~(-1), respectively. The annual carbon storage of mangroves, wetlands, and seagrass in Chinese coastal waters is 0.36–1.75 Tg C yr~(-1), with a dissolved organic carbon(DOC) output from seagrass beds of up to 0.59 Tg C yr~(-1). Removable organic carbon flux by Chinese macroalgae mariculture account for 0.68 Tg C yr~(-1) and the associated POC depositional and DOC releasing fluxes are 0.14 and 0.82 Tg C yr~(-1), respectively. Thus, in total, the annual output of organic carbon, which is mainly DOC, in the China Seas is 81.72–104.56 Tg C yr~(-1). The DOC efflux from the East China Sea to the adjacent oceans is 15.00–35.00 Tg C yr~(-1). The DOC efflux from the South China Sea is 31.39 Tg C yr~(-1). Although the marginal China Seas seem to be a source of atmospheric CO_2 based on the CO_2 flux at the sea-air interface, the combined effects of the riverine input in the area, oceanic input, depositional export,and microbial carbon pump(DOC conversion and output) indicate that the China Seas represent an important carbon storage area.     

Application of a multi‐temporal,LiDAR‐derived,digital terrain model in a landslide‐volume estimation

Sediments produced by landslides are crucial in the sediment yield of a catchment, debris flow forecasting, and related hazard assessment. On a regional scale, however, it is difficult and time consuming to measure the volumes of such sediment. This paper uses a LiDAR‐derived digital terrain model (DTM) taken in 2005 and 2010 (at 2 m resolution) to accurately obtain landslide‐induced sediment volumes that resulted from a single catastrophic typhoon event in a heavily forested mountainous area of Taiwan. The landslides induced by Typhoon Morakot are mapped by comparison of 25 cm resolution aerial photographs taken before and after the typhoon in an 83.6 km² study area. Each landslide volume is calculated by subtraction of the 2005 DTM from the 2010 DTM, and the scaling relationship between landslide area and its volume are further regressed. The relationship between volume and area are also determined for all the disturbed areas (V_L = 0.452A_L^1.242) and for the crown areas of the landslides (V_L = 2.510A_L^1.206). The uncertainty in estimated volume caused by use of the LiDAR DTMs is discussed, and the error in absolute volume estimation for landslides with an area >10⁵ m² is within 20%. The volume–area relationship obtained in this study is also validated in 11 small to medium‐sized catchments located outside the study area, and there is good agreement between the calculation from DTMs and the regression formula. By comparison of debris volumes estimated in this study with previous work, it is found that a wider volume variation exists that is directly proportional to the landslide area, especially under a higher scaling exponent. Copyright © 2013 John Wiley & Sons, Ltd.     

Influence of Surfactants on Degradation of 1‐(2‐Chlorobenzoyl)‐3‐(4‐chlorophenyl) Urea by Nanoscale Zerovalent Iron

Nanoscale zerovalent iron (NZVI) has been proved to be effective in the degradation of environmental pollutants and exhibits advantages in the removal of 1‐(2‐chlorobenzoyl)‐3‐(4‐chlorophenyl) urea (CCU), an analog of diflubenzuron. This present study focused on the influence of surfactants in the degradation procedure with NZVI in order to provide a simple and rapid removal method for CCU. Triton X‐100, Tween 20, Tween 80, sodium dodecyl sulfonate (SDS), and cetyltrimethylammonium bromide (CTAB) were investigated under anaerobic conditions. The experimental results demonstrated that the degradation rate increased sharply with the presence of SD during the first 15 min, up to 99.97% with addition of 0.01 g L^?1 SDS, whereas the presence of Triton X‐100, Tween 80, and Tween 20 resulted in a slight enhancement of the degradation of CCU. The enhancement strength of them was in the order Tween 20, Triton X‐100, and Tween 80. However, addition of the cationic surfactant CTAB resulted in a significant inhibitive effect. In contrast, the mixed surfactants did not result in the expected performance, and the performance was lower than that using some certain single surfactant among the mixed surfactants.     

Effects of rotation motions on strong-motion data

Rotation motion and its effects on strong-motion data, in most cases, are much smaller than that of translational motion and have been ignored in most analyses of strong-motion data. However, recent observations from near-fault and/or extreme large ground motions suggest that these effects might be underestimated and quantitative analyses seem to be necessary for improving our understating of these effects. Rotation motion-related effects include centrifugal acceleration, the effects of gravity and effects of the rotation frame. Detailed analyses of these effects based on the observed data are unavailable in the literature. In this study, we develop a numerical algorithm for estimating the effects of rotational motion on the strong-motion data using a set of six-component ground motions and apply it to a set of rotation rate-strong motion velocity data. The data were recorded during a magnitude 6.9 earthquake. The peak value of the derived acceleration and rotation rate of this dataset are about 186 cm/s/s and 0.0026 rad/s. Numerical analyses of data gives time histories of these rotational motion-related effects. Our results show that all the rotation angles are less than 0.01°. The maximum centrifugal acceleration, effect from gravity and effect of the rotation frame are about 0.03 and 0.14 cm/s/s, respectively. Both these two effects are much smaller than the peak acceleration 186 cm/s/s. This result might have been expected because our data are not near-field and wave motions are expected to be nearly plane waves. However, it is worth noticing that the centrifugal acceleration is underestimated and a small rotational effect can cause large waveform difference in acceleration data. The waveform difference before and after the correction for rotational motion can reach 16 cm/s/s (about 10 %).