胶州湾表层海水碳酸盐体系的季节变化

根据2007年8月(夏季),11月(秋季),2008年1月(冬季)和2010年4月(春季)在胶州湾海域测得的p H、溶解无机碳(DIC)、总碱度(Alk),以及通过以上参数计算得到的二氧化碳分压(p CO2)的数据,结合现场的化学、水文、生物等参数,探讨和分析了该海域的二氧化碳各参数的分布特征、季节变化和影响因素。结果表明:胶州湾p H、DIC、Alk和p CO2的年变化范围分别为:7.77—8.30,1949.2—2201.8μmol/kg,2033.9—2382.5μmol/kg和89.9—745.3μatm,均呈现明显的时空变化。温度是影响胶州湾碳酸盐体系的主要影响因素之一,同时陆地径流和降水会降低海水碳酸盐体系中各参数的含量,但是人类活动和生物活动也会在一定程度上增加DIC、Alk和p CO2的含量。     

海带磷酸甘露糖变位酶(PMM)基因的克隆与表达分析

磷酸甘露糖变位酶(PMM)是褐藻胶和岩藻聚糖合成过程中的关键酶之一。本研究利用c DNA末端快速克隆(RACE)技术,获得2条海带PMM基因(Sjpmm1,Sjpmm2)序列。其中,Sjpmm1的开放阅读框(ORF)长759 bp,其编码的Sj PMM1为卤酸脱卤酶(HAD)超家族成员,含252个氨基酸,分子量约为28.51 k Da;而Sjpmm2的ORF长1866 bp,其编码的Sj PMM2属于磷酸己糖变位酶超家族的成员,含621个氨基酸,分子量约为66.49 k Da。海带PMM的二级结构均以?-螺旋为主。进化分析表明,Sjpmm1来自于原始真核生物,而Sjpmm2来源于质体的第一次内共生作用。实时定量PCR分析发现,海带受到高温或低温胁迫时,Sjpmm1和Sjpmm2转录水平上升,以合成岩藻聚糖抵抗环境影响。此外,利用p MAL-c5X载体对Sj PMM1进行体外表达,得到高浓度的可溶性融合蛋白,为后续的Sj PMM功能分析提供基础。     

Improved regional groundwater flow modeling using drainage features: a case study of the central northern karst aquifer system of Puerto Rico (USA)

In northern Puerto Rico (USA), subsurface conduit networks with unknown characteristics, and surface features such as springs, rivers, lagoons and wetlands, drain the coastal karst aquifers. In this study, drain lines connecting sinkholes and springs are used to improve the developed regional model by simulating the drainage effects of conduit networks. Implemented in an equivalent porous media (EPM) approach, the model with drains is able to roughly reproduce the spring discharge hydrographs in response to rainfall. Hydraulic conductivities are found to be scale dependent and significantly increase with higher test radius, indicating scale dependency of the EPM approach. Similar to other karst regions in the world, hydraulic gradients are steeper where the transmissivity is lower approaching the coastline. This study enhances current understanding of the complex flow patterns in karst aquifers and suggests that using a drainage feature improves modeling results where available data on conduit characteristics are minimal.     

Quantities and Fluxes of Dissolved and Particulate Black Carbon in the Changjiang and Huanghe Rivers,China

Recent studies have suggested that large rivers play important roles in mobilizing and transporting black carbon (BC) from land to the ocean. However, the influence of the Changjiang and Huanghe, the two largest rivers in China, on the fate of BC has not been determined. In this paper, we present measurements of BC in both the dissolved and particulate phases in the Changjiang and Huanghe Rivers and in the coastal waters of the East China Sea (ECS). Our results show that dissolved black carbon (DBC) accounted for 3.0 ± 0.4 % and 4.8 ± 3.6 % of the total DOC pool in the Changjiang and Huanghe Rivers and 3.4 ± 0.6 % of the DOC pool in the coast of the ECS. In addition, particulate black carbon (PBC) accounted for 13 ± 0.9 % and 22 ± 11 % of the POC pool in the Changjiang and Huanghe Rivers, respectively. We calculate that the Changjiang and Huanghe transported 4.7 × 10¹⁰ gC and 1.7 × 10⁹ gC of DBC, and 2.0 × 10¹¹ gC and 1.2 × 10¹⁰ gC of PBC to the ECS and Bohai Sea in 2015. The large amounts of BC transported by the two rivers represent a major sink of anthropogenically derived organic carbon and could have a significant impact on the ecosystem and carbon cycling in China’s marginal seas.     

海洋产业结构与海域承载力的匹配关系探讨——以辽宁省为例

海洋产业结构和海域承载力之间存在多种耦合匹配关系,其匹配程度是影响海洋经济健康持续发展的重要因素,而准确地定量化评价这种匹配关系是准确把握海洋产业结构是否失衡的关键环节。文章将海域承载力引入海洋产业结构问题研究中,首先对海洋产业结构水平和海域承载力定量化测算,在此基础上运用基于数列的匹配度计算方法测度辽宁省海域承载状况与海洋产业结构水平的匹配度,探讨海洋产业结构与海域承载力之间的匹配关系。结果表明:辽宁省海域承载力与海洋产业结构水平匹配度数值较高,二者之间存在较匹配的关系。     

Bias estimation and correction for triangle-based surface area calculations

The calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data.     

Optimal routing for waste collection: a case study in Singapore

ABSTRACT

To collect and dispose growing amounts of municipal solid waste (MSW) changed to be a hot topic along with the rapid urbanization in past decades. Cities are more and more dependent on the incineration instead of landfilling due to the cost-efficiency and environmental concerns. Considering the limited number of incineration plants and complicated situation of transportation in both spatial and temporal dimensions in different cities, the optimal routing for waste collection turns to be meaningful research topic. In this research, the ant colony optimization (ACO)-based multi-objective routing model coupled with min-max model and Dijkstra’s algorithm is proposed to address the question of which route to take from these waste-generating points to the target incineration plant(s) considering travel time, accident probability (black spots), and population exposure, so as to support the routing decision-making. The model is successfully implemented in Singapore and the effectiveness of the model has also been justified. Besides, few limitations of this research have also been discussed, some of which would also be the future directions of our research, especially the design and integration of a web-based routing decision-making support system.