Integrating disciplinary research into an interdisciplinary framework: A case study in sustainability research

The complexity of today’s research problems increasingly demands that scientists move beyond the confines of their own discipline. In this special issue, the basics of a transdisciplinary framework are established and problems analyzed in a specific discipline are successfully integrated in this transdisciplinary network. The intent has been to go beyond only statements about the importance of enabling early stage researchers to work across disciplinary boundaries, and to show that important discoveries are being made at the intersection of disciplines. Embedding various research projects in a more global framework can meet the demands of an interdisciplinary or transdisciplinary approach (especially for early stage researchers) and contribute significantly to sustainable research.     

A study on the orbit of air temperature movement

In this paper, we describe an air temperature movement by mapping its time series to the polar coordinates: the value of the time series is plotted on the radial coordinate and the time on the angular coordinate. In this way, both the yearly and daily air temperature movements from five different locations of China are used to demonstrate that, in an intuitionistic view, air temperature movement is neither random nor chaotic, but has its own orbit. We then propose an elliptic orbit model for the air temperature movement. Our proposed model provides quite good results when it is applied to the evaluation of yearly air temperature movements in eight locations in various parts of the world, and the evaluation of daily air temperature movements in five locations in Hunan Province of China on December 17, 2005. Results show that our approach provides a concise and helpful model for air temperature analysis.     

Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social–economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.     

高效液相色谱法测定饮用水中的微囊藻毒素RR和LR

利用高效液相色谱法(HPLC)测定了藻毒素(MC-RR、LR)标准样品,并绘制了标准曲线,藻毒素浓度在0~5mg/L范围内,HPLC峰面积与藻毒素进样量呈线性关系,RR和LR相关系数分别为0·9986和0·9992,表明该方法可靠、灵敏度较高。对毒素的富集方法及分析条件进行了优化,并在藻类高发期的7~10月份,通过固相萃取(SPE)法对T市饮用水中的MCs进行浓缩富集后,用高效液相色谱仪和标准曲线测出MC-RR和MC-LR值。结果显示,T市高藻期饮用水中含有MCs,应引起足够的重视。     

博斯腾湖水质矿化度模型及预测研究

根据博斯腾湖１９８５～１９９５年水质监测数据和出入湖水量等水文数据观测值，采用水质扩散模型和盐量平衡关系推导出博斯腾湖大湖区（简称博湖）水质矿化度模型，并预测了几种情况下博湖水质矿化度及近几年变化趋势，分析了影响博湖水质矿化度的主要因素，为博湖的近期和远期环境保护规划、环境管理等提供科学依据．     

泰州市环境空气中挥发性有机物分布特征

于2018年4—9月对泰州市环境空气中挥发性有机物(VOCs)组分开展现场观测,结合观测数据分析该市大气中VOCs的时空分布特征。结果表明:观测期间泰州市环境空气中VOCs平均摩尔比为45.1 nmol/mol,其中含氧挥发性有机物占比为57.8%;受周边排放源和地理位置影响,下风向点位的VOCs测定值高于其他点位;VOCs月均最高值出现在6月,与臭氧月均最高值出现时间一致,7—9月气团出现老化,导致臭氧生成能力减弱;观测期间VOCs中甲苯/苯(T/B)比值范围为0.201 9～5.130 3,且大部分T/B比值2,说明溶剂、油气和液化石油气挥发等排放源对泰州市环境空气中VOCs的影响较为显著。     

博斯腾湖富营养化状态水平调查

通过水质特征和生物学特征的一个水文年规范化实地调查,采用Scirgesew参数分类标准和相关加权综合营养状态指数,对博斯腾湖水体富营养化状态水平进行了调查和评价。     

水环境数学模型在河流水质管理规划中的应用

本文讨论了水环境数学模型的选择及在水质管理规划中的使用方法,达到实用的目的.具有广泛的通用性和实用性.     

灰色系统模型GM（1,1）在大气环境浓度预测中的应用

运用灰色系统理论，结合实例详细介绍了ＧＭ（１，１）模型在大气环境浓度预测中的应用。     

浅谈GAM与GM（1,1）模型

对ＧＡＭ水环境预测模型提出了四点不同看法，与有关作者商榷，指出ＧＭ（１，１）模型与ＧＡＭ模型二者并无优劣之差，精度亦相当。