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.     

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．     

基于耦合模型的安徽经济与环境协调发展分析

构建了经济发展与生态环境评价指标体系，选用耦合协调度模型计算2010—2019年安徽省不同区域经济发展与生态环境的耦合协调度，并采用灰色关联度法计算评价指标与耦合协调度的关联系数。研究结果表明：全省经济发展与生态环境的耦合协调度整体呈改善趋势，耦合协调度集中分布在0.393~0.765，基本处于良好协调或低水平协调阶段。全省不同区域的生态环境指数均大于经济发展指数。省会城市、沿江江南地区耦合协调度高于江淮之间和淮河以北地区。工业"三废"排放量、环境质量、环境污染治理力度是影响经济发展与生态环境耦合协调度的重要因素，建议通过改善这些方面进一步提升经济与环境的耦合协调度水平。     

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。     

乌鲁木齐市汽车尾气污染状况调查

对乌鲁木齐市主要街道受汽车尾气污染的调查和综合分析得到CO、NOx、HC的污染规律,主要街道路中心空气中CO、NOx日均值已超出国家大气环境质量二级标准,市区主要街道的人行道空气中NOx含量已超出二级标准,Ⅱ、Ⅲ类道路人行道空气中CO含量已超标;在三类街道中,Ⅰ类街道车流量最大,Ⅱ类次之,Ⅲ类最少,而汽车排放尾气对街道空气污染状况为Ⅲ类最重、Ⅱ类次之、Ⅰ类最轻。     

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。     

灰色系统GM（1,1）模型在城市地下水总硬度预测中的应用

应用灰色系统GM(1,1)模型,对地下水中总硬度的变化作了预测.检验结果表明,该模型精度较高,是一种较好的预测方法.     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.