生态足迹影响因子的定量分析

人口的膨胀和人类工业化进程的加剧，使得人类向自然界获取越来越多的资源，同时向环境源源不断的输入废弃物，已经严重超过了自然生态系统的供给能力和环境容量，生态环境日益恶化，水土流失、草场退化、植被消亡、生物多样性锐减、全球变暖等情况已经严重到难以遏制的地步，危及到人类自身的生存。在和平与发展成为世界两大主题的今天，人们越来越关注可持续发展的问题。在现有资料的基础上，利用主成分分析方法，定量地讨论了中国各省（区市）1999年生态足迹大小与其影响因子间的关系。结果表明，生态足迹的大小是各省（区市）的大中型企业个数、全社会固定生产投资等众多因子共同作用的结果，其中总人口和GDP是生态足迹大小的主要影响因子，其因子载荷量分别达到了0.940和0.913。值得一提的是非农业人口与生态足迹的相关系数超过了农业人口，这说明由于消费模式和生活水平等的差异，非农业人口对生态足迹的影响大于农业人口对生态足迹的影响。在此基础上建立了生态足迹影响因子的多元线性回归模型，以期为生态足迹在进行区域可持续发展评价的方法上提供新的思路。     

Investigation of chlorinated phenols sorption mechanisms on different layers of the Danube alluvial sediment

The characteristics of the Danube river alluvial sediment are of great importance in assessing the risk for transport of pollutants to drinking water sources. Characterization of the sediment column layers has shown that the alluvial sediment, sampled near the city of Novi Sad, is a mesoporous sandy material with certain differences in the properties of individual layers. In order to investigate the sorption mechanisms of four chlorinated phenols (CPs) on the alluvial deposit, static sorption experiments were performed at pH 4, 7 and 10. The results of sorption experiments, confirmed by principal components analysis sugest different mechanisms govern the sorption process at different pH conditions. This can be attributed to the molecular characteristics of CPs, geosorbent properties and to variations in the surface charge of the sorbent at different pH conditions.     

中国“四化”建设对水足迹强度的影响分析

采用2000—2016年中国30省市（不包括香港、澳门、台湾、西藏四省区）的数据,构建四化指标体系,计算四化发展指数,进行地区四化水平评价,通过OLS和GMM回归模型考察四化发展对水足迹强度的影响,同时设置四化之间的交互项来识别四化指标之间的相互作用。主要结论如下：首先,从四化水平的地域发展差异上来看,中国四化水平总体上呈现出东部优于中部、中部优于西部的格局,水足迹强度的分布与之类似,说明四化水平发展与水足迹强度之间可能存在相关关系;其次,计量模型显示四化水平的提升对于水足迹强度的改善有积极影响,但每一化对于水足迹强度改善的影响大小不一;第三,以交互项衡量的四化相互作用对于水足迹强度的影响作用为正,表明四化发展对水足迹强度的改善效果逐渐趋于收敛。因此,优先提升落后地区的四化水平,以四化水平的提升推动水足迹强度的降低,能够有效降低我国总体水足迹强度水平。     

Relationship between physicochemical properties and dewaterability of hydrothermal sludge derived from different source

Sewage sludge (SS) and deinking sludge (DS) were used to comparatively study the hydrothermal dewatering of sludge with different components. For a better overview, an insight into the relationship between physicochemical properties and dewaterability of hydrothermal sludge was provided. Results found that not all kinds of sludge were suitably conditioned by hydrothermal treatment (HT) in term of the elevation of dewaterability. Higher hydrothermal temperature tended to enhance the dewaterability of SS rather than DS, which was supported by the variation of their physicochemical properties (including water distribution, bonding energy, extracellular polymeric substance (EPS), particles size, acid functional groups and zeta potential in this study). In addition, the changes in surface morphology suggested that the reverse effect of HT on sludge dewaterability was mainly due to their dewatering behavior. For SS, the destruction of EPS structure leaded to the release of bound water, thereby strengthening sludge dewatering. Conversely, “Bridging effect” generated by lignocellulose in DS was beneficial for sludge dewatering; however, the increasing hydrothermal temperature degraded part of lignocellulose and weakened “bridging effect”, finally resulting in worse dewaterability of DS.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Investigation of the Influence of Seagrass on Fisheries Catch Level Using Isotonic Regression

The relationship between seagrass area and the commercial catch of some economically important South Australian fish species is not known in any quantitative sense. However, there is evidence in the literature to suggest that seagrass decline will detrimentally impact on species abundance and composition, and consequently that the presence or absence of seagrass will compromise the commercial and recreational fishery. In this paper we describe the construction of models based on order restricted (isotonic) regression and discuss the role of seagrass area, in terms of its influence on the level of commercial catch through modifying the effect of fishing 'effort'.     

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.     

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.     

环境科学研究中两变量非线性相关时的线性回归方程

以环境科学研究两变量非线性相关的问题为对象，讨论建立线性回归方程的方法。有绝对误差均方和最小的最小二乘法、相对误差最小的新回归法。以简易的方法，建立相关性好、误差小的回归方程。介绍了两变量各种非线性相关时的线性回归方程在环境科学研究中的应用。     

ASM1中废水和污泥组分浓度的测定方法研究

对呼吸计量法应用于校准ASM1作了简明的阐述。在呼吸速率与模型组分关系的基础上,介绍了ASM1中废水与污泥各组分浓度的测定方法,并指出模型组分细化的意义。