华北地区汽车拥有量与CO2排放量关系研究

研究了华北地区5省（市、自治区）汽车拥有量和人均CO2排放量现状.采用变异系数分析方法对汽车拥有量和入均CO2排放量各地区差异大小变化趋势进行了分析,采用一元线性回归模型对华北地区未来10年汽车拥有量和人均CO2排放趋势及其地区差异大小变化趋势进行了预测,提出了相关建议,以期为该地区乃至全国经济和环境的和谐发展提供参考.     

高速铁路桥梁路段地面环境振动衰减特性研究

为了解高速铁路桥梁段列车运行引起的桥下地面环境振动衰减规律,对京津城际铁路247号桥墩处高速列车桥梁段列车运行时的环境振动进行测试,通过回归分析测试数据,得到了高速铁路桥梁段列车运行时所引起的地面环境振动衰减经验公式,并对现场测试数据与其他预测公式进行比较,分析了预测公式的有效性,为高速铁路引起地面环境振动的预防和治理提供参考。     

Agricultural causes of desertification risk in Minqin, China

This study uses statistical modeling techniques to develop a desertification risk index (RI) for Minqin County, Gansu Province, China. Twenty socio-economic factors were selected and compared with the RI results to explore the spatial and temporal variability of desertification risk in the study area and to identify possible local driving forces behind desertification risk. The explanatory factors were different in 1988, 1992 and 1997, possibly reflecting the role of temporal variation as a contributor to desertification. The average number of sheep per-household was found to be an important indicator of change in desertification risk, while changes in ridge crop planting area explained the distribution of the rate of change in desertification risk in 1988-1992. The results suggests that the RI was useful in expanding the understanding of spatial temporal desertification issues in Minqin County, as well as identifying a current set of agricultural activities related to desertification risk. Further, given the limited nature of consistent data and observations for the area, development of the RI also served to establish a baseline for future investigations into desertification change and the risks such change might pose for the region.     

Effects of Land Use on Threatened Species

Abstract:  There is widespread agreement that biodiversity loss must be reduced, yet to alleviate threats to plant and animal species, the forces driving these losses need to be better understood. We searched for explanatory variables for threatened-species data at the country level through land-use information instead of previously used socioeconomic and demographic variables. To explain the number of threatened species in one country, we used information on land-use patterns in all neighboring countries and on the extent of the country's sea border. We carried out multiple regressions of the numbers of threatened species as a function of land-use patterns, and we tested various specifications of this function, including spatial autocorrelation. Most cross-border land-use patterns had a significant influence on the number of threatened species, and land-use patterns explained the number of threatened species better than less proximate socioeconomic variables. More specifically, our overall results showed a highly adverse influence of plantations and permanent cropland, a weaker negative influence of permanent pasture, and, for the most part, a beneficial influence of nonarable lands and natural forest. Surprisingly, built-up land also showed a conserving influence on threatened species. The adverse influences extended to distances between about 250 km (plants) and 2000 km (birds and mammals) away from where the species threat was recorded, depending on the species. Our results highlight that legislation affecting biodiversity should look beyond national boundaries.     

长江宜昌水文站流量、含沙量和悬移质粒度关系

以长江宜昌水文观测站实测资料为基础，基于长江上游河道特点，用回归分析的方法研究了宜昌站流量、含沙量与悬移质泥沙粒度的复杂关系。通过对流量、含沙量多年年内变化的正相关特性的分析，建立了流量与含沙量之间相应的回归模型；通过对流量与悬移质粒度复杂关系的研究，揭示了二者明显的双值关系以及水流挟沙力的控制性影响。对水文数据年际变化的研究表明由于短时间尺度内长江上游流量没有阶段性变化，流量与含沙量之间相关性不是很好，丰水年不一定是丰沙年；悬移质粒度与流量变化基本上不存在周期性变化。     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

A composite receptor and dispersion model approach for estimation of effective emission factors for vehicles

It is difficult to estimate vehicular emission factors at traffic junctions for use in dispersion modelling studies. Firstly, because the vehicles are in various modes of operation and secondly, it is difficult to delineate the effects of other contributing sources, mainly the effects of road dust and deposited constituents, which are very prominent at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used in this study for apportioning the contributing sources. The measurement data consist of one year's temporal variation of suspended particulate matter (SPM), analysed for its trace metal constituents, and two gaseous components NO₂ and SO₂ at two traffic junctions in Mumbai (India). FA-MR apportioned 40% of the observed SPM to road dust and 15% to vehicular sources. Of the total Pb observed in the SPM, FA-MR apportioned 60% to vehicular sources and 20% to road dust. The field-observed vehicular counts, meteorological parameters and road geometry were used in California line source dispersion model to estimate the effective vehicular emission factor for Pb at one traffic junction. This derived emission factor was used to predict the Pb concentration at second (independent observation) traffic junction. The result was found to be more satisfactory than using default emission factors obtained from literature. Similarly, effective vehicular emission factor for NO₂ was also evaluated for one site and tested for predicting concentrations at the other site.     

三峡库区王家桥小流域土壤侵蚀因子初步研究

通过对王家桥小流域土壤，降雨，植被和地形等因子的分析，探讨了土壤抗侵蚀性能，分析了坡面侵蚀与各影响因子间的关系，建立了基于降雨基础上的全流域输沙幂函数回归方程。结果表明，本地区土壤因具有较强的渗透性，抗冲，抗蚀和抗剪切破坏的能力，而使其保持较小的可侵蚀性；坡面侵蚀中的主要影响因素是雨强和径流量（或降雨量）的大小；流域内土壤侵蚀的形式主要表现为暴雨作用下的重力侵蚀，在影响土壤侵蚀的诸多因子中降雨是关键性的，它的强度和年分布对年侵蚀模数的大小具有决定性作用。同时，通过分析各种雨强（平均雨强，最大雨强，最大15分钟雨强，最大30分钟雨强，最大60分钟雨强）对土壤侵蚀量的影响，结果表明，虽然最大60分钟雨强具有最优相关性，但在多元回归方程中，采用最大30分钟雨强回归效果最佳。     

影响因素对煤矿百万吨死亡率的回归分析及其应用

本文对煤矿百万吨死亡率关于其影响因素作回归线性假设,以美国1982～1991十年间的煤矿死亡事故数据为例,利用SPSS多元统计软件中的逐步回归分析法,拟合出百万吨死亡率关于所选出的两个显著影响因子的二元线性回归方程,结果表明该方程通过了各项假设检验,可用于百万吨煤死亡率的预测与控制.     

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude –0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude –0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude –0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude –0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at –0.3769.