我国副省级城市第三产业发展研究——以哈尔滨为例

第三产业发展水平是衡量一个国家经济实力的重要标准,综合评价第三产业,有助于正确认识我国各地区第三产业的发展水平。以我国15个副省级城市为研究对象,构建第三产业综合发展水平指标体系,运用主成分分析法,通过比较分析得出哈尔滨市第三产业发展中存在的问题,并提出相关对策建议。     

基于ISO 16750-2的汽车电源异常现象分析

本文通过研究汽车电源异常现象的来源,结合ISO 16750-2及各大车厂的要求,对各种汽车电源异常现象进行了分类和全面的阐述,使大家对ISO 16750-2有更加深入的理解。同时对ISO 16750-2:2010中出现的几个标误进行了分析说明,可供实验室和相关产品的供需双方参考。     

中国与美国对外直接投资区位选择的比较研究

基于邓宁的国际生产折衷理论,对比了中美两国投资规模、空间分布和发展阶段,利用灰色关联分析法比较了影响两国投资区位的因子.结果表明,影响美国对外投资的主要因子是人均GNP、CH和CO2的排放量,影响中国的主要因子是直接投资净流入量、航空运输量和CPI;美国对外直接投资属于市场导向型,而中国尚处于转型期,为对外投资第三阶段.     

高含硫天然气勘探开发潜在危害分析与防范

川东北地区大型天然气矿藏中硫化氢含量较高,且带有高剧毒性和高腐蚀性,勘探开发难度较大。同时气田所处区域的岩土条件十分脆弱,洪涝、大风、冰雹等气候灾害经常发生,易引发泥石流、滑坡,形成地质灾害。另外气田所在地人口密度大,给勘探开发设施布局和居民安全带来很大困难。文章通过对川东北地区天然气勘探、开发过程中存在的潜在危害进行详细分析,提出风险防范技术与对策,以供参考。     

大气细粒子(PM2.5)监测技术进展

大气细粒子(PM2.5)的污染对人和生态环境等影响巨大,迫切需要进行PM2.5质量浓度的监测;本文介绍了PM2.5国内外监测研究进展、监测网络应用情况以及监测技术;重点介绍了几种先进的技术:β射线法、振荡天平法和光散射法,并详细描述了各种监测设备的原理及结构,提出最好采用PM2.5在线监测仪进行日常监测。     

CFD分析在烟气脱硫中的实际运用与研究

为降低某公司烧结机干法脱硫装置烟道阻力,减少引风机出力,实现节能优化,运用计算流体力学软件,对引风机出口烟道内流场进行三维数值模拟,并根据分析结果进行烟道的优化改造。改造后,吸收塔入口负压由-200Pa提升至-560Pa,风机电流由328A降为301.4A,节能降耗显著。     

遗传投影寻踪回归在城市环境质量评价中的应用

引入投影降维的思想,将遗传投影寻踪与回归分析技术运用到城市环境质量评价中。将此技术与神经网络方法进行实例比较,投影寻踪回归方法不但可以合理地作出环境质量的综合评价,而且消除了神经网络方法中类别判断不够精确的影响。     

秦皇岛市“创模”对策研究及效益分析

介绍了秦皇岛市创建国家环境保护模范城市的意义,重点阐述了完成创模任务的主要对策,包括:构建以旅游业为中心、特色产业互融互促的现代产业体系;加快城乡一体化建设;提升城镇发展质量和水平;加强重大基础设施建设;积极推广清洁能源,深入开展工程节能;积极推进循环经济发展等。秦皇岛市创建国家环境保护模范城市,将会在环境、经济和社会三方面产生明显的综合效益。     

Watershed Regressions for Pesticides (warp) Models for Predicting Atrazine Concentrations in Corn Belt Streams1

Stone, Wesley W. and Robert J. Gilliom, 2012. Watershed Regressions for Pesticides (WARP) Models for Predicting Atrazine Concentrations in Corn Belt Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 970‐986. DOI: 10.1111/j.1752‐1688.2012.00661.x Abstract: Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region‐specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP‐CB) were developed for annual maximum moving‐average (14‐, 21‐, 30‐, 60‐, and 90‐day durations) and annual 95th‐percentile atrazine concentrations in streams of the Corn Belt region. The WARP‐CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model‐development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model‐development sites. The WARP‐CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine‐use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP‐CB models. The WARP‐CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine‐use intensities of 17 kg/km² of watershed area or greater.     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.