环境污染防治在建筑工程管理施工中的作用分析与研究

建筑活动是破坏环境资源、污染环境的主要活动之一,如何有效达到施工环境污染防治的目标已经逐渐成为社会关注的焦点话题。为有效防治施工环境污染,文中首先对典型施工环境污染的主要来源进行分析,然后提出施工环境污染防治目标的动态控制流程,并从设置动态控制目标、重视主动控制、多方面釆取有效措施方面对目标的动态控制进行分析。通过对施工环境目标的动态控制分析,使动态控制原理在项目环境管理领域有效运用,降低了建筑施工对环境的污染。     

PM_(2.5)严重超标下户外运动的风险评估模型设计研究

PM_(2.5)严重超标下进行户外运动可能会导致肺部疾病的风险,建立户外运动的风险评估模型,通过定量分析PM_(2.5)含量与户外运动的身体机能指标参量的关系,实现数学建模,指导户外运动锻炼。提出基于最大摄氧量定量递归分析的PM_(2.5)严重超标下户外运动的风险评估模型。构建户外运动风险评估约束参量,采用模板参数对照法和定量递归分析方法进行风险评估优化求解,结合模糊决策方法进行PM_(2.5)超标环境下的户外运动的风险评估建模,求得最大摄氧量、最大通气量等参数的最优值,从而指导户外运动训练。     

如何促进旅游业与生态环境耦合协调发展研究

对于正处于发展中状态的中国来说,旅游行业是国民经济发展主导性力量之一,并且越来越多旅游资源得到了极大开发利用,为提高当地经济水平起到了奠定性作用。然而基于调查却发现在此期间也存在着一些问题,如部分地区在进行旅游规划设计时过于强调经济效益,明确缺乏生态环境保护意识,再加上资源具有不可再生特点,极容易为生态环境发展带来不利影响,因而这就需要相关人员能够充分引起自身对该方面内容重视程度,促使旅游业与生态环境实现耦合协调发展。     

废水中硝基苯类化合物测定方法的探讨及新方法研究

探讨了废水中硝基苯类化合物的气相色谱法测定方法中的水样预处理操作繁琐,实验耗时长,且萃取时使用易挥发有毒有害的有机物二氯甲烷。另一种常见的测定方法还原-偶氮分光光度法,而染料、印染制革等工业废水的颜色较深,对测定造成干扰,通常通过蒸馏预处理消除水样颜色带来的干扰,而在蒸馏过程中因硝基苯类沸点较高不能将水样中的硝基苯类化合物全部蒸出,导致检测结果偏低。提出了一种经聚己内酰胺脱色,简单又快捷地消除水样中颜色的干扰,又避免了硝基苯类化合物蒸馏不完全的问题,再利用还原-偶氮分光光度法准确测定废水中硝基苯类化合物的方法。     

离子色谱UV-VIS检测柱后衍生高灵敏分析测定饮用水中六价铬对比研究

铬广泛存在于自然环境中,是一种重要的环境污染物,铬的毒性与其存在的价态有关,六价铬更易为人体吸收而且在体内蓄积,不同化合物的毒性也不相同。随着人民生活水平的提高,对水质要求也在提高,建立灵敏度更高的分析方法,满足高质量水质检测需要也显得更为迫切,科技攻关,技术保障,是解决问题的根源,饮用水中六价铬的测定方法有比色法,原子吸收法,催化极谱法等,国标法采用二苯碳酰二肼比色法,最低检测质量浓度为4ppb。离子色谱UV-VIS检测法采用的方法最低检测质量浓度为0.01 ppb,快速,灵敏度高。     

青海盐湖环境下高性能混凝土防腐措施的试验研究

采用传统防腐措施动弹模波动比较大,具有一定侵蚀影响,导致混凝土质量变化较大,且外观被侵蚀较为严重。提出基于水泥基渗透的结晶型防水措施,并对青海盐湖环境下高性能混凝土防腐措施进行试验研究。结合青海盐湖地质勘察报告,分析混凝土被侵蚀的主要影响因素,从物理方面和化学方面对措施展开分析。设定试验条件和配置,采用传统防腐措施和改进防腐措施对混凝土的动弹模、质量和外观进行实验对比。水泥基渗透的结晶型防水措施动弹模波动范围较小,无化学侵蚀影响、混凝土质量变化较小,且外观只有表面不光滑,其它地方并无明显变化,防腐效果较好。     

环保宣教影视作品制作中生态环保意识分析研究

传统环保宣教影视作品内容过于空泛,难以引起人们共鸣,近年来环境问题逐渐严重,带来一系列自然灾害,为人民生命财产带来巨大损失。影视作品能够起到传播观念和导向舆论的作用,将生态环保意识引入影视作品可以引发人们的思考。对环保宣教影视作品中生态环保意识进行分析,探讨了生态环保意识的建立过程以及融入过程,通过实验研究了环保宣教影视作品中引入生态环保意识对环境保护起到的作用。实验结果表明,引入生态环保意识的环保宣教影视作品能够更好地向社会大众宣传环保好处,更好地促进环境保护工程的实施。     

Tree species in relation to soil moisture regime in northwestern Ontario,Canada

Selected data on site, vegetation cover, and soil, including soil moisture regime (SMR), were collected from 2 167 field plots in northwestern Ontario, Canada. SMR provides an estimate of an averaged, annualized soil moisture supply throughout a complete vegetation cycle. SMR is based upon a relative scale that subjectively ranks sites from , 0, and 1 through 9 along a soil moisture continuum which relates to a dry to wet gradient. SMR may be generally correlated to tree growth, stand composition, degree of competition, nutrient availability and overall site quality.This paper reports on relationships between SMR and major tree species. Results highlight relationships between SMR class and the broad ecological ranges of several tree species. In northwestern Ontario, the determination of SMR can help resource managers to better understand the ecology of boreal sites.     

Comparative rice seed toxicity tests using filter paper,growth pouch-tm,and seed tray methods

Paper substrate, especially circular filter paper placed inside a Petri dish, has long been used for the plant seed toxicity test (PSTT). Although this method is simple and inexpensive, recent evidence indicates that it gives results that are significantly different from those obtained using a method that does not involve paper, especially when testing metal cations. The study compared PSTT using three methods: filter paper, Growth Pouch-TM, and seed tray. The Growth Pouch-TM is a commercially available device. The seed tray is a newly designed plastic receptacle placed inside a Petri dish. The results of the Growth Pouch-TM method showed no toxic effects on rice for Ag up to 40 mg L^–1 and Cd up to 20 mg L^–1. Using the seed tray method, IC₅₀ (50% inhibitory effect concentration) values were 0.55 and 1.4 mg L^–1 for Ag and Cd, respectively. Although results of filter paper and seed tray methods were nearly identical for NaF, Cr(VI), and phenol, the toxicities of cations Ag and Cd were reduced by using the filter paper method; IC₅₀ values were 22 and 18 mg L^–1, respectively. The results clearly indicate that paper substrate is not advisable for PSTT.     

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.