Distributions and sources of n-alkanes in PM2:5 at urban, industrial and coastal sites in Tianjin, China

Aliphatic hydrocarbons (n-alkanes) associated with fine particulate matter were determined in the ambient air of urban, industrial and coastal areas in Tianjin, China, where intensive coal burning for industrial and domestic purpose takes place. n-Alkane homologues from C12 to C35 were quantifiable in all samples with C20–C31 being the most abundant species. Average concentrations of the total n-alkanes were 148.7, 250.1 and 842.0 ng/m3 in July, April and January, respectively. Seasonal variations were mainly attributed to ambient temperature changes and coal combustion for residential heating. Among the three studied areas, the highest levels of n-alkanes were observed in the industrial complex in winter and spring, but in summer the coastal alkane concentration moved up to the highest. A mono-modal distribution for n-alkanes was observed in spring and summer with odd carbon number predominance and a maximum centered at C27–C31, suggesting the release of plant wax into the atmosphere. The bimodal distribution with maxima at C22 and C26 observed in winter indicated a substantial influence of fossil fuel sources. All the CPIs (CPI1, CPI2, CPI3) values, varying between 0.64 and 1.97, indicated the influence of anthropogenic emissions on fine organic aerosols. The estimated contributions of plant wax to total n-alkanes were on average of 12.9%, 19.1% and 26.1% for winter, spring and summer, respectively.     

A land use regression for predicting NO2 and PM10 concentrations in di erent seasons in Tianjin region, China

Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression (MLR) equations were established based on the most significant variables for NO2 in heating season (R2 = 0.74), and non-heating season (R2 = 0.61) in the whole study area; and PM10 in heating season (R2 = 0.72), and non-heating season (R2 = 0.49). Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO2, heating season), 0.035 (NO2, non-heating season), 0.068 (PM10, heating season), and 0.092 (PM10, non-heating season) in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 (NO2, heating season), 0.043 (NO2, non-heating season), 0.087 (PM10, heating season), and 0.096 (PM10, non-heating season). These intercept values might imply an area’s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. R2 values increased 0.09 (heating season) and 0.18 (non-heating season) for NO2, and 0.08 (heating season) and 0.04 (non-heating season) for PM10. In terms of R2, LUR model performed more e ectively in heating season than non-heating season in the study area and gave a better result for NO2 compared with PM10.     

基于HAZOP的保护层分析技术研究

介绍了“基于HAZOP的保护层分析技术”的工艺安全管理新模式。在此基础上,利用该方法对水煤浆气化装置进行了分析,识别出了装置存在的重大工艺危险,提出对策建议,对整个装置重大工艺安全事故的预防提供了有力的技术支持。     

基于GIS的中秦岭地区滑坡灾害易发性研究

本文以中秦岭地区为滑坡易发性评价对象,针对研究区的情况,选取了坡度、起伏度、地貌类型、岩性、土壤侵蚀强度、活动断裂距、地震动参数、地下水类型、多年平均降雨量9个评价因子,建立了研究区滑坡易发性评价的数据库。运用单因子概率比率模型对各因子和滑坡的相关性进行了评价,揭示了该区滑坡灾害类型特点及空间分布规律,明确了中秦岭地区滑坡地质灾害的极高易发、高易发三大聚集地区的位置。     

5·12汶川地震后陇南山区典型滑坡调查分析

本文以甘肃省北峪河流域的三家地滑坡、红土坡滑坡、曾家街滑坡作为主要研究对象,通过对上述三个典型滑坡的野外实地测量,结合对当地民众的走访以及在当地政府部门获取的相关数据,归纳分析得出该地区滑坡灾害的主要诱发因素,及其给人们带来的影响。     

磨盘山水库周边化肥农药污染状况调查

通过对磨盘山水库周边化肥、农药污染状况的调查和监测得出,每年施用化肥将加快水库的富营养化进程,必须预防其潜在危害。     

西安外商直接投资的变化特点与限制因素分析

本文通过对多年有关统计资料的计算和整理,从外商投资的阶段、资金、行业、来源及效益几个方面进行分析,表明９０年代以来,外商看好西安,西安外商直接投资的结构日趋合理。文章对西安外商投资的环境分析得出,经济规模、经济增长速率、经济开放度、基础设施是制约西安外商直接投资的主要限制因素。最后,本文提出了吸引外商直接投资的建议。     

打印机墨粉燃烧特性的热分析

通过扫描电镜(SEM)和傅立叶变换红外光谱(FT-IR)分别对墨粉的表面特征和成分进行分析,并对试样在不同升温速率下燃烧热失重行为进行了实验研究。分析研究表明,墨粉一旦达到着火点燃烧剧烈,燃烧过程处在动力区的时间较短,化学动力反应区在500℃以下。墨粉有不同升温速率下的燃烧特性,计算出燃烧反应动力学参数,为墨粉干法回收工艺过程燃烧防爆提供了较为可靠的基础数据。     

The relationships of urbanization to surface water quality in four lakes of Hanyang,China

By converting rural land into urban land, urbanization impacts on surface water quality, because cities produce more pollutants than farmlands, especially heavy metals. Ways to reduce urbanization impacts on water quality are now being highlighted worldwide. Considering that land use can be a source or sink in pollution runoff, an understanding of the relationship between urbanization and surface water quality, as well as effects of specific land uses on water quality, is crucial. Corresponding management and controlling steps can then be put forward towards non-point source (NPS) pollution control and urban sustainable development. China has experienced rapid urbanization, especially since the 1980s. However, the environmental impacts of this process are not fully investigated. Hanyang, Hubei Province was selected as a typical city to study the impacts of urbanization on lake water quality. Pearson's correlation analysis was performed to elucidate the correlation between different land uses and water quality indicators at both whole lake watershed and small catchment scales. The results indicated that land uses play different roles, either source or sink, in pollution flow processes. Bottomland had a negative and residential land a positive correlation to most water quality indicators, especially heavy metals. These proved to be indicative and crucial land uses in NPS pollution control. Finally, a strategy for regulating urban land uses is proposed for improving surface water quality in cities similar to Hanyang, in southern China.     

基于层次分析与集对理论的城市污染场地风险等级划分

城市污染场地风险评价过程充满不确定性因素,其中评价系统的客观随机性、评价因子选取的不完整以及监测数据的不精确都易导致评价结果失真,不能客观反映场地的实际风险情况。在综合考虑场地污染带来的潜在生态风险和人体健康风险的基础上,本文将递阶分层的评价指标体系用于场地污染风险分级,采用层次分析法确定了各层各指标的权重;并用模糊聚类思想建立了评价指标集,同时对综合评价指标进行量化,根据对综合评价指标的取值研究,将污染场地风险等级划分为低、中、较重、重和严重5类;构建了一个基于不确定信息的场地污染风险集对分析模型。实例验证表明,集对分析法与模糊综合评价法评价结果基本一致,评价手段合理,为场地污染风险等级划分提供了一种新方法。