海水入侵指标对比分析与评价—以珠江口地下水含水层为例

合理评价海水入侵对于地下水含水层管理和居民生活健康有着重要的意义。当前海水入侵评价应用较广泛的为包含单一因子和基于统计分析的多种水化学方法。本文综合阐述了海水入侵评价中所用的水化学指标、原理与方法,包括在国内评价体系中少见的一些方法,并以珠江口地下水含水层为例,对比了各种指标的有效性,评价了各类方法的优缺点以及在实际应用中可能产生的问题。结果显示,以Cl-和TDS作为简单直接的单因子可以快速评价海水入侵,尤其对于大范围的海水入侵评价十分有效。而和Ca2+,HCO₃-,SO₄2-等变化有关的指标具有地域性,在研究区对海水入侵的指示并不是十分敏感。在复杂评价因子中,与离子交换有关SAR,BEX以及GQI_SWI与Cl-相关性较高,可以较好的指示海水入侵及水岩相互作用。经过分析,受到地下含水层和地表水道双重介质入侵的影响,研究区海水入侵范围在过去几个年代有向北推进的趋势。本研究对于高速经济发展下的沿海地下水含水层管理和评价有重要的指导意义。     

基于逸度方法分析大连湾海域PFASs在海水-沉积物界面的扩散趋势

研究污染物在沉积物-水界面的扩散趋势对于开展其环境风险评估具有重要作用。本研究基于大连湾海域海水和沉积物样品中全氟烷基化合物（PFASs）的含量数据,应用变异系数（C_v）分析了浓度分布的空间变异程度,应用逸度分数（ff）分析了PFASs在沉积物-水界面扩散趋势,应用响应系数（RC）分析了有机碳含量对扩散趋势的影响。结果表明:大连湾海域海水和沉积物中9种PFASs的总浓度均为中等变异;在沉积物中的浓度空间差异略大,可能受历史排放污染物残留及污染事故的影响。在海水-沉积物界面扩散过程中,全氟烷基磺酸类和全氟烷基羧酸类化合物呈现相同的扩散特性,总体表现为ff值随PFASs链长的增加而逐渐降低的趋势。短链PFASs在大连湾海域倾向于存在于水体中,而长链PFASs倾向于扩散到沉积物中。有机碳是影响PFASs在沉积物-水界面扩散趋势的重要参数,且对长链PFASs的影响更明显。全氟辛烷磺酸作为一种持久性有机污染物在大连湾海域中主要处于平衡状态。     

昆明市地下水污染防治现状与对策

文章以昆明市为例,分析了昆明市地下水水质现状及变化趋势,总结了地下水污染防治工作成效和存在问题。分析表明,由于长期未受到充分重视,昆明市地下水已呈现出污染加重的趋势。近年来,昆明市地下水污染防治取得了一定的成绩,但仍然存在监管能力不足、监测网络覆盖不全的问题。在此基础上提出了开展地下水环境状况调查评估、磷石膏堆场地下水污染防控与修复试点和进一步完善地下水环境监测网络的对策建议,为昆明市和其他南方城市做好地下水污染防治工作提供借鉴。     

沈阳市农村生活污水处理设施有机废弃物归趋

采用实地观察法、文献调查法对沈阳市农村生活污水处理设施基本状况进行研究分析。结果显示:沈阳市已建成农村生活污水处理设施297处,有机废弃物产生量(干重)约为:剩余污泥637 t/a、湿地植物废弃物169 t/a。     

Seasonal variation and size distribution of biogenic secondary organic aerosols at urban and continental background sites of China

Size-resolved biogenic secondary organic aerosols(BSOA) derived from isoprene and monoterpene photooxidation in Qinghai Lake, Tibetan Plateau(a continental background site) and five cities of China were measured using gas chromatography/mass spectrometry(GC/MS). Concentrations of the determined BSOA are higher in the cities than in the background and are also higher in summer than in winter. Moreover, strong positive correlations(R2= 0.44–0.90) between BSOA and sulfate were found at the six sites,suggesting that anthropogenic pollution(i.e., sulfate) could enhance SOA formation,because sulfate provides a surface favorable for acid-catalyzed formation of BSOA. Size distribution measurements showed that most of the determined SOA tracers are enriched in the fine mode( 3.3 μm) except for cis-pinic and cis-pinonic acids, both presented a comparable mass in the fine and coarse( 3.3 μm) modes, respectively. Mass ratio of oxidation products derived from isoprene to those from monoterpene in the five urban regions during summer are much less than those in Qinghai Lake region. In addition, in the five urban regions relative abundances of monoterpene oxidation products to SOA are much higher than those of isoprene. Such phenomena suggest that BSOA derived from monoterpenes are more abundant than those from isoprene in Chinese urban areas.     

基于VA One船舶舱室动力设备空气噪声控制分析

目的研究舱室间壁板不同约束条件及不同敷设方式对隔声量的影响。方法用VA One建立两个舱室的统计能量分析(Statistical Energy Analysis,SEA)模型,对约束阻尼结构和自由阻尼结构的隔声性能进行分析。针对约束阻尼结构,讨论其两侧金属层厚度差对整个壁板的隔声性能的影响;对自由阻尼结构,分析金属层与阻尼层之间的相对位置对其隔声性能影响。结果约束阻尼结构隔声量A计权声压级比自由阻尼结构高。结论阻尼层厚度和壁板总厚度一定时,自由阻尼结构金属层与阻尼层之间的相对位置对实际的隔声性能并没有影响;约束阻尼结构,两侧金属层等厚敷设时,其隔声效果最优,且两侧厚度差越大隔声量越小。     

重庆九龙坡城区和城郊大气污染物浓度特征研究

为了解重庆市九龙坡区城区和城郊大气污染物浓度特征,分析了城区和城郊国控空气站点2014年3月~2015年2月典型污染物的浓度变化。结果表明,除O_3表现出夏高冬低的季节变化趋势外,其它污染物均表现出冬高夏低的季节变化趋势。PM_(2.5)污染最严重,城区和郊区日均值超标率分别为31.4%和33.4%。城区和城郊的PM_(2.5)浓度之间没有显著性差异(p0.05),城郊O_3浓度显著高于城区(p0.01),NO_2、SO_2、CO和PM_(10)则是城区显著高于城郊(p0.01)。     

环境保护因素对落后地区旅游开发的促进作用研究

为了提高落后地区旅游开发的质量,分别从低碳环保旅游以及生态旅游两方面,研究环保因素对落后地区旅游开发的促进作用。提出低碳环保旅游,并分析低碳环保对落后地区旅游业可持续发展的意义,在落后地区旅游开发中实施低碳饮食、低碳住宿、低碳交通以及低碳能源的旅游开发模式。对中国落后地区生态旅游现状和意义实施研究,分析生态旅游对落后地区旅游开发的促进作用。最终给出了落后地区实现生态旅游开发的建议,构建落后地区低碳环保的新型生态旅游模式,实现落后地区旅游的可持续发展。     

Analysing the sensitivity behaviour of two hydrology models

Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects) and the extent these parameters interact with each other (second-order effects). This method requires the specification of two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.