从"乡土情结"看农家乐的发展走向——以南充市近郊农家乐为例

乡村旅游是现代生态旅游的重要组成部分,农家乐是近20年我国发展最为迅速的乡村旅游形式之一。首先分析了“乡土情结”的由来和与农家乐旅游的紧密联系,进而指出“乡土情结”是旅游者到农家乐去旅游的根本动机,它具体体现在中国人身上就是“土地情结”和“家乡情结”。在此基础上,剖析了农家乐旅游者由“情结”产生的旅游动机和旅游偏好。这类旅游者势必要求体验原汁原味的农家生活情趣,借以慰藉自己的“乡土情结”;然后对南充市近郊农家乐的发展现状和存在的误区进行诊断,以此对南充市近郊农家乐的发展走向提出建议,那就是“走原味之路”。     

我国与欧洲钢铁企业安全管理的差异与对策研究

对我国和欧洲大型钢铁企业安全管理现状进行了分析,较全面地考察和讨论了欧洲大型钢铁企业先进的安全管理经验和安全理念,包括:企业的安全管理机构和管理模式、精干有效的安全管理人员配置、先进的安全管理理念和方法、安全指标与绩效、欧洲的行业安全管理等。在此基础上,从管理理念、发展科学技术、宣传教育、管理人员、责任和考核、消防和急救资源、外协管理、安全信息化等方面,提出提升我国企业安全管理水平的对策。     

南极中山站一氧化碳本底的监测与分析

利用东南极大陆沿岸中山站2008—2010年的CO浓度在线观测和相关资料,分析了大气中CO浓度的本底特征及其季节变化.结果显示,在不同风向和风速条件下CO浓度变化很小,表明风向和风速对CO监测结果的影响很小,也表明观测的CO浓度受局地污染源排放影响很小,可以代表南极中山站的本底浓度.中山站与其它南极站相似,CO浓度具有非对称的季节变化,月平均浓度最高值出现在春季(9—10月),最低值在夏末秋初(2—3月),月平均CO浓度在30～65 ppb之间.南极各站的年平均CO浓度的年际变化范围差异不大,均为2～3 ppb.     

硝酸盐连续回灌对生物反应器填埋场N2O产生的影响

异位硝化-原位反硝化是实现填埋场渗滤液脱氮处理的一种有效措施,但硝化反硝化过程中会产生强温室气体N2O.实验构建了3个新鲜垃圾生物反应器填埋场模拟装置,分别回灌NO-3-N浓度为50、100和300 mg·L-1的渗滤液,考察垃圾原位反硝化过程中N2O产生规律及其影响因素.结果表明,回灌不同浓度硝酸盐,N2O产生量均表现为初期浓度较大-下降-后期升高的规律;N2O产生量与回灌NO-3-N量正相关,其累积产生量分别为36 481、44 241、86 264μg,但反硝化消耗单位硝酸盐氮产生的N2O量(以N计)以及N2O转化率与回灌硝酸盐氮量呈负相关,N2O平均转化率分别为8.84‰、5.68‰和2.34‰.分析认为,各反应器垃圾降解后期反硝化碳源不足是N2O产生量高的主要原因.     

城市污水管网内生物菌群演替规律

建立了一套长1200 m的城市污水模拟管段,研究了城市污水管网中微生物菌群的沿程演替规律,分析了管网生物膜的pH、DO、厚度、结构及菌群构造的变化情况.结果表明,城市污水管网生物膜pH变化不大,DO由0.35 mg·L-1减小为0.26 mg·L-1,生物膜厚度沿程开始呈增长趋势,平均厚度由300μm增加至800 m处的平均最大厚度560μm,此后呈现降低趋势,这主要与水力冲刷及易吸收营养物质的减少有关,因水力冲刷作用,管网生物膜的初始结构表面平整,随管网长度增加,生物膜呈现疏松构造,从而增大了生物膜比表面积,有利于其生长发育.同样作用下,管网生物膜中的菌群数量也呈现逐渐减少的趋势,生物菌群也发生了较大的变化,其中,200 m处有球菌属、链球菌属、杆菌属、链杆菌属、芽孢杆菌属等菌群,依次400 m、600 m处有肠球菌、脱硫弧菌属、硫单胞菌属,1000 m以后以甲烷球菌、反硝化杆菌、脱氮硫杆菌等厌氧菌为主,沿程菌群种数减少后形成了稳定的菌群结构存在.     

Effects of seasonal climatic variability on several toxic contaminants in urban lakes: Implications for the impacts of climate change

Climate change is supposed to have influences on water quality and ecosystem. However, only few studies have assessed the effect of climate change on environmental toxic contaminants in urban lakes. In this research, response of several toxic contaminants in twelve urban lakes in Beijing, China, to the seasonal variations in climatic factors was studied. Fluorides, volatile phenols, arsenic, selenium, and other water quality parameters were analyzed monthly from2009 to 2012. Multivariate statistical methods including principle component analysis, cluster analysis, and multiple regression analysis were performed to study the relationship between contaminants and climatic factors including temperature, precipitation, wind speed, and sunshine duration. Fluoride and arsenic concentrations in most urban lakes exhibited a significant positive correlation with temperature/precipitation, which is mainly caused by rainfall induced diffuse pollution. A negative correlation was observed between volatile phenols and temperature/precipitation, and this could be explained by their enhanced volatilization and biodegradation rates caused by higher temperature. Selenium did not show a significant response to climatic factor variations, which was attributed to low selenium contents in the lakes and soils. Moreover, the response degrees of contaminants to climatic variations differ among lakes with different contamination levels. On average, temperature/precipitation contributed to 8%, 15%, and 12% of the variations in volatile phenols, arsenic, and fluorides,respectively. Beijing is undergoing increased temperature and heavy rainfall frequency during the past five decades. This study suggests that water quality related to fluoride and arsenic concentrations of most urban lakes in Beijing is becoming worse under this climate change trend.     

Methyl Orange removal by a novel PEI-AuNPs-hemin nanocomposite

A novel poly(ethyleneimine)/Au nanoparticles/hemin nanocomposite(PEI-AuNPs-Hemin)acting for Methyl Orange(MO) removal has been synthesized. PEI-AuNPs was prepared firstly and it was then linked to hemin through the coupling between carboxyl groups in hemin and amino groups in PEI without the activation of carboxyl groups. The high reactivity and stability of AuNPs contributed greatly in the formation of the amido bonds in the nanocomposite. Fourier transform infrared spectroscopy, transmission electron microscopy and UV–visible spectroscopy were used to characterize the PEI-AuNPs-Hemin.Results show that PEI-AuNPs-Hemin has strong adsorption for MO. Adsorption and degradation experiments were carried out at different p Hs, nanocomposite concentrations and UV irradiation times. Removal of MO in acidic solutions was more effective than in basic solutions. The real-time study showed that the MO degradation with the nanocomposite under UV irradiation was a fast process. In addition, the photocatalytic degradation mechanism was proposed. The study suggests that the PEI-AuNPs-Hemin may have promising applications in environmental monitoring and protection.     

湖南长株潭中度污染区土壤镉概率生态风险评价

湖南长株潭地区土壤修复试点工作已经开展,但并未关注重金属镉对生态系统的威胁,尤其是继续进行作物种植的中度污染区。文章选取长株潭地区三个县的中度污染区为研究区域,结合文献调研,利用美国ECO-TOX数据库获取了共计28个本地物种两个层次的毒性数据,利用物种敏感度分布(Species sensitivity distribution,SSD)模型和联合概率曲线(Joint probability curve,JPC)方法开展中度污染区的概率生态风险评价研究。结果表明,在LC/EC/IC水平50下,A县、B县、C县的概率生态风险值依次为1.65%、3.40%、5.80%;在NOEC/LOEC水平下,A县、B县、C县的概率生态风险值依次为7.46%、12.97%、19.50%。该结果说明,长株潭地区中度污染区土壤中重金属镉对生态系统具有一定的风险作用,同时该结果为生态系统保护目标制定提供科学依据。     

尼洋河流域水化学特征及其控制因素

为研究尼洋河流域水化学特征及其控制因素,2014年先后采集河水7组,井水13组,泉水10组,共计30组水样.综合运用数理统计、Piper三线图、Gibbs模型和离子比等方法,分析了尼洋河流域河水、泉水和井水的水文地球化学特征,并探讨了尼洋河流域的水化学演化规律.结果表明,河水、井水及泉水中阳离子均以Ca~(2+)、Mg~(2+)为主,占阳离子总量的84%以上;阴离子以HCO_3~-和SO_4~(2-)为主,占阴离子总量的97%以上;TDS介于79.11~290.48 mg·L~(-1)之间,平均值为165.21 mg·L~(-1),矿化度较低;水化学类型以HCO_3·SO_4(SO_4·HCO_3)-Ca·Mg(Mg·Ca)型水为主;水化学样品均分布在Gibbs模型左中部,说明该流域水化学离子组成受岩石风化作用控制;主成分分析及相关分析表明,尼洋河流域水化学组分受硅酸盐岩的溶解控制,碳酸盐岩的溶解也起到非常重要的作用.     

PMF和PCA/MLR法解析上海市高架道路地表径流中多环芳烃的来源

以上海市内环高架道路径流中的多环芳烃(PAHs)为研究对象,于2012年7~9月,实测了8场降雨的道路径流中的PAHs,了解了城市交通干道径流中PAHs的污染状况,并采用正定矩阵因子分解(PMF)和主成分分析/多元线性回归(PCA/MLR)两种模型对径流中的PAHs进行源解析.因子分析表明,径流中Σ16 PAHs的浓度范围为1.585~7.523μg·L-1,两种模型对于PAHs的来源有较为一致的判定.交通源为高架道路地表径流中PAHs的主要来源,PMF和PCA/MLR得到的源贡献率分别为37.7%和44.3%,其余3种来源石油源、燃气源和其他源,PMF得到的源贡献率依次为21.9%、26.4%、14.0%;与之对应,PCA/MLR得到的源贡献率依次为28.9%、18.3%、8.5%.PMF和PCA/MLR模型的计算值与实测值拟合较好,相关系数分别为0.961和0.997.