洞庭湖水质因子的多元分析

年1—12月在洞庭湖湖区采集360个水样,测定pH、ρ(DO)、ρ(BOD₅)、ρ(TP)、ρ(COD_Mn)、ρ(COD_Cr)、ρ(NH₃-N)、ρ(TN)、粪大肠菌群及ρ(Chla). 采用主成分分析法对采样断面水质因子进行分析. 结果表明:虞公庙、鹿角断面水质主要影响因子为ρ(DO)、ρ(BOD₅)、ρ(TP)、ρ(COD_Mn)、ρ(COD_Cr)、ρ(TN)、ρ(NH₃-N)及ρ(Chla);南嘴、目平湖、横岭湖和万子湖断面水质主要影响因子为ρ(TP);小河嘴断面水质主要影响因子为ρ(TP)、ρ(BOD₅)和ρ(Chla);东洞庭湖断面水质主要影响因子为ρ(BOD₅)和ρ(Chla);岳阳楼和洞庭湖出口断面水质主要影响因子为ρ(DO)、ρ(COD_Mn)、ρ(COD_Cr)、ρ(NH₃-N)和ρ(TN). 主成分综合得分对各断面水体受污染程度排序为虞公庙>鹿角>东洞庭湖>岳阳楼>洞庭湖出口>南嘴>横岭湖>目平湖>小河嘴>万子湖.      

Comparison of explosion parameters for Methane–Air mixture in different cylindrical flameproof enclosures

Flameproof enclosures having internal electrical components are generally used in classified hazardous areas such as underground coalmines, refineries and places where explosive gas atmosphere may be formed. Flameproof enclosure can withstand the pressure developed during an internal explosion of an explosive mixture due to electrical arc, spark or hot surface of internal electrical components. The internal electrical component of a flameproof enclosure can form ignition source and also work as an obstacle in the explosion wave propagation. The ignition source position and obstacle in a flameproof enclosure have significant effect on explosion pressure development and rate of explosion pressure rise. To study this effect three cylindrical flameproof enclosures with different diameters and heights are chosen to perform the experiment. The explosive mixture used for the experiment is stoichiometric composition of methane in air at normal atmospheric pressure and temperature.It is observed that the development of maximum explosion pressure (P_max) and maximum rate of explosion pressure rise (dp/dt)_ex in a cylindrical flameproof enclosure are influenced by the position of ignition source, presence of internal metal or non-metal obstacles (component). The severity index, K_G is also calculated for the cylindrical enclosures and found that it is influenced by position of ignition source as well as blockage ratios (BR) of the obstacles in the enclosures.     

Concentrations of some elements in the sediments of a river in Anhui Province,China

A total of 49 sediment samples from New Bian River near the city of Suzhou in northern Anhui Province, China, were analyzed by inductively coupled plasma-mass spectrometry for As, Cr, Pb, Cu, Fe, Zn, Mn, and Ni. Geoaccumulation indices (I_geo) were used to assess the influence of human activities. Correlation analysis and principal component analysis were also performed. The geoaccumulation indices were in the order of As > Cu > Cr > Ni > Mn > Zn > Pb > Fe, the first four of which had values above 0.5. We considered New Bian River to be moderately contaminated with As, Cr, Cu, and Ni. Correlation analysis and principal component analysis indicated that the presence of Fe, Cu, Ni, Mn, Zn, and Pb in New Bian River was caused by soil or rock weathering, whereas the elevated levels of As and Cr depended upon urban, agricultural, and industrial factors. The sampling stations around the towns were dominated by Component 2 (As and Cr), and the sampling stations that distributed along New Bian River were dominated by Component 1 (Fe, Cu, Ni, Mn, Zn, and Pb).     

Nutrient distribution in locations of the Gulf of Trieste (northern Adriatic sea) suspected of pollution

Nutrients (ammonium‐nitrogen, nitrite, nitrate, orthophosphate and dissolved silica) as well as some chemical‐physical parameters (temperature, pH, salinity, dissolved oxygen and total suspended matter) were determined in filtered seawater samples taken at the surface, at middle depth and at the bottom, in 6 open‐sea locations of the Gulf of Trieste (northern Adriatic Sea) close to pipeline exits that discharge wastewaters from treatment plants. The collected data allow a comparison of the quality of these open‐sea waters with coastal waters and constitute a reference for successive monitoring in these locations suspected of pollution. A multivariate statistical analysis by the principal component method shows that the considered ecosystem can be modeled with two factors: the first one is referenced to the marine environment, in particular to the decomposition of the organic debris in deep water; whereas the second factor is referenced to the input of freshwaters in the Gulf, driving nitrates from agricultural lands.     

Assessment of heavy metals in a soil contaminated by oil spill: a case study in Nigeria

Surface and subsurface soil samples contaminated with crude oils were collected from an impacted site at Bodo City in the Niger Delta, Nigeria, after a field reconnaissance survey. An uncontaminated soil sample collected 100 m from the impacted site, but within the same geographical area, was used as a control. Trace elements such as, As, Cu, Cr, Cd, Fe, Pb, Ba, Ni, V, Hg and cation-exchange capacity constituents of the contaminated and uncontaminated soils were determined by atomic absorption spectroscopy. Trace element concentrations were: Cu, 0.5–13.4 mg kg^{? 1}; Cr, 0.2–0.8 mg kg^{? 1}; Fe, 6.2–8.7 mg kg^{? 1}; Ba 80.0–108.0 mg kg^{? 1}; Ni, 0.6–4.8 mg kg^{? 1}; and V, 4.0–9.4 mg kg^{? 1}; cation-exchange capacity ranged from 43.6 to 57.2 mg kg^{? 1} in surface and subsurface soils. Results showed that eigenvalues for the two first principal components represent up to 49% of the total variance. A positive correlation of the first principal component with Cu, Cr and cation-exchange capacity shows pollution from oil spillage, while a positive correlation of the second principal component with Cr, Fe, V, and dissolved oxygen (DO) shows both oil pollution and allochthonous inputs.     

Accumulation trends of metals and a metalloid in the freshwater crayfish Astacus leptodactylus from Lake Yeniçağa (Turkey)

This study aims to determine the extent of metal pollution in Lake Yeniça?a (Bolu, Turkey) by investigating the accumulation trends of five metals (Al, Cu, Fe, Ni and Zn) and a metalloid (As) in gills, exoskeleton, hepatopancreas and abdominal muscles of the freshwater crayfish Astacus leptodactylus. Principal component analysis (PCA), cluster analysis (CA), correlation analysis and analysis of variance (ANOVA) were utilised to determine the accumulation profiles of each element over four seasons. The greatest element accumulation was found to occur in the gills. All elements in exoskeletal tissue displayed positive correlations with each other, a similar trend was also observed in the hepatopancreas samples. Strong (r=0.868) and very strong (r=0.960) positive correlations were found between the accumulations of Al and Fe in gills and the exoskeleton, respectively. Correlations in tissue accumulation rates are discussed in the context of metabolic roles and impacts associated with the elements tested. Elemental compositions of Yeniça?a water and sediment samples were also investigated to determine whether the composition of the surrounding environment matches the metal accumulation trends of tissue samples. We demonstrate that, by the criteria set by the United States Environmental Protection Agency, Lake Yeniça?a is heavily polluted in terms of As and Ni.     

吉林省典型城市大气中PAHs来源解析

通过对吉林省4个典型城市,即吉林、白城、四平和通化市大气颗粒物中多环芳烃（PAHs）的采样和分析,得到16种PAHs的成分谱,应用主因子分析和特征比值法对其进行定性研究,得到吉林省大气中PAHs的2种主要来源--车辆尾气和燃煤。并应用绝对主因子分析法进一步定量计算这2种源对PAHs的浓度贡献值及贡献率,结果表明：吉林省典型城市PAHs解析值为772.39μg·g-1,绝大多数解析值与监测值之间的比值接近于1,车辆尾气对各PAH的贡献率为2.6%~67.6%,燃煤源的贡献率为24.1%~121.2%。另外,该研究还计算了已识别的2种源对于不同环数的PAHs的贡献,2~3环的PAHs大部分来自于燃煤,约占总体的89%,车辆尾气仅占11%;4环的PAHs约58%来源于燃煤,42%来源于车辆尾气;5环的PAHs约45%来源于燃煤,55%来源于车辆尾气;6环PAHs来源于燃煤的占61%,来源于车辆尾气的占39%。     

武汉秋冬季大气PM2.5中多环芳烃的分布特征及来源

采集了2011—2012年武汉市工业区、交通区和植物园的3个功能区的秋冬2季大气PM2.5样品,采用超声提取预处理和GC/MS分析检测了PM2.5中27种PAHs,探讨了其时空分布特征,然后运用主成分分析/多元线性回归法解析了PAHs的来源.结果表明:PAHs的质量浓度范围为24.705~112.490 ng·m-3,PAHs的质量浓度分布呈现出工业区>交通区>植物园的规律;冬季PAHs质量浓度高于秋季等特征.不同环数PAHs质量浓度呈现出规律变化为:5环>4环>2-3环>6-7环,4环、5环的 PAH 含量比例高表明机动车尾气和煤燃烧排放是主要排放源.不同功能区化合物的比值指示来源略有不同,但总体指明了武汉主要污染源来自燃煤和机动车尾气的排放.源解析结果显示,工业区的污染源主要来自于燃煤,其贡献率为55%,其次为汽油燃烧、柴油燃烧、焦炉和轻质油燃烧.在交通区中,车辆尾气排放(34%)和天然气燃烧(25%)的贡献较大,其次是烹饪、燃煤及木材燃烧.植物园对照区的主要污染源分别是木材燃烧、燃煤、天然气燃烧、车辆排放和烹饪,其中木材燃烧(46%)的贡献最大.     

北京市城市生活垃圾组分预测

研究了北京市1991-1999年城市生活垃圾组分及变化特点.发现食品的含量最大,其次是塑料和纸类.以20世纪90年代生活垃圾产量和组分数据为基础,建立了生活垃圾组分人工神经网络预测模型.以预测的生活垃圾产量为输入值,分别对6个不同区域2001年、2005年、2008年与2010年的生活垃圾组分进行了预测,并用类比法进行了修正.考虑到由于城市发展规划不同区域生活垃圾比例变化情况,最终计算出全市生活垃圾组分变化.结果表明,在未来几年内北京市城市生活垃圾组分中,食品含量较高,但其比例逐年下降,纸类与塑料垃圾有增加,灰土所占比例减少.     

钢渣在污水治理中的应用

钢渣作为钢铁生产的主要固体废弃物,由于产生量大,成分复杂,直接排放将对环境造成严重影响,加强钢渣综合利用的研究对消除其环境污染具有积极的意义。本文通过总结近年来钢渣在污水治理方面的研究发展情况,为环境管理部门和环境保护工作者提供有益的参考。