Determination of toxic elements in waters and sediments from River Subin in the Ashanti Region of Ghana

Waters and sediments of Subin River, which flows through the industrial and commercial areas of Kumasi in the Ashanti region of Ghana, were geochemically investigated to ascertain heavy metal pollution levels due to anthropogenic activities. The study shows preoccupying pollution levels that constitute a threat to public and ecological systems. The waters of Subin River are neutral to slightly basic, inferred from pH values of 6.89–7.65). Electric conductivity (EC) of the waters ranges from 822 to 1,821 μs/cm and the range of total dissolved solids (TDS) is from 409 to 913 mg/l. Toxic elements contents of sediments and waters from 10 sites along the river were analysed by instrumental neutron activation analysis (INAA), and Al, As, Cd, Cr, Cu and Zn were determined. The concentrations of Al, As, Cd, Cr, Cu and Zn in the waters range between 4.02–15.18, 0.007–0.16, 0.002–0.05, 0.001–0.019, 1.32–7.04 and 4.28–10.2 mg/l, respectively. The contamination factors (CF) computed for the elements indicate that with the exception of sampling site S10, the sediments are polluted with Cd. Chromium contamination in the sediments is observed at S6 and S7, where the CF values were 1.39 and 1.52, respectively. The pollution load indices (PLI) were low (<1) and ranged from 0.14 to 0.75, suggesting that the overall sediment column of the river is not polluted.     

Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

The interaction effects of abiotic processes in the production of phytoplankton in a coastal marine region off Cochin are evaluated using multiple regression models. The study shows that chlorophyll production is not limited by nutrients, but their physiological regulations (responses to nutrients, pH, temperature and salinity) are mainly responsible for the increased biological production. The model explaining 77% of variability for chlorophyll a production is indicative of preconditioning of the coastal waters. The phytoplankton production is found to be sensitive to the environment, which varies seasonally. Further, the study suggests that supply of organic matter and grazing of zooplankton (not included) would improve the model efficiency. Despite this, the good agreement in the computed and measured chlorophyll a values shows that step-up multiple regression model is a useful tool to understand the influence of environmental variables on the production of phytoplankton in these coastal waters.     

Fecal source tracking by antibiotic resistance analysis on a watershed exhibiting low resistance

The ongoing development of microbial source tracking has made it possible to identify contamination sources with varying accuracy, depending on the method used. The purpose of this study was to test the efficiency of the antibiotic resistance analysis (ARA) method under low resistance by tracking the fecal sources at Turkey Creek, Oklahoma exhibiting this condition. The resistance patterns of 772 water-isolates, tested with nine antibiotics, were analyzed by discriminant analysis (DA) utilizing a five-source library containing 2250 isolates. The library passed various representativeness tests; however, two of the pulled-sample tests suggested insufficient sampling. The resubstitution test of the library individual sources showed significant isolate misclassification with an average rate of correct classification (ARCC) of 58%. These misclassifications were explained by low antibiotic resistance (Wilcoxon test P < 0.0001). Seasonal DA of stream E. coli isolates for the pooled sources human/livestock/deer indicated that in fall, the human source dominated (P < 0.0001) at a rate of 56%, and that human and livestock respective contributions in winter (35 and 39%), spring (43 and 40%), and summer (37 and 35%) were similar. Deer scored lower (17–28%) than human and livestock at every season. The DA was revised using results from a misclassification analysis to provide a perspective of the effect caused by low antibiotic resistance and a more realistic determination of the fecal source rates at Turkey Creek. The revision increased livestock rates by 13–14% (0.04 ≤ P ≤ 0.06), and decreased human and deer by 6–7%. Negative misclassification into livestock was significant (0.04 ≤ P ≤ 0.06). Low antibiotic resistance showed the greatest effect in this category.     

Modelling vegetation greenness responses to climate variability in a Mediterranean terrestrial ecosystem

Accurate knowledge of the quality and environmental impact of the highway runoff in Pear River Delta, South China is required to assess this important non-point pollution source. This paper presents the quality characterization and environmental impact assessment of rainfall runoff from highways in urban and rural area of Guangzhou, the largest city of Pear River Delta over 1 year’s investigation. Multiple regression and Pearson correlation analysis were used to determine influence of the rainfall characteristics on water quality and correlations among the constituents in highway runoff. The results and analysis indicates that the runoff water is nearly neutral with low biodegradability. Oil and grease (O&G), suspended solids (SS) and heavy metals are the dominant pollutants in contrast to the low level of nutrient constituents in runoff. Quality of highway runoff at rural site is better than that of at urban site for most constituents. Depth and antecedent dry period are the main rainfall factors influencing quality of highway runoff. The correlation patterns among constituents in highway runoff at urban site are consistent with their dominant phases in water. Strong correlations (r ≥ 0.80) are found among chemical oxygen demand (COD), total phosphorus, Cu and Zn as well as conductivity, nitrate nitrogen and total nitrogen. O&G, COD, SS and Pb in highway runoff at urban site substantially exceed their concentrations in receiving water of Pear River. The soil directly discharged by highway runoff at rural site has contaminated seriously by heavy metals in surface layer accompanying with pH conversion from original acidic to alkaline at present.     

大气环境数据分析预测方法对比研究

以西安市2006年9月27日至2008年5月3日每日的SO2平均浓度时间序列为例,应用时间序列分析对前555个数据进行拟合,得到合适的时间序列模型ARIMA(1,1,2);利用神经网络中的BP神经网络和RBF神经网络对同样的样本进行训练,用这三种方法对2008年4月4日至2008年5月3日的SO2日均浓度值进行了预测,并用同样的方法分析预测了同期PM10日均浓度值,最后比较了它们的预测效果。结果表明,利用这三种方法进行浓度预测都是可行的,其中RBF神经网络法的预测误差最小,效果最好。     

乌鲁木齐市水磨河水质状况评价与污染成因分析

利用单因子指数法和综合污染指数法,对乌鲁木齐市水磨河4个监测断面水质情况进行了评价。结果表明:搪瓷厂泉为超标水体;七纺桥为Ⅴ类水体,达到农业灌溉用水要求;联丰桥断面、米泉桥断面为劣Ⅴ类水体,没有达到农业灌溉用水要求。生活源和工业源化学需氧量、氨氮等污染物的排入对水体产生了严重影响,造成水体重污染。     

基于顺序注射分析技术的总氮在线分析仪

针对环境监测领域,研制了一种基于顺序注射分析技术的总氮在线分析仪。该分析仪以注射泵为分析过程的液体驱动定量单元,以多通道选向阀为流路切换单元,以发光二极管和硅光电管为检测单元。在该分析平台上,含氮化合物在高温高压条件下经过硫酸钾氧化成硝酸根,硝酸根被还原为亚硝酸根后与N-(1-萘基)乙二胺盐酸盐生成偶氮染料,再由光电检测单元测定吸光度值,经计算得到总氮含量。与现有的总氮在线分析技术相比,该分析仪具有检测单元简单、生产成本低、测量精度高、试剂消耗和废液产生量少等优点,适合于环境监测等领域的长时间在线分析。     

太原汾河景区跨桥断面水体污染的时空变化模式研究

选取太原汾河景区跨桥断面8个监测点,在2012年3月—2013年10月期间对河流8项水质参数进行监测,并结合派生的水体综合污染指数及主成分分析方法分析和评价景区水污染时空变化特征。结果表明,景区水质污染变化时空差异明显,上游污染相对较小,下游污染较重;春季溶解氧和p H值变化幅度最大,夏季除总氮和氨氮外,其他各污染物均有显著变化特征,秋季则以氨氮、总氮和总磷变化最为显著。主成分分析结果显示,虽然各监测点水体污染的关键影响因子不同,但总磷是太原市河流水体污染的共同解释因子。     

2013年春夏季莱州湾海水环境要素特征和富营养化评估

根据2013年5（春季）、8月（夏季）莱州湾海水环境要素的调查资料,采用富营养化指数、潜在性富营养化评价模式和灰色聚类分析方法研究环境要素特征和评估海水富营养化状况。结果表明,无机氮是莱州湾水质的主要污染要素,春夏季的N/P平均值分别为100.76、117.84,潜在性富营养化评价模式结果表明,春夏季各站位的营养级均只包括Ⅳ_P、Ⅵ_P两类,磷限制为莱州湾的营养盐结构特征;富营养化指数评价结果表明,春季和夏季E>1站位比例分别为65%、20%;灰色聚类分析结果表明,春季Ⅱ级、Ⅲ级的站位比例分别为95%、5%,夏季Ⅱ、Ⅲ级的站位比例分别为70%、25%,Ⅱ级中的部分站位具有较大潜在富营养化风险。     

常州市臭氧污染传输路径和潜在源区

利用NCEP全球再分析资料和HYSPLIT4模式,计算了2013—2015年常州市臭氧(O_3)超标日的气流后向轨迹。结合聚类分析方法和常州市PM2.5、PM10、SO2、NO2、O_3数据,分析了O_3超标日不同类型气团来源对各污染物浓度的影响,并利用引入权重因子后的潜在污染源贡献函数分析了影响常州市O_3超标的潜在污染源区分布特征。结果表明:常州市O_3超标期间易受到东南和西南方向气流影响,其中从东海和黄海途经浙江东北部、上海、江苏南部等地的东南气流占比达50%以上。自内陆途经黄山-湖州-宜兴到常州的气流对应的O_3平均质量浓度最高,为116μg/m3。自山东经枣庄-宿迁-淮安-泰州-苏州-无锡到常州的气流对应的O_3平均质量浓度最低,为78μg/m3,但该气流对应的SO2和NO2平均值为各聚类中的最高。影响常州市O_3的潜在污染源区主要在常州周边200 km以内的区域,且集中在从南京至上海的长江下游沿线区域和杭州湾区域;其中太湖湖区为重点污染源源区之一。O_3超标日影响常州NO2的潜在污染源区主要集中在江苏南部、浙江东北部和上海3个区域,太湖周边的常州、无锡、苏州和湖州等几个临近城市为潜在的重点污染源区。与影响常州O_3的WPSCF高值区相比,影响NO2的高值区分布范围更大、距离更远。影响常州O_3的潜在污染源区分布,与长江三角洲地区人为源大气污染物的高排放区域较为一致,说明长江三角洲地区的O_3污染与本区域的人为源大气污染物排放有着极为密切的关联。