An integrated multivariate statistical approach for the evaluation of spatial variations in groundwater quality near an unlined landfill

Environmental Science and Pollution Research - Groundwater is a major resource for water supply in Canada, and 43 of 68 Saskatchewan municipalities rely on groundwater or combined groundwater and...     

Fluoride removal and its impact on oral health in Sri Lanka's dry zone: Discussion and recommendation

Fifty percent of the dry zone areas in Sri Lanka have fluoride levels above 1 ppm. This paper discusses the ground conditions and recommends an appropriate range of fluoride in drinking water which can support preventive practices for improving the oral health of children 8-years old and younger. In efforts to address the Chronic Kidney Disease of Unknown etiology (CKDU), water treatment to reduce contaminant level in potable water has been implemented. Such treatment would also remove fluoride and has resulted in potable water with various fluoride levels, depending on concentrations in the raw water. While it is important to reduce fluoride levels, it is important to have appropriate residual levels for prevention of dental caries. It needs, however, to be noted fluoride in excess can cause dental fluorosis. In Sri Lanka's dry zone areas increasing prevalence of dental fluorosis with decreasing prevalence of dental caries has been noted. Consumption of tea and powdered milk could increase total intake of fluoride. Fluoridated toothpaste, when used properly, may, however, result in negligible intake of fluoride. Sri Lanka's hot tropical climate which results in substantial intake of fluids reinforces the need to consider reduction in water fluoride. Consideration of local studies and international standards indicate fluoride levels should be in the range of 0.225–0.500 ppm. In the range of 0.225–0.500 ppm, the prevalence of dental fluorosis and caries was only 14% and 8%, respectively, in an endemic district. When fluoride levels are above 0.500 ppm, the issue of dental fluorosis shall need to be addressed. When levels are below 0.225 ppm, oral health care services shall need to be directed at preventing dental caries.     

Changes in fetal heart rate pattern after intrauterine paracentesis in one twin with meconium peritonitis

We report a case of in utero paracentesis of ascites in a fetus with meconium peritonitis due to volvulus at 34 weeks which resulted in the correction of an abnormal fetal heart rate pattern and enabled vaginal delivery by preventing abdominal dystocia. The intrauterine intervention also helped to establish the diagnosis and potentially reduced the respiratory compromise after birth. Copyright © 2001 John Wiley & Sons, Ltd.     

响应土壤阴离子类型的盐碱土古细菌群落多样性研究

为初步解析我国黑龙江苏打盐碱地（HA）、新疆荒漠盐碱地（XD）、山西平原盐碱地（SY）、江苏滨海盐碱地（JD）和天津滨海盐碱地（TB）5个地区的土壤古细菌群落结构多样性,以及其响应土壤不同阴离子类型的分布特征,采用基于Illumin-Hiseq平台的高通量测序技术获得489~604个古细菌可操纵分类单元（OTU）.OTU多样性分析结果表明XD的古细菌群落物种丰富度最高,SY和HA分别具有最高和最低的古细菌群落多样性和均匀度;OTU的物种注释结果表明HA以奇古菌门（Thaumarchaeota）为优势菌门,其他土壤均以广古菌（Euryarchaeota）为优势;综合5个样品古细菌群落结构、优势古细菌属和土壤阴离子进行的典范对应分析（CCA）结果显示,XD古细菌群落结构和Haloterrigena属（XD中最优势,相对丰度20.2%）明显的响应土壤SO₄^2-浓度;HA古细菌群落结构和Nitrososphaera属（HA中最优势,62.3%）明显的响应土壤HCO₃^-/CO₃^2-浓度和pH;JD、SY和TB古细菌群落结构、Halorubrum属（JD和TB中最优势,24.4%和15.6%）,和Natronomonas属（SY和JD中第二优势,8.1%和9.2%）明显的响应土壤Cl^-浓度.研究结果说明我国不同地域分布的盐碱土受其土壤阴离子类型及其浓度的影响,呈现不同的古细菌群落结构和分布特征.研究结果为我国不同类型盐碱土古细菌资源的挖掘提供依据,也为揭示我国不同类型盐碱土古细菌群落的生态功能提供参考.     

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.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Monitoring of Phenol in Wastewater Bioremediation by HPLC

Bioremediation emphasizes the detoxification and destruction of toxic substances by microorganisms. Wastewater obtained from an industrial concern was solvent extracted with methyl alcohol and dichloromethane and analysed by GC/MS. Besides phenol, a large variety of organic compounds were detected. Under controlled laboratory conditions, the wastewater was innoculated with a mixed culture of microorganisms specially selected for their abilities to degrade phenol. Samples were collected at regular intervals from the stirred tank bioreactor and analysed for phenol by reverse phase HPLC with a C18 column. Results shows that from an initial phenol concentration of 987 ppm, slightly more than 50% was destroyed within 163 hours. The dry weight of the microorganisms and the plate count (CFU/ml) shows a steady increase from 0.5238 gms to 0.5355 gms and from 1.1E+9 to 1.94E+13 respectively over the same period. This suggested that the phenol was consumed by the microorganisms as the sole carbon source.     

Methane emission in a rice field of Thailand

ＭｅｔｈａｎｅｅｍｉｓｓｉｏｎｉｎａｒｉｃｅｆｉｅｌｄｏｆＴｈａｉｌａｎｄ￥ＲｏｎｇＸｉａｎｇ；Ｃｈｕｅｎ－ＨｏｗＮｇ（ＥｎｖｉｒｏｎｍｅｎｔａｌＥｎｇｉｎｅｅｒｉｎｇＰｒｏｇｒａｍ，ＳｃｈｏｏｌｏｆＥｎｖｉｒｏｎｍｅｎｔ，ＲｅｓｏｕｒｃｅｓａｎｄＤ...