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11.
Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut. 相似文献
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Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong 总被引:4,自引:0,他引:4
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 NOx 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, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NOx 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. 相似文献
15.
Effect of an industrial discharge on water quality and periphyton structure in a pampeam stream 总被引:1,自引:0,他引:1
Seasonal sampling was carried out at four sites on a pampeanstream that receives industrial effluent from two textile factories. To evaluate water quality, several physical and chemical parameters were examined and the periphyton growing oncattail (Typha latifolia L.) were analyzed.Water quality and periphyton structure differed significantlybetween sites upstream and downstream of the discharge. Differences in temperature and also in concentrations of phosphate, dissolved oxygen, and phaeopigment were detected. At the same time, changes in the dominant algae groups wereobserved. Downstream of the industrial discharge, the numberof Bacillariophyta decreased, while species of Cianophyta andEuglenophyta were more abundant. This abundance correlated withincreased phosphate and organic matter content and decreased oxygen concentration. Although this study did not detect a reduction in the number of species, similarity between stands decreased downstream of the industrial discharge. Changes incommunity structure were readily detected in this situation because the communities of the polluted and unpolluted zones were qualitatively different. Periphyton growing naturally on Typha latifolia is a useful indicator of the impact of waste waters on the biota and can also be used to evaluate water body recovery. 相似文献
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通过对2014—2016年湖体水质中氮素质量浓度分析,结合出入湖总氮浓度、水量、湖体水生生态等影响因素,发现太湖水体中总氮浓度呈现逐年下降的趋势,各监测点位总氮为0.530 mg/L~5.51 mg/L,时空分布不均,差异明显。时间上,总氮浓度表现为春季最高,夏季和秋季最低,且月均值变化曲线呈现出规律的正弦函数波形。空间上,总氮浓度大致表现出由西部湖区向东部湖区递减的趋势,呈现西部湖区﹥北部湖区﹥南部湖区﹥湖心区﹥东部湖区。要改善湖体水质,不仅要切断污染源,而且要加强水生生态功能修复。 相似文献
17.
Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators 总被引:4,自引:0,他引:4
Vølstad JH Roth NE Mercurio G Southerland MT Strebel DE 《Environmental monitoring and assessment》2003,84(3):219-242
In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation. 相似文献
18.
为了研究来自不同方向的沙尘暴对和田绿洲大气环境质量的影响,本文利用HYSPLIT后向轨迹模型和NCEP的GDAS全球气象要素数据,将和田绿洲西北部的墨玉县城作为模拟受点(37.26°N,79.72°E),计算2016年1月1日—2018年12月31日发生的每一次沙尘天气期间逐日18:00(世界时)36 h后向气流轨迹,轨迹计算起始高度设置为500 m,并结合相应的PM2.5、PM10、SO2、NO2、CO、O3浓度监测数据进行聚类分析,研究抵达该地区的沙尘暴的主要移动轨迹和污染物输送路径.同时,运用潜在源贡献因子分析法(PSCF)和权重污染轨迹分析法,分析了沙尘暴期间不同气流轨迹对墨玉县污染物浓度的影响,识别大气污染物的潜在源区,揭示不同源区对污染物浓度的贡献差异.结果表明,影响和田绿洲(墨玉县)的沙尘暴主要来自西北(WN)、北(N)、东北(EN)和东(E)4个方向;其中,来自东部的沙尘天气频率最高(60.2%),但主要以浮尘天气为主;扬沙和强沙尘暴主要来自西部方向,54.48%的强沙尘暴和38.53%的扬沙来自西和西北方向.不同沙尘源区和不同传输路径上的沙尘气溶胶对和田绿洲大气环境的影响不一样.沙尘天气期间,大气PM2.5和PM10的平均浓度相当于无沙尘天气期间的3~5倍,但对SO2、NO2、CO、O3质量浓度的影响不大;由西向东和由北向南的沙尘暴对墨玉县PM2.5和PM10质量浓度的贡献率最大;由东向西的沙尘暴由于路过和田市和洛浦县等工业污染源区,此簇沙尘暴气团将该区域SO2、NO2、CO等污染颗粒携带到墨玉县,因此,东-东南(E-ES)方向的沙尘暴对SO2、NO2、CO的贡献率分别为15.56%、20.55%和21.57%.本文定量印证了沙尘暴对和田绿洲大气环境质量的影响,可为绿洲区沙尘暴研究提供参考. 相似文献
19.
Quantitativestructure-activitystudyonthereductivedehalogenationpotencyofthehalogenatedaromaticsHuangQingguo;WangLiansheng;Han... 相似文献
20.
松花江大顶子山枢纽工程建设后,库区内水体流线及扩散规律的变化将影响库区内水质,受沿江排污影响,其规划目标的水环境容量将发生改变,因此对建库后上游水质总量控制进行研究很有意义. 相似文献