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51.
采集2015年南昌市冬季大气PM_(2.5)样品,利用电感耦合等离子体质谱仪(ICP-MS)测定样品中重金属(V、Mn、Cr、Co、Ni、Cu、Zn、Cd、Ba和Pb)的含量,分析重金属的分布特征和来源,并对重金属健康风险进行评价。结果表明:采样期间PM2.5浓度总平均值为(29.74±16.82)μg/m~3,其中省外办最高,武术学校最低;各重金属元素总体平均浓度从高到低次序为:ZnPbCuMnBaNiVCrCdCo。因子分析结果表明:PM_(2.5)中重金属元素的来源包括道路交通尘和冶金化工排放、机动车尾气以及混合源。健康风险评价结果显示:PM_(2.5)中Mn对人体健康存在非致癌风险,其他元素(Cr、Ba、Co、Pb、Cd、Cu、V、Zn、Ni)基本没有非致癌风险;Cr对人体有较明显的致癌风险,Cd、Ni和Co对部分年龄段的人群(尤其是成年人)存在一定的致癌风险。 相似文献
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文教区土壤环境质量直接影响学生以及职工的身体健康。本研究应用高效液相色谱仪对采集的西安市文教区表层土壤样品中的16种优控多环芳烃(PAHs)进行含量检测,分析其组分特征、来源及健康风险。结果表明,西安市文教区表层土壤中∑PAHs含量为0.290~4.147μg/g,平均值为1.515μg/g,7种致癌多环芳烃的含量为0.079~2.093μg/g,均值为0.593μg/g,土壤PAHs污染较为严重。其中4环的高环PAHs为土壤PAHs污染的主要物质,平均占∑PAHs含量的40.72%。源解析结果表明西安市文教区表层土壤中PAHs主要来源于石油燃烧、煤及生物质等的不完全燃烧。终生癌症风险评价表明西安市文教区表层土壤中PAHs污染对其生活在周围的人群产生的终生致癌风险性较小,但71.4%的样点达到严重污染水平,产生的间接影响应引起足够重视。 相似文献
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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
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