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临江河回水区营养盐及富营养化特征分析 总被引:2,自引:1,他引:1
以2008年3~9月对库区次级河流临江河回水区水质的调查为依据,分析了临江河回水区氮、磷营养盐的污染分布及富营养化特征。结果表明,临江河回水区氨氮(NH3-N)、总氮(TN)的浓度在7月中旬达到最小值,分别为1.963和5.128 mg/L, 之后在9月初出现峰值,而磷酸盐(PO3-4-P)和总磷(TP)的浓度却呈现出先增加后下降的变化规律;氮主要来自点源污染,而磷受面源污染影响较大;溶解性无机氮(DIN)和PO3-4-P是TN与TP的主要存在形态,平均分别占TN和TP的85.3%和77.8%,而DIN又以NH3-N为主。营养盐浓度呈现出回水区中游最高,回水末端次之,河口处最低的空间分布特征。叶绿素a(Chl-a)的浓度在4月和9月出现峰值,其空间分布特征与营养盐的类似。研究表明,临江河回水区在重度污染的情况下,即便是河流型水体也可能发生富营养化;流速对Chl-a浓度的显著影响呈指数关系。 相似文献
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在对淮南市窑河洼区环境水文地质调研基础上,对拟建窑河洼电厂灰场及邻区的浅层地下水环境质量现状进行了模糊数学评价。基于地下水水质模型,以F^-作为模拟因子,对地下水F^-浓度变化进行了数值模拟,对其5a后的污染范围和程度进行了预测评价。结果表明,模型较为可靠、合理,灰场建成后对场区及邻区地下水环境质量的短期影响不大,这为电厂灰场选址决策及电厂灰场建设后,可能引起地下水污染的范围和程度预测提供了科学依据。 相似文献
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深圳市区空气污染的人工神经网络预测 总被引:1,自引:0,他引:1
《环境工程学报》2015,(7)
利用深圳市2006至2013年的大气污染物监测浓度数据和气象资料,分析深圳市空气质量的逐月分布变化特征。采用Pearson相关分析,选择显著相关因子,分别以BP神经网络和RBF神经网络构建空气质量预测模型,对该市2013年SO2、NO2、PM103种空气污染物的月均值进行预测。实验结果表明,通过Pearson相关分析建立的预测模型有更高的预报精度。BP和RBF 2种网络预测效果都比较理想,对不同污染物的预测精度各有高低。但BP网络的构建和参数优化过程较为复杂且网络训练结果不稳定,而RBF网络构建和训练简单,时间短而结果稳定。在综合性能上,RBF网络用于环境空气污染物浓度的预测具有更强的适用性。 相似文献
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城市景观河流夏季污染状况及营养水平动态分析--以天津市津河为例 总被引:4,自引:0,他引:4
以CODMn、氨氮、总氮、总磷和叶绿素a等为主要指标。对天津市一条典型的景观河流——津河进行了调查分析。结果表明,津河水质介于地表水环境标准(GB3838—2002)的Ⅳ和Ⅴ类之间,营养状态为重富营养。且在多个采样点出现蓝藻水华。水体中的各项指标的变化趋势为,CODMn与叶绿素a都是在7月达到最高,随后逐渐降低。总溶解性氮夏季变化曲线呈倒“S’形。7月最低。8月最高。河道上游总溶解性磷一直比较稳定,而下游在8月上旬急剧升高。藻类演替过程大致为粉末微囊藻-皮状席藻-多种蓝藻。与改造之初相比。水质状况有所改善。 相似文献
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《Journal of the Air & Waste Management Association (1995)》2013,63(12):1571-1578
Abstract It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling. 相似文献
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人工湿地的去污机理复杂、呈高度非线性,故利用神经网络技术构建模型预测其长期运行效果。通过构建人工湿地复合基质模拟槽系统进行为期4个月的实验,监测得到56组COD去除率数据样本,经Matlab小波去噪后分别利用RBF和Elman网络构建动态神经网络模型,预测该系统对生活污水中COD去除效果。结果表明,RBF和Elman神经网络预测值的均方根误差分别为0.0186和0.0163,精度较高,该系统后期的COD去除率在49.4%~59.0%之间。 相似文献
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Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends 总被引:2,自引:0,他引:2
Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model. 相似文献
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Anastasia K. Paschalidou Spyridon Karakitsios Savvas Kleanthous Pavlos A. Kassomenos 《Environmental science and pollution research international》2011,18(2):316-327
In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial
basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed
to forecast hourly PM10 concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a
variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and
the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies.
The evaluation reveals that the MLP NN models display the best forecasting performance with R
2 values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R
2 values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes
with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the
models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality
if used on an operational basis. 相似文献
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The performance of three statistical methods: time-series, multiple linear regression and feedforward artificial neural networks models were compared to predict the daily mean ozone concentrations. The study here reported was based on data from one urban site with traffic influences and one rural background site. The studies were performed for the year 2002 and the respective four trimesters separately. In the multiple linear regression and feedforward artificial neural network models, the concentrations of ozone, the concentrations of its precursors (nitrogen oxides) and some meteorological variables for one and two days before the prediction day were used as predictors. For the application of these models in the validation step, the inputs of ozone concentration for one and two days before were replaced by the ozone concentrations predicted by the models. The results showed that time-series modelling was not profitable. In the development step, similar performances were obtained with multiple linear regression and feedforward artificial neural network. Better performance indexes were achieved with feedforward artificial neural network models in validation step. Concluding, feedforward artificial neural network models were more efficient to predict ozone concentrations. 相似文献
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通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1.11,训练样本的模拟值与实测值的相关系数为0.93,测试样本的模拟值与实测值的相关系数为0.87,模拟值与实测值的相关系数均高于显著水平为α=0.05与α=0.01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。 相似文献
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基于人工神经网络的街道峡谷NO_x浓度的数值模型研究 总被引:1,自引:0,他引:1
通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1.11,训练样本的模拟值与实测值的相关系数为0.93,测试样本的模拟值与实测值的相关系数为0.87,模拟值与实测值的相关系数均高于显著水平为α=0.05与α=0.01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。 相似文献