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1.
土壤和底泥中砷、铬、锰测定的前处理技术   总被引:6,自引:0,他引:6  
试验了土壤和底泥中砷的前处理技术,其目的是能对土壤、底泥中砷、铬、锰在一次前处理中制备成试液,比色法分析。试验表明,用H2SO4-H3PO4-H2O2进行前处理是可行的。方法简单、挥发酸雾少,用标准参考物质检验证明,分解完全,数据准确,有粒较好一致性。  相似文献   

2.
①空间分布特征:有明显区域差异。近海地区pH均值6.51~6.12,酸雨频率2.5%~5.9%。市区pH均值<5.1,最低值为3.66,频率为44.1%~50.6%。②时间分布特征:从6县(市)属镇的pH均值,1991~1995年逐年下降,1995~1997年逐年上升。③季、月变化:降水酸度为春季>冬季>秋季>夏季,3月份酸雨频率最高(70%),pH均值为4.77。酸性降水的季节变化与两广地区类似。④化学特征:降水中阴离子以SO2-4占绝对优势,占阴离子总含量的80%,SO2-4与NO-3的比值…  相似文献   

3.
高压蒸汽消化法测定废水化学需氧量的再研究   总被引:2,自引:0,他引:2  
用"自控式高压蒸汽消解器"作为高压蒸汽消化法测定废水化学需氧量的消化装置,详细研究了消化条件,最后确定,消化体系中(1/6)K2Cr2O7浓度为01mol/L,H2SO4浓度为10.1mol/L,催化剂Ag+浓度为0.03mol/L,消化温度为130℃,恒温时间为20min。用此方法和条件测定了12种单纯有机化合物和16种不同工业废水的化学需氧量,与标准回流法相比,相对误差在-5.5-6.0%之间。  相似文献   

4.
NH_3-N分析中显色后稀释测定误差的探讨任晓梅(江苏扬州市环境监测站,扬州225)02)溶液碱度变化影响了纳氏反应平衡,是产生误差的主要原因。稀释5倍时,pH降低0.54,误差<5%;而稀释SO倍时,与原浓度溶液的pH值相差1.4,相对误差则高达8?..  相似文献   

5.
本文研究了气相色谱/石墨炉(Ⅲ型)原子吸收光谱联用的工作条件对测定(C2H4)4Pb的影响,选择了分析条件并建立了其分析方法。该方法的最小检测量为8.6×10-11g,回收率为100.8~111.4%。已用于汽油中铅化学形态的分析,取得了满意的结果。  相似文献   

6.
能见度分级约束下的大气气溶胶光学厚度特征   总被引:4,自引:0,他引:4  
利用PIS太阳光谱仪观测了北京地区1993年3月—1995年3月晴天和少云天气的太阳直接辐射光谱,波长范围为0.40-1.04μm。光谱分辨率1.25nm,共有1064组数据。观测期间,将地面能见度分为五级。由太阳直射谱获得了各能见度下大气气溶胶光学厚度谱。研究表明,北京地区大气气溶胶光学厚度虽然其总体季节统计规律为春夏大,秋冬小,然而,当加入能见度分级约束后,各能见度下气溶胶光学厚度的季节变化,近于消失。这表明少数“反常”垂直结构不影响能见度分级的平均结果。而不分级的光学厚度季节起伏主要由各季节的几率能见度决定。文中还把年平均五种能见度下的光谱光学厚度与LOWTRAN模式作了对比。由光学厚度谱反演出了气溶胶粒子谱分布,为建立我国北方局地气溶胶模式构造了基本框架  相似文献   

7.
为研究杭州PM2.5污染来源特征,利用2013—2019年杭州市PM2.5监测数据和气象观测数据,分析了杭州市2013—2019年PM2.5浓度变化,选取本地积累型和输入型2种PM2.5污染过程,结合单颗粒气溶胶飞行时间质谱仪(SPAMS)和在线离子色谱数据,探讨杭州市PM2.5化学组分和污染来源。结果表明:每年秋冬季(11月至次年3月)杭州以东北风、西北风及偏南风为主,风速低于4 m/s时,大气扩散条件差,受本地污染物积累影响,PM2.5浓度容易出现超标;风速较大且为东北风和西北风时,受上游污染输入影响,易出现PM2.5重度污染。本地积累型和输入型案例中,PM2.5化学组分中占比最大的为NO3-、SO42-和NH4+;PM2.5浓度上升过程中,二次NO3-和SO42-转换率明显上升,其中NO3-上升更为显著,二次气溶胶污染严重。2次案例中,PM2.5来源贡献占比前3位均为机动车尾气源、燃煤源和工业工艺源,其中本地积累型PM2.5浓度上升阶段,机动车尾气源占比会明显上升;输入型案例中,输入阶段机动车尾气源占比显著上升,燃煤源贡献也小幅上升。  相似文献   

8.
在对淄博市19个空气质量监测站点监测数据进行分析后,提出了一种基于机器学习的复合模型——灰色关联度分析(GRA)-改进的完备总体经验模态分解(ICEEMD)-长短期记忆网络(LSTM)模型。通过分析淄博市2019年大气污染物和气象数据,选用LSTM模型预测PM2.5浓度。由于传统单一模块机器学习模型具有训练时间较长和预测精度较低的问题,提出了复合LSTM模型。该模型由3部分组成:GRA,用于PM2.5浓度影响因素变量筛选;ICEEMD,用于PM2.5分解、分量筛选和原始大气污染物及气象数据处理;LSTM,用于PM2.5浓度预测。预测结果表明:淄博市中部丘陵地带PM2.5浓度高于南部山区和北部平原,东部高于西部;淄博市逐月PM2.5浓度呈“U”形分布,1月最高,8月最低;淄博市PM2.5浓度受PM10和CO影响较大,受湿度和温度影响较小。对比单一LSTM模型和GRA-LSTM模型,GRA-ICEEMD-LSTM模型...  相似文献   

9.
A new algorithm was developed for retrieving sea surface temperature (SST) in coastal waters using satellite remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua platform. The new SST algorithm was trained using the Artificial Neural Network (ANN) method and tested using 8 years of remote sensing data from MODIS Aqua sensor and in situ sensing data from the US coastal waters in Louisiana, Texas, Florida, California, and New Jersey. The ANN algorithm could be utilized to map SST in both deep offshore and particularly shallow nearshore waters at the high spatial resolution of 1 km, greatly expanding the coverage of remote sensing-based SST data from offshore waters to nearshore waters. Applications of the ANN algorithm require only the remotely sensed reflectance values from the two MODIS Aqua thermal bands 31 and 32 as input data. Application results indicated that the ANN algorithm was able to explaining 82–90% variations in observed SST in US coastal waters. While the algorithm is generally applicable to the retrieval of SST, it works best for nearshore waters where important coastal resources are located and existing algorithms are either not applicable or do not work well, making the new ANN-based SST algorithm unique and particularly useful to coastal resource management.  相似文献   

10.
This article describes the application of on-line nonlinear monitoring of a sequencing batch reactor (SBR). Three-way batch data of SBR are unfolded batch-wisely, and then a adaptive and nonlinear multivariate monitoring method is used to capture the nonlinear characteristics of normal batches. The approach is successfully applied to an 80 L SBR for biological wastewater treatment, where the SBR poses an interesting challenge in view of process monitoring since it is characterized by nonstationary, batchwise, multistage, and nonlinear dynamics. In on-line batch monitoring, the developed adaptive and nonlinear process monitoring method can effectively capture the nonlinear relationship among process variables of a biological process in a SBR. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the adaptive and nonlinear monitoring technique showed lower false alarm rate and physically meaningful, that is, robust monitoring results.  相似文献   

11.
基于L-M神经网络的道路交通噪声预测研究   总被引:1,自引:1,他引:0  
神经网络具有很强的预测功能.根据石家庄公路交通噪声的实测数据,利用L-M优化算法的多层神经网络预测模型进行道路交通噪声的预测,经检验,计算值与实测值接近,预测精度令人满意.  相似文献   

12.
As the regulations of effluent quality are increasingly stringent, the on-line monitoring of wastewater treatment processes becomes very important. Multivariate statistical process control such as principal component analysis (PCA) has found wide applications in process fault detection and diagnosis using measurement data. In this work, we propose a consensus PCA algorithm for adaptive wastewater treatment process monitoring. The method overcomes the problem of changing operating conditions by updating the covariance structure recursively. The algorithm does not require any estimation compared to typical multiway PCA models. With this method process disturbances are detected in real time and the responsible measurements are directly identified. The presented methodology is successfully applied to a pilot-scale sequencing batch reactor for wastewater treatment.  相似文献   

13.
The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.  相似文献   

14.
Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO2) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO2 distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO2 concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO2 concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO2. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO2 is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.  相似文献   

15.
基于决策树技术及在线监测的水质预测   总被引:1,自引:2,他引:1       下载免费PDF全文
利用北方某城市水源的水质在线监测系统,建立了基于决策树技术,具有较强可视性和实际应用,以及能预测次日源水中叶绿素水平的决策树模型.该模型将某城市水源在线监测的溶解氧和太阳辐射照度数据转换计算为每日平均标准偏差及均值,并与每日定时取样测定的叶绿素含量一起作为预测因子,通过将115组数据的前100组数据作为训练集建立预测次日叶绿素水平决策树模型,并采用后15组数据进行模型的仿真预测检验,结果只有3 d的预测出错,预测准确率达80%.并讨论了模型建立对数据的要求及解读预测规则等问题.  相似文献   

16.
The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.  相似文献   

17.
This article evaluates the performance of a protocol to monitor riparian forests in western Oregon, United States based on thequality of the data obtained from a field survey. Precision isthe criteria used to determine the quality of 19 field and 6 derived metrics. The derived metrics were calculated from thefield data. The survey consisted of 110 riparian sites on publicand private lands that were sampled during the summers of 1996 and 1997. In order to calculate metric precision, some of the field plots were re-measured. Metric precision was defined in terms of the coefficient of variability (CV) and standard deviation and then compared with a pre-defined data quality objective (DQO). A metric was considered precise if the CV met or exceeded the DQO. The geomorphology metrics were not precisewhile the forest stand inventory metrics and forest cover metrics, with some exceptions, were precise. The precision formany of the field and derived metrics compared favorably withthe level of precision for similar metrics reported in the literature. Recommendations are made to improve the precision for some metrics and they include changing the way precision is calculated, re-defining the field protocol, or improving field training.  相似文献   

18.
Wildfires are a major disturbance in the Mediterranean Basin and an ecological factor that constantly alters the landscape. In this context, it is crucial to understand where wildfires are more likely to occur as well as the drivers guiding them in complex landscapes such as the Mediterranean area. The objectives of this study are to estimate wildfire probability occurrence as a function of biophysical and human-related drivers, to provide an assessment of the relative impact of each driver and analyze the performance of machine learning techniques compared to traditional regression modeling. By employing an Artificial Neural Network model and fire data (2004–2012), we estimated wildfire probability across two geographical regions covering most of the Italian territory: Alpine and subalpine region and Insular and peninsular region. The high classification accuracy (0.68 for the Alpine and subalpine region and 0.76 for the Insular and peninsular region) and good performances of the technique (AUC values of 0.82 and 0.76, respectively) suggest that our model can be used in the areas studied to assess wildfire probability occurrence. We compared our model with a logistic function, which showed a weaker predictive power (AUC values of 0.78 for the Alpine and subalpine region and 0.65 for the Insular and peninsular region) compared to the Artificial Neural Network. In addition, we assessed the importance of each variable by isolating it in the model. The importance of an individual variable differed between the two regions, underscoring the high diversity of wildfire occurrence drivers in Mediterranean landscapes. Results show that in the Alpine and subalpine region, the presence of forest is the most important variable, while climate resulted as being the most important variable in the Insular and peninsular region. The majority of areas recently affected by large wildfires in both regions have been correctly classified by the ANN model as ‘high fire probability’. Hence, the use of an Artificial Neural Network is efficient and robust for understanding the probability of wildfire occurrence in Italy and other similar complex landscapes.  相似文献   

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