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1.
提出一种在环境影响评价中更好地应用地面定时风资料的方法,即将定时风与自记风按季节进行回归分析,建立回归方程,经回归方程订正后,定时风的风向频率与污染系数比订正前更接近于自记风的实况,能更有效的实用环境影响评价工作。  相似文献   

2.
空气质量数值模型的构建及应用研究进展   总被引:3,自引:0,他引:3       下载免费PDF全文
综述了近年来国内外空气质量模型的发展历程、空气质量数值模型构建的关键技术以及应用研究。指出了目前空气质量数值预报及应用主要面临气象条件,尤其是大气边界层模拟、大气污染物排放源和大气物理化学过程等问题。提出,应当通过规范化制作和完善排放源清单数据,建立统一的排放源分析标准,提高排放源数据的准确性;多向发展观测手段,加大监测密度和频率,并进行实验室化学分析,提出适合我国的大气物理化学机制。就空气质量模式而言,对模式方案进行优化,以及使用气象、卫星资料同化等技术手段,将其与观测相结合,构建监测与预报系统相结合的统一体系,应用于多平台。  相似文献   

3.
为提高环境数值预报水平,构建了一个针对污染物扩散的模拟数据同化系统。采用集合卡尔曼滤波方法对二维平流扩散模型的状态变量进行了实时校正,实现污染物浓度的实时模拟预报,完成了敏感性实验中集合数目变化、观测方差变化和同化窗口长度变化研究。比较考察观测点位置与污染源距离不同时的预报效果,探讨了优化条件下的同化策略,提出一种根据距离远近动态调节卡尔曼增益权重的方法。在集合数目较小时,可降低计算代价,得到优化的同化效果。  相似文献   

4.
空气质量自动监测二氧化硫不确定度分析   总被引:1,自引:0,他引:1  
不确定度是反映某一测量方法,在一定置信概率条件下测量所产生的不确定量。根据测量原理建立数学模型,分析各种不确定度分量的来源,评定标准不确定度,确定合成不确定度和扩展不确定度,通过不确定影响分量的分析,找出影响测量结果的最大不确定度分量,重点控制其分量,保证测量的准确性和精度,同时也可通过重新评估显著性不确定分量,找出方法存在的不足和问题,提出逐步控制不确定分量的步骤和方法,改善测量方法和手段提高测量准确性和精度,不断减少测量的不确定量。  相似文献   

5.
一类非等间隔序列灰色建模方法的改进   总被引:1,自引:0,他引:1  
对一类非等间隔序列的灰色建模方法进行了改进,提出在原序列前一个平均间隔单位位置上假设有一虚拟值零,然后改用线性回归来求解模型参数,实例表明,不仅简化了建模方法,模型精度亦有所提高。  相似文献   

6.
以检验和提高环境监测培训活动的质量为目的,利用Kirkpatrick 4层评估模型,提出了构建环境监测培训效果评估体系的思路和方法,对如何明确评估对象、确定评估主体以及设计评估方案等问题进行了探讨。  相似文献   

7.
模拟由污染源排放的污染物对其周围地区的大气污染程度,已经提出许多计算公式,如高斯模式,萨顿扩散模式,以及针对不同排放方式的点源扩散公式和面源扩散公式等。但这些公式在使用中,由于污染源的情况千差万别,往往与模型相差甚多;此外,这些公式中所需要的气象参数,如大气稳定度和空气的湍流等的准确确定十分困难,也直接影响模型的计算精度。特别当污染源以多种方式排放时,选择合适的源强参数就更为困难。 本文提出了只需要源点浓度、风向、风速和距离的浓度衰减公式。该公式在对我市包钢氟污染的计算中,对二十一个点的监测数据进行计算,取得满意的结果。  相似文献   

8.
探讨了比例下降模型和灰色预测型线性规划模型机理及比例下降-灰色预测型线性规划模型建模和求解的过程。根据大气污染控制规划实例,在该区域内污染源按等比例下降方式排放污染物构建模型,运用单纯形法对模型求解并对规划结果进行灵敏度分析。结果表明,比例下降-灰色预测线性规划模型适宜含有不确定性信息的区域大气污染控制规划,尤其在空间尺度较大的污染控制区内,针对不同污染源规划相应的污染控制方案时具有适应性。  相似文献   

9.
利用环境监测实验室积累的数据,通过线性拟合法、GUM法和控制图法对水中化学需氧量的不确定度进行了评定。结果表明:3种不确定度评定方法的评定结果相似。在量化过程中存在两种主要不确定度评定的类型:一种是不确定度的正向传播,另一种是模型不确定度和参数不确定度的反向评定。GUM法明显是正向的不确定度,线性拟合法和控制图法是反向不确定度。GUM法应用复杂且烦琐,操作性差;相比,控制图法和线性拟合法更加简单实用,可代替GUM法来评估监测实验室的不确定度。  相似文献   

10.
为实现对水系入河排污口有效、准确的自动检测,提出一种基于改进MobileNetV3-SSD的深度学习模型。在MobileNetV3-SSD模型的基础上,使用K-means聚类算法和遗传算法,对先验框的宽高比进行调整,使得预测框更好地匹配真实框。引入多尺度特征融合模块,提高模型对小排污口的检测能力。引入改进的CBAM注意力模块,减少模型在排污口检测时计算的参数数量。使用可变形卷积替代普通卷积,自适应地捕获不同排污口的形态与尺度信息,提升模型的特征提取能力。实验结果表明,改进后MobileNetV3-SSD模型的平均精度为89.36%,F1分数为91.88%,较改进前分别提升4.83%和5.03%。  相似文献   

11.
蛭石在除氮技术中的应用研究   总被引:1,自引:0,他引:1  
研究了蛭石在实验条件下去除污水中氨氮的方法,以及污水中pH、温度、浓度、蛭石目数等对去除氨氮的影响,为蛭石作为一种新型填料的应用提供了基础数据和理论依据。  相似文献   

12.
环境监察和环境监测是环境保护工作的重要组成部分,监察体现了环保工作的权威性,监测则保证了环保工作的科学性。全面论述加强环境监察监测联动的重要意义,从思想和技术方面探讨加强环境监察监测联动的可行方法,对如何建立科学的环境监察与环境监测联动机制,快速、准确查处环保违法行为具有一定的指导意义。  相似文献   

13.
In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.  相似文献   

14.
In recent years, geophysics is increasingly used to study the flow and transport processes in the vadose zone. Particularly, when the vadose zone is made up of rocks, it is difficult to install sensors in the subsurface to measure hydrological state variables directly. In these cases, the electrical resistivity tomography (ERT) represents a useful tool to monitor the hydrodynamics of the infiltration and to estimate hydraulic parameters and state variables, such as hydraulic conductivity and water content. We propose an integrated approach aimed at predicting water content dynamics in calcarenite, a sedimentary carbonatic porous rock. The uncoupled hydrogeophysical approach proposed consists in 4D ERT monitoring conducted during an infiltrometer test under falling head conditions. Capacitance probes were installed to measure water content at different depths to validate the estimations derived from ERT. A numerical procedure, based on a data assimilation technique, was accomplished by combining the model (i.e., Richards’ equation) with the observations in order to provide reliable water content estimations. We have used a new data assimilation method that is easy to implement, based on the ensemble Kalman filter coupled with Brownian bridges. This approach is particularly suitable for strongly non-linear models, such as Richards’ equation, in order to take into account both the model uncertainty and the observation errors. The proposed data assimilation approach was tested for the first time on field data. A reasonable agreement was found between observations and predictions confirming the ability of the integrated approach to predict water content dynamics in the rocky subsoil.  相似文献   

15.
A fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO4 phosphorus, NO3 nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.  相似文献   

16.
This study aims to apply Moderate Resolution Imaging Spectroradiometer (MODIS Data) to monitor water quality parameters including chlorophyll-a, secchi disk depth, total phosphorus and total nitrogen at Chaohu Lake. In this paper, multivariate regression analysis, Back Propagation neural networks (BPs), Radial Basis Function neural networks (RBFs) and Genetic Algorithms-Back Propagation (GA-BP) were applied to investigate the relationships between water quality parameters and the MODIS bands combinations. The study results indicated that a simple, efficient and acceptable model could be established through multivariate regression analysis, but the model precision was relatively low. In comparison, BPs, RBFs and GA-BP were significantly advantageous in terms of sufficient utilization of spectra information and model reliance. The relative errors of BPs, RBFs and GA-BP were below 35%. Based on method comparison, it can be concluded that GA-BP is more suitable for simulation and prediction of water quality parameters by applying genetic algorithm to optimize the weight value of BP network. This study demonstrates that MODIS data can be applied for monitoring some of the water quality parameters of large inland lakes.  相似文献   

17.
A data worth model is presented for the analysis of alternative sampling schemes in a special project where decisions have to be made under uncertainty. This model is part of a comprehensive risk analysis algorthm with the acronym BUDA. The statistical framework in BUDA is Bayesian in nature and incorporates both parameter uncertainty and natural variability. In BUDA a project iterates among the analyst, the decision maker, and the field work. As part of the analysis, a data worth model calculates the value of a data campaign before the actual field work, thereby allowing the identification of an optimum data collection scheme. A goal function which depicts the objectives of a project is used to discriminate among different alternatives. A Latin hypercube sampling scheme is used to propagate parameter uncertainties to the goal function. In our example the uncertain parameters are the parameters which describe the geostatistical properties of saturated hydraulic conductivity in a Molasse environment. Our results indicated that failing to account for parameter uncertainty produces unrealistically optimistic results, while ignoring the spatial structure can lead to an inefficient use of the existing data.  相似文献   

18.
Ongoing marine monitoring programs are seldom designed to detect changes in the environment between different years, mainly due to the high number of samples required for a sufficient statistical precision. We here show that pooling over time (time integration) of seasonal measurements provides an efficient method of reducing variability, thereby improving the precision and power in detecting inter-annual differences. Such data from weekly environmental sensor profiles at 21 stations in the northern Bothnian Sea was used in a cost-precision spatio-temporal allocation model. Time-integrated averages for six different variables over 6 months from a rather heterogeneous area showed low variability between stations (coefficient of variation, CV, range of 0.6–12.4%) compared to variability between stations in a single day (CV range 2.4–88.6%), or variability over time for a single station (CV range 0.4–110.7%). Reduced sampling frequency from weekly to approximately monthly sampling did not change the results markedly, whereas lower frequency differed more from results with weekly sampling. With monthly sampling, high precision and power of estimates could therefore be achieved with a low number of stations. With input of cost factors like ship time, labor, and analyses, the model can predict the cost for a given required precision in the time-integrated average of each variable by optimizing sampling allocation. A following power analysis can provide information on minimum sample size to detect differences between years with a required power. Alternatively, the model can predict the precision of annual means for the included variables when the program has a pre-defined budget. Use of time-integrated results from sampling stations with different areal coverage and environmental heterogeneity can thus be an efficient strategy to detect environmental differences between single years, as well as a long-term temporal trend. Use of the presented allocation model will then help to minimize the cost and effort of a monitoring program.  相似文献   

19.
The primary objective of this study was to provide a detailed framework to use the spatio-temporal kriging to model the spatio-temporal variations of salinity data and predict saltwater intrusion into freshwater aquifers in the vicinity of deserts. EC data, measured in extraction wells in the Mahvelat plain located in the Northeastern part of Iran, available from 2007 to 2013, were used to demonstrate the developed framework. The source of data was not a well-designed measurement network. Therefore, to homogenize the data, spatial analysis was used to find EC distribution in the area in each year of study. To conduct the spatial analysis, a guideline and a systematic process were developed to select an appropriate kriging method and optimize its parameters. This process can be applied to different variables. After spatial analysis of EC data for all the years of the analysis period using empirical Bayesian kriging (EBK) method with manually optimized parameters, spatio-temporal and corresponding variogram analysis was conducted using R software. This process was based on a separable product-sum model applied to the data from 2007 to 2012. The data of 2013 and the data available for the years 1999 and 2006 were used for evaluating the performance of the spatio-temporal model. The EC distribution maps, developed for different years until 2021, show a high level of EC in the north, south, and west of the study area and growing saltwater intrusion into the central freshwater aquifer. This result can be attributed to the over-exploitation of the aquifer and hydraulic head and gradient distribution in the area. The framework provided in this study for spatio-temporal analysis of unstructured EC data is useful for groundwater managers in making proper decisions.  相似文献   

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