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1.
张家口地震台垂直摆倾斜仪固体潮半月波分析 总被引：1，自引：1，他引：0
张家口地震台垂直摆能记录到清晰固体潮半月波。从原始曲线可见,观测曲线与理论曲线形态相近,幅度基本一致,只是观测曲线因气压影响欠光滑。利用频谱分析和小波分析方法提取该台垂直摆观测资料中的半月波变化特征,频谱分析结果显示:垂直摆观测资料清晰检测到半月波MF,幅值近17×10~(-3)″。小波分析结果显示:分离出的垂直摆固体潮半月波与地倾斜理论固体潮的半月波形态一致,幅度相当,相位吻合。张家口地震台垂直摆能观测到清晰的固体潮半月波,是多方面技术改造和精心维护的结果。 相似文献
2.
Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis
Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day’s AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000–2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons. 相似文献
3.
The frequent occurrence of sudden water pollution accidents brings enormous risks to water environment safety. Therefore, there is great need for the modeling and development of early warning systems and rapid response procedures for current water pollution situation in China. This paper proposes an emergency response system based on the integration of Geographic Information System (GIS) technology and a hydraulic/water-quality model. Using the spatial analysis and three-dimensional visualization capabilities of GIS technology, we calculated pollutant diffusion measures, and visualized and analyzed the simulation results, in order to provide the services of early warning and emergency response for sudden water pollution accidents in the Xiangjia Dam area on the Yangtze River. The results show that the proposed system offers reliable technological support for emergency response to sudden water pollution events, and it shows good potential for wide applications in various aspects of water resources protection. 相似文献
4.
目前我国对化工园区风险分析的理论研究尚处于起步阶段。针对化工园区这一特殊区域,从整体角度出发,分析研究了化工园区内多个重大事故风险源造成的区域风险,并进行了实例应用。综合考虑多个重大事故风险源产生的风险,分析了其产生风险的叠加效应,绘制出化工园区的个人风险等值线;同时根据ALARP（合理可接受风险水平）准则对化工园区的风险水平进行评估,并据此给出化工园区安全规划、风险控制和应急救援工作的实施建议。所提出的化工园区重大事故风险分析方法,在建立和完善化工园区风险分析中具有一定的理论和现实意义。 相似文献
5.
An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution 总被引：4，自引：4，他引：0
Heng L. Li Guo H. Huang Yun Zou 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(6):789-803
High concentrations of air pollutants in the ambient environment can result in breathing problems with human communities.
Effective assessment of health-impact risk from air pollution is important for supporting decisions of the related detection,
prevention, and correction efforts. However, the quality of information available for environmental/health risk assessment
is often not satisfactory enough to be presented as deterministic numbers. Stochastic method is one of the methods for tackling
those uncertainties, by which uncertain information can be presented as probability distributions. However, if the uncertainties
can not be presented as probabilities, they can then be handled through fuzzy membership functions. In this study, an integrated
fuzzy-stochastic modeling (IFSM) approach is developed for assessing air pollution impacts towards asthma susceptibility.
This development is based on Monte Carlo simulation for the fate of SO2 in the ambient environment, examination of SO2 concentrations based on the simulation results, quantification of evaluation criteria using fuzzy membership functions, and
risk assessment based on the combined fuzzy-stochastic information. The IFSM entails (a) simulation for the fate of pollutants
in ambient environment, with the consideration of source/medium uncertainties, (b) formulation of fuzzy air quality management
criteria under uncertain human-exposure pathways, exposure dynamics, and SPG-response variations, and (c) integrated risk
assessment under complexities of the combined fuzzy/stochastic inputs of contamination level and health effect (i.e., asthma
susceptibility). The developed IFSM is applied to a study of regional air quality management. Reasonable results have been
generated, which are useful for evaluating health risks from air pollution. They also provide support for regional environmental
management and urban planning. 相似文献
6.
Associations between outdoor air quality and birth weight: a geostatistical sequential simulation approach in Coastal Alentejo,Portugal 总被引：3，自引：2，他引：1
Manuel Castro Ribeiro Pedro Pinho Esteve Llop Cristina Branquinho Amílcar Soares Maria João Pereira 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):527-540
Several environmental health studies suggest birth weight is associated with outdoor air pollution during gestation. In these studies, exposure assignments are usually based on measurements collected at air quality monitoring stations that do not coincide with health data locations. So, estimated exposures can be misleading if they do not take into account the uncertainty of exposure estimates. In this article we conducted a semi-ecological study to analyze associations between air quality during gestation and birth weight. Air quality during gestation was measured using a biomonitor, as an alternative to traditional air quality monitoring stations data, in order to increase spatial resolution of exposure measurements. To our knowledge this is the first time that the association between air quality and birth weight is studied using biomonitors. To address exposure uncertainty at health locations, we applied geostatistical simulation on biomonitoring data that provided multiple equally probable realizations of biomonitoring data, with reproduction of observed histogram and spatial covariance while matching for conditioning data. Each simulation represented a measure of exposure at each location. The set of simulations provided a measure of exposure uncertainty at each location. To incorporate uncertainty in our analysis we used generalized linear models, fitted simulation outputs and health data on birth weights and assessed statistical significance of exposure parameter using non-parametric bootstrap techniques. We found a positive association between air quality and birth weight. However, this association was not statistically significant. We also found a modest but significant association between air quality and birth weight among babies exposed to gestational tobacco smoke. 相似文献
7.
8.
Reliance on motor vehicle travel and the internal combustion engine has provided mobility, but the public health costs are
substantial: Road deaths, adverse deleterious health effects from air pollution and noise, reduction in physical exercise,
and toxic hazards associated with the refining, transport, use and disposal of petrochemicals. For traumatic road injury,
kinetic energy is the pathogen. Risks for injury and death rise with the second and fourth power of increases in velocity
upon impact; emissions of many air pollutants also increase exponentially with speed. Models derived from vector transmission
in infectious diseases have proven useful for defining risks and designing interventive strategies. These models predict the
number of lives saved and injuries prevented from a package of low-cost, effective measures, which can be quickly implemented.
Eradication of road deaths and elimination of air pollution emissions are achievable public health goals.
Speed camera systems produce sustainable levels of detection deterring speeding, and thereby reducing human injury and environmental
damage. “Education” and building more roads, part of the scenario “predict and provide,” have not been shown to reduce injury
risks. Building more roads, which in the long run, promotes urban sprawl and congestion, does not reduce travel time.
High speed toll roads and circular beltways, which involve trade-offs among time-saving, risk of injury, and diversion of
traffic from population centers, need to be re-evaluated and compared to alternative strategies based on modal shifts. We
suggest that revenues resulting from massive use of speed cameras can serve as the first step for funding the first steps
of sustainable transportation policies based on developing alternatives to private vehicle use and trucking. Such alternatives
could lead to even further reductions in injury and death and adverse effects of air pollution. More involvement by epidemiologists
in overseeing and evaluating strategies can expedite progress towards the goal of eradication of deaths from road injury,
and at the same time, reduce emissions of air pollutants.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
9.
Ihsan Kaya Cengiz Kahraman 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(4):529-541
Air pollution is one of the most important threats for the humanity. It can damage not only human health but also Earth’s
ecosystem. Because of the harmful effects of air pollution, it should be controlled very carefully. To do the risk assessment
of air pollution in Istanbul, the process capability indices (PCIs) which are very effective statistics to summarize the performance
of process are used in this paper. Fuzzy PCIs are used to determine the levels of the air pollutants which are measured in
different nine stations in Istanbul. Robust PCIs (RPCIs) are used when air pollutants have correlation. Fuzzy set theory has
been applied for both PCIs and RPCIs to have more sensitive results. More flexible PCIs obtained by using fuzzy specification
limits and fuzzy standard deviation are used to evaluate the air pollution’s level of Istanbul. Additionally some evaluation
criteria have been constructed for fuzzy PCIs to interpret the air pollution. 相似文献
10.
This paper introduces a new geostatistical model for counting data under a space-time approach using nonhomogeneous Poisson processes, where the random intensity process has an additive formulation with two components: a Gaussian spatial component and a component accounting for the temporal effect. Inferences of interest for the proposed model are obtained under the Bayesian paradigm. To illustrate the usefulness of the proposed model, we first develop a simulation study to test the efficacy of the Markov Chain Monte Carlo (MCMC) method to generate samples for the joint posterior distribution of the model’s parameters. This study shows that the convergence of the MCMC algorithm used to simulate samples for the joint posterior distribution of interest is easily obtained for different scenarios. As a second illustration, the proposed model is applied to a real data set related to ozone air pollution collected in 22 monitoring stations in Mexico City in the 2010 year. The proposed geostatistical model has good performance in the data analysis, in terms of fit to the data and in the identification of the regions with the highest pollution levels, that is, the southwest, the central and the northwest regions of Mexico City. 相似文献
11.
A method is presented to design monitoring networks for detecting groundwater pollution at industrial sites. The goal is to detect the pollution at some distance from the site’s boundary so that it can be cleaned up or hydrologically contained before contaminating groundwater outside the site. It is assumed that pollution may occur anywhere on the site, that transport is by advection only and that no retardation and chemical reactions take place. However, the approach can be easily extended to include designated (and uncertain) source areas, dispersion and reactive transport. The method starts from the premise that it is impossible to detect 100% of all the contaminant plumes with reasonable costs and therefore seeks a balance between the risk of pollution and network density. The design approach takes account of uncertainty in the flow field by simulating realisations of conductivity, groundwater head and associated flow fields, using geostatistical simulation and a groundwater flow model. The realisations are conditioned to conductivity and head observations that may already be present on the site. The result is an ensemble of flow fields that is further analysed using a particle track program. From this the probability of missing a contaminant plume originating anywhere on the terrain can be estimated for a given network. From this probability follows the risk, i.e. the expected costs of an undetected pollution. The total costs of the monitoring strategy are calculated by adding the risk of pollution to the costs of installing and maintaining the monitoring wells and the routinely performed chemical analyses. By repeating this procedure for networks of varying well numbers, the best network is chosen as the one that minimises total cost. The method is illustrated with a simulated example showing the added worth of exploratory wells for characterising hydraulic conductivity of a site. 相似文献
12.
Haidong Yang Dongguo Shao Biyu Liu Jianhua Huang Xianbao Ye 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(2):507-522
Sudden water pollution accidents in surface waters occur with increasing frequency. These accidents significantly threaten people’s health and lives. To prevent the diffusion of pollutants, identifying these pollution sources is necessary. The identification problem of pollution source, especially for multi-point source, is one of the difficulties in the inverse problem area. This study examines this issue. A new method is designed by combining differential evolution algorithm (DEA) and Metropolis–Hastings–Markov Chain Monte Carlo (MH–MCMC) based on Bayesian inference to identify multi-point sudden water pollution sources. The effectiveness and accuracy of this proposed method is verified through outdoor experiments and comparison between DEA and MH–MCMC. The average absolute error of the sources’ position and intensity, the relative error and the average standard deviations obtained using the proposed method are less than those of DEA and MH–MCMC. Moreover, the relative error and the sampling relative error under four different standard deviations of measurement error (σ = 0.01, 0.05, 0.1, 0.15) are less than 2 and 0.11 %, respectively. The proposed method (i.e., DEMH–MCMC) is effective even when the standard deviation of the measurement error increases to 0.15. Therefore, the proposed method can identify sources of multi-point sudden water pollution accidents efficiently and accurately. 相似文献
13.
D. Oettl R. A. Almbauer P. J. Sturm G. Pretterhofer 《Stochastic Environmental Research and Risk Assessment (SERRA)》2003,17(1-2):58-75
Although the strict legislation regarding vehicle emissions in Europe (EURO 4, EURO 5) will lead to a remarkable reduction
of emissions in the near future, traffic related air pollution still can be problematic due to a large increase of traffic
in certain areas. Many dispersion models for line-sources have been developed to assess the impact of traffic on the air pollution
levels near roads, which are in most cases based on the Gaussian equation. Previous studies gave evidence, that such kind
of models tend to overestimate concentrations in low wind speed conditions or when the wind direction is almost parallel to
the street orientation. This is of particular interest, since such conditions lead generally to the highest observed concentrations
in the vicinity of streets. As many air quality directives impose limits on high percentiles of concentrations, it is important
to have good estimates of these quantities in environmental assessment studies. The objective of this study is to evaluate
a methodology for the computation of especially those high percentiles required by e.g. the EU daughter directive 99/30/EC
(for instance the 99.8 percentile for NO2). The model used in this investigation is a Markov Chain – Monte Carlo model to predict pollutant concentrations, which performs
well in low wind conditions as is shown here. While usual Lagrangian models use deterministic time steps for the calculation
of the turbulent velocities, the model presented here, uses random time steps from a Monte Carlo simulation and a Markov Chain
simulation for the sequence of the turbulent velocities. This results in a physically better approach when modelling the dispersion
in low wind speed conditions. When Lagrangian dispersion models are used for regulatory purposes, a meteorological pre-processor
is necessary to obtain required input quantities like Monin-Obukhov length and friction velocity from routinely observed data.
The model and the meteorological pre-processor applied here, were tested against field data taken near a major motorway south
of Vienna. The methodology used is based on input parameters, which are also available in usual environmental assessment studies.
Results reveal that the approach examined is useful and leads to reasonable concentration levels near motorways compared to
observations.
We wish to thank Andreas Schopper (Styrian Government) for providing air quality values, M. Kalina for providing the raw
data of the air quality stations near the motorway and J. Kukkonen for providing the road site data set from the Finish Meteorological
Institute (FMI). The study was partly funded by the Austrian science fund under the project P14075-TEC. 相似文献
14.
This study introduces a method using a multi-goal fuzzy cognitive map (FCM) and multi-criteria decision making based on sensitivity analysis to assess the risks associated with working accidents in underground collieries. Safety, stoppage in operation, and operational and capital costs are considered as the main goals during the FCM process with significant emphasis on safety. Workplace accidents data from Kerman underground collieries are statistically evaluated to find the degrees of occurrence probability, severity, and work-disability duration as the main risk factors. The causes and effects of accidents are analyzed using FCM based on three goals and the effects of risk factors. A sensitivity analysis on the weights of the goals is conducted with the aim of increasing the workplace safety in TOPSIS environment after solving the designed multi-goal FCM. Results indicate that “gas poisoning,” “roof fall,” and “debris and destruction” take the first three ranks and impose high risks to the system. By contrast, “collision, hit, and crash” presents the lowest risk among all accidents. 相似文献
15.
16.
Probabilistic seismic risk assessment for spatially distributed lifelines is less straightforward than for individual structures. While procedures such as the ‘PEER framework’ have been developed for risk assessment of individual structures, these are not easily applicable to distributed lifeline systems, due to difficulties in describing ground‐motion intensity (e.g. spectral acceleration) over a region (in contrast to ground‐motion intensity at a single site, which is easily quantified using Probabilistic Seismic Hazard Analysis), and since the link between the ground‐motion intensities and lifeline performance is usually not available in closed form. As a result, Monte Carlo simulation (MCS) and its variants are well suited for characterizing ground motions and computing resulting losses to lifelines. This paper proposes a simulation‐based framework for developing a small but stochastically representative catalog of earthquake ground‐motion intensity maps that can be used for lifeline risk assessment. In this framework, Importance Sampling is used to preferentially sample ‘important’ ground‐motion intensity maps, and K‐Means Clustering is used to identify and combine redundant maps in order to obtain a small catalog. The effects of sampling and clustering are accounted for through a weighting on each remaining map, so that the resulting catalog is still a probabilistically correct representation. The feasibility of the proposed simulation framework is illustrated by using it to assess the seismic risk of a simplified model of the San Francisco Bay Area transportation network. A catalog of just 150 intensity maps is generated to represent hazard at 1038 sites from 10 regional fault segments causing earthquakes with magnitudes between five and eight. The risk estimates obtained using these maps are consistent with those obtained using conventional MCS utilizing many orders of magnitudes more ground‐motion intensity maps. Therefore, the proposed technique can be used to drastically reduce the computational expense of a simulation‐based risk assessment, without compromising the accuracy of the risk estimates. This will facilitate computationally intensive risk analysis of systems such as transportation networks. Finally, the study shows that the uncertainties in the ground‐motion intensities and the spatial correlations between ground‐motion intensities at various sites must be modeled in order to obtain unbiased estimates of lifeline risk. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
17.
Urban air quality is an issue of major concern across many cities in India. In particular, high levels of particulate matter (both SPM and RSPM) are responsible for noncompliance to air quality standards. Air quality modeling is an effective tool to simulate the air quality of a region and to predict air quality concentrations under different scenarios. Kanpur city which is a top‐ten urban conglomerate in India (based on population) is chosen for the application of the ISCST3 model and simulation of air quality. Sectored emission loads are estimated for transport, industrial, power, and domestic sectors, which provide an estimate of the major contributors to air pollution with specific reference to particulate matter, which is a major pollutant of concern. A detailed scenario analysis is carried out to estimate the changes in emissions that would take place due to various interventions. Dispersion modeling is carried out using the ISCST3 model, to estimate the concentrations of SPM all over the city under different scenarios. Emission inventory and meteorological data served as input to the model, and the air quality is predicted for various seasons and intervention scenarios. The modeled values for the scenario without intervention results in an underestimation of 48%, which is due to unaccountable or unidentified sources, trans‐boundary movement of SPM, and model calibration errors. To overcome the error, the model is calibrated with the observed values and results are obtained for other scenarios using the calibration factor. The paper demonstrates only the research direction currently used to simulate air quality in Indian cities. However, further refinement and research is required before it could be used for more accurate predictions. 相似文献
18.
Modeling the stochastic dependence of air pollution index data 总被引：1，自引：1，他引：0
The air pollution index (API) is a common tool, which is often used for determining the quality of air in the environment. In this study, a discrete-time Markov chain model is applied for describing the stochastic behaviour of API data. The study reported in this paper is conducted based on the data collected from Klang city in Malaysia for a period of 3 years (2012–2014). Based on the API data, we considered a five-state Markov chain for depicting the five different states of the air pollution. We identified the Markov chain is an ergodic Markov chain and determined the limiting distribution for each state of the air pollution. In addition, we have identified the mean first passage time from one state to another. Based on the limiting distribution and the mean return time, we found that the risk of occurrences for unhealthy events is small. However, the risk remains notably troubling. Therefore, the standard of air quality in Klang falls within a margin that is considered healthy for human beings. 相似文献
19.
Distributed modeling of monthly air temperatures over the rugged terrain of the Yellow River Basin 总被引：1，自引：0，他引：1
Our analyses of the monthly mean air temperature of meteorological stations show that altitude, global solar radiation and
surface effective radiation have a significant impact on air temperature. We set up a physically-based empirical model for
monthly air temperature simulation. Combined the proposed model with the distributed modeling results of global solar radiation
and routine meteorological observation data, we also developed a method for the distributed simulation of monthly air temperatures
over rugged terrain. Spatial distribution maps are generated at a resolution of 1 km×1 km for the monthly mean, the monthly
mean maximum and the monthly mean minimum air temperatures for the Yellow River Basin. Analysis shows that the simulation
results reflect to a considerable extent the macro and local distribution characteristics of air temperature. Cross-validation
shows that the proposed model displays good stability with mean absolute bias errors of 0.19°C–0.35°C. Tests carried out on
local meteorological station data and case year data show that the model has good spatial and temporal simulation capacity.
The proposed model solely uses routine meteorological data and can be applied easily to other regions.
Supported by China Meteorological Administration key Project on New Technique Diffusion (Grant No. CMATG2006Z10) and Jiangsu
Key Laboratory of Meteorological Disasters (Grant No. KLME050102) 相似文献
20.
针对2011年12月初北京及华北持续近一周的严重大雾天气这一热点事件,从城市群大雾过程气溶胶区域影响的视角,基于"973"项目"北京及周边地区大气-水-土环境污染机理与调控原理"的研究工作,就北京及周边地区大雾天气与大气气溶胶区域影响的关系等方面进行了讨论.研究表明,北京城市大雾前低空SO2和NO2浓度存在"积聚"与"突增"现象.北京及周边地区冬季雾日数和气溶胶光学厚度则呈正相关,并具有"同位相"的年际变化趋势.研究同时发现北京及其南部周边的冬季气溶胶高值区呈南北向带状分布,其与北京周边居民户数高值区有所吻合,反映了冬季北京城市气溶胶颗粒物的远距离影响源区及大尺度输送效应.统计分析指出,冬季北京气溶胶颗粒物PM10、PM2.5主要影响成分是SO2和NOX,且有关研究也表明,电厂、采暖和工业面源是SO2的三大本地排放源,而机动车、电厂、工业为NOX的三大本地排放源,上述大气PM10、PM2.5主成分污染源亦与雾水样本化学分析结果相吻合,即冬季由于燃煤在生活能源中的比例较大,北京雾水中硫元素和碳元素的含量都较高.因此,北京冬季大雾不仅与北京城区气溶胶及其污染排放影响存在相关关系,而且与北京周边天津、河北、山东等地气溶胶及大气污染物的远距离输送和气溶胶区域影响效应有着重要的联系.因此,北京雾霾天气及相关大气污染的治理工作首先要着眼于局地污染物的减排,但同时如何做好区域大气污染的协同治理也是不容忽视的问题. 相似文献