Evaluation of risk concepts in partial duration series

Conventional design practice aims at obtaining optimal estimates of floods with specified exceedance probabilities. Such estimates are, however, known on the average to be exceeded more frequently than expected. Alternatively, methods focusing on the expected exceedance probability can be used. Two different methods are considered here; the first is based on the sample distribution of true exceedance probabilities. The second is a Bayesian analogue using the likelihood function and a noninformative prior to describe the variability of exceedance probabilities. Appropriate analytical solutions are presented in both cases using the partial duration series approach.     

Evaluation of risk concepts in partial duration series

Conventional design practice aims at obtaining optimal estimates of floods with specified exceedance probabilities. Such estimates are, however, known on the average to be exceeded more frequently than expected. Alternatively, methods focusing on the expected exceedance probability can be used. Two different methods are considered here; the first is based on the sample distribution of true exceedance probabilities. The second is a Bayesian analogue using the likelihood function and a noninformative prior to describe the variability of exceedance probabilities. Appropriate analytical solutions are presented in both cases using the partial duration series approach.     

Daily flow time series patching or extension: a spatial interpolation approach based on flow duration curves

Abstract

An algorithm is described that was initially developed as a simple method for patching and extending observed time series of daily streamflow. It is based on the use of 1-day flow duration curves for each month of the year and on the assumption that flows occurring simultaneously at sites in reasonably close proximity to each other correspond to similar percentage points on their respective duration curves. The algorithm has been incorporated into a model that allows flows at a destination site to be estimated from flows occurring at several source sites. The model has been applied to six groups of catchments within southern Africa and the resulting streamflow simulations compare favourably with those obtained using a semi-distributed, physically-based, daily time step rainfall-runoff model. The current limitations of the approach and its future potential value are discussed.     

Time series analysis and forecasting for air pollution in small urban area: an SARIMA and factor analysis approach

Despite the existing public and government measures for monitoring and control of air quality in Bulgaria, in many regions, including typical and most numerous small towns, air quality is not satisfactory. In this paper, factor analysis and Box–Jenkins methodology are applied to examine concentrations of primary air pollutants such as NO, NO₂, NO_x, PM10, SO₂ and ground level O₃ in the town of Blagoevgrad, Bulgaria within a 1 year period from 1st September 2011 to 31st August 2012, based on hourly measurements. By using factor analysis with PCA and Promax rotation, a high multicollinearity between the six pollutants has been detected. The pollutants were grouped in three factors and the degree of contribution of the factors to the overall pollution was determined. This was interpreted as the presence of common sources of pollution. The main part of the study involves the performance of time series analysis and the development of univariate stochastic seasonal autoregressive integrated moving average (ARIMA) models with recording on a hourly basis as seasonality. The study also incorporates the Yeo–Johnson power transformation for variance stabilizing of the data and model selection by using Bayersian information criterion. The obtained SARIMA models demonstrated very good fitting performance with regard to the observed air pollutants and short-term predictions for 72 h ahead, in particular in the case of ozone and particulate matter PM10. The presented statistical approaches allow the building of non-complex models, effective for short-term air pollution forecasting and useful for advance warning purposes in urban areas.     

Bias and variance of quantile estimates from a partial duration series

Partial duration series methods have been used to estimate the T-year event Z_T of a hydrological variable. In this paper the maximum likelihood estimate of Z_T is considered for the model with a Poisson number of peaks and independently, exponentially distributed peak magnitudes. The mean and the variance of are evaluated by finite summations. Approximations to the bias of are discussed. Although the use of a first-order asymptotic expression for var( ) has been criticized by earlier workers, the results from this study indicate that the error is small for the most common values of T. It is further shown that small departures from the assumed model can have a large impact on var( ).     

On the distribution of flood volume in partial duration series analysis of flood phenomena

A methodology based on the theory of stochastic processes is applied to the analysis of floods. The approach will be based on some results of the theory of extreme values over a threshold. In this paper, we focus on the estimation of the distribution of the flood volume in partial duration series analysis of flood phenomena, by using a bivariate exponential distribution of discharge exceedances and durations over a base level.     

On the distribution of flood volume in partial duration series analysis of flood phenomena

A methodology based on the theory of stochastic processes is applied to the analysis of floods. The approach will be based on some results of the theory of extreme values over a threshold. In this paper, we focus on the estimation of the distribution of the flood volume in partial duration series analysis of flood phenomena, by using a bivariate exponential distribution of discharge exceedances and durations over a base level.     

Fuzzy robust process capability indices for risk assessment of air pollution

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.     

Indices for the assessment of environmental pollution of the Baltic Sea coasts: integrated assessment of a multi-biomarker approach

Two mathematical methods to assess the "health status" of flounder (Platichthys flesus), eelpout (Zoarces viviparus) and blue mussel (Mytilus spp.) populations of the Baltic Sea were applied on selected biomarker data collected during the EU project "BEEP" (Biological Effects of Environmental Pollution on Marine Coastal Ecosystems). The Bioeffect Assessment Index (BAI) and the Integrated Biomarker Index (IBR) combine different biomarkers to single values, which can be used to describe the toxically-induced stress level of populations in different areas. Both indices determined here produced essentially similar results, which in most cases agreed with the known contamination levels in the different study areas. Advantages and limitations of index applications and interpretations are critically discussed. The use of indices provides comprehensive information about biological effects of pollution in marine organisms and may therefore serve as a useful tool for environmental management by ranking the pollution status of marine coastal areas.     

Dependence of air pollution on meteorological conditions based on magnetic susceptibility measurements: a case study from Warsaw

This study reports systematic analysis of air pollution concentration by particulate matter (PM) in relation to annual changes of metrological conditions. The concentration of PM collected on filters was evaluated by magnetic properties. Long series of filters collected during 1977, 1980, 1981 and 1985 in Warsaw have been used for measurement. Such long time series allowed a better determination of correlation between seasonal susceptibility changes and meteorological conditions and to verify the repeatability of annual behavior. The magnetic susceptibility normalized by mass (??) has been used as a proxy of pollution. Monthly variations of ?? (??_m) demonstrated slow and small seasonal changes disturbed by quick, high peaks. The filters with high susceptibilities contain a mixture of magnetic single and multidomain grains which is characteristic for anthropogenic pollution, with the frequency-dependent susceptibility ??_fd being less than 4%. The low susceptibility samples are characterized by admixture of small amount of pseudo-single domain (PSD) and possible small amount of superparamagnetic (SP) grains. Detailed analysis of reduced mean monthly susceptibility ??_R revealed that temperature and absolute humidity correlate positively in cold period of year and negatively in warm months. The linear correlation coefficients range from 0.7 to 0.9 and from 0.3 to 0.4 for negative and positive linear correlation, respectively. The dispersion of pollutants in a city depends on the way of ventilation. The big cities like Warsaw are urban heat islands which act the most effective way for the warm period and for the cold time. The correlation of ??_R with absolute humidity is similar to that for temperature but for different reasons. The presence of big amount of water vapor particles in the atmosphere can favor the adhesion of smaller ferromagnetic grains to their surface which can accelerate the process of falling down of such agglomerates. Such a process can take place during wintertime. We do not observe clear correlation between ??_R and precipitation.     

The implementation of Bayesian structural additive regression models in multi-city time series air pollution and human health studies

In this study, a novel Bayesian semiparametric structural additive regression (STAR) model is introduced in multi-city time series air pollution and human health studies. This modeling approach can simultaneously take into account the fixed effects, random effects, nonlinear smoothing functions and spatial functions in an integrated model framework. This study focuses on examining the powerful functionalities of this approach in modeling air pollution and mortality data of 100 U.S. cities from 1987 to 2000. Compared with previous studies, the modeling approach used in this study yields consistent findings of nation-level and city-level PM₁₀ (particulate matter less than 10?μm) effects on mortality. Notably, cities with significantly elevated mortality rates were concentrated in the Northeastern U.S. This modeling approach also emphasizes the important functionality of the spatial function in visualizing disease mapping. Model diagnostics were performed to confirm the availability of the STAR model. We also found consistent findings by using different hyperparameters in the sensitivity analysis. To sum up, the implementation of this modeling approach has achieved the goals of applying a spatial function and obtaining robust results in the multi-city time series air pollution and human health study.     

Magnetic signature of indoor air pollution: Household dust study

The combination of magnetic and geochemical methods was used to determine the mineralogy, grain size and domain structure of magnetic particles in indoor dust collected in 195 sites in Warsaw, Poland. Data show an asymmetric distribution of magnetic susceptibility (χ) in the wide range of 20–1514 × 10^?8 m³ kg^?1. Comparison of magnetic parameters shows that the internal dust contains outside pollution characteristic for air and soil. More than 90% of indoor dust samples were characterized by roughly uniform magnetic mineralogy, typical for fine grained magnetite (diameter of 0.2–5 μm), and grain size between pseudo-single-domain and small multi-domain with small contribution of superpara-magnetic particles (～10%). Samples with χ larger than 220 × 10^?8 m³ kg^?1 contain mainly magnetite and an anthropogenic metallic Fe with T _C > 700°C. The indoor dust contains, characteristic for the urban areas, spherical magnetic particles originated from fossil fuel combustion processes and mixture of irregular angular iron-oxides grains containing other elements, including Na, Ca, Al, Si, K, S, Mn, Cl, and Mg.     

基于EMD的GNSS时间序列异常值探测算法

考虑到传统谐波模型难以精确地描述GNSS坐标时间序列的非线性时变季节性变化,进一步影响了异常值探测.本文提出了一种基于经验模态分解(Empirical Mode Decomposition,EMD)和四分位距统计量(Interquartile Range,IQR)的组合异常值探测算法.新算法的基本思想是:先采用EMD算法将GNSS坐标时间序列分解为若干不同频率的IMF(Intrinsic Mode Function)分量,并采用相关系数法进行信噪分离,最后采用IQR准则对残差序列进行异常值探测.模拟实验分析结果表明,传统算法仅能探测到83％的异常值,而新算法能够探测到91％的异常值.陆态网络实测数据分析结果进一步验证了新算法较传统算法能更加有效地探测出GNSS坐标时间序列的异常值.     

An approach to the assessment of local trace metal pollution in the Mediterranean marine atmosphere

Assessing the extent of local trace metal pollution in marine atmospheric particulates presents a number of difficulties, and one potential approach to the problem has been evaluated by reference to a series of aerosols from the lower troposphere over the Eastern Mediterranean. This region, which is less remote from the continents than open-ocean areas, has two significantly different atmospheric particulate catchments: being bordered in the north by nations having industrialized, semi-industrialized and rural economies, and in the south by the North African desert belt. The elemental chemistry of the particulates is illustrated in terms of the distributions of Fe and Pb, which are presented in the form of enrichment factor diagrams, and is shown to be essentially controlled by the dilution of a ‘European’ background material, common to ‘remote’ regions of the latitudinal belt, with crust-derived, desert components. Major local perturbations in the dilution relationship can occur when either crust-derived or pollutant components strongly dominate the total particulate population. The extent of these local perturbations can be tentatively assessed using appropriate enrichment factor diagrams, which offer a framework within which to interpret the elemental chemistry of marine atmospheric particulates providing sufficient data is available for their construction. By using such an EF diagram it is suggested that the Eastern Mediterranean atmospheric particulates have not suffered local pollution on a gross scale with respect to Pb.     

随机结构动力可靠度分析的极值概率密度方法

提出了随机结构动力可靠度分析的极值概率密度方法。基于概率密度演化的基本思想,构造一个虚拟随机过程,使得随机结构动力反应的极值为该虚拟随机过程的截口随机变量。进而．采用概率密度演化方法,建立概率密度演化方程并求解给出随机结构动力反应的极值分布。在安全域内积分即可给出结构动力可靠度,当安全界限为随机变量时,采用这一方法几乎不增加额外的工作量,与随机模拟结果的比较表明,本文建议方法具有良好的精度和效率。     

A Kriging-based approach for locating a sampling site—in the assessment of air quality

Air quality assessment studies have either high sampling or analyses costs. In these studies the representation of a city by a single sampling point is still a serious problem, especially in the metropolitans of developing countries, because of the absence of equipments required for sampling at different locations. In this paper, the smoke data measurements of long years are used in the determination of the region for the representation of the city by drawing contours through the Kriging method. Then, the selection of the sampling site in this region is done on the basis of the criteria recommended by the EPA. By this way, the data taken from this sampling point are used for assessing the average city concentrations with lower sampling and analyses costs. This information is valuable for monitoring the air quality and defining environmental policies, although the local distribution and the extreme concentration values over the city are not measurable.     

中国清洁空气行动对PM2.5污染的影响

随着经济快速发展,中国近十年来大气污染事件频发,严重危害居民公共健康.为应对严重的大气污染问题,切实改善空气质量,国务院于2013年颁布了《大气污染防治行动计划》(简称《大气十条》)(国务院, 2013),要求到2017年全国地级及以上城市可吸入颗粒物(PM10)年均浓度比2012年下降10%以上,优良天数逐年提高。     

Impact of clean air action on PM2.5 pollution in China

<正>China suffers from severe air pollution in the past decades,characterized by high-levels of fine particulate matter(PM_(2.5)) concentrations. To mitigate PM_(2.5) pollution, the Chinese government issued the Air Pollution Prevention and Control Action Plan (referred to as the Clean Air Action hereinafter) in 2013, which requires the three key regions,