Regression models for exceedance data via the full likelihood

Many situations in practice require appropriate specification of operating characteristics under extreme conditions. Typical examples include environmental sciences where studies include extreme temperature, rainfall and river flow to name a few. In these cases, the effect of geographic and climatological inputs are likely to play a relevant role. This paper is concerned with the study of extreme data in the presence of relevant auxiliary information. The underlying model involves a mixture distribution: a generalized Pareto distribution is assumed for the exceedances beyond a high threshold and a non-parametric approach is assumed for the data below the threshold. Thus, the full likelihood including data below and above the threshold is considered in the estimation. The main novelty is the introduction of a regression structure to explain the variation of the exceedances through all tail parameters. Estimation is performed under the Bayesian paradigm and includes model choice. This allows for determination of higher quantiles under each covariate configuration and upper bounds for the data, where appropriate. Simulation results show that the models are appropriate and identifiable. The models are applied to the study of two temperature datasets: maxima in the U.S.A. and minima in Brazil, and compared to other related models.     

近53年黄河流域降水时空分布特征

利用黄河流域76个气象台站近53 a（1960-2012）的逐日降水资料,采用国际上通用的极端降水事件指数,应用一元线性回归法、移动平均法和径向基函数空间插值法,研究了黄河流域极端降水指数时空变化特征。结果表明：（1）时间上,黄河流域年平均降水量在过去53 a下降趋势较为显著,降水倾向率为-7.2 mm/10a;极端降水量变化趋势表现较为稳定,极端降水倾向率为-0.64 mm/10a,呈不断降低趋势;极端降水强度倾向率为-0.078 mm/10a,呈不断下降趋势;极端降水比率总体表现为微弱增长趋势,倾向率为0.49 mm/10a。（2）空间上,降水量空间分布具有明显的差异性,由北至南呈阶梯式逐渐增多趋势,其中降水量最少的地区是以银川为代表的周边区域,最多的地区为黄河流域南部区域;极端降水量从北至南也具有逐渐增多态势,与降水量具有相似的空间分布特点,且极端降水量越多的地区降水总量也相对较多;极端降水强度表现出由流域西部向东部逐渐增多的趋势,西部最低值为不到20 mm/d,逐渐向东过渡到最大值为76 mm/d;极端降水比率的分布呈由北向南逐渐递减的特点,并且出现了以银川为中心的极大值和以西安为中心的极小值分布格局。     

Hierarchical modeling for extreme values observed over space and time

We propose a hierarchical modeling approach for explaining a collection of spatially referenced time series of extreme values. We assume that the observations follow generalized extreme value (GEV) distributions whose locations and scales are jointly spatially dependent where the dependence is captured using multivariate Markov random field models specified through coregionalization. In addition, there is temporal dependence in the locations. There are various ways to provide appropriate specifications; we consider four choices. The models can be fitted using a Markov Chain Monte Carlo (MCMC) algorithm to enable inference for parameters and to provide spatio–temporal predictions. We fit the models to a set of gridded interpolated precipitation data collected over a 50-year period for the Cape Floristic Region in South Africa, summarizing results for what appears to be the best choice of model.

Time-varying models for extreme values

We propose a new approach for modeling extreme values that are measured in time and space. First we assume that the observations follow a Generalized Extreme Value (GEV) distribution for which the location, scale or shape parameters define the space–time structure. The temporal component is defined through a Dynamic Linear Model (DLM) or state space representation that allows to estimate the trend or seasonality of the data in time. The spatial element is imposed through the evolution matrix of the DLM where we adopt a process convolution form. We show how to produce temporal and spatial estimates of our model via customized Markov Chain Monte Carlo (MCMC) simulation. We illustrate our methodology with extreme values of ozone levels produced daily in the metropolitan area of Mexico City and with rainfall extremes measured at the Caribbean coast of Venezuela.     

Imputing Unmeasured Explanatory Variables in Environmental Epidemiology With Application to Health Impact Analysis of Air Pollution

This paper presents the results of a reconsideration of earlier work that finds an association between daily hospital admissions for respiratory distress and daily concentrations of sulphate (lag 1) as well as daily maximum concentrations of ozone (lags 1 and 3). These associations are found even after clustering the data by hospital of admission and accounting for the effects of temperature. We use an adaptation of their generalized estimating equation technique for clustered data, that daily data being for southern Ontario summers from 1983 to 1988. Like them, we adjust for daily maximum temperatures. However, unlike the earlier work returned to ours includes daily average humidity as a potential explanatory variable in our model. Our analysis also differs from theirs in that we cluster the data by census subdivision to reduce the risk of confounding pollutant levels with population size within regions. Moreover, we log-transform the explanatory variables and then high-pass filter the resulting data. We also deviate from the earlier analysis by taking account of measurement error incurred in using surrogate measures of the explanatory variables. To do so we use new methodology designed for our study but of potential value in other applications. That methodology requires a spatial predictive distribution for the unmeasured explanatory variables. Each day about 700 missing measurements for each of these variables can then be imputed over the geographical domain of the study. With these imputations we get a measure of imputation error through the covariance of the predictive distribution. Along with the predictive distribution we require an impact model to link-up with the predictive distribution. We describe that model and show how it uses the imputed measurements of the missing values of the explanatory variables. We also show how through that model, uncertainty about these values is reflected in our analysis and in commensurate uncertainties in the inferences made. Apart from its substantive objectives, our analysis serves to test the new methods with the earlier results serving as a foil. The reassuring qualitative agreement between our findings and the earlier results seems encouraging.     

甘肃黄土高原各级降水和极端降水时空分布特征

利用甘肃黄土高原地区1960－2011年12个及周边4个站点的逐日降水观测资料,用阈值检测方法计算出甘肃黄土高原地区极端降水的阈值并运用气候线性趋势、多项式拟合、反距离加权法、Mann-Kendall检验、Morlet小波、R／S方法分析了近52a甘肃黄土高原各级雨量和R95极端降水的时空变化特征及对未来进行预测。结果表明：①近52a来甘肃黄土高原春季以小雨为主,夏、秋季以暴雨为主,冬季以大雪为主,R95极端降水主要集中在夏季;②春、秋季主要以大雨降水强度最强,夏季暴雨降水强度最强,冬季则为大雪;R95极端降水强度夏季降水强度最强。③小雪呈显著增加,微雨、中雨、大雨、微雪、R95极端降水量均呈显著减少;④微雨量、小雨量、中雨量、大雨量、R95极端降水量与年降水总量有很好的相关性,对年降水总量有很好的响应;⑤在空间分布上,各量级降水和R95极端降水大部分存在减小趋势,减小区主要集中在西南部和东北部;⑥各量级降水和R95极端降水突变年份不尽相同;⑦各量级降水和R95极端降水在不同的时间序列存在长短不同的周期震荡;⑧R／S分析表明不同量级降水和R95极端降水的未来变化趋势和过去一致。     

南亚热带森林3层次物种多度之广义泊松分布模型

生物群落中物种多度分布(species abundance distribution)呈典型的倒J形,即其中存在许多稀有种、少量常见种.物种多度分布模型研究有助于解决森林生态恢复中的物种配置等实际问题.本研究考察了一种过分散(over-dispersion,或称超分布,即方差大于均值)的离散型分布,即具有λ和α两个参数...     

Performance evaluation of NOAA-EPA developmental aerosol forecasts

To aid air quality model development and assess air quality forecasts, the Meteorological Development Laboratory (MDL) provided categorical verification metrics for developmental aerosol predictions. The National Air Quality Forecasting Capability (NAQFC) generated 48 h (of) gridded hourly developmental predictions for the lower 48 states (CONUS) domain in 12 km horizontal spacing. The NAQFC uses the North American Mesoscale (NAM) model with EPA’s Community Multiscale Air Quality (CMAQ) model to produce predictions of ground level aerosol concentrations. We used bilinear interpolation to calculate predicted daily maximum values at the location of the observation sites. We compared these interpolated predicted values to the observed daily maximum to produce 2 × 2 contingency tables, with a threshold of 40 μg/m³ during the months of March–August, 2007. The model showed some degree of skill in predicting aerosol exceedances. These results are preliminary as the NAQFC model for aerosol prediction is in the developmental stage. A more comprehensive performance evaluation will be accomplished in 2008, when more data become available. Our verification metrics included categorical analyses for Fraction Correct (FC) or percent correct (FC × 100), Threat Score (TS) or Critical Success Index (CSI), Probability of Detection (POD), and the False Alarm Rate (FAR), Mean Absolute Error (MAE) and mean algebraic error or bias, where bias is forecast minus observation. Graphic products included weekly statistics for the CONUS displayed in the form of bar charts, scatterplots, and graphs. In addition, we split the CONUS into six geographic regions and provided regional statistics on a monthly basis. MDL produced spatial maps of daily 1-h maximum predicted aerosol values overlaid with the corresponding point observations. MDL also provided spatial maps of the daily maximum of the 24-h running average. We derived the 24-h running average from the 1-h average predicted aerosol values and observations.     

Environmental equity and the distribution of toxic release inventory and other environmentally undesirable sites in metropolitan New York City

We study the question of environmental equity via generalized linear modeling for the metropolitan New York City region and ask whether, after accounting for socioeconomic status, particular racial/ethnic populations bear a disproportionate burden of hosting environmentally undesirable sites. Our data consist of population demographics for 2216 census tracts linked to 354 environmentally undesirable facilities, including toxic release inventory sites, hazardous waste treatment, storage, and disposal facilities, and other common urban problem sites such as landfills, incinerators, bus garages and sewage treatment plants. Using generalized linear and additive modeling techniques, we find that racial/ethnic demographics, in particular the Hispanic percentage of a tract's population, are significantly associated with the presence of potentially environmentally adverse sites. This leads us to the conclusion that, over the whole metropolitan New York City area, the Hispanic population is proximate to more sites than other populations. At the same time, we find that both Hispanics and African-Americans are more proximate to these sites in the Bronx and Queens. However, we also find indications that Hispanics and African-Americans are less likely to be proximate to the sites in Manhattan. We establish an empirical relationship that warrants additional study in order to establish the causes for the population distribution and whether a basis for a claim of discrimination exists.     

辽宁本溪大气颗粒物浓度特征

利用本溪大气成分监测站2008年3月至2009年2月大气颗粒物监测仪GRIMM180的连续监测资料,对该地区大气颗粒物的质量浓度变化、谱分布特征以及大气颗粒物与气象因素的关系进行分析研究.结果表明,本溪PM10和PM2.5平均质量浓度分别为0.086 mg.m-3和0.058 mg.m-3,其日平均质量浓度变化幅度较大;冬季和夏季质量浓度日变化均呈现明显的双峰双谷特征;数浓度谱分布较好地符合Junge分布;PM10日平均值超标率为8.7%,且大气颗粒物主要是以细粒子的形式存在;随风速的增大大气颗粒物质量浓度基本呈现逐渐减小的趋势,当风速〉0.6 m.s-1时,大气颗粒物质量浓度随风速增大下降明显,风速〉3.0 m.s-1时,下降的趋势减缓;降水过程对大气颗粒物有清除作用,其中对粗粒子的清除效果非常明显.     

太原市大气中PM10的监测与分布

2001年3月下旬，在太原市建成区均匀布设15个网格点，使用便携式空气质量监测仪，连续5天监测了各点大气中可吸入颗粒物（PM10）的浓度。结果表明：监测期间PM10的浓度范围为0．191－0．660mg/m^3，全市日平均浓度为0．373mg/m^3，上午平均值是下午的2倍；PM10浓度空间分布整体上比较均匀，不同功能区的污染程度依次为：电厂＞化工工＞太钢工业区＞校园商业区＞太行居民我。着重探讨了污染现状的成因，并提出了改善大气质量的建议。     

Bayesian modelling of extreme wind speed at Cape Town,South Africa

In the framework of generalized extreme value (GEV) distribution, the frequentist and Bayesian methods have been used to analyse the extremes of annual maxima wind speed recorded by automatic weather stations in Cape Town, Western Cape, South Africa. In the frequentist approach, the GEV distribution parameters were estimated using maximum likelihood, whereas in the Bayesian method the Markov Chain Monte Carlo technique with the Metropolis–Hastings algorithm was used. The results show that the GEV model with trend in the location parameter appears to be a better model for annual maxima data. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from other weather stations. The results from the Bayesian analysis show that posterior inference might be affected by the choice of priors and hence by the distance between a weather station used to formulate the priors and the point of interest.     

Generalized extreme value regression for ordinal response data

This paper introduces a flexible skewed link function for modeling ordinal response data with covariates based on the generalized extreme value (GEV) distribution. Commonly used probit, logit and complementary log-log links are prone to link misspecification because of their fixed skewness. The GEV link is flexible in fitting the skewness in the response curve with a free shape parameter. Using Bayesian methodology, it automatically detects the skewness in the response curve along with the model fitting. The flexibility of the proposed model is illustrated by its application to an ecological survey data about the coverage of Berberis thunbergii in New England. We employ the latent variable approach by Albert and Chib (J Am Stat Assoc 88:669–679, (1993) to develop computational schemes. For model selection, we employ the Deviance Information Criterion (DIC).     

Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Species distribution models (SDMs) based on statistical relationships between occurrence data and underlying environmental conditions are increasingly used to predict spatial patterns of biological invasions and prioritize locations for early detection and control of invasion outbreaks. However, invasive species distribution models (iSDMs) face special challenges because (i) they typically violate SDM's assumption that the organism is in equilibrium with its environment, and (ii) species absence data are often unavailable or believed to be too difficult to interpret. This often leads researchers to generate pseudo-absences for model training or utilize presence-only methods, and to confuse the distinction between predictions of potential vs. actual distribution. We examined the hypothesis that true-absence data, when accompanied by dispersal constraints, improve prediction accuracy and ecological understanding of iSDMs that aim to predict the actual distribution of biological invasions. We evaluated the impact of presence-only, true-absence and pseudo-absence data on model accuracy using an extensive dataset on the distribution of the invasive forest pathogen Phytophthora ramorum in California. Two traditional presence/absence models (generalized linear model and classification trees) and two alternative presence-only models (ecological niche factor analysis and maximum entropy) were developed based on 890 field plots of pathogen occurrence and several climatic, topographic, host vegetation and dispersal variables. The effects of all three possible types of occurrence data on model performance were evaluated with receiver operating characteristic (ROC) and omission/commission error rates. Results show that prediction of actual distribution was less accurate when we ignored true-absences and dispersal constraints. Presence-only models and models without dispersal information tended to over-predict the actual range of invasions. Models based on pseudo-absence data exhibited similar accuracies as presence-only models but produced spatially less feasible predictions. We suggest that true-absence data are a critical ingredient not only for accurate calibration but also for ecologically meaningful assessment of iSDMs that focus on predictions of actual distributions.     

基于GIS的中国API指数时空分布规律研究

以2009年1月至2012年12月的中国120个重点城市的API监测数据为基础,通过选取最优的空间插值方法获得各月及各季节的API空间分布图,并运用剖面线分析等空间数据分析方法研究其空间分布规律。结果表明,API在不同时段的变化较大;存在着明显的季节变化趋势,总体上冬季最高、春季次之、夏季最低,且夏秋季基本均分布在100以内;中国空气质量状况总体良好,API主要分布在51~100之间,但有较多地区分布在80~100间,空气环境质量仍不容乐观;API分布着以兰州和西宁为中心、陕西省西安市为中心及乌鲁木齐市和吐鲁番市为中心的几个空气质量较差的高值集聚的＂热点＂区域及海南省、广东省及福建省的沿海区域、西藏自治区等几个空气质量较好的低值集聚的＂冷点＂区域;在N-S、WE、NW-SE、NE-SW四个剖面线方向上相同季节的分布趋势基本一致,且同一方向上春夏秋冬四季的分布也大致相同,基本呈现＂低-高-低＂的分布趋势且北方地区明显高于南方地区。     

‘Effortless Perfection:’ Do Chinese cities manipulate air pollution data?

This paper uses unique data on daily air pollution concentrations over the period 2001–2010 to test for manipulation in self-reported data by Chinese cities. First, we employ a discontinuity test to detect evidence consistent with data manipulation. Then, we propose a panel matching approach to identify the conditions under which irregularities may occur. We find that about 50% of cities reported dubious PM₁₀ pollution levels that led to a discontinuity at the cut-off. Suspicious data reporting tends to occur on days when the anomaly is least detectable. Our findings indicate that the official daily air pollution data are not well behaved, which provides suggestive evidence of manipulation.     

A building-based data capture and data mining technique for air quality assessment

Recently, a building-based air quality model system which can predict air quality in front of individual buildings along both sides of a road has been developed. Using the Macau Peninsula as a case study, this paper shows the advantages of building-based model system in data capture and data mining. Compared with the traditional grid-based model systems with input/output spatial resolutions of 1–2 km, the building-based approach can extract the street configuration and traffic data building by building and therefore, can capture the complex spatial variation of traffic emission, urban geometry, and air pollution. The non-homogeneous distribution of air pollution in the Macau Peninsula was modeled in a high-spatial resolution of 319 receptors·km^-2. The spatial relationship among air quality, traffic flow, and urban geometry in the historic urban area is investigated. The study shows that the building-based approach may open an innovative methodology in data mining of urban spatial data for environmental assessment. The results are particularly useful to urban planners when they need to consider the influences of urban form on street environment.     

Ranked set sample design for environmental investigations

Ranked set sampling can provide an efficient basis for estimating parameters of environmental variables, particularly when sampling costs are intrinsically high. Various ranked set estimators are considered for the population mean and contrasted in terms of their efficiencies and useful- ness, with special concern for sample design considerations. Specifically, we consider the effects of the form of the underlying random variable, optimisation of efficiency and how to allocate sampling effort for best effect (e.g. one large sample or several smaller ones of the same total size). The various prospects are explored for two important positively skew random variables (lognormal and extreme value) and explicit results are given for these cases. Whilst it turns out that the best approach is to use the largest possible single sample and the optimal ranked set best linear estimator (ranked set BLUE), we find some interesting qualitatively different conclusions for the two skew distributions     

Study on particulate matter air pollution,source origin,and human health risk based of PM10 metal content in Volos City,Greece

Investigations on the fluctuation in PM₁₀ air pollution in Volos, a medium-sized industrialized port city in the Mediterranean, are presented for the 5-year period between 2009 and 2014. The levels detected have been examined in relation to legislatively set limits, sampling year, and day of the week. A PM₁₀ spring sampling campaign has been performed in 2014 and metals and other elements in the PM₁₀ mass have been quantified. Source origin has been attempted for the latter sampling campaign and human health risk has been assessed. Results show compliance with the mean annual value of 40 μg m^?3 of 2008/50/EC for the city; however, exceedances of the daily quality standard of 50 μg m^?3 were frequently recorded. Shifts in PM₁₀ concentrations and in contributing sources have been recorded; nevertheless, longer duration data series are needed for safe deductions. Element measurements have enabled source identification for early summer of 2014, with Earth's crust minerals and anthropogenic sources being the main factors. Cumulative non-carcinogenic risk may exceed the threshold value of 1. Possible involvement of sea salt aerosol and desert dust long-range transport has also been assessed. These results may furnish databases on PM pollution of Greek cities as well as other Mediterranean urban centers with similar characteristics.     

The effect of pollution on crime: Evidence from data on particulate matter and ozone

We estimate the effect of short-term air pollution exposure (PM_2.5 and ozone) on several categories of crime, with a particular emphasis on aggressive behavior. To identify this relationship, we combine detailed daily data on crime, air pollution, and weather for an eight-year period across the United States. Our primary identification strategy employs extremely high dimensional fixed effects and we perform a series of robustness checks to address confounding variation between temperature and air pollution. We find a robust positive effect of increased air pollution on violent crimes, and specifically assaults, but no relationship between increases in air pollution and property crimes. The effects are present in and out of the home, at levels well below Ambient Air Pollution Standards, and PM_2.5 effects are strongest at lower temperatures. The results suggest that a 10% reduction in daily PM_2.5 and ozone could save $1.4 billion in crime costs per year, a previously overlooked cost associated with pollution.