Analysis of nonlinear associations between prenatal methylmercury exposure from fish consumption and neurodevelopmental outcomes in the Seychelles Main Cohort at 17 years

The Seychelles Child Development Study has been examining the relationship between prenatal methylmercury (MeHg) exposure from consuming fish during pregnancy and child development. This study re-analyzes seven outcomes in the 17 year Main Cohort data to determine if there are nonlinear or non-homogeneous (subgroup) associations that were not identified in the linear analysis. We adopted two statistical approaches. First, we carried out an additive nonlinear analysis assuming homogeneous prenatal MeHg-outcome relationships to explore overall associations. Second, we applied the regression tree to the Woodcock–Johnson Calculation subtest (it was significantly associated in earlier analyses) and identified 4 clusters based on covariates. Then we used additive models to assess the prenatal MeHg association in each of the four clusters for all seven outcomes. This approach assumes nonlinear associations in each cluster and non-homogeneous associations between clusters. The additive nonlinear analysis yielded prenatal MeHg curves similar to the linear analysis. For the regression tree analysis, the curves relating prenatal MeHg to outcomes between the 4 clusters differed and some crossed at higher prenatal MeHg levels, suggesting non-homogeneity in the upper range of exposure. Additionally, some of the curves suggested a possible non-linear relationship within the range of exposure we studied. This non-linear analysis supports the findings from the linear analysis. It shows little evidence to support an adverse association of prenatal MeHg exposure through maternal consumption of fish contaminated with natural background levels. However, the tree analysis suggests that the prenatal exposure/outcome relationship may not be homogeneous across all individuals and that some subpopulations may have an adverse association in the upper range of the exposures studied. More robust data in the higher levels of exposure in this cohort are needed to confirm this finding.     

A note on variational multiscale methods for high-contrast heterogeneous porous media flows with rough source terms

In this short note, we discuss variational multiscale methods for solving porous media flows in high-contrast heterogeneous media with rough source terms. Our objective is to separate, as much as possible, subgrid effects induced by the media properties from those due to heterogeneous source terms. For this reason, enriched coarse spaces designed for high-contrast multiscale problems are used to represent the effects of heterogeneities of the media. Furthermore, rough source terms are captured via auxiliary correction equations that appear in the formulation of variational multiscale methods [23]. These auxiliary equations are localized and one can use additive or multiplicative constructions for the subgrid corrections as discussed in the current paper. Our preliminary numerical results show that one can capture the effects due to both spatial heterogeneities in the coefficients (such as permeability field) and source terms (e.g., due to singular well terms) in one iteration. We test the cases for both smooth source terms and rough source terms and show that with the multiplicative correction, the numerical approximations are more accurate compared to the additive correction.     

Inferring catchment flow path responses using a data-driven model: an exploratory study based on a generalized additive model

Identifying physical catchment processes from streamflow data, such as quick- and slow-flow paths, remains challenging. This study is designed to explore whether a flexible nonparametric regression model (generalized additive model, GAM) can be used to infer different flow paths. This assumes that the data relationship in data-driven models is also a reflection of catchment physical processes. The GAM, using time-lagged flow covariates, was fitted to synthetic rainfall–runoff data simulated using simple linear reservoirs. Partial plots of the time-lagged covariates show that the model could differentiate simple and more complex flow paths in simulated synthetic data with short and long memory systems and varying between dry and wet climates. Further analysis of data from real catchments showed that the model could differentiate catchments dominated by slow flow and by quick flow. Therefore, this study indicates that GAM can be used to identify catchment storages and delay processes from streamflow data.     

A framework to estimate sediment loads using distributions with covariates: Beaurivage River watershed (Québec,Canada)

A framework to estimate sediment loads based on the statistical distribution of sediment concentrations and various functional forms relating distribution characteristics (e.g. mean and variance) to covariates is developed. The covariates are used as surrogates to represent the main processes involved in sediment generation and transport. Statistical models of increasing complexity are built and compared to assess their relative performance using available sediment concentration and covariate data. Application to the Beaurivage River watershed (Québec, Canada) is conducted using data for the 1989–2004 period. The covariates considered in this application are streamflow and calendar day. A comparison of different statistical models shows that, in this case, the log‐normal distribution with a mean value depending on streamflow (power law with an additive term) and calendar day (sinusoidal), a constant coefficient of variation for streamflow dependence and a constant standard deviation for calendar day dependence provide the best result. Model parameters are estimated using the maximum likelihood estimation technique. The selected model is then used to estimate the distribution of annual sediment loads for the Beaurivage River watershed for a selected period. A bootstrap parametric method is implemented to account for uncertainties in parameter values and to build the distributions of annual loads. Comparison of model results with estimates obtained using the empirical ratio estimator shows that the latter were rarely within the 0·1–0·9 quantile interval of the distributions obtained with the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd.     

Correction of TRMM 3B43 monthly precipitation data over the mountainous areas of Peru during the period 1998–2007

In an attempt to estimate the spatial and temporal behaviour of rainfall over the mountainous areas of the Peruvian Andes, a new in situ monthly rainfall dataset has been collected (1998–2007) and compared with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data for regions located above 3000 m. The reliability of the TRMM 3B43 data varies depending on the root mean squared error ratio (%RMSE) and correlation coefficient. Because of the discrepancy between the two datasets, the use of additive and multiplicative correction models is proposed for the TRMM 3B43 data. In the Peruvian mountain ranges, these correction models better approximate TRMM rainfall monthly values, as already verified for annual values. Copyright © 2011 John Wiley & Sons, Ltd.     

THE COMPLEX RESISTIVITY SPECTRA OF MODELS CONSISTING OF TWO POLARIZABLE MEDIA OF DIFFERENT INTRINSIC PROPERTIES*

Theoretical model study shows that when an earth model is composed of two (Cole-Cole) polarizable media, its normalized complex resistivity spectrum is approximately a multiplicative combination of the contributions of the two media. This also applies on inversion, but the two dispersions thus obtained are apparent rather than intrinsic dispersions. In models consisting of two media, either a multiplicative or an additive combination of Cole-Cole functions fits the complex apparent resistivity spectrum. On inversion each combination gives similar parameters except for the apparent chargeability of the component with the shorter time constant. However, this can be compensated simply. In this sense the two representations are almost equivalent. We show that the apparent spectrum due to a finite polarizable body is actually a true Cole-Cole dispersion, as is usually assumed in practice. The behavior of a complex apparent resistivity spectrum and its corresponding apparent Cole-Cole dispersion parameters is influenced by the variation of the dilution factor with frequency. Hence when estimating intrinsic parameters from the nomogram, based on constant (frequency-independent) dilution factors, particular care is required to correctly relate apparent parameters to intrinsic parameters.     

Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management

There is increasing demand for models that can accurately predict river temperature at the large spatial scales appropriate to river management. This paper combined summer water temperature data from a strategically designed, quality controlled network of 25 sites, with recently developed flexible spatial regression models, to understand and predict river temperature across a 3,000 km² river catchment. Minimum, mean and maximum temperatures were modelled as a function of nine potential landscape covariates that represented proxies for heat and water exchange processes. Generalised additive models were used to allow for flexible responses. Spatial structure in the river network data (local spatial variation) was accounted for by including river network smoothers. Minimum and mean temperatures decreased with increasing elevation, riparian woodland and channel gradient. Maximum temperatures increased with channel width. There was greater between‐river and between‐reach variability in all temperature metrics in lower‐order rivers indicating that increased monitoring effort should be focussed at these smaller scales. The combination of strategic network design and recently developed spatial statistical approaches employed in this study have not been used in previous studies of river temperature. The resulting catchment scale temperature models provide a valuable quantitative tool for understanding and predicting river temperature variability at the catchment scales relevant to land use planning and fisheries management and provide a template for future studies.     

Hillslope response to tectonic forcing in threshold landscapes

Hillslopes are thought to poorly record tectonic signals in threshold landscapes. Numerous previous studies of steep landscapes suggest that large changes in long‐term erosion rate lead to little change in mean hillslope angle, measured at coarse resolution. New LiDAR‐derived topography data enables a finer examination of threshold hillslopes. Here we quantify hillslope response to tectonic forcing in a threshold landscape. To do so, we use an extensive cosmogenic beryllium‐10 (¹⁰Be)‐based dataset of catchment‐averaged erosion rates combined with a 500 km² LiDAR‐derived 1 m digital elevation model to exploit a gradient of tectonic forcing and topographic relief in the San Gabriel Mountains, California. We also calibrate a new method of quantifying rock exposure from LiDAR‐derived slope measurements using high‐resolution panoramic photographs. Two distinct trends in hillslope behavior emerge: below catchment‐mean slopes of 30°, modal slopes increase with mean slopes, slope distribution skewness decreases with increasing mean slope, and bedrock exposure is limited; above mean slopes of 30°, our rock exposure index increases strongly with mean slope, and the prevalence of angle‐of‐repose debris wedges keeps modal slopes near 37°, resulting in a positive relationship between slope distribution skewness and mean slope. We find that both mean slopes and rock exposure increase with erosion rate up to 1 mm/a, in contrast to previous work based on coarser topographic data. We also find that as erosion rates increase, the extent of the fluvial network decreases, while colluvial channels extend downstream, keeping the total drainage density similar across the range. Our results reveal important textural details lost in 10 or 30 m resolution digital elevation models of steep landscapes, and highlight the need for process‐based studies of threshold hillslopes and colluvial channels. Copyright © 2012 John Wiley & Sons, Ltd.     

Use of Remotely-Derived Bathymetry for Modelling Biomass in Marine Environments

The paper presents results on the influence of geometric attributes of satellite-derived raster bathymetric data, namely the General Bathymetric Charts of the Oceans, on spatial statistical modelling of marine biomass. In the initial experiment, both the resolution and projection of the raster dataset are taken into account. It was found that, independently of the equal-area projection chosen for the analysis, the calculated areas are very similar, and the differences between them are insignificant. Likewise, any variation in the raster resolution did not change the computed area. Although the differences were shown to be insignificant, for the subsequent analysis we selected the cylindrical equal area projection, as it implies rectangular spatial extent, along with the automatically derived resolution. Then, in the second experiment, we focused on demersal fish biomass data acquired from trawl samples taken from the western parts of ICES Sub-area VII, near the sea floor. The aforementioned investigation into processing bathymetric data allowed us to build various statistical models that account for a relationship between biomass, sea floor topography and geographic location. We fitted a set of generalised additive models and generalised additive mixed models to combinations of trawl data of the roundnose grenadier (Coryphaenoides rupestris) and bathymetry. Using standard statistical techniques—such as analysis of variance, Akaike information criterion, root mean squared error, mean absolute error and cross-validation—we compared the performance of the models and found that depth and latitude may serve as statistically significant explanatory variables for biomass of roundnose grenadier in the study area. However, the results should be interpreted with caution as sampling locations may have an impact on the biomass–depth relationship.     

Copula-based geostatistical modeling of continuous and discrete data including covariates

It is common in geostatistics to use the variogram to describe the spatial dependence structure and to use kriging as the spatial prediction methodology. Both methods are sensitive to outlying observations and are strongly influenced by the marginal distribution of the underlying random field. Hence, they lead to unreliable results when applied to extreme value or multimodal data. As an alternative to traditional spatial modeling and interpolation we consider the use of copula functions. This paper extends existing copula-based geostatistical models. We show how location dependent covariates e.g. a spatial trend can be accounted for in spatial copula models. Furthermore, we introduce geostatistical copula-based models that are able to deal with random fields having discrete marginal distributions. We propose three different copula-based spatial interpolation methods. By exploiting the relationship between bivariate copulas and indicator covariances, we present indicator kriging and disjunctive kriging. As a second method we present simple kriging of the rank-transformed data. The third method is a plug-in prediction and generalizes the frequently applied trans-Gaussian kriging. Finally, we report on the results obtained for the so-called Helicopter data set which contains extreme radioactivity measurements.     

Probabilistic-fuzzy health risk modeling

Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA). Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function. The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods.     

Error analysis for the evaluation of model performance: rainfall–runoff event time series data

This paper provides a procedure for evaluating model performance where model predictions and observations are given as time series data. The procedure focuses on the analysis of error time series by graphing them, summarizing them, and predicting their variability through available information (recalibration). We analysed two rainfall–runoff events from the R‐5 data set, and evaluated 12 distinct model simulation scenarios for these events, of which 10 were conducted with the quasi‐physically‐based rainfall–runoff model (QPBRRM) and two with the integrated hydrology model (InHM). The QPBRRM simulation scenarios differ in their representation of saturated hydraulic conductivity. Two InHM simulation scenarios differ with respect to the inclusion of the roads at R‐5. The two models, QPBRRM and InHM, differ strongly in the complexity and number of processes included. For all model simulations we found that errors could be predicted fairly well to very well, based on model output, or based on smooth functions of lagged rainfall data. The errors remaining after recalibration are much more alike in terms of variability than those without recalibration. In this paper, recalibration is not meant to fix models, but merely as a diagnostic tool that exhibits the magnitude and direction of model errors and indicates whether these model errors are related to model inputs such as rainfall. Copyright © 2004 John Wiley & Sons, Ltd.     

Diatoms from the genus Achnanthidium in flowing waters of the Appalachian Mountains (North America): Ecology, distribution and taxonomic notes

Diatoms from the genus Achnanthidium are abundant in rivers, streams, and springs of the Appalachian Mountains. They inhabit clean and polluted waters, including those affected by acid mine drainage. The identification of Achnanthidium taxa is difficult due to their small cell size and insufficient information in the diatom floras. We studied the taxonomy and ecology of Achnanthidium in Appalachian rivers by analyzing a data set of benthic diatom samples and corresponding water chemistry data collected during several water-quality surveys from 181 sampling sites. Ten species were identified using scanning electron and light microscopy: A. alpestre (Lowe & Kociolek) Lowe & Kociolek, A. atomus (Hustedt) Monnier, Lange-Bertalot, & Ector, A. deflexum (Reimer) Kingston, A. duthii (Sreenivasa) Edlund, A. eutrophilum (Lange-Bertalot) Lange-Bertalot, A. cf. gracillimum (Meister) Lange-Bertalot, A. cf. latecephalum Kobayasi, A. minutissimum (Kützing) Czarnecki (sensu lato), A. reimeri (Camburn) comb. nov., and A. rivulare Potapova & Ponader. The distribution of common taxa in relation to water chemistry was studied by fitting non-parametric regression models (generalized additive models, GAM, and non-parametric multiplicative regression models, NPMR) to species relative abundances. Studied Achnanthidium species differed considerably in their responses to water chemistry. These results suggest that species-level identifications will lead to more accurate bioassessments.     

Apparent/spurious multifractality of data sampled from fractional Brownian/Lévy motions

Many earth and environmental variables appear to be self‐affine (monofractal) or multifractal with spatial (or temporal) increments having exceedance probability tails that decay as powers of − α where 1 < α ≤ 2. The literature considers self‐affine and multifractal modes of scaling to be fundamentally different, the first arising from additive and the second from multiplicative random fields or processes. We demonstrate theoretically that data having finite support, sampled across a finite domain from one or several realizations of an additive Gaussian field constituting fractional Brownian motion (fBm) characterized by α = 2, give rise to positive square (or absolute) increments which behave as if the field was multifractal when in fact it is monofractal. Sampling such data from additive fractional Lévy motions (fLm) with 1 < α < 2 causes them to exhibit spurious multifractality. Deviations from apparent multifractal behaviour at small and large lags are due to nonzero data support and finite domain size, unrelated to noise or undersampling (the causes cited for such deviations in the literature). Our analysis is based on a formal decomposition of anisotropic fLm (fBm when α = 2) into a continuous hierarchy of statistically independent and homogeneous random fields, or modes, which captures the above behaviour in terms of only E + 3 parameters where E is Euclidean dimension. Although the decomposition is consistent with a hydrologic rationale proposed by Neuman (2003), its mathematical validity is independent of such a rationale. Our results suggest that it may be worth checking how closely would variables considered in the literature to be multifractal (e.g. experimental and simulated turbulent velocities, some simulated porous flow velocities, landscape elevations, rain intensities, river network area and width functions, river flow series, soil water storage and physical properties) fit the simpler monofractal model considered in this paper (such an effort would require paying close attention to the support and sampling window scales of the data). Parsimony would suggest associating variables found to fit both models equally well with the latter. Copyright © 2010 John Wiley & Sons, Ltd.     

基于精确震源函数的解调包络多尺度全波形反演

本文提出解调包络方法来重构地震记录中缺失的低频信号,同时该方法能够降低全波形反演的非线性程度;提出伴随状态震源函数反演方法来得到精确的震源函数,并推导了梯度计算公式;解调包络方法结合低通滤波技术,实现了从低频到高频的多尺度反演策略,有效缓解了全波形反演的周波跳跃问题.数值算例证明了解调包络、伴随状态震源函数反演方法和低通滤波多尺度反演策略的可行性及优越性.震源函数反演精度测试结果表明:即使观测记录在缺失低频信息的情况下,也能反演得到精确的震源函数.缺失低频测试和抗噪能力测试结果表明:即使地震数据中缺失9Hz以下的低频信号或者信噪比极低的情况下,利用反演得到的精确震源函数进行解调包络多尺度全波形反演,同样可以得到高精度的全波形反演结果.与Hilbert包络全波形反演对比结果表明:解调包络在重构低频和降低伴随震源主频方面具有一定优势.     

Capturing the time-dependence in the precipitation process for weather risk assessment

This work investigates the seasonal, long-term and time-dependence properties of the precipitation process using daily precipitation amount records in Calgary and develops occurrence–amount models which capture the complex properties of the process. Data show that: (a) the probability of precipitation occurrence not only depends on the occurrence of the previous days, but also depends on the amount values. Moreover this dependence varies with season. (b) The expected amount and its volatility depend on the occurrence as well as the amount on the previous days—these dependencies reveal seasonal patterns. (c) There is a strong long-term dependence in Calgary’s data. The proposed models in this work satisfy these properties. A large set of covariates is built to capture the complexity of various properties of the precipitation process. A grouped sequential model selection approach is used to pick the appropriate covariates which works by assigning the predictors to various groups and sequentially exploring them. This framework is assessed by comparing the simulations from the models to the observed data. This confirms a satisfactory agreement.     

Partitioned multiobjective risk modeling of carcinogenic compounds in groundwater

Quantifying human cancer risk arising from exposure to contaminated groundwater is complicated by the many hydrogeological, environmental, and toxicological uncertainties involved. In this study, we used Monte Carlo simulation to estimate cancer risk associated with tetrachloroethene (PCE) dissolved in groundwater by linking three separate models for: (1) reactive contaminant transport; (2) human exposure pathways; and (3) the PCE cancer potency factor. The hydrogeologic model incorporates an analytical solution for a one-dimensional advective–dispersive–reactive transport equation to determine the PCE concentration in a water supply well located at a fixed distance from a continuous source. The pathway model incorporates PCE exposure through ingestion, inhalation, and dermal contact. The toxicological model combines epidemiological data from eight rodent bioassays of PCE exposure in the form of a composite cumulative distribution frequency curve for the human PCE cancer potency factor. We assessed the relative importance of individual model variables through their correlation with expected cancer risk calculated in an ensemble of Monte Carlo simulations with 20,000 trials. For the scenarios evaluated, three factors were most highly correlated with cancer risk: (1) the microbiological decay constant for PCE in groundwater, (2) the linear groundwater pore velocity, and (3) the cancer potency factor. We then extended our analysis beyond conventional expected value risk assessment using the partitioned multiobjective risk method (PMRM) to generate expected-value functions conditional to a 1 in 100,000 increased cancer risk threshold. This approach accounts for low probability/high impact outcomes separately from the conventional unconditional expected values. Thus, information on potential worst-case outcomes can be quantified for decision makers. Using PMRM, we evaluated the cost-benefit relationship of implementing several postulated risk management alternatives intended to mitigate the expected and conditional cancer risk. Our results emphasize the importance of hydrogeologic models in risk assessment, but also illustrate the importance of integrating environmental and toxicological uncertainty. When coupled with the PMRM, models integrating uncertainty in transport, exposure, and potency constitute an effective risk assessment tool for use within a risk-based corrective action (RBCA) framework.     

基于小波的地闪首次回击辐射场的多重分形分析

利用2002年夏季青海野外观测慢天线电场变化仪资料,应用小波变换模极大方法对地闪首次回击辐射场的检测及多重分形谱特征进行了分析.发现小波分解在小尺度上的时间变异系数在回击主脉冲峰处（回击点）呈现明显的尖峰,据此可对回击点进行快速可靠的检测;地闪回击辐射场多重分形谱可用推广的多重分形二项倍增串级公式比较精确地拟合,最小标度指数及谱宽度平均分别为-011和15,是重要的回击特征参数,而小波及多重分形应是闪电信号处理的重要工具.     

基于“剪切+张裂”一般位错模型频率域求解微震震源机制

研究微地震的震源机制,获得压裂区域的破裂方向、尺度和应力状态等信息,在非常规油气开采过程中具有重要意义.对于微震,通常采用剪切位错或者矩张量模型对震源进行描述.本文从其实际发震机制出发,使用了"剪切+张裂"的一般位错点源模型,并基于此模型发展了一种利用全波形信息,通过波形振幅谱相关和初至约束,在频率域求解微震震源机制的方法.该方法适用于地面和井中观测,能够在得到常规震源参数（断层走向、倾角和滑动角）的同时给出裂缝断层剪切和张裂错动的距离信息,更直观体现破裂程度.理论数值测试证明方法有效、可行,在未滤波的情况下,实际数据的波形拟和结果仍较为一致,同时还发现错动距离与应力降等常规破裂参数并不严格相关,说明剪切、张裂错距可作为独立的新参数来定量评估水压致裂效果,指导工程开发进行.