Nonparametric Threshold Model of Zero-Inflated Spatio-Temporal Data with Application to Shifts in Jellyfish Distribution

There is increasing scientific interest in studying the spatial distribution of species abundance in relation to environmental variability. Jellyfish in particular have received considerable attention in the literature and media due to regional population increases and abrupt changes in distribution. Jellyfish distribution and abundance data, like many biological datasets, are characterized by an excess of zero counts or nonstationary processes, which hampers their analyses by standard statistical methods. Here we further develop a recently proposed statistical framework, the constrained zero-inflated generalized additive model (COZIGAM), and apply it to a spatio-temporal dataset of jellyfish biomass in the Bering Sea. Our analyses indicate systematic spatial variation in the process that causes the zero inflation. Moreover, we show strong evidence of a range expansion of jellyfish from the southeastern to the northwestern portion of the survey area beginning in 1991. The proposed methodologies could be readily applied to ecological data in which zero inflation and spatio-temporal nonstationarity are suspected, such as data describing species distribution in relation to changes of climate-driven environmental variables. Some supplemental materials including an animation of jellyfish annual biomass and web appendices are available online.     

An Efficient Approach to Spatiotemporal Analysis and Modeling of Air Pollution Data

A statistically efficient approach is adopted for modeling spatial time-series of large data sets. The estimation of the main diagnostic tool such as the likelihood function in Gaussian spatiotemporal models is a cumbersome task when using extended spatial time-series such as air pollution. Here, using the Innovation Algorithm, we manage to compute it for many spatiotemporal specifications. These specifications refer to the spatial periodic-trend, the spatial autoregressive moving average, the spatial autoregressive integrated and fractionally integrated moving average Gaussian models. Our method is applied to daily pollutants over a large metropolitan area like Athens. In the applied part of our paper, we first diagnose temporal and spatial structures of data using non-likelihood based criteria, such as the empirical autocorrelation and covariance functions. Second, we use likelihood and non-likelihood based criteria to select a spatiotemporal model among various specifications. Finally, using kriging we regionalize the resulting parameter estimates of the best-fitted model in space at any unmonitored location in the Athens region. The results show that a specific autoregressive integrated moving average spatiotemporal model can optimally perform in within and out of spatial sample estimation. Supplemental materials for this article are available from the journal website.     

土壤有机质空间变异与农用地自然质量关系研究

该文以广州市花都区赤坭镇为试验区,根据典型农用地类型的土壤采样基于Cokriging方法进行土壤有机质精确插值与空间变异分析,并将结果与农用地分等成果进行相关分析。结果表明,以土壤有机质和农用地类型为协因子的球状模型Cokriging总体插值精度最高,试验区内土壤有机质分布格局是四周丘陵和水库区含量较低,巴江河两岸的平原地区、西南角的山前冲积平原含量较高,这也是灌溉水田密集的区域;试验区内土壤有机质含量菜地分布比较均匀、空间相关性很强,而水浇地分布差异较大、空间变异性较高;土壤有机质分布在南北、东北-西南两个方向相关性较强具有各向同性特征,在东南-西北、东西方向具有各向异性特征,受到随机因素的影响变异性较大。土壤有机质Cokriging插值结果与试验区内农用地自然质量等指数的相关性很显著,菜地与农用地自然质量等指数相关性最强,灌溉水田其次,旱地最差。     

Testing Backpropagation Neural Network Approach in Interpolating Missing Daily Precipitation

This study proposes a method, backpropagation (BP) neural network, for interpolating missing values in daily precipitation time series. Firstly, the BP neural network is adopted to interpolate missing daily rainfall data at three selected stations in Yantai, Shandong, China. Then, the temporal and spatial variations in precipitation extremes across Shandong are analyzed by utilizing the complete daily rainfall dataset derived from accurate propagation at 24 meteorological stations. The results show that the long-term trends in five selected extreme precipitation indices calculated from interpolated daily rainfall data are generally consistent with those from original nonmissing values. And the spatial patterns of trends in precipitation extremes also show better performance for BP neural network approach in interpolating missing daily rainfall gaps. Those suggest that this BP neural network algorithm can obtain a good fit in terms of space-time variability of regional precipitation extremes, in case that the correlation coefficients between the target stations with missing values and reference stations with complete daily rainfall dataset are relatively large. These findings could be crucial for investigating regional frequency of heavy rainfall and water resource management.     

Spatio-temporal modeling of fine particulate matter

Studies indicate that even short-term exposure to high concentrations of fine atmospheric particulate matter (PM_2.5) can lead to long-term health effects. In this article, we propose a random effects model for PM_2.5 concentrations. In particular, we anticipate urban/rural differences with regard to both mean levels and variability. Hence we introduce two random effects components, one for rural or background levels and the other as a supplement for urban areas. These are specified in the form of spatio-temporal processes. Weighting these processes through a population density surface results in nonstationarity in space. We analyze daily PM_2.5 concentrations in three midwestern U.S. states for the year 2001. A fully Bayesian model is implemented, using MCMC techniques, which enables full inference with regard to process unknowns as well as predictions in time and space.     

Critical Toxicity Level of Arsenic and Elemental Composition of Arsenic-Induced Chlorosis in Hydroponic Sorghum

In this study, NO₂ and SO₂ levels during a specific winter episode in the city of Istanbul were researched with regard to the respective impact of weather conditions. The city under study has the largest population and is one of the most polluted cities of Turkey. Furthermore, Istanbul is vulnerable to trans-boundary transport of air pollutants from Europe, because its location is on the eastern end of the Continent, where westerly winds prevail. However, there has neither been any comprehensive study nor any regulation to fix and solve the air quality problem in the city so far. A framework was prepared to model air quality using MM5 for meteorological modeling and CMAQ for transport and chemistry modeling. This study was supported by the tracer and the trajectory investigations over the area of interest. The model results underestimated the concentration of air pollutants over Istanbul as expected. But, in spite of the coarse emission inventory, the similarity of the time series between the model and observation values during the simulation period is very encouraging for future studies. That is to say, the tracer and the trajectory studies indicated that trans-boundary sources are responsible for the poor air quality of Istanbul under specific weather conditions. It must be pointed out, though, that a high-resolution national emission inventory is needed to get more accurate results.     

空气总悬浮颗粒物浓度的遥感信息模型研究

空气总悬浮颗粒物遥感信息模型是使用遥感信息模型的方法来模拟空气总悬浮颗粒物在空间上的分布。通过对空气总悬浮颗粒物来源和分布影响的因子分析,认为地表覆盖情况因子对空气总悬浮颗粒物来源影响最大,降雨强度和风速因子对空气总悬浮颗粒物分布影响最大,因此根据此三个因子建立了空气总悬浮颗粒物遥感信息模型。然后根据对厦门市高分辨遥感的分类数据和空气总悬浮颗粒物的分布数据得到了空气总悬浮颗粒物遥感信息模型的地理参数。通过对公式结果验证认为该模型较好的模拟了空气总悬浮颗粒的分布,为空气总悬浮颗粒物浓度的分布研究提出一种新思路。     

基于改进HRNet的遥感影像冬小麦语义分割方法

冬小麦在影像中呈现田块碎小且分布零散等空间特征,同时影像包含的复杂地物对冬小麦识别造成干扰,易出现识别精度低且边界分割模糊等问题。为及时准确获取大范围冬小麦空间分布信息,该研究以高分二号卫星影像作为数据源,提出一种CAHRNet(change attention high-resolution Net)语义分割模型。采用HRNet(high-resolution Net)替换ResNet作为模型的主干网络,网络的并行交互方式易获取高分辨率的特征信息;联合OCR(object-contextual representations)模块聚合上下文信息,以增强像素点与目标对象区域的关联性;3）引入坐标注意力（coordinate attention）机制,使网络模型充分利用有效的空间位置信息,以保留分割区域的边缘细节,提高对分布零散、形状多变的冬小麦田块的特征提取能力。试验结果表明,在自制的高分辨率遥感数据集上,CAHRNet模型的平均交并比（mean intersection over union,MIoU）和像素准确率（pixel accuracy, PA）分别达到81.72%和97.0...     

基于时空克里格的土壤重金属时空建模与预测

土壤重金属或其他生态环境属性在时间和空间上均存在连续性和变异性,而目前的研究忽略了它们在时间维的变异。为了在预测时使用多时期采样数据,该文提出使用时空克里格方法对土壤重金属进行时空建模及预测,着重介绍了经验半方差值的计算、理论变异模型的形式及参数拟合、时空克里格估值算法、估值方差和精度随邻近点数量的变化及时空克里格制图。以武汉市青山区土壤重金属为例介绍了时空克里格建模及预测的流程。结果表明,时空克里格方法能够很好地描述土壤重金属在空间、时间和时空上3个部分的变异特征,能够利用其他时期的数据对预测时间点的属性进行插值,而多时期的属性空间分布图能够很好地反映土壤重金属的分布变化规律。该研究可为资源环境生态时空建模及预测研究提供参考。     

Measurement and Modelling of Particulate Pollution over Kashmir Himalaya,India

In major cities, air quality is of significant concern because of its negative effect on the health of the region’s living conditions, climate, and economy. Recent studies show the significance of the data on microlevel pollution which includes severe air pollutants and their impacts on human. Conventional methods of measuring air quality need skilled personnel for accurate data measurement that are based on stationary and limited measuring station networks. However, it is costly to seize the spatio-temporal variability and to recognize pollution hotspots that are necessary to develop real-time exposure control strategies. Due to the restricted accessibility of information and the non-scalability of standard techniques for air pollution monitoring, a real-time system with both higher spatial and temporal resolution is crucial. In recent times, unmanned aerial vehicles (UAVs) mounted with various sensors have been implemented for on-site air quality surveillance as they can offer new methods and research possibilities in air pollution and emission tracking, as well as in the study of environmental developments. An extensive literature review has been conducted, and it was observed that there are types of UAVs and types of sensors that are used for air quality monitoring for the parameters like CO, SO₂, NO₂, O₃, PM_2.5, PM_1.0, and black carbon. Low-cost wireless sensors have been using for monitoring purpose in the past studies, and when results obtained are validated with the stationary monitoring instruments, the coefficient of correlation (R²) is found to be varied from 0.3 to 0.9. The difficulties, however, are not just technical, but at present time, policies and laws, which vary from country to country, symbolize the major challenge to the extensive use of UAVs in air quality/monitoring studies.

     

Regional‐scale characterization of the geomorphic control of the spatial distribution of soil organic carbon in cropland

The heterogeneity of the spatial distribution of soil organic carbon (SOC) at the landscape scale is generally not considered in regional or national SOC dynamics models. In cropland this heterogeneity is controlled largely by topography, which influences the distribution of water, energy and sediments, and thus the SOC dynamics. Sediment redistribution rates have increased strongly since the mechanization of agriculture. The over‐simplification of landscape processes in regional models of C dynamics may add to the uncertainty in C balances. Therefore, a better characterization of the importance of landscape‐scale effects on the SOC distribution throughout a region is needed. This study characterized the relative importance of geomorphology in the SOC horizontal and vertical variability across croplands in the Belgian loess belt region. A large legacy dataset of soil horizons was exploited together with 147 recently sampled profiles. Mean SOC depth profiles for different soil types were compared. Various topographic attributes were computed from a digital elevation model, and their influence on SOC was quantified through simple linear models. Finally, SOC content was mapped at three depth layers through multiple linear models, and results were cross‐validated. The legacy dataset allowed identification of significant differences in the mean SOC profile according to texture, drainage or profile development classes. A clear relationship between SOC content and topographic attributes was demonstrated, but only for the recently sampled profiles. This may be explained by a substantial error in the location of the profiles of the legacy dataset. This study thus shows evidence that the major control of the vertical distribution of SOC is related to topography in a region where observed heterogeneities for other commonly involved factors are limited. However, the large amount of unexplained variability still limits the usefulness of the spatial prediction of SOC content, and suggests the importance of additional influencing factors.     

利用数字土壤制图技术评价桉树林土壤肥力

精细的土壤肥力空间信息有助于森林质量精准管理.本研究以广西高峰桉树林场为研究区,利用数字土壤制图技术对研究区主要肥力因子空间分布进行数字制图,在此基础上,应用灰色关联度模型对桉树人工林土壤肥力进行了综合评价.结果表明:研究林场的土壤肥力质量较好,土壤肥力水平2级和3级林地面积占总面积的68.6％,1级和4级林地面积占2...     

玄武湖菹草种群空间格局分析及其环境效应

大型水生植物的分布格局与空间尺度有着密切关系,传统的分析方法只能分析一种尺度下的格局。引入点格局分析,以种群空间分布坐标点图为基础,分析各种尺度下的种群格局。运用点格局分析对玄武湖菹草种群的空间分布格局进行研究,并结合N、P等水质指标探讨其环境效应。结果表明,玄武湖菹草种群分布集群特征明显,且在尺度232-344m内聚集最为明显。从水质监测结果看,菹草种群有利于改善水体环境,无种群区的TN、TP和NO3-N浓度明显高于有种群区;溶解氧（DO）则表现为有种群区高于无种群区。菹草在空间分布上的明显规律性,会进一步导致水质参数的空间异质性。     

Seasonal Abundance of Particle-Phase Organic Pollutants in an Urban/Industrial Atmosphere

Polycyclic aromatic hydrocarbons (PAHs); their derivatives nitro, and methyl-PAHs; n-alkanes; and organic acids were investigated in the aerosol samples collected during two field campaigns conducted at three sampling stations in an industrialized city in southern Italy. The main sources affecting the atmosphere and its toxicity were investigated by means of the diagnostic ratios of: specific particulate-phase PAHs, marker compounds among nitro-PAHs, alkanes, and acids, the dominant wind direction, daily and seasonal abundance of carcinogenic organic substances. The potential importance of the non-regulated pollutants to assess the air quality was confirmed; in fact the carcinogenic organic compounds showed to have scarce correlation with particulate matter (PM) concentration. An exceptionally high variability of toxic compounds at a daily scale was due to meteorological condition causing periods of extremely high pollution levels.     

Effect of the landscape context on the density and persistence of a predator population in a protected area subject to environmental variability

Protected areas are the most important tool for the conservation of biodiversity. However, many species are area-demanding and their populations seldom meet their space requirements in reserves. In this context, the unprotected exterior becomes an important part of their home range, and variations in habitat quality of the surroundings of a protected area might affect the dynamics of populations. Using a spatially explicit simulation model, we studied the effect of the surrounding landscape of a protected area on the density and persistence of a predator population inhabiting inside the reserve in different conditions of environmental variability. We simulated individuals of a predator population, their herbivorous prey and a vegetative substrate in a landscape comprised of a square protected area and different types of habitat quality outside the reserve. We studied the combination of three substrate qualities of protected area (inside) with three of the landscape context and three levels of variability of productivity. Our results showed that there were strong effects of both the relative quality of the surrounding landscape and of the environmental variability on the density and persistence of the simulated population inside the protected area. More importantly, we showed that complex patterns emerge when spatial heterogeneity and temporal variability interact with population dynamics. Specifically, under high environmental variability, when the protected area had a high habitat quality, the highest population persistence was not attained when the exterior was also of high quality, but when the surroundings had an intermediate quality. The latter result suggests that, under the mentioned conditions, small enhancements in the quality of the matrix may have, for some species, better effects on increasing persistence in small reserves than large and costly enhancements.     

Soil moisture variability along transects over a well-developed gully in the Loess Plateau,China

Knowledge of soil moisture distributions in gullies, which are highly variable spatially and temporally, is important for both restoring vegetation and controlling erosion in them, but little attention has been paid to this spatio-temporal variability to date. Therefore, we examined soil moisture profiles and their variability along three transects traversing sidewalls of a well-developed gully with steep slopes in a hilly area of the Chinese Loess Plateau. We took intensive measurements at 20-cm intervals from 0 to 160 cm depth, using a portable time domain reflectometer, from September 3 to October 20 2009 and from April 5 to July 20 2010. The results indicate that the mean, standard deviation and coefficient of variation of moisture content vary with time, their responses to precipitation vary at different depths, and moisture content is most variable when mean values are moderate (15–20%). Revised fitting functions developed and introduced by Famiglietti et al. (2008) captured with confidence the relationship between spatial variability (SD and CV) and spatial mean of moisture content (RMSE ranging from 0.0015 to 0.0293). Soil moisture clearly varied along the transects, the vertical distribution of soil moisture differed in different seasons, and correlation analysis showed that soil texture influenced the variability of surface soil moisture more strongly than terrain attributes (except during distinct rainfall events, when this pattern reversed). The results presented here should improve understanding of spatio-temporal variations in soil moisture profiles in well-developed gullies in the Loess Plateau, and potentially elsewhere.     

基于温湿度与遥感植被指数的冬小麦赤霉病估测

为明晰江淮区域大田冬小麦赤霉病的发生特征,建立冬小麦赤霉病遥感估测模型,该文分析了冬小麦赤霉病病情指数与气候因素(不同时间尺度日均气温和日均空气相对湿度)、生长参数(生物量、叶面积指数和叶片叶绿素含量)和光谱信息(NDVI、RVI和DVI)之间的互作关系。结果表明:1)不同时间尺度日均气温之间存在较好相关性,5日均气温与冬小麦赤霉病病情指数间的相关系数最大为0.77。与日均气温相类似,不同时间尺度日均空气相对湿度之间也存在不同程度的相关性,5日均空气相对湿度与赤霉病病情指数间的相关性最大,其相关性高于5日均气温。2)冬小麦生物量、叶面积指数和叶片叶绿素含量与赤霉病病情指数之间均呈线性正相关关系,且均达到显著水平,说明冬小麦群体密度大、郁闭程度高以及长势过旺是赤霉病易发的主要农学诱因。3)遥感植被指数NDVI(normalized difference vegetation index)、RVI(ratio vegetation index)和DVI(difference vegetation index)分别与冬小麦叶面积指数、生物量和叶片叶绿素含量之间有较好相关性,可以利用NDVI、RVI和DVI分别替换叶面积指数、生物量和叶片叶绿素含量参与建模。4)综合5日均气温、5日均空气相对湿度、NDVI、RVI和DVI 5个敏感因子,构建基于温湿度与遥感植被指数的冬小麦赤霉病病情指数估测模型,模型的估测值与实测值较为一致,RMSE为5.3%,相对误差为9.54%。说明本研究所建立的估测模型可以实现对冬小麦始花期赤霉病的有效估测,该研究可为江淮区域冬小麦生产中防病减灾的信息获取提供方法参考。     

Probabilistic Assessment of Regional Mercury Exposure

A methodology for the probabilistic assessment of regional exposure to mercury is presented. The methodology is based on a multimedia model of the atmospheric fate and transport of mercury over a continental scale, and the aquatic simulation of the transformation and bioaccumulation of mercury in lakes in the region of interest. A probabilistic approach allows one to take into account the spatial variability of environmental characteristics and the variability of dose-related variables (e.g., fish consumption, body weight) among individuals. The methodology is demonstrated for total mercury deposition in the Great Lakes region of the U.S.     

辽河流域降雨侵蚀力的时空变化分析

降雨侵蚀力是反映流域降雨侵蚀能力的综合指标之一。根据辽河流域10个气象站的日降雨量资料,利用日降雨侵蚀力模型估算辽河流域的降雨侵蚀力。结果表明：辽河流域降雨侵蚀力的空间变异与降雨量的空间分布趋势基本一致,由东南向西北递减,变化于1000—3800MJ·mm／（hm^2·h·a）之间;降雨侵蚀力年内集中度高,6—8月3个月约占全年的80％;降雨侵蚀力年际变化大,年际变率Cv在0．367—0．649之间,采用时序系列的Mann—Kendall检验表明,降雨侵蚀力并无显著变化趋势;特别是在流域水土流失严重的西辽河地区,年降雨侵蚀力较小,但年内集中程度大,年际变化更突出。     

Modelling Some Heavy Metals Air Concentration in Europe

Metals, as well as other air toxic pollutants, can be responsible for a range of human health effects via inhalation or ingestion. European normatives regulate lead, arsenic, cadmium, mercury and nickel ambient air levels in order to prevent potential damage to human health and ecosystems; annual target levels of 500, 6, 5 and 20?ng/m³ for Pb, As, Cd and Ni are set for these pollutants in directives 2008/50/CE and 2004/107/CE. Air quality models constitute a powerful tool to understand tropospheric dynamic and to assign concentration values to areas where no measurement is available. However, not many models include heavy metals in their code, and mainly results for Pb, Cd and Hg have been published. In this paper, we present preliminary results on modelling Pb, Cd, As, Ni, Cu, Zn, Cr and Se air background concentration in Europe using the CHIMERE model, at a 0.2° resolution and the evaluation of the model performance in order to see its capability to reproduce observed levels. This evaluation was performed by comparing simulated values with observations at the EMEP monitoring sites, as only values at background sites can be captured at the 0.2° model resolution. Important uncertainties mainly related to emissions should be solved in order to obtain an improvement of model performance; more recent annual totals, information on snap activities for each metal, higher spatial resolution and a better knowledge of the temporal emission behaviour is necessary to adequately model these air pollutants. Also a better treatment of these particles considering more realistic metal size distribution, more refined deposition processes or some chemical processes regarding Se could result in better model results. A larger number of stations and a better temporal coverage of observations are also important to carry out a better statistical analysis of model performance.