Assessment of alternative adsorption models and global sensitivity analysis to characterize hexavalent chromium loss from soil to surface runoff

We investigate our ability to assess transfer of hexavalent chromium, Cr(VI), from the soil to surface runoff by considering the effect of coupling diverse adsorption models with a two‐layer solute transfer model. Our analyses are grounded on a set of two experiments associated with soils characterized by diverse particle size distributions. Our study is motivated by the observation that Cr(VI) is receiving much attention for the assessment of environmental risks due to its high solubility, mobility, and toxicological significance. Adsorption of Cr(VI) is considered to be at equilibrium in the mixing layer under our experimental conditions. Four adsorption models, that is, the Langmuir, Freundlich, Temkin, and linear models, constitute our set of alternative (competing) mathematical formulations. Experimental results reveal that the soil samples characterized by the finest grain sizes are associated with the highest release of Cr(VI) to runoff. We compare the relative abilities of the four models to interpret experimental results through maximum likelihood model calibration and four model identification criteria (i.e., the Akaike information criteria [AIC and AIC_C] and the Bayesian and Kashyap information criteria). Our study results enable us to rank the tested models on the basis of a set of posterior weights assigned to each of them. A classical variance‐based global sensitivity analysis is then performed to assess the relative importance of the uncertain parameters associated with each of the models considered, within subregions of the parameter space. In this context, the modelling strategy resulting from coupling the Langmuir isotherm with a two‐layer solute transfer model is then evaluated as the most skilful for the overall interpretation of both sets of experiments. Our results document that (a) the depth of the mixing layer is the most influential factor for all models tested, with the exception of the Freundlich isotherm, and (b) the total sensitivity of the adsorption parameters varies in time, with a trend to increase as time progresses for all of the models. These results suggest that adsorption has a significant effect on the uncertainty associated with the release of Cr(VI) from the soil to the surface runoff component.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Mobile Agent系统结构及其关键技术

Mobile Agent技术是一门新兴的技术,具有移动性、智能性和异步计算的特点,已成为计算机网络和分布式系统最具活力的发展方向.现对Mobile Agent的概念、系统结构及其关键技术进行了分析和探讨.     

DISASTER ANALYSIS AND COUNTERMEASURES OF LAND SUBSIDENCE CAUSED BY COAL CUTTING IN CHINA

In recent years, many coal-producing countries have paid great attention to the land subsidence causedby coal cutting. In China, because of the dense population in coalfield areas, the land subsidence hazard is more seri-ous. After a brief analysis on the mechanism of land subsidence, this paper gives a comprehensive and systematical ac-count on all kinds of hazards caused by the land subsidence in China. The study shows that land subsidence has endan-gered land, buildings, traffic and communication lines, dykes and dams. It also causes damage to ecological and socialenvironment. In order to lessen the hazard of land subsidence, preventive measures should be taken to reduce the col-lapse amount, such as extraction with stowing, banded mining system, succession and coordination mining system, orhigh-pressure mudflow between rock strata. Measures of reinforcing or moving certain buildings should also be taken toreduce the destructive degree. In order to harness the subsidence land and bring them under control for fanning, mea-sures should be taken such as filling with spoil or fine breeze, excavating the deeper and covering the shallower land.     

新疆塔中南坡奥陶系的地层缺失和沉积相变化

按照奥陶系内部6个组沉积的时间片段拟定塔中南坡不同区块存在不同程度的缺失。部分关键层段的牙形石和几丁虫组合特征证明一间房组和恰尔巴克组在塔中部分井区是存在的,但恰尔巴克组的分布范围最狭窄。总体上,塔中南坡隆起区地层缺失较多,古城墟隆起基本完整。据缺失状况和岩相展布,显示塔中南坡的沉积单元具有由东往西迁移的特征,且各时段迁移的距离与速度存在较大差异。     

Distribution of the C37 tetra-unsaturated alkenone in Lake Qinghai, China: A potential lake salinity indicator

The alkenone unsaturation index U^K′₃₇ has been applied to reconstruct past temperature changes in both marine and lacustrine systems. However, few studies have addressed whether the relative abundance of the C_37:4 alkenone to the total C₃₇ production (%C_37:4) can reflect surface salinity changes in lacustrine systems. Here we present long-chain C₃₇ alkenone distribution patterns in surface sediments from Lake Qinghai, China. Surface sediments were sampled over a large range of surface salinity changes (1.7-25 g/l) within Lake Qinghai and its surrounding lakes, while temperature differences at these sampling locations should be relatively small. We have found that %C_37:4 varies from 15% to 49% as surface salinity decreases. We tentatively describe this %C_37:4-salinity link with a general linear regression: %C_37:4 = 53.4 (±7.8) − 1.73 (±0.45) × S (n = 28, r² = 0.62), although step-wise %C_37:4 changes in response to salinity variation may exist. U^K′₃₇ values vary between 0.10 and 0.16 at these sites and the inferred range of lake water temperature changes is ∼2-3 °C, suggesting that U^K′₃₇ largely reflects temperature signal across a large salinity range, consistent with previous findings that U^K′₃₇ can indicate temperature changes over a large diversity of environmental settings. We have also found that U^K′₃₇ values are correlated with salinity changes (r² = 0.4), and thus cannot exclude potential temperature effect on %C_37:4 and salinity effect on U^K′₃₇ in this study. However, even extreme estimates of temperature differences within the lake are still unable to explain the observed %C_37:4 changes. We therefore suggest that %C_37:4 could be used to infer past lake salinity changes at a regional scale.     

Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system

We describe empirical results from a multi-disciplinary project that support modeling complex processes of land-use and land-cover change in exurban parts of Southeastern Michigan. Based on two different conceptual models, one describing the evolution of urban form as a consequence of residential preferences and the other describing land-cover changes in an exurban township as a consequence of residential preferences, local policies, and a diversity of development types, we describe a variety of empirical data collected to support the mechanisms that we encoded in computational agent-based models. We used multiple methods, including social surveys, remote sensing, and statistical analysis of spatial data, to collect data that could be used to validate the structure of our models, calibrate their specific parameters, and evaluate their output. The data were used to investigate this system in the context of several themes from complexity science, including have (a) macro-level patterns; (b) autonomous decision making entities (i.e., agents); (c) heterogeneity among those entities; (d) social and spatial interactions that operate across multiple scales and (e) nonlinear feedback mechanisms. The results point to the importance of collecting data on agents and their interactions when producing agent-based models, the general validity of our conceptual models, and some changes that we needed to make to these models following data analysis. The calibrated models have been and are being used to evaluate landscape dynamics and the effects of various policy interventions on urban land-cover patterns.     

大凌河流域河道生态需水量估算

本文从大凌河流域存在的环境问题出发,分别估算了各水资源分区的河道生态需水量（包括生态基流量、自净需水和输沙需水）及各区的河道生态需水贡献量。提出了使用偏枯年（75％保证率）水文数据估算河道生态需水量的方法．弥补了Tennant法、枯水季最小流量法等方法计算结果偏大或偏小的不足。结果表明,大凌河的4个河段（建凌站以上、建凌站至朝阳站、朝阳站至义县站、义县站至锦县段）的河道分区需水量分别为0．55×10^8m^3、5．21×10^8m^3、3．9×10^8m^3和1．34×10×10^8m^3。锦县水文站以上河道生态需水总量11×10^8m^3,占地表水总资源量的59．75％,因此．大凌河地表水资源取用率应在40％以下。