A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

基于SARIMA模型和普通Kriging法对杭州市主城区PM2.5短期预测和制图

基于SARIMA模型对杭州市主城区内的7个站点和周边3个站点的PM_(2.5)浓度进行4 h平均的短期预报,并利用普通Kriging法对杭州市主城区PM_(2.5)进行空间插值和制图.在建立SARIMA模型时,用批量自动化的方法,使用R语言编程对夏季和冬季各360期的数据进行SARIMA(p,d,q)×(P,D,Q)6模型的参数的确定和拟合,来预测下一期的值.在10个站点分别进行120期的预测,做出真实与预测的时间序列图,在精度分析中,夏季和冬季PM_(2.5)浓度总体的平均绝对误差(MAE)分别为8.4μg·m~(-3)和14.8μg·m~(-3).在Arc GIS中,对每期的数据使用普通Kriging法,用球面模型拟合半变异函数,决定各个站点的权重,来对杭州市区内未知空间样点进行插值,最后生成完整的杭州市主城区PM_(2.5)短期预测图.本研究创新性地将SARIMA模型广义化运用到小尺度时间序列中,预报效果较好,并且批量自动化预测和制图的方法,可为今后的预测制图产品化提供技术支持.     

稳健GM(1,1)模型在工业固废产生量预测中的应用

本文提出了稳健灰色模型,并用该模型建立了山东省工业固体废物产生量数学模型。结果表明,稳健ＧＭ（１,１）模型比通常的ＧＭ（１,１）模型更具预测应用价值。     

Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America

Centrality metrics evaluate paths between all possible pairwise combinations of sites on a landscape to rank the contribution of each site to facilitating ecological flows across the network of sites. Computational advances now allow application of centrality metrics to landscapes represented as continuous gradients of habitat quality. This avoids the binary classification of landscapes into patch and matrix required by patch-based graph analyses of connectivity. It also avoids the focus on delineating paths between individual pairs of core areas characteristic of most corridor- or linkage-mapping methods of connectivity analysis. Conservation of regional habitat connectivity has the potential to facilitate recovery of the gray wolf (Canis lupus), a species currently recolonizing portions of its historic range in the western United States. We applied 3 contrasting linkage-mapping methods (shortest path, current flow, and minimum-cost-maximum-flow) to spatial data representing wolf habitat to analyze connectivity between wolf populations in central Idaho and Yellowstone National Park (Wyoming). We then applied 3 analogous betweenness centrality metrics to analyze connectivity of wolf habitat throughout the northwestern United States and southwestern Canada to determine where it might be possible to facilitate range expansion and interpopulation dispersal. We developed software to facilitate application of centrality metrics. Shortest-path betweenness centrality identified a minimal network of linkages analogous to those identified by least-cost-path corridor mapping. Current flow and minimum-cost-maximum-flow betweenness centrality identified diffuse networks that included alternative linkages, which will allow greater flexibility in planning. Minimum-cost-maximum-flow betweenness centrality, by integrating both land cost and habitat capacity, allows connectivity to be considered within planning processes that seek to maximize species protection at minimum cost. Centrality analysis is relevant to conservation and landscape genetics at a range of spatial extents, but it may be most broadly applicable within single- and multispecies planning efforts to conserve regional habitat connectivity.     

An Optimization Method for Estimating Constituent Mean Concentrations in Base Flow‐Dominated Flow

Pollutant coefficients have been widely used to assess runoff nonpoint source pollution from individual land uses (e.g., agricultural, residential) of a watershed. Pollutant coefficients, known as event mean concentrations (EMCs), were developed by the U.S. Environmental Protection Agency's Nationwide Urban Runoff Program (NURP) to serve as a national measure for characterizing pollutant loading in a receiving water body. The term “baseflow pollutant coefficient (BPC)” is used in this study as a surrogate for EMC to describe mean concentration of pollutants in base flow‐dominated flow. A method for characterizing base flow quantity and quality for different land uses was explored using inverse modeling with two optimization techniques (a least square method and a genetic algorithm [GA] optimization), land use information, and streamflow quantity and quality data. The inverse model was formulated as a constrained minimization problem and demonstrated with data for 15 watersheds in Indiana. Results showed that estimated pollutant coefficients are comparable to the published literature. This indicates that the proposed method has the potential to effectively estimate constituent mean concentrations for pollutant load determination in gauged and ungauged watersheds, albeit more analysis with larger and more robust datasets is desirable to further refine and validate the accuracy of the approach.     

Sensitivity of some groups of ordinary chernozem soil microorganisms to γ irradiation

The effect of γ irradiation at doses of 1, 5, 10, and 20 kGy on the microflora of ordinary chernozem soil has been studied. The time course of the restoration of the microorganism numbers over 180 days after γ irradiation has been studied in model experiments. The microorganism radioresistance decreases in the following order: amylolytic bacteria > Azotobacter > spore-forming bacteria > ammonifiers > micromycetes. A dose of 20 kGy has a 96% suppressing effect on all microorganisms. Model experiments on the restoration of the irradiated soil microflora have revealed different rates of increase in the numbers of microorganism groups depending on the dose and incubation time.     

基于地统计模型的上海大气污染物多建模方法的比较

地统计模型被广泛应用于环境空气污染物暴露模拟,但不同建模方法及其模拟结果之间的对比研究较少.基于上海2016~2019年55个环境空气监测点位的NO₂和PM_2.5观测数据,以及交通路网、排放源兴趣点和卫星数据等地统计变量,应用偏最小二乘回归（PLS）、监督学习线性回归（SLR）和机器学习随机森林（RF）这3种建模方法创建年暴露模型,并进一步应用普通克里金插值（OK）法分析模型残差,构建复合模型.应用交叉验证对模型的模拟效果进行检验,选取每一种建模方法的最优模型结构（是否应用OK）作为最终模型.结果表明,NO₂模型中表现最好的是RF-OK （R_mse²为0.70~0.82）和PLS-OK模型（R_mse²为0.78~0.84）;PM_2.5模型中PLS模型（R_mse²为0.62~0.71）优于SLR-OK （R_mse²为0.40~0.79）和RF-OK （R_mse²：0.31~0.56）模型.应用3种建模方法对上海1 km网格开展年暴露模拟和对比,NO₂模型间模拟结果的相关性（r为0.82~0.91）高于PM_2.5模拟结果的相关性（r为0.66~0.96）.基于3种模型2019年的模拟结果,评估了上海NO₂和PM_2.5的人群暴露水平.     

南水北调大型渡槽结构抗震安全性研究

阐述了南水北调中大型渡槽的抗震问题,分别介绍了基于常微分方程求解器(ODE)和有限元方法的大型渡槽动力计算模型、动力特性分析和设置铅芯橡胶支座(LRB)的隔震分析。研究表明,基于ODE求解器的解析和半解析方法有很好的精度和可靠性,可用于大型渡槽的初步设计。结合所得的研究成果,对南水北调的渡槽抗震提出了合理化建议。     

差分光学吸收光谱法测量大气污染气体的研究

介绍了差分光学吸收光谱(DOAS)法测量大气污染气体的基本原理,描述了收发一体的DOAS系统的设计以及在同一波段对多种被测气体浓度同时进行反演的数据处理方法,并且对周围环境中的SO₂,O₃和NO₂进行了长期监测,得到的结果与实际情况相符合.