GIS在水污染控制中的研究与探讨

地理信息系统(GIS)是专门用于地理空间信息处理和管理的计算机技术系统，造成城市水污染的污染源及其相关数据均具有空间分布特性，这决定了GIS可在该领域发挥重要作用。GIS能支持与水环境有关的地理空间数据的获取、管理、分析、模拟和显示，以解决复杂的水环境综合治理问题和水污染控制问题；同时，建立完善的空间数据库和属性数据库，为环境保护部门和科研部门提供研究与决策支持。本文就GIS在水污染控制这一领域的应用做一定探讨。     

RESEARCH: Comparison of Temporal Trends in Ambient and Compliance Trace Element and PCB Data in Pool 2 of the Mississippi River, USA, 1985–1995

6+ ), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium (Se), zinc (Zn), and polychlorinated biphenyls (PCBs). Water-column, bed-sediment, and fish-tissue (fillets) data collected by five government agencies comprised the ambient data set; effluent data from five registered facilities comprised the compliance data set. The nonparametric Mann-Kendall trend test indicated that 33% of temporal trends in all data were statistically significant (P < 0.05). Possible reasons for this were low sample sizes, and a high percentage of samples below the analytical detection limit. Trends in compliance data were more distinct; most trace elements decreased significantly, probably due to improvements in wastewater treatment. Seven trace elements (Cr, Cd, Cu, Pb, Hg, Ni, and Zn) had statistically significant decreases in wastewater and portions of either or both ambient water and bed sediment. No trends were found in fish tissue. Inconsistency in trends between ambient and compliance data were often found for individual constituents, making overall similarity between the data sets difficult to determine. Logistical differences in monitoring programs, such as varying field and laboratory methods among agencies, made it difficult to assess ambient temporal trends.     

Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

We assessed the occurrence of a common river bird, the Plumbeous Redstart Rhyacornis fuliginosus, along 180 independent streams in the Indian and Nepali Himalaya. We then compared the performance of multiple discrimant analysis (MDA), logistic regression (LR) and artificial neural networks (ANN) in predicting this species’ presence or absence from 32 variables describing stream altitude, slope, habitat structure, chemistry and invertebrate abundance. Using the entire data (=training set) and a threshold for accepting presence in ANN and LR set to P≥0.5, ANN correctly classified marginally more cases (88%) than either LR (83%) or MDA (84%). Model performance was assessed from two methods of data partitioning. In a ‘leave-one-out’ approach, LR correctly predicted more cases (82%) than MDA (73%) or ANN (69%). However, in a holdout procedure, all the methods performed similarly (73–75%). All methods predicted true absence (i.e. specificity in holdout: 81–85%) better than true presence (i.e. sensitivity: 57–60%). These effects reflect species’ prevalence (=frequency of occurrence), but are seldom considered in distribution modelling. Despite occurring at only 36% of the sites, Plumbeous Redstarts are one of the most common Himalayan river birds, and problems will be greater with less common species. Both LR and ANN require an arbitrary threshold probability (often P=0.5) at which to accept species presence from model prediction. Simulations involving varied prevalence revealed that LR was particularly sensitive to threshold effects. ROC plots (received operating characteristic) were therefore used to compare model performance on test data at a range of thresholds; LR always outperformed ANN. This case study supports the need to test species’ distribution models with independent data, and to use a range of criteria in assessing model performance. ANN do not yet have major advantages over conventional multivariate methods for assessing bird distributions. LR and MDA were both more efficient in the use of computer time than ANN, and also more straightforward in providing testable hypotheses about environmental effects on occurrence. However, LR was apparently subject to chance significant effects from explanatory variables, emphasising the well-known risks of models based purely on correlative data.     

数据仓库在水环境质量评价系统中的应用前景

水环境质量评价工作涉及的数据量大,数据分散,评价标准多,分析模型多。现有的基于数据库系统的水环境质量评价系统面向的对象单一,分析功能弱。数据仓库系统可以把分散的数据经过抽取、转换,装载进中心数据仓库,建立多维数据模型,利用OLAP的分析功能完成各种水质评价工作,更好的辅助决策者决策。介绍了基于数据库的水环境质量评价系统的缺点,数据仓库的特点,初步设想了其在评价系统中的具体实现。     

Boosting large-scale river connectivity restoration by planning for the presence of unrecorded barriers

Conservation decisions are invariably made with incomplete data on species’ distributions, habitats, and threats, but frameworks for allocating conservation investments rarely account for missing data. We examined how explicit consideration of missing data can boost return on investment in ecosystem restoration, focusing on the challenge of restoring aquatic ecosystem connectivity by removing dams and road crossings from rivers. A novel way of integrating the presence of unmapped barriers into a barrier optimization model was developed and applied to the U.S. state of Maine to maximize expected habitat gain for migratory fish. Failing to account for unmapped barriers during prioritization led to nearly 50% lower habitat gain than was anticipated using a conventional barrier optimization approach. Explicitly acknowledging that data are incomplete during project selection, however, boosted expected habitat gains by 20–273% on average, depending on the true number of unmapped barriers. Importantly, these gains occurred without additional data. Simply acknowledging that some barriers were unmapped, regardless of their precise number and location, improved conservation outcomes. Given incomplete data on ecosystems worldwide, our results demonstrate the value of accounting for data shortcomings during project selection.     

电脑散热风扇灰尘中多溴二苯醚的污染特征和环境健康风险评价

不同模型对土壤污染物空间分布预测精度具有重要影响,针对现有方法不能较好模拟土壤污染物较强的空间变异特征以及缺乏对影响污染物空间分布的关键环境因子识别,本研究基于随机森林（RF）模型,通过融合多源环境要素,开展了某冶炼厂周边农田土壤砷含量空间分布预测研究,并与反距离加权（IDW）和逐步线性回归模型（STEPREG）相比较.结果表明,研究区农田土壤砷污染范围较广,污染严重区域主要分布在研究区南部,3种模型模拟的砷污染空间分布虽总体趋势相似,但局部区域差异明显,IDW和STEPREG模型不能很好地反映研究区土壤污染的强空间变异特征,RF模型模拟结果较好的表达局部高污染区域的细部变化.不同环境要素对农田土壤砷含量空间分布影响的重要性不同,研究区环境变量和地形变量是影响土壤砷含量空间分布的关键环境因子.交叉验证结果表明,RF模型相对IDW和STEPREG模型具有最小的均方根误差（RMSE）、平均绝对误差（MAE）、平均误差（ME）和最大的R²,RF模型的RMSE、MAE、ME较IDW模型分别降低了10.8%、5.5%和88.1%,较STEPREG模型分别降低了17.8%、18.4%和94.7%,表明采用RF模型对研究区农田土壤砷含量预测精度最高,取得了最优的预测效果.本研究结果能够为土壤重金属污染空间分布制图提供方法学参考.     

城市防灾应急信息数据管理研究

以防灾应急信息系统的数据管理研究为背景,重点研究城市防灾应急工作中所涉及的城市信息、灾害信息以及防灾应急信息的数据管理。调查分析国内外防灾应急数据管理的现状,在此基础上综合考虑数据库应包含的数据类型及内容,从数据应用的角度出发,思考数据管理的规范流程,并提出数据管理过程中应注意的重要事项和适用的建议。针对城市防灾应急工作中存在的问题,提出综合管理城市建设及防灾减灾中相关的各类信息和数据的建议,阐述数据集成管理的重要性与优势,旨在解决防灾应急工作中复杂的数据管理问题,希望能够为城市防灾应急工作启发新思想和新方法。     

苏通大桥结构健康状态评估技术研究与应用(3):主梁损伤定位

为提高基于模态参数的损伤识别方法的损伤敏感性和噪声鲁棒性,将多源数据融合技术引入到苏通大桥主梁损伤定位方法中。基于D-S证据理论对模态柔度和模态应变能指标进行数据融合,并以苏通大桥扁平钢箱梁为分析对象,对融合后损伤定位指标的应用效果进行了讨论。结果表明:基于数据融合的损伤定位方法具有较强的损伤敏感性,只需要较少的低阶模态信息就能识别主梁的早期损伤;数据融合后,损伤定位指标可以在较强的噪声环境下准确地识别斜拉桥钢箱梁的损伤,具有较好的工程实用性。     

基于虚拟仪器技术的振动台模型试验98通道动态信号采集系统研制

土工结构振动台模型试验除了需采集常规的加速度、位移、应变信号外,还需采集模型场地土中振动孔隙水压力、动土压力等信号,由于同步采集的动态信号的多样性和复杂性,一般的振动台信号采集系统难以满足这样的多种动态信号同步采集要求。基于传感器融合技术和虚拟仪器技术,研发了一套适用于多种类型信号输入的动态信号同步采集系统,可实现80通道动态信号、18通道数字信号的同步采集、回放和频谱分析等功能,并具有界面友好、使用维护方便等特点。