应用ANFIS预测蠕虫反应器的污泥减量速率并优化其运行条件

利用水生蠕虫的捕食作用可以有效地实现污泥减量.为了研究环境条件波动对蠕虫捕食污泥减量效率的影响,应用自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN)模型分别预测蠕虫反应器的污泥减量速率.结果表明,溶解氧浓度(D0)、温度(T)、蠕虫密度和污泥负荷是蠕虫捕食过程的主要影响因素,通过性能比较得出ANFIS模型预测值与实验测定值间具有更好的一致性,其相关系数(r)为0.82,绝对平均误差百分比(MAPE)为71.5％,均方根误差(RMSE)为16.7.根据ANFIS模型的预测结果,得出蠕虫反应器的最适运行条件为:DO 1.8 ～3.1mg·L-1,温度18.4～21.7℃,蠕虫密度低于1.7 g·cm-2(以湿重计),污泥负荷563～734 mg·g-1(以TSS计),在此操作条件下获得的污泥减量速率均高于100 mg· g-1·d-1.     

Value of information analysis for adequate monitoring of carbon dioxide storage in geological reservoirs under uncertainty

As monitoring is essential for the proper management of geological storage of carbon dioxide (CO₂), the ability to value information from monitoring is indispensable to adequately design a monitoring program. It is necessary to judge whether the expected improvement in management is worth the cost of monitoring. The value of information (VOI) is closely related to the possible increase in expected utility gained by gathering the information, the concept of which can be applied to such judgement. Although VOI analysis has been extensively studied in the context of decision analysis, its application to the management of carbon dioxide capture and storage (CCS) operations is rare. This paper introduces and discusses the methodology of VOI analyses in the context of monitoring CO₂ storage. A motivating problem with discrete probabilities is used to illustrate the concept of VOI. It is demonstrated that information is not always of value; for information to be worthwhile, monitoring under uncertainty must satisfy certain conditions. This concept is then extended to continuous probability distributions. The effects of prior uncertainty and information reliability on the VOI are examined. It is shown that an excessive improvement in information accuracy yields little value and that the optimal level of reliability can be inferred. VOI analyses provide quantitative insights into the value of information-gathering activities and therefore can be an objective means to adequately design and impartially justify a monitoring program.     

Dealing with microtopography of an ombrotrophic bog for simulating ecosystem-level CO2 exchanges

Peatlands contain approximately 25% of the global soil carbon (C), despite covering only 3% of the earth's land surface. In order to evaluate the role of peatlands in global C cycling, models of ecosystem biogeochemistry are required, but peatland ecosystems present a number of unique challenges, particularly how to deal with the large variability that occurs at scales of one to several metres. In models, spatial variability is considered either explicitly for each individual unit and the outputs averaged, referred to as flux upscaling, or implicitly by weighting model parameters by the fractional occurrence of the individual units, referred to as parameter upscaling. The advantage of parameter upscaling is that it is much more computationally efficient: a requirement for hemispheric scale simulations. In this study we determined the differences between modelling a raised bog peatland with hummock-hollow microtopography using flux and parameter upscaling. We used the McGill Wetland Model (MWM), a process-based ecosystem C model for peatlands, configured for hummocks and hollows separately and then a weighted mixture of both. The simulated output based on flux and parameter upscaling was compared with eddy-covariance tower measurements. We found that net ecosystem production (NEP) for hollows was much larger than that for hummocks because total ecosystem respiration (TER) for hummocks was greater while gross primary production (GPP) did not differ significantly between the two topographic features. However, despite differences in components of NEP between hummocks and hollows, there was no statistically significant difference between the NEP based on flux and parameter upscaling using the MWM. Both flux and parameter upscaling show equivalent capability to capture the magnitude, direction, seasonality and inter-annual variability. The root-mean-square-errors (RMSE) are 0.66, 0.45, and 0.49 g C m⁻² day⁻¹, respectively for GPP, TER and NEP based on the flux upscaling, while 0.67, 0.44, and 0.48 g C m⁻² day⁻¹, respectively based on the parameter upscaling. The degree of agreement (d*) is 0.96, 0.97, and 0.88, respectively for GPP, TER and NEP based on the flux upscaling, while 0.96, 0.97, and 0.89, respectively based on the parameter upscaling. This result suggests that differences in processes caused by peatland microtopography scale linearly, which means an ecosystem-level model set-up (i.e. parameter upscaling scheme), is sufficient to simulate the C cycling.     

A simple plankton model for the Oregon upwelling ecosystem: Sensitivity and validation against time-series ocean data

Simple plankton models serve as useful platforms for testing our understanding of the mechanisms underlying ecosystem dynamics. A simple, one-dimensional plankton model was developed to describe the dynamics of nitrate, ammonium, two phytoplankton size-classes, meso-zooplankton, and detritus in the Oregon upwelling ecosystem. Computational simplicity was maintained by linking the biological model to a one-dimensional, cross-shelf physical model driven by the daily coastal upwelling index. The model sacrificed resolution of regional-scale and along-shore (north to south) processes and assumed that seasonal productivity is primarily driven by local cross-shelf Ekman transport of surface waters and upwelling of nutrient-rich water from depth.Our goals were to see how well a simple plankton model could capture the general temporal and spatial dynamics of the system, test system sensitivity to alternate parameter set values, and observe system response to the effective scale of potential retention mechanisms. Model performance across the central Oregon shelf was evaluated against two years (2000-2001) of chlorophyll and copepod time-series observations. While the modeled meso-zooplankton biomass was close in scale to the observed copepod biomass, phytoplankton was overestimated relative to that inferred from the observed surface chlorophyll concentration. Inshore, the system was most sensitive to the nutrient uptake kinetics of diatom-size phytoplankton and to the functional grazing response of meso-zooplankton. Meso-zooplankton was more sensitive to alternate parameter values than was phytoplankton. Reduction of meso-zooplankton cross-shelf advection rates (crudely representing behavioral retention mechanisms) reduced the scale of model error relative to the observed seasonal mean inshore copepod biomass but had little effect of the modeled meso-zooplankton biomass offshore nor upon phytoplankton biomass across the entire shelf.     

Adaptive neuro fuzzy inference system for classification of water quality status

An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set (nine weeks, total 845 observations) was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.     

基于改进遗传算法率定水质参数的研究

提出一种新的优化准则函数,结合改进的加速遗传算法,能够求得全局最优的水质参数。实例计算结果表明,新算法具有较高的精确度和良好的优化性能。     

环境监测数据审核中各类数据逻辑关系的研究

环境质量监测数据审核是环境监测的最后一步,也是最重要的一环,是质量保证工作的重要环节。文章论述了环境监测数据审核的重要性。联系工作实际通过对环境质量监测各类数据的规律性、相关性进行研究,提出数据审核应注意的问题以及需要掌握的技巧,并详细的介绍数据审核包括的内容,重点介绍数据审核中水环境质量的审核和研究。重点要说明水的多项监测数据中存在哪些逻辑关系并且用科学的方法去证明这种逻辑关系,在证明的过程中利用历年的大量监测数据去比较分析证明。     

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N₂O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N₂O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N₂O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N₂O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N₂O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N₂O emissions from arable soils at regional or continental scales.     

基于置信规则的村镇应急避难场所暴雨灾害链风险诊断*

为解决村镇应急避难场所面临暴雨灾害时的风险问题,基于故障树和置信规则库推理方法,提出暴雨灾害链和村镇应急避难场所功能破坏链相结合的场所避难功能失效风险诊断模型。根据事故致因理论推理灾害节点变量,通过故障树描述灾害链,运用关联规则从历史灾害数据中挖掘规则,建立置信规则库系统,构建村镇应急避难场所功能失效风险诊断模型,并以四川省某寄宿制学校为例进行模型验证。研究结果表明:该模型可实现不同证据组合下村镇应急避难场所功能失效风险的诊断推理;实例的模型诊断结果与实际情况吻合,证实该模型能够科学地诊断村镇应急避难场所面临暴雨等恶劣自然条件时存在的风险,可为村镇应急避难场所规划设计和应急管理提供理论支撑。