污水土地处理CAD系统设计

针对污水土地处理设计实际,以提高设计速度和质量为目标,结合计算机辅助设计技术和软件技术的发展,采用成熟而先进的参数化设计技术及ADS开发工具,对土地处理设计中的几个关系问题进行了探讨。     

Using culturomics and social media data to characterize wildlife consumption

Wildlife provides food, medicine, clothing, and other necessities for humans, but overexploitation can disrupt the sustainability of wildlife resources and severely threaten global biodiversity. Understanding the characteristics of consumer behavior is helpful for wildlife managers and policy makers, but the traditional survey methods are laborious and time-consuming. In contrast, culturomics may more efficiently identify the features of wildlife consumption. As a case study of the culturomics approach, we examined tiger bone wine consumption in China based on social media and Baidu search engine data. Tiger bone wine is one of the most purchased tiger products; its consumption is closely related to tiger poaching, which greatly threatens wild tiger survival. We searched a popular social media website for the term “tiger bone wine” and focused on posts that were originally created from 1 January 2012 to 31 December 2018. We filtered and classified posts related to the purchase, sale, or consumption of tiger bone wine and extracted information on providers, consumption motivations, year of production, and place of origin of the tiger bone wines based on the texts and photos of these posts. We found 756 posts related to tiger bone wine consumption, 113 of which mentioned providers of tiger bone wine, including friends (53%), elder relatives (37%), peer relatives (7%), and others (3%). Out of the 756 posts, 266 indicated the motivations of tiger bone wine consumption. Tiger bone wines were consumed as a tonic (34%), medicine (23%), game product (30%), and a symbol of wealth (28%). Some posts indicated ≥2 consumption motivations. These findings were consistent with the search queries from Baidu index. Such information could help develop targeted strategies for tiger conservation. The culturomics approach illustrated by our study is a rapid and cost-efficient way to characterize wildlife consumption.     

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.     

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.     

A statistical evaluation of non-ergodic variogram estimators

Geostatistics is a set of statistical techniques that is increasingly used to characterize spatial dependence in spatially referenced ecological data. A common feature of geostatistics is predicting values at unsampled locations from nearby samples using the kriging algorithm. Modeling spatial dependence in sampled data is necessary before kriging and is usually accomplished with the variogram and its traditional estimator. Other types of estimators, known as non-ergodic estimators, have been used in ecological applications. Non-ergodic estimators were originally suggested as a method of choice when sampled data are preferentially located and exhibit a skewed frequency distribution. Preferentially located samples can occur, for example, when areas with high values are sampled more intensely than other areas. In earlier studies the visual appearance of variograms from traditional and non-ergodic estimators were compared. Here we evaluate the estimators' relative performance in prediction. We also show algebraically that a non-ergodic version of the variogram is equivalent to the traditional variogram estimator. Simulations, designed to investigate the effects of data skewness and preferential sampling on variogram estimation and kriging, showed the traditional variogram estimator outperforms the non-ergodic estimators under these conditions. We also analyzed data on carabid beetle abundance, which exhibited large-scale spatial variability (trend) and a skewed frequency distribution. Detrending data followed by robust estimation of the residual variogram is demonstrated to be a successful alternative to the non-ergodic approach.     

Bayesian analysis under ordered functions of parameters

Ordered parameter problems arise in a wide variety of real world situations and are dealt with extensively in the literature. Traditional frequentist methods for dealing with these problems are rather complicated theoretically, especially when sample sizes are small. Bayesian methods are not widely used because high dimensional numerical integration is often required. However, Markov chain Monte Carlo methods provide alternatives to such numerical integration and also deal with ordered parameter problems in a straightforward manner. Little is known about the situation where functions of parameters are ordered. Such problems may seem to be of little practical concern initially, but one can readily see their importance in situations where ordering is placed on the means and variances of several normal or Gamma populations. For the Gamma distribution we will present real examples where we will analyze monthly precipitation data from San Francisco, California and Oakland Mills, Iowa. For the San Francisco data we will simultaneously order both monthly precipitation means and variances. For the Iowa data we will place ordering on seasonal average while still estimating monthly means. Our results show that we would obtain sharper, more accurate inference when order restrictions are employed.     

山东省碳减排系统仿真及政策优化研究

为考察减排政策对能源消耗及碳排放的影响,以经济、能源、人口和环境要素构建山东省能源消耗碳排放系统仿真模型。在验证模型有效性的基础上,进行单维度情景与多维度综合情景仿真模拟。结果显示:在设定情景参数下,产业结构、科技投入与能源结构调整能够有效促进碳减排,但减排效果不同,其中科技投入如果表现为清洁技术投入,则更有利于碳减排目标的实现;单因素调整的减排效果有一定的局限性,多因素综合模拟时,当条件满足第二产业固定资产投资比例为49.43%,第三产业投资比例达到46.94%,科技投入比例为2.70%,三次产业能源消耗结构比为74.87%:15.27%:9.86%,可接近实现山东省“十三五”低碳经济发展目标。     

Characteristics of plankton Hg bioaccumulations based on a global data set and the implications for aquatic systems with aggravating nutrient imbalance

• Hg bioaccumulation by phytoplankton varies among aquatic ecosystems. • Active Hg uptake may exist for the phytoplankton in aquatic ecosystems. • Impacts of nutrient imbalance on food chain Hg transfer should be addressed. The bioaccumulation of mercury (Hg) in aquatic ecosystem poses a potential health risk to human being and aquatic organism. Bioaccumulations by plankton represent a crucial process of Hg transfer from water to aquatic food chain. However, the current understanding of major factors affecting Hg accumulation by plankton is inadequate. In this study, a data set of 89 aquatic ecosystems worldwide, including inland water, nearshore water and open sea, was established. Key factors influencing plankton Hg bioaccumulation (i.e., plankton species, cell sizes and biomasses) were discussed. The results indicated that total Hg (THg) and methylmercury (MeHg) concentrations in plankton in inland waters were significantly higher than those in nearshore waters and open seas. Bioaccumulation factors for the logarithm of THg and MeHg of phytoplankton were 2.4–6.0 and 2.6–6.7 L/kg, respectively, in all aquatic ecosystems. They could be further biomagnified by a factor of 2.1–15.1 and 5.3–28.2 from phytoplankton to zooplankton. Higher MeHg concentrations were observed with the increases of cell size for both phyto- and zooplankton. A contrasting trend was observed between the plankton biomasses and BAF_MeHg, with a positive relationship for zooplankton and a negative relationship for phytoplankton. Plankton physiologic traits impose constraints on the rates of nutrients and contaminants obtaining process from water. Nowadays, many aquatic ecosystems are facing rapid shifts in nutrient compositions. We suggested that these potential influences on the growth and composition of plankton should be incorporated in future aquatic Hg modeling and ecological risk assessments.     

Mechanistic insight into the biofilm formation and process performance of a passive aeration ditch (PAD) for decentralized wastewater treatment

• A Passive Aeration Ditch was developed to treat decentralized wastewater. • A model was developed to describe the process performance. • A high C/N ratio facilitates microbial growth but nitrification deteriorates. • A high salinity decreases both organic and nitrogen contaminants removal. Decentralized wastewater containing elevated salinity is an emerging threat to the local environment and sanitation in remote coastal communities. Regarding the cost and treatment efficiencies, we propose a passive aeration ditch (PAD) using non-woven polyester fabric as a feasible bubbleless aerator and biofilm carrier for wastewater treatment. Consideration has been first given to PAD’s efficacy in treating saline decentralized wastewater, and then to the impact of chemical oxygen demand-to-nitrogen (C/N) ratio and salinity on biofilm formation. A multispecies model incorporating the salinity effect has been developed to depict the system performance and predict the microbial community. Results showed that the PAD system had great capacity for pollutants removal. The biofilm thickness increased at a higher C/N ratio because of the boost of aerobic heterotrophs and denitrifying bacteria, which consequently improved the COD and total nitrogen removal. However, this led to the deterioration of ammonia removal. Moreover, while a higher salinity benefited the biofilm growth, the contaminant removal efficiencies decreased because the salinity inhibited the activity of aerobic heterotrophs and reduced the abundance of nitrifying bacteria inside the biofilm. Based on the model simulation, feed water with salinity below 2% and C/N ratio in a range of 1 to 3 forms a biofilm that can reach relatively high organic matter and ammonia removal. These findings not only show the feasibility of PAD in treatment of saline decentralized wastewater, but also offer a systematic strategy to predict and optimize the process performance.     

应用系统动力学原理制定小流域经济建设决策

本文应用系统动力学原理，就太平沟小流域资源的开发利用进行了系统分析，绘制了ＳＤ模型流程图，编制了ＳＤ模型程序，经微机模拟仿真，确定了“综合型”发展为该流域资源开发利用方向，为该类型区小而域经济建设及两高一优农业发展提供了科学依据和方法。