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.     

Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

Toxicological Assessment of Wastewater and Runoff from the Baikal Pulp and Paper Mill

Russian Journal of Ecology -     

硫化亚铁处理含砷废水中砷的形态变化

本文对用硫化亚铁处理含砷废水时，在气相、液相和固相中砷的形态进行了研究结果表明，在气相中无ＡａＨ３，但在Ａｓ（Ｖ）废水的液相中有Ａｓ（Ｖ）和Ａｓ（Ⅲ）共同存在，在固相中有Ａｓ（０），ＦｅＡｓＯ４（ＦｅＡｓＯ３），Ａｓ２Ｓ３（Ａｓ２Ｓ５）和吸附砷，砷的形态变化表明，硫化亚铁处理含砷废水能取得良好的效果，是沉淀、沉淀转化、氧化还原、吸附共沉淀和中和五种反应共同作用的结果。     

焦化废水处理过程中固相物质的形成及处置方法评价

焦化废水处理过程中产生的焦油、污泥和结晶杂盐等固相物质,既有资源属性,又有污染特性,但目前缺乏基于能源、经济及环境影响方面的评估.本研究阐述了3类固相物质的形成机制,建立了质量当量计算及处置方法评价模型.以宝武集团韶关钢铁股份有限公司焦化厂（二期）焦化废水处理工程的A/O/H/O（厌氧/好氧/水解/好氧）流化床工艺作为考察对象,利用工程运行参数和水质统计数据进行固相物质的产量推算,结果发现,焦油、物化污泥、生物污泥（含水率为80%）和工业杂盐的产率分别为0.186、5.80、4.24和1.97 kg·m^-3.通过处置方法评价模型明确了焦油焚烧、污泥热解、结晶杂盐分盐提纯后工业应用是最佳处置方案,在60 m³·h^-1废水处理规模的固相物质处置过程中,每年约产生1177 MWh的能源,获得135.0万元的经济效益,排放627.0 t CO₂,表明能源回收、经济效益和环境影响的协同存在.     

固定化微藻流化床反应器去除低碳污水中氨氮的潜力

以固定化微藻颗粒为原料,通过搭建流化床反应器强化微藻对氨氮（NH₄⁺-N）的去除,设计了藻种、污水上升流速、光周期和光照强度四组单一变量实验,系统地研究了不同条件下微藻去除NH₄⁺-N的能力.结果表明,当以固定化斜生栅藻为原料、污水上升流速为6.8m/h、光周期为8：16h和光照强度为4800Lux时,NH₄⁺-N去除效果最优（96.7%）.在最优操作条件下,探究了COD为200mg/L时微藻去除NH₄⁺-N的潜力,结果表明,当NH₄⁺-N初始浓度不高于50mg/L时,NH₄⁺-N去除率高于95%.本实验建立了一套半连续微藻流化床实验方法,该方法显著减弱了微藻在生物同化过程中对有机碳源的依赖性,为低COD条件下微藻生物脱氮工艺的设计提供了技术参考和理论基础.     

模拟土壤淋洗废液中重金属的选择性去除与淋洗液的回收研究

土壤淋洗废液中污染物的选择性去除是实现淋洗液回收的关键.本文以硫化钠（Na₂S）、乙基黄原酸钾（PEX）、二甲基二硫代氨基甲酸钠（DTC）作为重金属沉淀剂处理电子垃圾污染土壤模拟淋洗废液,在筛选出Na₂S作为理想重金属沉淀剂的基础上,研究初始pH值、反应温度、沉淀剂浓度等因素对Na₂S分离络合态重金属的影响,并通过软件模拟和产物表征等方式推测反应机理.结果表明：Na₂S对柠檬酸络合态重金属的分离顺序依次为Cu、Pb、Cd,符合硫化物溶度积原则;反应温度、初始pH值对Na₂S处理络合态重金属无显著影响,重金属去除率均保持在90%以上,柠檬酸的回收率约为95%;Visual MINTEQ模拟结果显示,S^2-投加前液相中重金属主要以H₂CA^-、HCA^2-和CA^3-结合形式存在,S^2-投加后液相中重金属则以HS^-和S^2-结合形式存在;扫描电镜及能谱（SEM-EDS）表明,S^2-与金属阳离子按物质的量比1：1生成硫化物沉淀物,沉淀主要呈团聚、成簇和圆片状存在;X射线衍射（XRD）分析结果表明,沉淀产物中存在CuS、CdS、PbS和CuPbS₂.本研究结果可为化学沉淀法处理重金属污染土壤淋洗废液提供技术参考.     

高原高寒污水处理系统的微生物群落特征

为研究高原高寒污水处理系统活性污泥的微生物群落结构及多样性,以拉萨、云南、四川的3座高原污水厂作为实验组,同时以重庆2座非高原污水厂作为对照,采用PCR-DGGE技术对比分析了高原与非高原污水厂的微生物特性.研究表明：高原污水厂样品与非高原样品在聚类中展现出了较为疏远的关系,微生物群落区别明显.受强紫外线辐照的抑制,高原高寒污水处理系统微生物多样性的平均水平显著低于非高原污水厂,较低的微生物多样性是导致高原高寒地区污染物去除效果不佳的一项重要原因.群落构成方面,共鉴定出16个优势菌属,对应Proteobacteria、Bacteroidetes、Firmicutes、Verrucomicrobia 4个门.组间差异分析结果发现,高原组中丰度显著偏高的菌属只有Prosthecobacter,该菌在污水厂内分布广泛,且能够适应高原低温的条件.对于大多数活性污泥微生物而言,高原强紫外线是不利的生存条件.因此,减少高原露天污水处理系统的紫外辐照,是提升污水处理效能的一个潜在措施.     

Performance of different macrophytes in the decontamination of and electricity generation from swine wastewater via an integrated constructed wetland-microbial fuel cell process

Plants constitute a major element of constructed wetlands(CWs).In this study,a coupled system comprising an integrated vertical flow CW(IVCW) and a microbial fuel cell(MFC) for swine wastewater tre atment was developed to research the effects of macrophytes commonly employed in CWs,Canna indica,Acorus calamus,and Ipomoea aquatica,on decontamination and electricity production in the system.Because of the different root types and amounts of oxygen released by the roots,the rates of chemical oxygen demand(COD) and ammonium nitrogen(NH_4~+-N) removal from the swine wastewater differed as well.In the unplanted,Canna indica,Acorus calamus,and Ipomoea aquatica systems,the COD removal rates were 80.20%,88.07%,84.70%,and 82.20%,respectively,and the NH_4~+-N removal rates were 49.96%,75.02%,70.25%,and 68.47%,respectively.The decontamination capability of the Canna indica system was better than those of the other systems.The average output voltages were 520±42,715±20,660±27,and 752±26 mV for the unplanted,Canna indica,Acorus calamus,and Ipomoea aquatica systems,respectively,and the maximum power densities were 0.2230,0.4136,0.3614,and0.4964 W/m~3,respectively.Ipomoea aquatica had the largest effect on bioelectricity generation promotion.In addition,electrochemically active bacteria,Geobacter and Desulfuromonas,were detected in the anodic biofilm by high-throughput sequencing analysis,and Comamonas(Proteobacteria),which is widely found in MFCs,was also detected in the anodic biofilm.These results confirmed the important role of plants in IVCW-MFCs.