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.     

“Hey Friend,Buy Green”: Social Media Use to Influence Eco-Purchasing Involvement

This study addresses the use of message carriers to convey corporate social responsibility information to promote eco-purchasing involvement. The work tests various media formats transmitted via social media. The effects of corporate vs. peer communication in a corporate social responsibility campaign in stimulating self-reported eco-purchasing involvement are indicated. The research varies communication dimensions of a fictional corporate social responsibility campaign sent through social media. Multiple media formats; new release; article; and advertisement were tested, as was the inclusion of social media persuasive sentiment. The analysis of source/format combinations and source/format/sentiment combinations found statistically significant differences for consumer-to-consumer communication in stimulating eco-purchasing involvement in certain circumstances. This research suggests an emerging role for the consumer communicator as a content co-creator and validates the effective use of articles and news releases instead of advertising on social media, yielding managerial and scholarly implications.     

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     

生物基聚合物PHBV和PLA复合材料在不同介质中的生物降解及其影响因素

传统工艺的塑料生产不仅依赖石油资源的持续开发利用,同时给环境造成了前所未有的压力,近年来生物基聚合物（聚（3-羟基-3-戊酸酯）-PHBV,聚乳酸-PLA）日渐成为传统石油基塑料的替代产品.本文采用呼吸测试手段,旨在揭示均质复合材料在不同环境介质（土壤、熟化堆肥、水体）条件下及有机添加剂（木质素）,无机添加剂（蒙脱石）和天然有机物链增长剂（Joncryl）作用下的生物降解特征.结果表明：当链增长剂Joncryl添加量为5%时,对所有介质PHBV和PLA复合材料产生显著抑制作用.Joncryl添加量为0.2%时,未对所测样品的生物降解行为产生干扰作用.在熟化堆肥介质中,PLA复合材料比PHBV基质混合物的生物降解速率明显降低.有机木质纤维添加剂（榛子壳粉末）单独在聚合物中添加或者和链增长剂Joncryl以及非有机添加剂（Dellite72T）共同作用下都可促进PLA聚合物中各组分的相容连接性.实验结果表明,新型添加剂在不同介质中以二元或三元添加的方式对生物降解过程产生重要影响,该研究将为新型材料使用后的生物降解效应提供理论依据.     

滤纸法测定悬浮物的干扰及其消除

提出了滤纸法测定悬浮物时，水样的酸碱度，取样量的多少对悬浮物测定有直接的影响。通过实验验证，对于偏酸，偏碱的水样用酸碱中和后再过滤即可消除影响，取样量应根据悬浮物的不同而定。     

湖州市北污水处理厂污泥处理运行管理

重点介绍湖州市北污水处理厂采用带式压滤机进行污泥脱水处理的应用情况及运行管理,并进行了小结。     

恶臭污染防治技术及工程实践

目前，恶臭污染对人类生活的危害日益加剧，文中介绍了几种恶臭污染物的防治技术。对生物滴滤池 活性炭吸附工艺进行了探讨，工程实例表明具有良好的治理效果。     

上海市雨水污染控制初探

水环境的严重污染和饮用水资源的匮乏，已对上海市的持续发展构成了直接和潜在威胁。在借鉴国外雨水处理利用的研究，应用成果，综合考虑上海市水环境，城市建设及经济发展现状和地区特点的基础上，对上海地区城市雨水污染及其特性，控制雨水污染措施等作了阐述。     

浅淡大阻力配水系统的技术改造

大阻力配水系统滤池由于采用穿孔钢管配水 ,而穿孔钢管容易腐蚀、结垢 ,导致孔眼堵塞 ,再若高位水箱反冲强度掌握不当 ,会使承托层和滤料冲洗混乱 ,滤池不能正常运行。针对这一问题 ,湖州市自来水公司与浙江省玉环净化厂联手 ,利用该厂滤板、滤头及有关专利新技术 ,对自来水公司的 6万t/d快速滤池进行改造 ,运行半年来 ,取得了满意的结果。     

多孔介质中污染物运移空间变异最优估计

论述了空间变异性分析的原理和方法,着重阐述不同估值方法在多孔介质污染物运移空间变异分析中的应用.实践表明,地质统计学方法能够弥补经典概率统计方法在评价区域化变量空间变异性方面的不足,从而对多孔介质中污染物运移的空间变异性做出更切合实际的分析与评价.