江汉平原土地资源诅咒效应研究

将资源诅咒的研究领域由传统的矿产资源转向土地资源研究问题域,以近10a江汉平原18个县(市)的统计数据为依据,运用诅咒系数对其进行资源诅咒的实证分析。结果显示,近年来发展滞后的荆州市所辖的监利县、江陵县以及其他7个县市的土地资源诅咒系数大于1,确实存在资源诅咒现象,且江陵县与监利县还属于严重诅咒区。通过对选取指标作进一步的计算分析,得出导致其产生资源诅咒的原因主要为:以土地资源为依托的农业没有得到高效发展,而受到发展观念与投入等束缚,其二、三产业发展同样滞后。根据影响资源诅咒地区产业发展的具体因素,有针对性地给出化解土地资源诅咒的对策建议,为今后资源诅咒地区打破诅咒束缚,持续快速发展指明方向。     

Research status,challenges and future prospects of renewable synthetic fuel catalysts for CO2 photocatalytic reduction conversion

Environmental Science and Pollution Research - In recent years, with global climate change, the utilization of carbon dioxide as a resource has become an important goal of human society to achieve...     

电气石强化生物膜系统处理中药废水的启动及生物膜特性分析

将电气石与生物膜技术结合构建电气石强化生物膜系统处理中药废水,通过电气石调节生物膜微环境,以增强微生物代谢活性,从而提高了反应系统的处理能力。结果表明：电气石强化厌氧流化床(AFBR)反应系统经历160 d完成中药废水的启动实验,反应系统COD去除率达到87.8%,容积负荷达到5.34 kg·(m³·d)⁻¹,生物膜产甲烷活性达到126.4 mL·(g·d)⁻¹;电气石强化好氧流化床(FBR)反应系统统经历35 d完成启动实验后,出水COD稳定在76.5 mg·L⁻¹,反应系统对应的COD去除率和容积负荷分别为90.3%和1.4 kg·(m³·d)⁻¹。中药废水依次经AFBR和FBR处理后,出水水质满足《中药类制药工业水污染物排放标准》(GB 21906-2008)排放要求。以上结果可为实际工程项目提供理论依据和参考。     

TEOM法与实验室称重法监测大气中TSP的对比研究

用数理统计的方法计算出TEOM法与称重法监测结果的偏差,用F检验法检验TEOM法与称重法监测结果之间的致性,用r检测法检验TEOM法与称重法监测结果之间的相关性,用t检验法检验TEOM法与称重法监测结果之间的差异,通过对比研究,找出TEOM法与称重法之间的相互关系和规律。     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

活性炭常温吸附实验研究

活性炭因有较大的比表面积和经济易得性,常作为气体吸附法净化气体的吸附剂。SO2是大气环境质量指示的一个重要大气环境因子,其排放被严格控制。SO2的吸附受多种因素影响,本研究选取商品粒状活性炭作为样品,在常温、常压、相对湿度RH=0%、用SO2标气和N2模拟配制气体的条件下,进行了活性炭物理吸附SO2的穿透曲线和吸附容量的研究,结果显示该商品活性炭实验吸附穿透曲线和吸附容量随其颗粒直径的减小而增加,吸附曲线也由陡峭变平坦。所得实验结果为商品活性炭的使用提供实验数据支撑。     

水解酸化-磷酸铵镁法-EGSB组合工艺处理大豆蛋白废水

采用水解酸化-磷酸铵镁（MAP）法-EGSB组合工艺对大豆蛋白废水进行处理,考察了水解酸化系统运行效果,并对其运行参数进行优化;提出MAP法对高浓度氨氮废水的处理方法,并评选其处理高浓度氨氮废水的适宜条件。结果表明,水解酸化系统运行稳定,最适pH为7.0,最佳水力停留时间为12 h,氨氮转化率高达95.8%;MAP法处理高浓度氨氮废水的优选条件为n（Mg）：n（N）：n（P）=1：1：0.8,此条件下氨氮去除率为88.3%,磷酸去除率为76.7%;EGSB反应器经过三个月的启动后可稳定运行,有机负荷高达到9.88 kg·（m3·d）-1,COD去除率达到90.0%左右。水解酸化-MAP-EGSB组合工艺在处理大豆蛋白废水时可获得连续稳定的处理效果,为大豆蛋白废水处理的工程化提供了基础依据。     

Comparison of microbial community between two shallow freshwater lakes in middle Yangtze basin, East China

In order to investigate the role of the microbial community in aquatic ecology and nutrient transformations in the development of eutrophication in large shallow freshwater lakes along Yangtze River, the microbial community in the depth-related sediment in Lake Chaohu and Lake Longganhu were compared. Lake Chaohu is one of the three most polluted lakes in China. However, the neighboring Lake Longganhu, a mesotrophic lake, is relatively pristine. The total phosphorous (TP) and total nitrogen (TN) concentration in water was detected at 0.193 mgl(-1) and 3.035 mgl(-1) for Lake Chaohu, 0.051 mgl(-1) and 0.774 mgl(-1) for Lake Longganhu, respectively. The population of the microorganisms with various ecological nutrient transforming functions (e.g. phosphate solubilizing, denitrifying and cellulose decomposing) and a batch of environmental parameters concerning the nutrient accumulating and transforming (e.g. total organic carbon, total nitrogen, and total phosphorous concentrations) were assayed in the depth-related sediment samples from several defined points in both lakes. The sediment samples from Lake Chaohu showed higher density of actinomycetes (P<0.05) and phosphate-solubilizing bacteria (P<0.001) and less profusion of denitrifying bacteria (P<0.05) and cellulolytic microbes (P<0.001), compared with those of Lake Longganhu. The data suggested that the current microbial community in the sediment of Lake Chaohu is in favor of sustaining or further accelerating the process of the lake eutrophication. A possible positive feedback loop which consists of sustained growth of microorganisms and gradual decline of lake eutrophic status is worth further discussing.