组合工艺去除垃圾渗滤液中的DBP和DEHP

探讨了采用实验室规模的生物处理组合工艺(上流式厌氧污泥床+曝气生物滤池+缺氧反应器+膜生物反应器,UASB+BAF+ANO+MBR)处理垃圾渗滤液过程中,邻苯二甲酸二丁酯(DBP)和邻苯二甲酸二(2-乙基己基)酯(DE-HP)两种内分泌干扰物在各工艺段的去除效率和机理。测定结果表明,对于DBP和DEHP浓度分别为164.4μg/L和215.0μg/L的原水,总出水浓度分别降至11.8μg/L和10.4μg/L,去除率分别达到92.9%和95.2%,处理效果良好。其中DBP在处理工艺中逐级降解,主要是微生物的降解作用。MBR是DEHP的主要去除工艺段,去除比例达到56.6%,膜截留效果明显。采用生物处理组合工艺可实现对垃圾渗滤液中DBP和DEHP的同时高效去除。     

聚丙烯酸水凝胶对氯化-1-丁基-3-甲基咪唑离子液体的吸附热力学和动力学研究

通过水溶液聚合法制备了聚丙烯酸水凝胶（polyacrylic acid,PAA）,研究了溶液酸碱度和温度对聚丙烯酸水凝胶吸附氯化1-丁基3-甲基咪唑离子液体的影响。结果表明,水凝胶对氯化1-丁基3-甲基咪唑离子液体的吸附量随溶液pH值增大而逐渐增大,pH=2～4时增幅明显,而pH大于4时变化趋于缓慢;随温度的升高而逐渐降低。该等温吸附行为基本符合Langmuir吸附等温式,由二级吸附动力学模型获得的吸附过程活化能（Ea=52.470 kJ/mol）可以看出,PAA对氯化1-丁基3-甲基咪唑的吸附属于化学吸附范畴。     

微波及其组合工艺污泥预处理上清液中碳酸盐对磷回收的影响

本研究考察了微波及其组合工艺(微波-酸、微波-碱、微波-过氧化氢和微波-过氧化氢-碱)污泥预处理的碳酸盐溶出特征,并研究了碳酸盐对鸟粪石法磷回收的影响。结果表明,微波及其组合工艺进行污泥预处理时,碳酸盐的释放与pH值显著相关,但与预处理方法无关。模拟实验结果表明,当在浓度≤150 mg/L时,碳酸盐对鸟粪石结晶的磷/镁摩尔比(Mg/P=0.74~1.16)具有一定影响,但对鸟粪石法磷回收率(>90%)和鸟粪石结晶形态(呈典型的斜方晶型)没有显著影响。     

Dioxins and their fingerprint in size-classified fly ash fractions from municipal solid waste incinerators in China—Mechanical grate and fluidized bed units

The distribution of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs), in brief dioxins, has seldom been addressed systematically in fly ash from municipal solid waste incinerators (MSWIs). This study shows the amount and fingerprint of PCDD/Fs in fly ash from four different Chinese MSWIs, that is, three mechanical grate units and one circulating fluidized bed unit. In these fly ash samples, dioxins-related parameters (international toxic equivalent quantity, total amount of PCDD/Fs, individual isomer classes, and 17 toxic 2,3,7,8-substituted congeners) all tend to increase with decreasing particle size for mechanical grate incinerators, yet only for the finest fraction for fluidized bed units. Moreover, the fluidized bed incinerator seems superior to grate incineration in controlling dioxins, yet a comparison is hampered by internal differences in the sample, for example, the fluidized bed fly ash has much lower carbon and chlorine contents. In addition, the presence of sulfur from mixing coal as supplemental fuel to the MSW may poison the catalytic steps in dioxins formation and thus suppress the formation of dioxins. With more residual carbon and chlorine in the fly ash, it is easier to form dioxins during cooling. Nevertheless, there is no apparent relation between Fe, Cu, and Zn contents and that of dioxins in fly ash.

Implications This paper is of interest because it presents the amounts and distribution of PCDD/Fs in fly ash samples from some typical waste incineration plants in China, featuring distinct incinerator types, combustion conditions, fuel composition, or residual carbon, chloride, and heavy metal contents in fly ash.     

聚合氯化铝和聚合硫酸铁对水中亚硒酸盐的去除研究

为达到快速有效去除饮用水中亚硒酸盐的目的,通过混凝法考察了聚合氯化铝与聚合硫酸铁对模拟硒污染饮用水源中亚硒酸盐的去除效果,探讨了初始浓度、投加量以及pH对去除效果的影响。结果表明,混凝工艺对水中亚硒酸盐的去除效果明显,在初始硒浓度为184／μg／L时,聚合氯化铝和聚合硫酸铁对亚硒酸盐的最大去除率分别为70％和97％。聚合硫酸铁对亚硒酸盐的混凝去除效果明显好于聚合氯化铝,其最佳除硒pH为8。     

Theoretical evaluation of the configurations and Raman spectra of 209 polychlorinated biphenyl congeners

Though polychlorinated biphenyls (PCBs) have distributed as threats in the environment to human beings for several decades, monitoring of trace level PCBs in-field is still a challenge. As a potential method for monitoring PCBs at trace levels, Raman spectroscopy has been used to detect several PCBs in the laboratory. To facilitate the development of rapid detection of PCBs by Raman spectroscopy, it is essential to investigate the Raman spectra of all PCB congeners. Herein, the stable configurations and vibrational spectra of all the PCB congeners were calculated by Gaussian 03 program package. Based on molecular symmetry, PCBs are classified into seven groups. The structural features and the normal vibration modes for each group are discussed. Taking the C(2)-2 group as an example, the wavenumber ranges of the various normal vibration modes in the Raman spectra of PCBs were analyzed. The accuracy of calculated results was verified by experimental Raman spectra of PCB77 standard. This study can elucidate further information to promote the development of Raman spectroscopy in environmental monitoring.     

Sensitivity of potential natural vegetation in China to projected changes in temperature,precipitation and atmospheric CO2

A sensitivity study was performed to investigate the responses of potential natural vegetation distribution in China to the separate and combined effects of temperature, precipitation and [CO₂], using the process-based equilibrium terrestrial biosphere model BIOME4. The model shows a generally good agreement with a map of the potential natural vegetation distribution based on a numerical comparison using the ΔV statistic (ΔV = 0.25). Mean temperature of each month was increased uniformly by 0–5 K, in 0.5- or 1-K intervals. Mean precipitation of each month was increased and decreased uniformly by 0–30%, in 10% intervals. The analyses were run at fixed CO₂ concentrations of 360 and 720 ppm. Temperature increases shifted most forest boundaries northward and westward, expanded the distribution of xeric biomes, and confined the tundra to progressively higher elevations. Precipitation increases led to a greater area occupied by mesic biomes at the expense of xeric biomes. Most vegetation types in the temperate regions, and on the Tibetan Plateau, expanded westward into the dry continental interior with increasing precipitation. Precipitation decreases had opposite effects. The modelled effect of CO₂ doubling was to partially compensate for the negative effect of drought on the mesic biomes and to increase potential ecosystem carbon storage by about 40%. Warming tended to counteract this effect, by reducing soil carbon storage. Forest biomes showed substantial resilience to climate change, especially when the effects of increasing [CO₂] were taken into account. Savannas, dry woodland and tundra biomes proved sensitive to temperature increases. The transition region of grassland and forest, and the Tibetan plateau, was the most vulnerable region.     

Impacts of climate change on Chinese ecosystems: key vulnerable regions and potential thresholds

China is a key vulnerable region of climate change in the world. Climate warming and general increase in precipitation with strong temporal and spatial variations have happened in China during the past century. Such changes in climate associated with the human disturbances have influenced natural ecosystems of China, leading to the advanced plant phenology in spring, lengthened growing season of vegetation, modified composition and geographical pattern of vegetation, especially in ecotone and tree-lines, and the increases in vegetation cover, vegetation activity and net primary productivity. Increases in temperature, changes in precipitation regime and CO₂ concentration enrichment will happen in the future in China according to climate model simulations. The projected climate scenarios (associated with land use changes again) will significantly influence Chinese ecosystems, resulting in a northward shift of all forests, disappearance of boreal forest from northeastern China, new tropical forests and woodlands move into the tropics, an eastward shift of grasslands (expansion) and deserts (shrinkage), a reduction in alpine vegetation and an increase in net primary productivity of most vegetation types. Ecosystems in northern and western parts of China are more vulnerable to climate changes than those in eastern China, while ecosystems in the east are more vulnerable to land use changes other than climate changes. Such assessment could be helpful to address the ultimate objective of the United Nations Framework Convention on Climate Change (UNFCCC Article 2).     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.