沈阳市农村生活污水处理设施有机废弃物归趋

采用实地观察法、文献调查法对沈阳市农村生活污水处理设施基本状况进行研究分析。结果显示:沈阳市已建成农村生活污水处理设施297处,有机废弃物产生量(干重)约为:剩余污泥637 t/a、湿地植物废弃物169 t/a。     

黄河流域生态环境保护投资分析及优化建议

以黄河流域生态环保投资为研究对象,统计并分析近年来全社会和中央财政投入情况,从时间、空间和领域3个维度分析变化趋势,发现存在投资总量不足、效益跨区域外部性、地方投入渠道受制约等问题。从政府、企业、社会和金融机构4个主体层次分别提出相应的对策建议,以期为保障黄河流域生态环保资金投入提供支撑。     

深圳市综合水质指数研究及其在水资源资产评估上的应用

自然资源资产评估技术是开展自然资源资产负债表实践和实行环境责任离任审计的重要技术支撑。水资源作为一项重要自然资源,如何有效评估水资源资产是不可回避的问题。论文以深圳市为研究区域,一是采用综合污染指数法和主成分分析法,以总氮、氨氮、总磷、化学需氧量、生化需氧量、固体悬浮物为指标参数构建了深圳市综合水质指数以评价水资源综合水质。二是以该综合水质评估模型为基础,通过采用治污成本法分析综合水质与治污成本间的关系,从而构建深圳市景观水和饮用水资源的价值量评估模型。论文还以深圳市大鹏新区为试点开展了水资源价值量评估,评估结果显示,2015年大鹏新区景观水和饮用水资源的价值量分别为3.60亿、0.35亿元。     

Mercury flows in large-scale gold production and implications for Hg pollution control

Large-scale gold production(LSGP) is one of the five convention-related atmospheric mercury(Hg) emission sources in the Minamata Convention on Mercury. However, field experiments on Hg flows of the whole process of LSGP are limited. To identify the atmospheric Hg emission points and understand Hg emission characteristics of LSGP, Hg flows in two gold smelters were studied. Overall atmospheric Hg emissions accounted for 10%–17% of total Hg outputs and the Hg emission factors for all processes were 7.6–9.6 kg/ton. There were three dominant atmospheric Hg emission points in the studied gold smelters, including the exhaust gas of the roasting process, exhaust gas from the environmental fog collection stack and exhaust gas from the converter of the refining process. Atmospheric Hg emissions from the roasting process only accounted for 16%–29% of total emissions and the rest were emitted from the refining process. The overall Hg speciation profile(gaseous elemental Hg/gaseous oxidized Hg/particulate-bound Hg) for LSGP was 34.1/57.1/8.8. The dominant Hg output byproducts included waste acid, sulfuric acid and cyanide leaching residue. Total Hg outputs from these three byproducts were 80% in smelter A and 84% in smelter B. Our study indicated that previous atmospheric Hg emissions from large-scale gold production might have been overestimated.Hg emission control in LSGP is not especially urgent in China compared to other significant emission sources(e.g., cement plants). Instead, LSGP is a potential Hg release source due to the high Hg output proportions to acid and sludge.     

油气集输管道内防腐技术应用进展

针对油气田集输管道的内腐蚀问题,分别介绍了耐蚀材料、衬里技术、涂镀层技术与药剂防腐技术等管道内防腐技术及其现场应用效果,指出了耐蚀金属材料防腐效果显著,但存在经济效益差的缺点。为降低成本,选用双金属复合管替代耐蚀金属材料,但其焊缝位置腐蚀失效频发,成为制约其应用的薄弱环节。耐蚀非金属材料防腐效果显著,但耐高温性能与力学性能较差,受温度、压力与CO_2、H_2S、固体颗粒等介质成分的影响,衬里技术与涂镀层技术的应用范围受到限制。药剂防腐技术的防护效果与药剂类型、加药工艺密切相关,需要根据腐蚀工况监测结果进行实时调整。针对上述内防腐技术存在的问题,提出了未来内防腐技术的发展方向为改进现有内防腐技术存在的不足,提升其防护效果。同时,应开发防腐效果显著、经济成本低、施工简便、易于推广应用的防腐材料与防腐工艺技术。     

好氧颗粒污泥对有机物吸附类型的研究

为探索好氧颗粒污泥对有机物的去除机制,进行好氧颗粒污泥对有机物的吸附性能及吸附类型研究,在实验室条件下,采用动态吸附方式,利用SBAR反应器分析好氧颗粒污泥对有机物的去除情况。结果表明,好氧颗粒污泥存在初期吸附作用,最佳吸附时间为30~45 min,COD去处率60%;通过对失活前后好氧颗粒污泥对有机物吸附性能测定以及红外光谱分析,探究好氧颗粒污泥对有机物的吸附类型,表明好氧颗粒污泥对有机物的吸附是一个包含生物吸附和化学吸附的复杂过程。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.