稠油火驱开采技术节能减排效果分析

火驱作为稠油热采的有效接替技术,与注蒸汽开采技术相比,具有采收率高,能耗低,CO_2排放少的优势。文章从火驱驱油机理特征阐述了火驱与注蒸汽技术的不同点,从燃料开采能耗、热效率、注入剂资源、数据对比四个方面分析火驱比注蒸汽热效率高,无燃料开采成本,能耗是注蒸汽的50%;从CO_2气体排放方式和排放量对比可知,火驱能够减少温室气体排放,为油田节能减排、环保生产起到积极作用。     

降低循环冷却水水耗的生产措施

为了有效降低循环冷却水系统补充水量、排污量，节约水处理剂的消耗、降低冷却水处理成本，关键在于提高循环水的浓缩倍数。本文对影响循环水浓缩倍数的因素进行了分析，总结了哈尔滨石化公司循环水场存在的问题。同时本文给出了提高循环水浓缩倍数的方法，上述方法能取得显著的环境效益和经济效益。     

污水低氧脱氮机制及其应用研究进展

阐述了污水低氧脱氮的基本原理,即抑制或去除亚硝酸盐氧化菌(NOB),同时保留氨氧化菌(AOB),并保持其活性;探讨了污水低氧脱氮实现途径;详细介绍了几种典型的污水低氧脱氮工艺(短程硝化(SHARON)工艺、厌氧氨氧化(ANAMMOX)工艺、好氧反氨化(DEMON)工艺、低氧自养硝化反硝化(OLAND)工艺、甲烷营养型硝化反硝化工艺和亚硝酸盐型完全自养脱氮(CANNON)工艺)的应用研究进展;最后对污水低氧脱氮处理工艺的工程运用进行了展望.     

云南省能源消费及二氧化碳排放分析

根据云南省能源消费量,计算了由能源消费产生的CO2排放量及单位GDP碳排放强度。结果表明:近10年来,随着社会经济的快速发展,云南省能源消费及能源消费导致的CO2排放总量也呈现出显著上升的趋势。云南的碳排放强度高于全国平均水平,但人均碳排放量低于全国平均水平。以工业为主的第二产业是能源消费及碳排放的主体,其碳排放占总量的75%。     

Dietary patterns and blood levels of PCDDs, PCDFs, and dioxin-like PCBs in 1656 Japanese individuals

The association between dietary patterns and blood dioxin levels has not been fully investigated. The present study population consisted of 755 men and 901 women (aged 15-73 years) living in 90 different areas of 30 prefectures of Japan. Dietary habits were assessed by inquiring about the consumption frequency of 28 foods, food groups and beverages. In addition, the blood levels of 29 polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzo-furans (PCDFs), and dioxin-like polychlorinated biphenyl (DL-PCBs) congeners were determined by high-resolution gas chromatography/mass spectrometry. The median total toxicity equivalent (TEQ) in the blood, which was calculated on the basis of the toxicity equivalency factors of WHO (2005), was 16 pg TEQ g⁻¹ lipid. Principal component analysis identified five dietary patterns: Healthy diet (high intake of vegetables and fruits); Meat/High fat intake (high intake of meat, meat products, and eggs); Seafood and Alcohol (high intake of fish, shellfish, and alcoholic beverages); Miscellaneous; and Milk products and Alcohol intake (high intake of milk, Milk products, and alcoholic beverages). After adjusting for sex, age, body mass index, and smoking habits, the Seafood and Alcohol pattern scores were significantly related to higher blood levels of total TEQ and PCDDs/PCDFs/DL-PCBs, and the Milk products and Alcohol pattern scores were correlated with higher blood levels of DL-PCBs. More detailed analysis showed that the intake frequencies for alcoholic beverages and seafood were independently and positively associated with total TEQ and the TEQ of PCDFs and DL-PCBs. The association between alcoholic beverage intake and PCDDs was also significant. Analysis of dietary patterns may be useful for identifying the dietary characteristics of individuals with a high dioxin body burden.     

A human health risk assessment of rare earth elements in soil and vegetables from a mining area in Fujian Province,Southeast China

Contaminated food through dietary intake has become the main potential risk impacts on human health. This study investigated concentrations of rare earth elements (REEs) in soil, vegetables, human hair and blood, and assessed human health risk through vegetables consumption in the vicinity of a large-scale mining area located in Hetian Town of Changting County, Fujian Province, Southeast China. The results of the study included the following mean concentrations for total and bio-available REEs of 242.92 ± 68.98 (135.85–327.56) μg g⁻¹ and 118.59 ± 38.49 (57.89–158.96) μg g⁻¹ dry weight (dw) in agricultural soil, respectively, and total REEs of 3.58 ± 5.28 (0.07–64.42) μg g⁻¹ dw in vegetable samples. Concentrations of total REEs in blood and hair collected from the local residents ranged from 424.76 to 1274.80 μg L⁻¹ with an average of 689.74 ± 254.25 μg L⁻¹ and from 0.06 to 1.59 μg g⁻¹ with an average of 0.48 ± 0.59 μg g⁻¹ of the study, respectively. In addition, a significant correlation was observed between REEs in blood and corresponding soil samples (R² = 0.6556, p < 0.05), however there was no correlation between REEs in hair and corresponding soils (p > 0.05). Mean concentrations of REEs of 2.85 (0.59–10.24) μg L⁻¹ in well water from the local households was 53-fold than that in the drinking water of Fuzhou city (0.054 μg L⁻¹). The health risk assessment indicated that vegetable consumption would not result in exceeding the safe values of estimate daily intake (EDI) REEs (100−110 μg kg⁻¹ d⁻¹) for adults and children, but attention should be paid to monitoring human beings health in such rare earth mining areas due to long-term exposure to high dose REEs from food consumptions.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

人口与消费对碳排放影响的分析模型与实证

从消费压力人口视角探讨碳排放问题,有利于正确判断和把握气候变化压力的人文因素,以及人口与消费可持续发展的动力学机制.本文通过对STIRPAT模型的扩展,应用岭回归方法计量分析人口、消费及技术因素对碳排放的影响.对我国1980至2007年碳排放情况的统计实证结果表明,扩展的STIRPAT模型对中国国情有较高的解释力.居民消费水平、人口城市化率、人口规模三个因素对我国碳排放总量的变化影响明显;现阶段我国居民消费水平与人口结构变化对碳排放的影响力已高于人口规模变化的影响力,居民消费水平与消费模式等人文因素的变化有可能成为我国碳排放的新的增长点;技术进步因素在此模型中对我国该阶段碳排放的解释力有限,表明我国未来通过技术进步减缓碳排放的潜力巨大.     

电解法处理含镍废水及纯镍的回收

采用电解回收法处理Ni2+含量为2g/L的废水,在阴极回收纯镍。研究了电解电流、极距、NH4Cl浓度、pH值等因素对Ni2+去除率、槽压、阴极能耗的影响,得出最优工艺参数为:温度20℃,电解时间20min时、电解电流300mA,极距15mm,NH4Cl浓度5g/L,pH值8.0,该条件下Ni2+去除率为96.926%,槽压16.21V,阴极能耗22.418kW.h/kg,在阴极可得到沉积6.45μm厚的镍板。