城市大气环境质量比较评价分级方法

以城市大气环境实测数据为背景,经过Box－Cox变换,选取标准正态分布的代表点,得到大气环境质量分级标准值及对应概率。数据正规化后,从多种因素和总量统计进行大气环境质量综合比较分级,按综合分级值M_ij从（0.0～1.0）均分原则,分为清洁级、尚清洁级、轻度污染级、中度污染级、重度污染级和极度污染级。     

石墨探针采样/石墨探针炉原子吸收光谱测定APM中痕量铜

采用一定规格的石墨探针可直接收集大气微料物质（ＡＰＭ）。然后,立即采用石墨探针炉原子吸收光谱测定收集在探针上的ＡＰＭ中痕量铜。方法简便,快速。在０－５５０ｎｇ／ｍＬ范围内,铜的浓度与峰面积吸光度呈良好的线性关系。铜的特征量为２２．０ｐｇ,检出限为６．６ｎｇ／ｍＬ。分析标准参比材料,铜的回收率达９８％,相对标准偏差为３．３％。     

改良型Carrousel氧化沟工艺生物脱氮除磷效果研究

为了提高改良型Carrousel氧化沟工艺污水处理厂的脱氮除磷效果,结合某污水处理厂3年的运行实践,讨论了该工艺的处理效果,生物脱氮除磷原理及影响出水效果的因素.分析表明将DO控制在0.3～0.7 mg/L范围内,能够使出水中的TN浓度低于20 mg/L;在氧化沟中发生的同步硝化反硝化反应(SND)对总氮的去除的贡献占总系统脱氮的66%;该系统剩余污泥的含磷率为3.0%,生物细胞中平均含磷量可达细胞干重的4.2%;总磷去除率与污泥龄具有很好的线性关系,加大污泥排放量可以提高除磷效果.     

温度对热水解预处理高含固污泥特性的影响

以含固率为10%的污泥为对象,研究不同温度和时间条件下热水解过程中有机物转化规律对污泥特性的影响。结果表明:随着热水解时间的延长和温度的升高,污泥中挥发性悬浮固体(VSS)的水解率逐渐升高。在热水解时间为20 min时,温度由150℃升高到180℃,VSS水解率由22.9%增至38.4%,污泥中VSS由46.28 g/L降低到40.63 g/L。水解液组分分析表明,溶解性化学需氧量(SCOD)主要组成为蛋白质(50%)、碳水化合物(15%~20%)、可挥发性脂肪酸(15%~20%)。水解液中氮主要以有机氮的形式存在。在热水解条件达到165℃、50 min时,氨氮浓度为1.16 g/L,之后,随着温度的升高和时间的延长,氨氮浓度基本不变。     

海水电池处理模拟海水养殖废水

随着中国海水养殖业的迅猛发展,海水养殖废水排放量与日俱增,近海水域环境严重恶化。采用海水电池处理模拟海水养殖废水,以镁为电池负极,钛为电池正极。镁负极失去电子产生Mg2+,与废水中的NH4+、PO43-结合生成磷酸铵镁（MgNH4PO4·6H2O,MAP）,将废水中的NH4+和PO43-去除。结果表明,当pH为9.5,氮磷配比为1：1.1时,氮的去除率可达85.34% ,磷的去除率可达98.46%。利用正交实验研究pH、初始氨氮浓度、氮磷比和COD4个因素对氮磷去除的影响,氨氮初始浓度、COD分别对氮、磷的去除影响最大。多个电池串联时,总输出电压是各电池电压之和。     

上流式缺氧污泥床和好氧生物膜组合脱氮工艺的研究

将上流式颗粒污泥床(USB)用于反硝化和生物膜法用于自养硝化处理蔗糖配水和小区生活污水,反硝化污泥床去除有机物和硝态氮具有节省需好氧去除有机物的能耗的优势,同时好氧生物膜法硝化效率高。试验结果表明,当工艺进水的有机负荷小于2kgCOD/m3·d时,出水COD均小于60mg/L,好氧单元进水有机负荷和氨氮负荷分别小于13kgCOD/m3·d和09kgNH3N/m3·d时,出水氨氮小于5mg/L;COD/NO-3N是影响反硝化的关键因素,处理蔗糖配水时,COD/NO-3N大于5时反硝化脱氮完全,而COD/NO-3N为10时,生活污水作为电子供体仍然脱氮不完全;有机物含量过高导致好氧单元硝化效果降低,HRT是影响好氧单元硝化效率的主要因素,HRT缩短为15h时,氨氮去除率降低了85%左右;同时处理蔗糖配水和生活污水的反硝化菌活性相当。     

碳氮磷比例失调城市污水的同步脱氮除磷

为解决现行同步脱氮除磷工艺处理南方地区碳、氮、磷比例失调城市污水中,因C/N、C/P偏低,碳源不足而降低脱氮除磷效率的难题,试验以碳源偏低的广州市城市污水为研究对象,采用厌氧/好氧交替运行的SBR系统,通过对厌氧、好氧时段的合理调控,在无需额外添加碳源的条件下,有机物、氨氮、总氮和总磷的平均去除率分别可达90%、72%、41%和99%,不仅能使有机物和氮的出水指标达到国家排放标准,而且总磷出水浓度能达0.5 mg/L以下。通过进一步分析同步高效脱氮除磷的影响因素和控制条件,得出合理污泥龄的控制是实现同步脱氮除磷的关键,厌氧/好氧交替运行的方式不仅强化了磷的释放和吸收,而且降低了碳源偏低和硝酸盐对同步脱氮除磷影响的结论。     

Composition of wet and bulk deposition in Erzurum, Turkey

Seasonal variations in the chemical characteristics of wet and bulk deposition samples collected in Erzurum were investigated for the period March 2002-January 2003. Major cations (Ca2+, K+, Mg2+) and major anions (SO4(2-),NO3-) were determined in bulk and wet deposition samples; pH was also measured in wet deposition. The average pH of the wet deposition at Erzurum was 6.6 due to extensive neutralization of the acidity. A strong relationship between pH and SO4(2-) concentrations was observed in all seasons; however, only a weak relationship was found between pH and NO3-. On a seasonal basis, the correlation between Ca2+ and SO4(2-) concentrations was stronger in winter than in summer. Seasonal variations of ions were examined in both wet and bulk deposition samples. Although maximum concentrations of anions generally occurred during winter and spring, cation concentrations peaked in summer for both wet and bulk deposition. Results indicated that Ca2+ was the dominant cation and SO4(2-) the dominant anion in all deposition samples at Erzurum. Even though correlations among the crustal ions (calcium, magnesium and potassium) were high, the relationship between anthropogenic ions (sulfate and nitrate) was less clear in bulk deposition.     

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.     

我国固定源大气颗粒物监测技术的现状与改进建议

针对国内固定源大气颗粒物监测技术的现状及不足,通过对资料总结发现国内固定源排放颗粒物的监测技术现状中存在国标精度要求较低和技术细节不足等问题,并重点从分级采样和大气低浓度颗粒物检测方面深入剖析相关技术的不足与需求,并结合国内外的经验,从国标的修订与补充、分级采样技术体系的建立和低浓度大气颗粒物采样方法的改进等3个方面提出技术与设备方面的改进建议。