Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

潮汐流人工湿地对高污染河水的处理功效

为了研究潮汐流人工湿地对高污染河流污染物的去除效果及其影响因素,实验构建了潮汐流人工湿地系统。运行春夏两季以来,系统对TN、NH3-N、TP、COD和BOD5的平均去除率分别为49.14%、48.34%、79.40%、81.52%和83.39%;其中系统对TN和NH3-N的去除受到温度的影响,且温度变化对NH3-N去除效果的影响大于TN;在实验周期内,系统对TN、TP、COD和BOD5的去除均受进水污染负荷的影响,但却有着较好的抗负荷冲击能力;潮汐流人工湿地能够显著提高系统内溶解氧,使其不再成为去除污染物的限制性因素。     

菖蒲和空心菜在处理微污染河水潜流人工湿地中的应用

为了探讨修复微污染河水的潜流湿地中植物对污染物去除效果的影响及其生长变化,在野外条件下构建2座分别栽种菖蒲和空心菜的水平潜流人工湿地,并以未栽种植物的湿地作空白。分析了湿地中污染物的去除效果,考察了湿地中植物的生物量、根系活力和氮磷含量的变化。植物湿地中污染物净化效果优于空白湿地,菖蒲和空心菜湿地对氨氮(NH+4-N)、总氮(TN)、总磷(TP)和高锰酸盐指数(CODMn)的平均去除率分别为61.1%和57.5%,31.5%和39.7%,24.7%和25.5%,20.4%和20.7%。实验结果表明,湿地中菖蒲的根系鲜重是空心菜的4.2倍,但其根系活力低于空心菜。2种植物均可在湿地中正常生长,但受湿地中营养盐浓度的限制性影响,移栽后的植物组织氮磷含量与移栽前相比下降了11.8%~20.3%。植物在净化微污染河水的潜流人工湿地中对N、P的去除起重要作用。     

投加化学药剂改善城市黑臭河流水质的研究

该研究通过投加化学药剂比选、药剂投加浓度和投加方式优化试验,考察了投加化学药剂对城市黑臭河流水质的改善效果和适用性。结果表明,投加过氧化钙能够快速提高水体溶解氧含量,同时降低水体总磷含量。在该试验条件下,投加400 mg/L的过氧化钙后水中的溶解氧迅速升高,但1天后即恢复到投加前水平。由于投加过氧化钙增氧的成本相对较高,并且水体流动性会影响过氧化钙的增氧效果,投加过氧化钙方法仅适用于对封闭的污染水体进行快速增氧和修复。     

微污染水源水处理技术探析

近年来,我国不少地区饮用水水源水质日益恶化,水质净化面临新的问题。通过对微污染水源水处理技术的分析,阐明了微污染水处理的基本理论,探讨了强化传统处理工艺、预处理技术、深化处理技术的作用效果与机理,展望了我国饮用水源水处理发展趋势。     

HS-SPME-GC-MS法测定黑臭河流中二甲基三硫醚

该研究在优化分析条件的基础上,采用顶空固相微萃取(HS-SPME)和气相色谱/质谱(GC-MS)联用方法测定了黑臭河流水体中二甲基三硫醚的含量。通过研究萃取头涂层、萃取温度、萃取时间、水样体积和气相色谱进样口温度对二甲基三硫醚萃取效率的影响,得到了二甲基三硫醚最佳预处理和检测条件为:采用CAR-PDMS(75μm)萃取头,取20 mL待测水样在45℃恒温搅拌下顶空萃取30 min,进样口温度为250℃。采用优化后的方法对实际城市黑臭河流水样进行了检测,测得的二甲基三硫醚的浓度范围为5 853～8 939 ng/L。     

强化混凝与活性炭联用处理赣江微污染水源水试验

采用强化混凝与活性炭联用技术对赣江微污染水源水处理进行试验研究。试验结果表明,强化混凝与活性炭联用较常规工艺能减小出水浊度,显著提高CODMn和UV254的去除率,并有效控制出水余铝含量。原水CODMn为4.10~4.25mg/L左右,UV254为0.2~0.3mg/L时,中试系统出水浊度降低了12%左右,出水CODMn和UV254分别为0.5~0.7mg/L和0.015~0.025mg/L,各自去除率较传统工艺分别提高了36%和40%左右。试验出水余铝浓度均<0.2mg/L,NH3-N去除率没有明显提高。     

有机污染场地微生物修复研究进展

结合国内外研究,从典型有机污染物微生物修复机理、降解菌株的筛选、微生物修复技术,以及微生物修复技术的表面活性剂和电化学强化等方面综述了微生物修复技术的研究进展,并对其发展方向和趋势做出展望。     

以有机污染为主的河流治理技术研究进展

借鉴国外污染河流治理与修复的经验,并结合我国当前严重的河流污染形势,介绍了以有机污染为主的河流治理技术的研究进展。根据污染河水处理系统与河道的相对空间关系,河流治理技术可分为三类,第一类是将河水引出河道水系,引入附近的污水处理厂进行处理的异地处理法,其中截污工程是异地处理法的关键;第二类是在河道内建设处理系统,沿程进行河水净化的原位处理法,如河道内的曝气法、投菌法、生物膜法和化学法等;第三类是在河岸带上建设处理系统,将河水分流其中进行处理的旁路处理法,如建于河岸上的人工湿地处理系统、氧化塘以及多种形式的生物床或生物反应器等,旁路处理法起着人工强化河岸带的作用,是目前受污染河流治理中值得关注的一条新思路。受污染河流的具体处理方法及空间位置的选择,需要根据实际情况而定。     

Dependence of Ozone Biomonitoring on Meteorological Conditions of Different Sites in Catalonia (N.E. Spain)

Evaluation of visual symptoms of ozone damage was conducted in the network of bioindicator rural stations of Catalonia (NE Spain) every 14 days from May to October. Damage rates of ozone (and consequently, ozone biomonitoring capacity of bioindicators) were found to vary highly in time and space depending on the local environmental and meteorological conditions. Lower ozone damage to foliage was produced when meteorological conditions favour stomata resistance. Modulatory effects of meteorological conditions need to be considered in biomonitoring and when modeling plant ozone doses and damage.