Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

能源富集区市域经济发展水平空间格局演变——基于晋陕蒙甘宁地区

采用熵权TOPSIS法对我国晋陕蒙甘宁这一典型能源富集区各地级以上城市的市域经济发展水平进行综合评价,进而使用马尔可夫转移概率分析方法和ESDA空间分析方法,探索该地区市域经济发展水平时空分异格局的演化特征,结果显示：（1）该地区的市域经济发展差距在波动中下降;（2）各等级城市在不同时期演化规律截然相反,随时间推移,除低等级之外的各等级城市均表现出较高的等级下降风险;（3）该地区城市的市域经济水平曾存在一定空间负相关,到2015年这种关系则不再显著;（4）该地区呈现以局部为核心区的格局,但西部地区明显趋冷;（5）市域经济系统整体处于耦合协调度较低的状态,发展不平衡、不协调的问题较突出。     

腐植酸中不同分子量组分与As(Ⅲ)的络合性能

采用超滤方法将腐植酸（HA）分成5个不同分子量组分,利用平衡透析法研究HA中不同分子量组分与As（III）的络合特征和作用机理,计算络合过程的条件分配系数、表观稳定常数.结果表明,腐植酸组分的分子量对HA与As（III）的络合过程有显著的影响,当分子量小于10kDa时,HA与As（III）络合能力明显强于其他条件.其中羧基和酚羟基在HA与砷络合中起重要作用,为主要络合位点.采用Scatchard分析和双位点配体模型对络合结果进行拟合,发现HA中不同分子量组分和As（III）的络合过程中均存在强和弱2种络合位点.     

颗粒物分界层Mie散射激光雷达识别的sigmoid算法

基于成都市2013年6月~2015年5月期间由Mie散射激光雷达探测的大气消光系数廓线资料,发现混合层以上在颗粒物消光和分子消光之间一致存在一个S型的过渡区,利用sigmoid函数对此分布形态进行模拟,通过计算该函数上下曲率最大点所在的高度,据此提出了颗粒物分界层Mie散射激光雷达识别的sigmoid算法.针对该算法模拟效果的分析表明,颗粒物分界层过渡区附近大气消光系数理论廓线和实测廓线保持了高度的相关性,二者在春夏秋冬四季的相关系数（R）分别为0.9971±0.0052、0.9935±0.0167、0.9979±0.0038和0.9990±0.0021（均通过α=0.05的显著性检验）.基于sigmoid算法计算的颗粒物分界层过渡区与成都市温江站探空资料得到的逆温层之间存在很好的对应关系.     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

新疆生态功能区划初探

“新疆生态功能区划”参考了“中国综合生态环境区划方案”，以《生态功能区划暂行规程》为依据，充分考虑新疆独特的地理分布格局，将新疆分为5个生态区，18个生态亚区，79个生态功能区。“新疆生态功能区划”对科学有效地管理新疆生态环境，因地制宜地实施保护和治理策略，保证社会经济可持续发展具有重要意义。     

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.     

太湖水质氮素质量浓度时空差异特征及影响因素分析

通过对2014—2016年湖体水质中氮素质量浓度分析,结合出入湖总氮浓度、水量、湖体水生生态等影响因素,发现太湖水体中总氮浓度呈现逐年下降的趋势,各监测点位总氮为0.530 mg/L～5.51 mg/L,时空分布不均,差异明显。时间上,总氮浓度表现为春季最高,夏季和秋季最低,且月均值变化曲线呈现出规律的正弦函数波形。空间上,总氮浓度大致表现出由西部湖区向东部湖区递减的趋势,呈现西部湖区﹥北部湖区﹥南部湖区﹥湖心区﹥东部湖区。要改善湖体水质,不仅要切断污染源,而且要加强水生生态功能修复。     

阳离子共聚物的反相悬浮聚合

采用反相悬浮聚合方法合成颗粒状的阳离子型高分子絮凝剂。选用丙烯酰胺(Am)和氯化[N,N,N-三甲基乙醇丙烯酸酯]盐(AQ)两种单体,研究反应体系的特征及影响分子量的基本因素,诸如温度、单体浓度、引发剂浓度、分散剂浓度、水相和油相的体积比(Vw/Vo)对分子量的影响。为了选择出最佳原料比,本文对两种单体的原料比进行了研究。实验中还发现亚硫酸钠是一种优良的缩短反应诱导期、加速反应进程的试剂。     

Quantitative structure - activity study on the reductivedehalogenation potency of the halogenated aromatics

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