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.     

Analysis of BTEX and other substituted benzenes in water using headspace SPME-GC-FID: method validation

The analysis of BTEX and other substituted benzenes in water samples using solid phase microextraction (SPME) and quantification by gas chromatography with flame ionization detection (GC-FID) was validated. The best analytical conditions were obtained using PDMS/DVB/CAR fibre using headspace extraction (HS-SPME) at 50 [degree]C for 20 min without stirring. The linear range for each compound by HS-SPME with GC/FID was defined. The detection limits for these compounds obtained with PDMS/DVB/CAR fibre and GC/FID were: benzene (15 ng L(-1)), toluene (160 ng L(-1)), monochlorobenzene (54 ng L(-1)), ethylbenzene (32 ng L(-1)), m-xylene (56 ng L(-1)), p-xylene (69 ng L(-1)), styrene (35 ng L(-1)), o-xylene (42 ng L(-1)), m-dichlorobenzene (180 ng L(-1)), p-dichlorobenzene (230 ng L(-1)), o-dichlorobenzene (250 ng L(-1)) and trichlorobenzene (260 ng L(-1)). This headspace SPME-GC-FID method was compared with a previously validated method of analysis using closed-loop-stripping analysis (CLSA). The headspace SPME-GC-FID method is suitable for monitoring the production and distribution of potable water and was used, in field trials, for the analysis of samples from main intakes of water (surface or underground) and from the water supply system of a large area (Lisbon and neighbouring municipalities).     

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.     

The organic composition of diesel particulate matter, diesel fuel and engine oil of a non-road diesel generator

Diesel-powered equipment is known to emit significant quantities of fine particulate matter to the atmosphere. Numerous organic compounds can be adsorbed onto the surfaces of these inhalable particles, among which polycyclic aromatic hydrocarbons (PAHs) are considered potential occupational carcinogens. Guidelines have been established by various agencies regarding diesel emissions and various control technologies are under development. The purpose of this study is to identify, quantify and compare the organic compounds in diesel particulate matter (DPM) with the diesel fuel and engine oil used in a non-road diesel generator. Approximately 90 organic compounds were quantified (with molecular weight ranging from 120 to 350), which include alkanes, PAHs, alkylated PAHs, alkylbenzenes and alkanoic acids. The low sulfur diesel fuel contains 61% alkanes and 7.1% of PAHs. The identifiable portion of the engine oil contains mainly the alkanoic and benzoic acids. The composition of DPM suggests that they may be originated from unburned diesel fuel, engine oil evaporation and combustion generated products. Compared with diesel fuel, DPM contains fewer fractions of alkanes and more PAH compounds, with the shift toward higher molecular weight ones. The enrichment of compounds with higher molecular weight in DPM may be combustion related (pyrogenic).     

上海汽车制造业涂装废气VOCs排放特征

于2015年10月对上海市嘉定区4家(A、B、C、D)汽车制造企业涂装废气ρ(VOCs)和组成特征进行了调查分析。结果表明,A、B、C、D厂涂装废气排放口ρ(VOCs)总为0. 743～6. 11 mg/m~3,主要检出物和最高检出值分别为:A厂二甲苯2. 06 mg/m~3、B厂二甲苯0. 578 mg/m~3、C厂甲苯2. 59 mg/m~3、D厂庚烷0. 274 mg/m~3;芳香烃类是A、B、C厂排放比例最高的VOCs组分,烷烃类是D厂排放比例最高的VOCs组分。指出,原、辅料种类影响排放物的主要成分,废气处理工艺类型影响排放物主要成分和浓度。     

环境样品中痕量锰的催化动力学测定法

对环境样品中痕量锰的催化动力学测定方法进行简要评述，引述文献１６篇。     

A study of the elemental leachability and retention capability of compost

In this work a comparison is made between the different approaches that can be taken to evaluate the mobility of elements in compost. The practical consequences of the results obtained are also discussed in terms of methods for cleaning up compost and using compost in environmental remediation. The mobility of potentially toxic elements in compost is evaluated by leaching with four selected eluents, i.e. diluted sulfuric acid, oxalate, citrate and EDTA. In contrast to the chelating agents, diluted sulfuric acid was found to generally have a low leaching capability for removal of heavy metals from compost. This implies that the risk of heavy metal leaching caused by natural rainfall is likely to be low. The results obtained in the leaching experiment were compared with previous results obtained from sequential fractionation. This comparison confirmed that both methods gave similar results for predicting the lability of elements in compost. A non-linear regression analysis of the leaching curves was also conducted. The leaching curves for elements with high lability could be fitted with a two components model. The labile components identified by the kinetic model are approximately in accordance with the fractions obtained from the first step of the sequential extraction method. The kinetic speciation method is shown to be a relatively rapid and simple procedure for compost which gives more information about element lability than simple leaching experiments. The leaching reagents used in this work were not effective enough to be used for cleaning up compost with a high metal content. Compost was however shown to have a high affinity for heavy metals, with the order of affinity of metal for the compost being very similar to that seen for humic acid. Compost may therefore prove to be a good remediation material for metal contaminated waste.     

HNO_3─KI─MIBK萃取／原子吸收光谱法测定固体废物浸提液中的微量银

采用选择性较好的ＨＮＯ３—ＫＩ—ＭＩＢＫ萃取体系对铋渣和尾矿渣浸出液中的痕量银进行预分离富集后，用火焰原子吸收法（ＡＡＳ）进行测定。此法不仅变异系数小（约为３．１％），而且检出限比直接火焰ＡＡＳ法降低两个数量级，达０．５ｎｇ／ｍｌ．另外，还就作品细度对银测定结果的影响原因做了初步探讨。     

合肥地区重金属污染生物监测的初步研究 --以大蜀山铅镉的白鹭监测为例

将合肥地区大蜀山的白（Egretta garzetta）卵和组织样品，用原子吸收法时样品中的Pb、Cd等重金属污染物进行了检测，结果表明，这2种重金属元素在卵和组织中均有不同程度的检出，其中Cd在卵的内容物中残留量较低。而且这2种重金属污染物在鹭卵不同部分的残留量均有差异；同其他地区类似的研究结果相比，合肥地区鹭卵中重金属残留水平相时较高。     

用好防灾科技学院教育平台创建和弘扬防震减灾文化-恭贺母校升本暨校庆30周年

本文对防震减灾文化的组成、创建动力及其方法进行了探讨.初次提出了防震减灾文化的概念,并对创建防震减灾文化的目的和如何创建、弘扬防震减灾文化提出了看法和观点.