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.     

青铜峡铝厂氟污染对广武乡玉米影响的调查分析

监测了青铜峡铝厂东南方向４．５ｋｍ的广武乡玉米叶，得知含氟量为４３．９４－１６６．１ｍｇ／ｋｇ，对照样品树新林场玉米叶的含氟均值为１３．１５ｍｇ／ｋｇ，广武乡的玉米受到了严重的氟污染，农作物减产严重。     

小清河水体和沿岸地下水中有机污染物的监测及其毒性分析

本文对小清河水体和沿岸地下水中的有机污染物进行了色－质联用分析，共检出有机污染物１３类９３种。并对其毒性效应进行了分析和评价。     

Determination of PAHs by On-Line Polarization Synchronous Fluorescence with HPLC

A novel analytical method has been established for on-line simultaneous determination of fluorene and acenaphthene,chrysene and benzo[a]anthracene (B[a]A) by polarization synchronous fluoromet.y (PSF) coupled to high performance liquid chromatography (HPLC). The detection limits were: 0.039, 0.046, 0.016 and 0.042 mgL-1 for fluorene, acenaphthene, chrysene and B[a]A, respectively. The proposed method has been successfisily applied to simultaneous determination of these PAHs in environmental air and marine sediment samples.     

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).     

Determination of Trace Organochlorinated Pesticides and Polychlorinated Biphenyls in Water

A modified steam distillation liquid-liquid extraction (SDE) device is developed for the enrichment of trace organochlorinated pesticides (OCPs) and polychlorinated biphenyls (PCBs) in up to one litre of water samples. One ml of n-hexane is sufficient to extract the analytes. The extract can be analysed directly by a capillary gas chromatography-electron capture detector. The recoveries range from 84.6 to 102.3% for OCPs and 80.5 to 106.2% for PCBs, respectively. Matrice materials such as lipids and humic substances were removed by the SDE method from the extract and no further cleanup and concentration steps were needed.     

Preliminary studies of a fast screening method for polycyclic aromatic hydrocarbons in soil by using solvent microextraction-gas chromatography

Solvent microextraction (SME) was applied to the extraction of polycyclic aromatic hydrocarbons (PAHs) from spiked and real environmental soil samples with different matrices. Soil sample was mixed with 7 mL of acetone and 14 mL of water to allow partitioning of the PAHs from the soil to the liquid phase. A 2 microL octane drop suspended from a microsyringe needle tip was then immersed into the stirred solution-soil mixture for extraction. After an 11 min extraction, the octane drop was withdrawn into the syringe and injected directly into the GC for identification and quantification. The whole analysis procedure took 27 min, with an extraction time of 11 min, and a GC separation time of 16 min. A second extraction could be undertaken whilst the GC is running, hence the GC run time currently limits the sample throughput. In this method, a small amount of organic solvent was used for the extraction process, which produced little waste. The limits of detection for lower molecular weight (< 230) PAHs range from 0.13 to 0.36 mg kg-1, and for higher molecular weight (> 250) PAHs are estimated to be between 0.5 and 1.0 mg kg-1, with RSD values generally under 20%. Due to the small volumes of organic solvent used, the consumable cost per extraction is only US$ 0.12. This is the first report of the application of SME to solid samples, and the first report of the use of SME for the analysis of PAHs.     

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。