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.     

固相微萃取-毛细管气相色谱法快速同步分析水中有机氯及有机磷农药

固相微萃取是一种快速、简便,集萃取浓缩进样于一体的样品前处理技术,具有分析时间短、灵敏度高、无需有机溶剂的优点.用固相微萃取富集水中8种有机氯及7种有机磷农药,毛细管气相色谱ECD检测器分离分析,整个分析过程只需26min,检出限可达0.002～1μg/L,已用于地表水中有机氯及有机磷农药含量的测定.     

Desert Water Harvesting to Benefit Wildlife: A Simple, Cheap, And Durable Sub-Surface Water Harvester for Remote Locations

A sub-surface desert water harvester was constructed in the sagebrush steppe habitat of south-central Idaho, U.S.A. The desert water harvester utilizes a buried micro-catchment and three buried storage tanks to augment water for wildlife during the dry season. In this region, mean annual precipitation (MAP) ranges between about 150–250 mm (6″–10″), 70% of which falls during the cold season, November to May. Mid-summer through early autumn, June through October, is the dry portion of the year. During this period, the sub-surface water harvester provides supplemental water for wildlife for 30–90 days, depending upon the precipitation that year. The desert water harvester is constructed with commonly available, “over the counter” materials. The micro-catchment is made of a square-shaped, 20 mL. “PERMALON” polyethylene pond liner (approximately 22.9 m × 22.9 m = 523 m²) buried at a depth of about 60 cm. A PVC pipe connects the harvester with two storage tanks and a drinking trough. The total capacity of the water harvester is about 4777 L (1262 U.S. gallons) which includes three underground storage tanks, a trough and pipes. The drinking trough is refined with an access ramp for birds and small animals. The technology is simple, cheap, and durable and can be adapted to other uses, e.g. drip irrigation, short-term water for small livestock, poultry farming etc. The desert water harvester can be used to concentrate and collect water from precipitation and run-off in semi-arid and arid regions. Water harvested in such a relatively small area will not impact the ground water table but it should help to grow small areas of crops or vegetables to aid villagers in self-sufficiency.     

CTL-12型化学需氧量速测仪与标准法测定CODCr之比较

使用CTL—12型化学需氧量速测仪分析废水化学需氧量，简便、快速，且方法可行，完全能满足环境监测技术要求。     

膜、柱串联固相萃取-高效液相色谱法测定水中多环芳烃(PAHs)的研究

建立了用膜-柱串联固相萃取(SPE)技术,甲醇和水作为流动相进行梯度洗脱,紫外和荧光检测器串联的高效液相色谱法(HPLC)分析水中EPA优先监控的16种PAHs的方法.     

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.     

Trace Metal Concentration in Roadside Surface Soil and Tree Back: A Measurement of Local Atmospheric Pollution in Abuja, Nigeria

Cadmium, copper, lead, nickel and zinc concentrations were analysed by atomic absorption spectrophotometry in surface soil and tree bark from different districts of Abuja, Nigeria, in order to determine the atmospheric trace metal input in the area.Elevated concentrations of some of the studied metals were observed in the soil and tree bark samples from the commercial/high traffic areas of the city compared to backgroundvalues. In soil samples, the average concentration of the metals were 0.6±0.4, 18.0±4.0, 281±39, 16±4 and66±23 g g^-1 dry weight for Cd, Cu, Pb, Ni and Zn, respectively, whilst the average concentrations in tree bark were 0.3±0.2, 12±4, 133±32, 13±3 and 61±10 g g^-1 dry weight for Cd, Cu, Pb, Ni and Zn, respectively. The trend in trace metal levels suggested that automobile emissions are a major source of these metals as the highest concentrations of Pb and Zn were recorded in the commercial areas of the city known for their high traffic densities. The levels of metal in the study area were relativelylow compared to levels found in some larger and older cities in various countries worldwide.     

Monitoring of Butter and Ghee (Clarified Butter Fat) for Pesticidal Contamination from Cotton Belt of Haryana, India

Butter (45) and ghee (55) samples were collected from rural and urban areas of cotton growing belt of Haryana and analysed for detecting the residues of organochlorine, synthetic pyrethroid and organophosphate insecticides. The estimation was carried out by using multi residue analytical technique employing GC-ECD and GC-NPD systems equipped with capillary columns. Butter samples were comparatively more contaminated (97%) than ghee (94%), showing more contamination with organochlorine insecticides from urban samples. About 11% samples of butter showed endosulfan residues above MRL value and 2% samples had residues of synthetic pyrethroids and organophosphates each above their respective MRL values. In ghee, residues of HCH & DDT both and of endosulfan exceeded the MRL values in 5 and 20% samples, respectively. Among organophosphates, only chlorpyriphos was detected with 9% samples showing its residue above MRL value. Irrespective of contamination levels, residues above the MRL values were more in ghee. More extensive study covering other agricultural regions/zones of Haryana has been suggested to know the overall scenario of contamination of milk products.     

Air Pollution and Heat Exposure Study in the Workplace in a Glass Manufacturing Unit in India

Air pollution in the workplace environment due to industrial operation have been found to cause serious occupational health hazard. Similarly, heat stress is still most neglected occupational hazard in the tropical and subtropical countries like India. The hot climate augments the heat exposure close to sources like furnaces. In this study an attempt is made to assess air pollution and heat exposure levels to workers in the workplace environment in glass manufacturing unit located in the State of Gujarat, India. Samples for workplace air quality were collected for SPM, SO₂, NO₂ and CO₂ at eight locations. Results of workplace air quality showed 8-hourly average concentrations of SPM: 165–9118 μg/m³, SO₂: 6–9 μg/m³ and NO₂: 5–42 μg/m³, which were below the threshold limit values of workplace environment. The level of CO₂ in workplace air of the plant was found to be in the range 827–2886 μg/m³, which was below TLV but much higher than the normal concentration for CO₂ in the air (585 mg/m³). Indoor heat exposure was studied near the furnace and at various locations in an industrial complex for glass manufacturing. The heat exposure parameters including the air temperature, the wet bulb temperature, and the globe parameters were measured. The Wet Bulb Globe Temperature (WBGT), an indicator of heat, exceeded ACGIH TLVs limits most of the time at all the locations in workplace areas. The recommended duration of work and rest have also been estimated.     

Cu、Pb、Zn火焰原子吸收多谱线分析在环境监测中的应用

对铜的 3条谱线 (2 49.2 nm、2 61 .4nm,3 2 4.8nm ) ,铅的 2条谱线 (2 83 .3 nm、2 1 7.0 1 nm )以及锌的 2条谱线(3 0 7.6nm、2 1 3 .9nm ) ,共 7条谱线的灵敏度、检测限、线性范围进行了归纳性总结 ,并对多种共存元素可能存在的干扰情况进行了研究 ,认为利用元素不同分析谱线的分析灵敏度差异 ,根据被测元素在样品消解液中含量 ,选择合适的谱线进行分析 ,既降低工作强度 ,又能获得满意结果