Separation of selected PAHs by using high performance capillary electrophoresis with modifiers

The effect of varying SDS concentration in a micellar electrokinetic capillary chromatographic system (MECC) is investigated. In addition, the introduction of -cyclodextrin as modifier to the electrophoretic medium, containing SDS and a phosphate-borate buffer, is shown to give satisfactory separation of the seven selected PAHs. The separation efficiency is increased with the addition of -cyclodextrin.     

Development of monitoring technology for airborne particulate matter

Particulate matter suspended in the air has adverse effects onhuman health. Its level of concentration is an important parameter in evaluating the degree of hazard it poses to the atmosphere. Conventional methods used in measuring particulatematter are often filter-based, which indicates some disadvantagesbecause such a base requires labor and time. In this study, to achieve real-time measurements, a new electrical method was developed for measuring PM10 and PM2.5 concentrations. The basicprinciple is to electrically charge particles passing through thePM inlet using a corona charger and measure the currents createdby charged particles to obtain the number concentration of particulate matter. A new type inlet based on the particle cupimpactor configuration was designed and its performance was evaluated. A unipolar diffusion charger was developed and thecharger's efficiency was determined experimentally in terms ofPn, which represents the penetration through the charger,P, times the average charge number acquired by a particle,n, for different particle sizes. The correlation was constructed between the PM10 (or the PM2.5) mass concentrationsand the electrical currents due to particles, which were chargedby the diffusion charger.     

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.     

超临界二氧化碳萃取－GC／MS测定土壤中的多环芳烃

本文开发了一种采用超临界二氧化碳萃取土壤中多环芳烃、不须经过纯化步骤，直接可用于ＧＣ／ＭＳ分析的简便、高效的方法。本实验中超临界革取的流体是二氧化碳，改善剂是５％的二氯甲烷／甲醇，萃取温度为１２０℃、压力为３４ＭＰａ。ＧＣ／ＭＳ分析时除了采用外标外，还加入了６种同位素ＰＡＨｓ内标以校正各段ＰＡＨｓ的响应因子。采用本方法成功地测定了我国未开垦森林土壤中的ＰＡＨｓ。     

Arsenic speciation in human hair: a new perspective for epidemiological assessment in chronic arsenicism

The analysis for arsenic in hair is commonly used in epidemiological studies to assess exposure to this toxic element. However, poor correlation between total arsenic concentration in hair and water sources have been found in previous studies. Exclusive determination of endogenous arsenic in the hair, excluding external contamination has become an analytical challenge. Arsenic speciation in hair appears as a new possibility for analytical assessing in As-exposure studies. This study applied a relative simple method for arsenic speciation in human hair based on water extraction and HPLC-HG-ICP-MS. The concentration of arsenic species in human hair was assessed in chronically As(V)-exposed populations from two villages (Esqui?a and Illapata) of the Atacama Desert, Chile. The arsenic concentrations in drinking water are 0.075 and 1.25 mg L(-1), respectively, where As(V) represented between 92 and 99.5% of the total arsenic of the consumed waters. On average, the total arsenic concentrations in hair from individuals of Esqui?a and Illapata were 0.7 and 6.1 microg g(-1), respectively. Four arsenic species, As(III), DMA(V), MMA(V) and As(V), were detected and quantified in the hair extracts. Assuming the found species in extracts represent the species in hair, more than 98% of the total arsenic in hair corresponded to inorganic As. On average, As(III) concentrations in hair were 0.25 and 3.75 microg g(-1) in Esqui?a and Illapata, respectively; while, the As(V) average concentrations were 0.15 and 0.45 microg g(-1) in Esqui?a and Illapata, respectively. Methylated species represent less than 2% of the extracted As (DMA(V)+ MMA(V)) in both populations. As(III) in hair shows the best correlation with chronic exposure to As(V) in comparison to other species and total arsenic. In fact, concentrations of As(total), As(III) and As(V) in hair samples are correlated with the age of the exposed individuals from Illapata (R= 0.65, 0.69, 0.57, respectively) and with the time of residence in this village (R= 0.54, 0.71 and 0.58, respectively).     

Floristics, performance and prognosis of historical trees in the urban forest of Guangzhou City (China)

Outstanding historical trees embedded in cities constitute pertinent environmental assets, yet they are widely threatened in third-world cities. Inadequate understanding of this valuable natural-cum-cultural heritage hinders proper conservation. A case study of Guangzhou in south China evaluated floristic composition, age profile and biomass structure of historical trees, assessed their performance in major habitats (institutional, park and roadside), and established a prognosis for future growth and management. The 348 historical trees examined belonged to only 25 species, vis-à -vis 254 trees in the entire urban forest, dominated by five species and native members. Roadside had more trees, followed by institutional and park, with merely the most common four species shared by all habitats. The limited commonality reflected tree-performance differentiation by habitats exerting selection pressure on species. The institutional growth-regime was more conducive to nurturing high-caliber specimens, whereas park is less capable. Individual species achievement by habitats, derived from tree-count ranking and relative-abundance indices, could inform species choice and tree conservation. Few trees exceeded 300 years of age in the millennium-old city, echoing a history of intense tree—city conflicts. Potential life-span, trunk and crown diameters indicated ample opportunities for further expansion of biomass and landscape impacts, which would be straitjacketed by the tightening urban fabric.     

Environmental and biological monitoring of methyl ethyl ketone (MEK)

This study was conducted to evaluate the usefulness of various biological parameters for monitoring of workers exposed to methyl ethyl ketone (MEK). Fifty male workers from a large magnetic videotape factory participated in this study. Personal air samples were collected using 3M organic vapor monitors and analysed for MEK by gas chromatography with flame ionisation detector (FID). 10 mL of urine; blood (1 mL) and exhaled air were also collected at the end of an 8-hour workshift. The headspace GC method was applied for measurement of urinary and blood MEK. MEK in expired air was analysed directly by using a GC/FID.The correlation coefficients (r) between environmental MEK and all other biological parameters measured show significant positive relationships. The r for environmental MEK and urine MEK was 0.84; for blood 0.73 and for breath 0.64. The correlation coefficients between blood and urine was 0.72; blood and breath was 0.88 and urine and breath 0.60. These findings suggest that measurements of unmetabolised MEK in blood, exhaled air and urine can be used for biological monitoring of MEK exposure. Nevertheless, laboratory methodological assessment is in favour of measuring urinary MEK as it is non-invasive and does not have to be analysed immediately after collection.     

Global and Regional Distribution of Carbon Monoxide from MOPITT: Seasonal Distribution at 700 hPa

The Measurement of Pollution in the Troposphere (MOPITT) instrument is an eight-channel gas correlation radiometer, which was launched on the Earth Observing System (EOS) Terra satellite in 1999. Carbon monoxide (CO) is one of the important trace gases because its concentration in the troposphere directly influences the concentrations of tropospheric hydroxyl (OH), which controls the lifetimes of tropospheric trace gases. CO traces the transport of global and regional pollutants from industrial activities and large scale biomass burning. The global and regional distributions of CO were analyzed using the MOPITT data for East Asia, which were compared with the ozone distributions. In general, seasonal CO variations are characterized by a peak in the spring, which decrease in the summer. This work also revealed that the seasonal cycles for CO are at a maximum in the spring and a minimum in the summer, with average concentrations ranging from 118 to 170 ppbv. The monthly average for CO shows a similar profile to that for O3. This fact clearly indicates that the high concentration of CO in the spring is possibly due to one of two causes: the photochemical production of CO in the troposphere, or the transport of the CO into East Asia. The seasonal cycles for CO and O3 in East Asia are extensively influenced by the seasonal exchanges of different air mass types due to the Asian monsoon. The continental air masses contain high concentrations of O3 and CO, due to the higher continental background concentrations, and sometimes to the contribution from regional pollution. In summer this transport pattern is reversed, where the Pacific marine air masses that prevail over Korea bring low concentrations of CO and O3, which tend to give the apparent summer minimums.     

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.     

210Po bioaccumulation in coastal sand dune wild legumes--Canavalia spp. of southwest coast of India

In view of considerable natural background radioactivity reported from southwest coast of India, the current study documents bioaccumulation of 210Po in two dominant coastal sand dune perennial mat-forming wild legumes, Canavalia cathartica and C. maritima. Root, stem, leaf, mature beans, dry seeds and rhizosphere sand were analyzed for 210Po concentration. 210Po activity decreased in the order of leaves > roots > seeds > stems > beans. The highest 210Po activity was recorded in rhizosphere sand samples (5.78-5.88 Bq kg(-1)) followed by the leaf samples (3.27-3.07 Bq kg(-1)), while it was lowest in mature beans (0.13-0.20 Bq kg(-1)). 210Po activities or tissue moisture between plant species were not significantly different (p > 0.05; t-test). But 210Po activity vs. moisture differed significantly in all tissues (p = 0.0001), vegetative tissues (root, stem and leaf) (p = 0.0016), seeds (p = 0.0393) and proteins in seeds (p = 2.355 x 10(-6)) indicating the importance of moisture and protein in 210Po accumulation. Although 210Po has affinity for proteins, it did not concentrate too much in seed proteins of Canavalia. Concentration of 210Po in mature beans is at safe levels as fisher folk only consume tender pods occasionally. 210Po activity in Canavalia is compared with other plant materials. As the landraces of C. cathartica and C. maritima are distributed throughout pantropical coastal areas, the current study emphasizes on considering them as bioindicators to monitor 210Po in coastal sand dune biomes and in turn the health of coastal population.