Assessment of uncertainty of NO2 measurements by the chemiluminescence method and discussion of the quality objective of the NO2 European Directive

Hereafter, an assessment of the ability of the chemiluminescence method to measure ambient NO2 with an accuracy within 15%, as requested by the data quality objective of European directive 1999/30/CE, is presented. In general, uncertainty is evaluated using the response to reference materials or by means of inter-comparisons used to determine some statistics like repeatability, reproducibility and calibration bias. These are incomplete approaches and the method of the Guide to the Expression of Uncertainty in Measurement, advised by the Directive, should be preferred. In fact, even if it requires a large data set, it allows the relative influence of all possible sources of uncertainty to be studied. The extent of NO2 uncertainty is mainly dependent on the level of NO. It is decreased by NOx and the correlation between NOx and NO. Furthermore, the uncertainty budget reveals that the contribution of accuracy of calibration standard, linearity, converter efficiency and drift of the analyser between calibration checks to the overall uncertainty is less important than the contribution of interference, mainly humidity and PAN in rural areas. The relative expanded uncertainty of the NO2 hourly average exceeds 30% for NO2 concentrations lower than 40 microg m(-3). Nevertheless, the data quality objective of 15% is reached for 200 microg m(-3), the hourly limit value of the European directive. On the contrary, at the limit value on the annual average, 40 microg m(-3), the data quality objective is not met if NO is higher than 100 microg m(-3). However, the data quality objective could be reached by correcting the measurements with the bias due to interference.     

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.     

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.     

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.     

Modelling of Materials Policies

This paper discusses the REAP1 model and its application for the analysis of CO₂ reduction and waste management policies for Japanese petrochemicals. The pros and cons of this modelling approach in comparison to other tools is elaborated. This is followed by a discussion of the model code and the modelling results. The results show that CO₂ policies can have significant impacts on waste flows and waste policies can have significant CO₂ benefits. As a consequence both effects must be considered in policy assessment. Pricing instruments are recommended instead of regulations because of the complex physical relations in the materials life cycle that extend beyond sector boundaries. A taxation approach is superior to a subsidy approach because rebound effects can be avoided.     

Seasonal variations in nickel and vanadium in Mont Blanc snow and ice dated from the 1960s and 1990s

Ni and V have been determined in snow and ice collected at a high altitude location (Col du D?me) near the summit of Mont Blanc on the French-Italian border; dated from the 1960s and 1990s. Ni and V were simultaneously determined by inductively coupled plasma sector field mass spectrometry. Measured concentrations range from 6 to 700 pg g(-1) and 4 to 1,100 pg g(-1) for Ni and V, respectively. The results show pronounced seasonal variations in the concentrations of both metals, with high concentration values in summer layers and much lower values for winter layers. These seasonal variations are linked especially with the existence of inversion layers during winter months. Ni and V concentrations in excess of the contributions from rock and soil dust (Ni(excess), V(excess)) appear to be mainly associated with anthropogenic inputs, with pronounced seasonal variations. Large variations in the V(excess)/Ni(excess) ratio are observed, with a higher ratio in summer than in winter. This shows differences in anthropogenic inputs at Col du D?me during the different parts of the year. The above ratio was compared with the corresponding ratios for oil combustion from stationary sources and the exhaust from gasoline and diesel engines. It appears that Ni and V concentrations at Col du D?me are probably the result of changing combinations of contributions from oil combustion for power generation, industrial and residential uses, on one side, and automobile and truck traffic, on the other side, with possibly a significant contribution from Ni smelters in Russia during winter months.     

Preliminary air pollution monitoring in San Miguel, Buenos Aires

Passive diffusion samplers were employed in San Miguel(Buenos Aires Metropolitan Area) for a preliminary airpollution monitoring. The highest loads were observedin downtown, compared with an urban background site.Total suspended particulate matter (TSPM) varied from0.257 to 0.033 mg cm^-2 month^-1; dust was examinedfor particle nature and size distribution. A similartrend was observed for nitrogen dioxide (NO₂) andTSPM spatial distribution, suggesting that traffic isthe major pollution source. Sulphur dioxide (SO₂)values were low and rather homogeneous. Levels for theinvestigated pollutants are below EPA's guide linevalues. Geographic (flat area, near to Rio de LaPlata) and climatologic factors (rainfalls andvariable wind directions) contribute to disperse pollutants.