Ozone facilitated dechlorination of 2-chloroethanol and impact of organic solvents and activated charcoal

The ozone-initiated oxidation of 2-chloroethanol was followed by monitoring the consumption of the halogenated organic substrate. Gas chromatographic analysis of the ozonated products showed an increase in conversion from about 1 % after 3 h of ozone treatment to about 22 % after 12 h. The yields of major ozonated products identified and quantified namely acetaldehyde, acetic acid, and chloride ion increased proportionately as a function of ozone treatment time. The percent conversion of 2-chloroethanol in the presence of acetic acid or ethyl acetate were found to be higher than those under solvent-free conditions with similar products obtained. The use of activated charcoal during the ozonolyis of 2-chloroethanol showed a significant increase in the percent conversion of the substrate compared to solvent free ozonation. Based on the experimental findings, the overall mechanism for the reaction between 2-chloroethanol and ozone is described.     

Arsenic and other trace elements in two catfish species from Paranaguá Estuarine Complex, Paraná, Brazil

Concentrations of arsenic and four additional trace elements (Cu, Cr, Ni, and Zn) were determined by inductively coupled plasma–optical emission spectrometry in the muscular tissue of the yellow catfish (Cathorops spixii) and the urutu catfish (Genidens genidens) from Paranaguá Estuarine Complex, Brazil (PEC). The PEC can be characterized by an environment of high ecological and economic importance in which preserved areas of rainforest and mangroves coexist with urban activities as ports and industries. The average concentrations (in milligram per kilogram dry weight) of elements in the muscle tissue of C. spixii are as follows: Zn (31), As (17), Cu (1.17), Cr (0.62), and Ni (0.28). Similar concentrations could be found in G. genidens with exception of As: Zn (36), As (4.78), Cu (1.14), Cr (0.51), and Ni (0.14). Fish from the geographic northern rural region (Guaraqueçaba–Benito) display higher As concentrations in the muscle tissues than fish found in the south-western (urban) part of the PEC. An international comparison of muscle tissue concentrations of trace elements in fish was made. Except for Ni in C. spixii, a tendency of decrease in element concentration with increasing size (age) of the fish could be observed. According to the National Health Surveillance Agency of Brazil, levels of Cr and As exceeded the permissible limits for seafood. An estimation of the provisional tolerable weekly intake of As was calculated with 109 % for C. spixii and with 29 % for G. genidens.     

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.     

Sharing the geo-Referenced Results of Climate Change Impact Research

The Prairie Adaptation Research Collaborative (PARC) has implemented an Internet Map Server (IMS) at the PARC web site (www.parc.ca) to 1) disseminate the geo-referenced results of PARC sponsored research on climate change impacts and adaptation, and 2) address data, information and knowledge management within the PARC network of researchers and partners. PARC facilitates interdisciplinary research on adaptation to the impacts of climate change in the Canadian Prairie Provinces. The web site is intended as a platform for sharing information and encouraging discussion of climate change impacts and adaptation. The IMS enables scientists and stakeholders to apply simple climate change scenarios to geo-referenced biophysical and social data, and dynamically create maps that display the geographic distribution of potential impacts of climate change. With a limited capacity for spatial analysis, most geo-processing and the climate impact modeling is done offline within a GIS environment. The IMS will serve the output from climate impact models, such that the model results can be customized by the web site user and be most readily applied to the planning and analysis of adaptation strategies.     

Flue gas treatability studies: a tool for techno-economic control of industrial air pollution

Air pollution problems in developing countries have gained larger fraction in the last decade especially due to non functioning and non implementation of effective air pollutioncontrol devices in industries. In industrial wastewater management, adequate treatability studies are conducted to arrive at a techno-economic treatment option. However no suchstudies were done for reducing air pollution or emission fromindustries until now in India. Little information was availableabout such studies in other countries. This article provides information about a novel technique known asflue gas treatability studies and to undertake such studies, a pilot scale system is installed in Air Pollution Control Divisionof M/s National Environmental Engineering research Institute, NEERI, Nagpur-20, India. This study is a tool for techno-economicselection of air pollution control systems specially for small/medium scale industrial emissions.     

Laboratory-polluted soils: a methodological approach to establish equilibrium conditions for different metal chemical forms in soils

An approach based on the number of extractions performed over time was applied to study the time necessary to attain equilibrium conditions between the different chemical forms present in two natural soils laboratory-loaded with heavy metals (Cu(II), Pb(II), Zn(II)). The influence of some soil parameters, such as pH value, cation exchange capacity and organic matter content, on the equilibrium time was evidenced both by the different nature of the soils and by the treatment of soils with paper mill sludge. Equilibrium conditions were determined by studying the metal partitioning in the soil on a case by case basis. The behaviour of the three metals was not the same, and the conditions of equilibrium among the different chemical forms were the result of the amount of heavy metals added as well as of the nature of the soil.     

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EF_G) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EF_G techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EF_G technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.     

Prediction of the solubility of zinc, copper, nickel, cadmium, and lead in metal-contaminated soils

Risk assessment of metal-contaminated soil depends on how precisely one can predict the solubility of metals in soils. Responses of plants and soil organisms to metal toxicity are explained by the variation in free metal ion activity in soil pore water. This study was undertaken to predict the free ion activity of Zn, Cu, Ni, Cd, and Pb in metal-contaminated soil as a function of pH, soil organic carbon, and extractable metal content. For this purpose, 21 surface soil samples (0–15 cm) were collected from agricultural lands of various locations receiving sewage sludge and industrial effluents for a long period. One soil sample was also collected from agricultural land which has been under intensive cropping and receiving irrigation through tube well water. Soil samples were varied widely in respect of physicochemical properties including metal content. Total Zn, Cu, Ni, Cd, and Pb in experimental soils were 2,015?±?3,373, 236?±?286, 103?±?192, 29.8?±?6.04, and 141?±?270 mg kg^?1, respectively. Free metal ion activity, viz., pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺, as estimated by the Baker soil test was 9.37?±?1.89, 13.1?±?1.96, 12.8?±?1.89, 11.9?±?2.00, and 11.6?±?1.52, respectively. Free metal ion activity was predicted by pH-dependent Freundlich equation (solubility model) as a function of pH, organic carbon, and extractable metal. Results indicate that solubility model as a function of pH, Walkley–Black carbon (WBC), and ethylenediaminetetraacetic acid (EDTA)-extractable metals could explain the variation in pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺ to the extent of 59, 56, 46, 52, and 51 %, respectively. Predictability of the solubility model based on pH, KMnO₄-oxidizable carbon, and diethylenetriaminepentaacetic acid-extractable or CaCl₂-extractable metal was inferior compared to that based on EDTA-extractable metals and WBC.     

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC_e 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.     

Assessment of oxidative stress markers and concentrations of selected elements in the leaves of Cassia occidentalis growing wild on a coal fly ash basin

Assessment of oxidative stress levels and tissue concentrations of elements in plants growing wild on fly ash basins is critical for realistic hazard identification of fly ash disposal areas. Hitherto, levels of oxidative stress markers in plants growing wild on fly ash basins have not been adequately investigated. We report here concentrations of selected metal and metalloid elements and levels of oxidative stress markers in leaves of Cassia occidentalis growing wild on a fly ash basin (Badarpur Thermal Power Station site) and a reference site (Garhi Mandu Van site). Plants growing on the fly ash basin had significantly high foliar concentration of As, Ni, Pb and Se and low foliar concentration of Mn and Fe compared to the plants growing on the reference site. The plants inhabiting the fly ash basin showed signs of oxidative stress and had elevated levels of lipid peroxidation, electrolyte leakage from cells and low levels of chlorophyll a and total carotenoids compared to plants growing at the reference site. The levels of both protein thiols and nonprotein thiols were elevated in plants growing on the fly ash basin compared to plants growing on the reference site. However, no differences were observed in the levels of cysteine, reduced glutathione and oxidized glutathione in plants growing at both the sites. Our study suggests that: (1) fly ash triggers oxidative stress responses in plants growing wild on fly ash basin, and (2) elevated levels of protein thiols and nonprotein thiols may have a role in protecting the plants from environmental stress.