Characterisation of PM10, PM2.5 and Benzene Soluble Organic Fraction of Particulate Matter in an Urban Area of Kolkata, India

In this study, the relationship between inhalable particulate (PM₁₀), fine particulate (PM_2.5), coarse particles (PM_{2.5 – 10}) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003–2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3–5 m above ground near highly trafficked and congested areas. The 24 h average PM₁₀ and PM_2.5 samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM_2.5 and PM₁₀ were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM₁₀ and PM_2.5 and inverse correlation was observed between particulate matter (PM₁₀ and PM_2.5) and wind speed. Statistical analysis of air quality data shows that PM₁₀ and PM_2.5 are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM₁₀ and PM_2.5 and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM₁₀) and fine particulate (PM_2.5) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM₁₀ (BSM10) and benzene soluble organic fraction of PM_2.5 (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed.     

Using Ion-Exchange Resins to Study Soil Response to Experimental Watershed Acidification

Ion-exchange resins (IER) offer alternative approaches to measuring ionic movement in soils that may have advantages over traditional approaches in some settings, but more information is needed to understand how IER compare with traditional methods of measurement in forested ecosystems. At the Bear Brook Watershed in Maine (BBWM), one of two paired, forested watersheds is treated bi-monthly with S and N (28.8 and 25.2kgha⁻¹yr⁻¹ of S and N, respectively). Both IER and ceramic cup tension lysimeters were used to study soil solution responses after ∼11 years of treatment. Results from both methods showed treatments resulted in the mobilization of base cations and Al, and higher SO₄—S and inorganic N in the treated watershed. Both methods indicated similar differences in results associated with forest type (hardwoods versus softwoods), a result of differences in litter quality and atmospheric aerosol interception capacity. The correlation between lysimeter and IER data for individual analytes varied greatly. Significant correlations were evident for Na (r=0.75), Al (r=0.65), Mn (r=0.61), Fe (r=0.57), Ca (r=0.49), K (r=0.41) and NO₃—N (r=0.59). No correlation was evident between IER and soil solution data for NH₄—N and Pb. Both IER and soil solution techniques suggested similar interpretations of biogeochemical behavior in the watershed.     

Comparison of 24 h averaged VOC monitoring results for residential indoor and outdoor air using Carbopack X-filled diffusive samplers and active sampling--a pilot study

Analytical results obtained by thermal desorption GC/MS for 24 h diffusive sampling of 11 volatile organic compounds (VOCs) are compared with results of time-averaged active sampling at a known constant flow rate. Air samples were collected with co-located duplicate diffusive sampling tubes and one passivated canister. A total of eight multiple-component sampling events took place at fixed positions inside and outside three private homes. Subsequently, a known amount of sample air was transferred from the canister to an adsorbent tube for analysis by thermal desorption GC/MS. Results for the 11 most prevalent compounds--Freon 11, 1,3-butadiene, benzene, toluene, tetrachloroethene, ethylbenzene, m,p-xylene, o-xylene, 4-ethyltoluene, 1,3,5-trimethylbenzene, and p-dichlorobenzene--show that the ratio of average study values (diffusive sampling to active sampling) is 0.92 with 0.70 and 1.14 extreme ratios. Absolute percent difference for duplicate samples using diffusive sampling was <10% for the four most prevalent compounds. Agreement between the two sampling approaches indicates that the prediction of approximately constant diffusive sampling rates based on previous laboratory studies is valid under the field conditions.     

Estimating sampling frequency in pollen exposure assessment over time

A time series model was fitted to the pollen concentration data collected in the Greater Cincinnati area for the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). A traditional time series analysis and temporal variogram approach were applied to the regularly spaced databases (collected in 2003) and irregularly spaced ones (collected in 2002), respectively. The aim was to evaluate the effect of the sampling frequency on the sampling precision in terms of inverse of standard error of the overall level of mean value across time. The presence of high autocorrelation in the data was confirmed and indicated some degree of temporal redundancy in the pollen concentration data. Therefore, it was suggested that sampling frequency could be reduced from once a day to once every several days without a major loss of sampling precision of the overall mean over time. Considering the trade-offs between sampling frequency and the possibility of sampling bias increasing with larger sampling interval, we recommend that the sampling interval should take values from 3 to 5 days for the pollen monitoring program, if the goal is to track the long-term average.     

Cancer and Non-Cancer Health Risk from Eating Cassava Grown in Some Mining Communities in Ghana

Food crops such as cassava, cocoyam and other tuber crops grown in mining communities uptake toxic or hazardous chemicals such as arsenic, and cadmium, from the soil. Cassava is a stable food for Ghanaians. This study evaluated human health risk from eating cassava grown in some mining communities in Ghana such as Bogoso, Prestea, Tarkwa and Tamso, which are important mining towns in the Western Region of Ghana. The study evaluated cancer and non-cancer health effects from eating cassava grown in the study areas in accordance with US Environmental Protection Agency’s Risk Assessment guidelines. The results of the study revealed the following: cancer health risk for Tamso, 0.098 (RME – Reasonable Maximum Exposure) and 0.082 (CTE – Central Tendency Exposure). This means that approximately 10 and 8 out of 100 resident adults are likely to suffer from cancer related cases by RME and CTE parameters respectively. For Prestea, we have 0.010 and 0.12, which also means that approximately 1 out of 100 and 10 resident adults out of 100 are also likely to suffer from cancer related diseases by RME and CTE parameters. The results of the study obtained were found to be above the acceptable cancer risk range of 1× 10⁻⁶ to 1× 10⁻⁴, i.e., 1 case of cancer out of 1 million or 100,000 people respectively.     

Development of an extraction method for perchlorate in soils

Perchlorate originates as a contaminant in the environment from its use in solid rocket fuels and munitions. The current US EPA methods for perchlorate determination via ion chromatography using conductivity detection do not include recommendations for the extraction of perchlorate from soil. This study evaluated and identified appropriate conditions for the extraction of perchlorate from clay loam, loamy sand, and sandy soils. Based on the results of this evaluation, soils should be extracted in a dry, ground (mortar and pestle) state with Milli-Q water in a 1 ratio 1 soil ratio water ratio and diluted no more than 5-fold before analysis. When sandy soils were extracted in this manner, the calculated method detection limit was 3.5 microg kg(-1). The findings of this study have aided in the establishment of a standardized extraction method for perchlorate in soil.     

SAOZ measurements of NO2 at Aberystwyth

We present in this paper fifteen years' measurements, from March 1991 to September 2005, of stratospheric NO2 vertical columns measured by a SAOZ zenith-sky visible spectrometer. The instrument spent most of its time at Aberystwyth, Wales, with occasional excursions to other locations. The data have been analysed with the WinDOAS analysis program with low-temperature high-resolution NO2 cross-sections and fitting a slit function to each spectrum. Because of a change in detector in May 1998 there is some uncertainty about the relative changes before and after this date, which are partially constrained by the results of an intercomparison exercise. However, the effect of the Mt Pinatubo aerosol cloud is very evident in the data from 1991-94, with a decrease of 10% in NO2 in the summer of 1992 (the SAOZ was located in Lerwick, Scotland during the winter of 1991-92 and observed very low NO2 values but these cannot be directly compared to the Aberystwyth data). To focus more on interannual and long-term variations in NO2, a seasonal variation comprising an annual and semi-annual component was fitted to the morning and evening twilight separately from 1995 to the present. This fit yielded average NO2 columns of 4.08 x 10(15) cm(-2) and 2.68 x 10(15) cm(-2) for the evening and morning twilight, respectively, with a corresponding annual amplitude of +/-2.08 x 10(15) cm(-2) and +/-1.50 x 10(15) cm(-2). Departures from the fitted curve show a trend of 6% per decade, consistent with that reported elsewhere, for the period 1998-2003, but in the past two years a distinct interannual variation of amplitude of approximately 8% has emerged.     

The Metallic Composition of Aerosols at Three Monitoring Sites in Korea During Winter 2002

In the present study, the distribution patterns of various metals were analyzed and compared using PM samples collected concurrently from three monitoring sites located in Korea (Seoul, Busan, and Jeju island) in December 2002. As these sites can represent metal pollution with different degrees of anthropogenic activities, their concentration levels were distinguished in a systematic manner in the order of Jeju, Busan, and Seoul. By comparing the present data sets with those measured previously from other locations in Korea and around the world, we attempted to diagnose the general status of elemental pollution on the Korean peninsula. Through an application of different statistical approaches, the major processes controlling elemental levels were assessed for each of the three study sites. The results indicated the importance of both crustal and anthropogenic sources in all sites with their relative roles varying significantly from each other. The results of the metal analysis data, when examined in relation to back trajectory analysis, confirmed that their concentration changes are affected quite sensitively with air mass movement patterns. The overall results of this study consistently indicated the contribution of a strong anthropogenic source area (e.g., China) to the observed metal concentration levels in the study area, but the strengths of such signals vary considerably across the Korean peninsula.     

A Rapid Method for Assessing the Environmental Performance of Commercial Farms in the Pampas of Argentina

The generation of reliable updated information is critical to support the harmonization of socio-economic and environmental issues in a context of sustainable development. The agro-environmental assessment and management of agricultural systems often relies on indicators that are necessary to make sound decisions. This work aims to provide an approach to (a) assess the environmental performance of commercial farms in the Pampas of Argentina, and (b) propose a methodological framework to calculate environmental indicators that can rapidly be applied to practical farming. 120 commercial farms scattered across the Pampas were analyzed in this study during 2002 and 2003. Eleven basic indicators were identified and calculation methods described. Such indicators were fossil energy (FE) use, FE use efficiency, nitrogen (N) balance, phosphorus (P) balance, N contamination risk, P contamination risk, pesticide contamination risk, soil erosion risk, habitat intervention, changes in soil carbon stock, and balance of greenhouse gases. A model named Agro-Eco-Index was developed on a Microsoft-Excel support to incorporate on-farm collected data and facilitate the calculation of indicators by users. Different procedures were applied to validate the model and present the results to the users. Regression models (based on linear and non-linear models) were used to validate the comparative performance of the study farms across the Pampas. An environmental dashboard was provided to represent in a graphical way the behavior of farms. The method provides a tool to discriminate environmentally friendly farms from those that do not pay enough attention to environmental issues. Our procedure might be useful for implementing an ecological certification system to reward a good environmental behavior in society (e.g., through tax benefits) and generate a commercial advantage (e.g., through the allocation of green labels) for committed farmers.     

A Simulation Study to Assess the Sensitivity of a Forest Health Monitoring Network to Outbreaks of Defoliating Insects

The sensitivity of the United States Forest Health Monitoring network to outbreaks of defoliating insects was examined by means of a simulation study. A model constructed specifically for the study was used to generate a wide variety of defoliation patterns in forested landscapes. Forest configuration was that of Minnesota, USA, as expressed by the GAP land cover classification. Combinations of model parameters were based on a Latin Hypercube sample. The relationship between the average number of plots defoliated and outbreak characteristics was then examined via multiple regression. Both theoretical and model results pointed to a strong, linear relationship between the average number of plots defoliated and outbreak size. Model results provided additional insight, suggesting a significant relationship between the average number of plots defoliated and other outbreak characteristics after outbreak size was taken into account.