Induction of cytochrome P4501A and endocrine disrupting effects of school incinerator residues

The emission of the dioxin-like compounds from on-site waste incinerators of seven schools in Kyonggi Province of Korea was evaluated by determination of the cytochrome P4501A(CYP1A) catalytic activity and antiestrogenic activity using cell culturemicrobioassay. The residue samples were extracted in a Soxhlet apparatus using toluene for 20 hr. The concentrated crude extractswere fractionated with a basic alumina column. Dioxin-like compounds were then extracted. Induction of CYP1A activity in a rat(H4IIE) hepatoma cell line was used as indicator of biologicaleffect of incinerator residues and measured as 7-ethoxyresorufin-O-deethylase(EROD) activities. The EROD activities of fraction II extracts (one of the two extracts) in the H4IIE cells were from 0.044±0.002 to 4.424±0.351 ng-TEQ g^-1 (TCDD Toxicity equivalent), showing relatively high inducing capacity. Antisetrogenicity of the extracts was measured as decrease in E₂-induced cell proliferation. Most of the extracts showed antiestrogenic activity in MCF7-BUS cell.The TEQ levels of the incinerator residues and the antiestrogenicactivities were in good correlation, strongly suggesting that thepotent toxic emissions were indeed produced from the on-site school waste semi-incinerators and could cause the antiestrogenicity.     

Comparison of Two Manual Methods of Nitrogen Dioxide Determination in Ambient Air

Sodium arsenite (modified Jacobs and Hochheiser method; hereafter referred as SA), the most widely used manual monitoring method for determination of nitrogen dioxide (NO₂) in ambient air, particularly in developing countries has been evaluated and compared with US EPA recommended equivalent method of TGS-ANSA (hereafter referred as ANSA). NO₂ concentrations generated from laboratory test atmosphere were analyzed by these two methods and were compared statistically. Laboratory evaluations showed that SA method has high sensitivity to different sampling conditions, which normally vary during actual field monitoring. Hence, correction factors for absorption efficiency were estimated for SA and ANSA methods. Absorption efficiency of NO₂ in SA method was found to be much lower (64%) as against the reported value of 82% at the method recommended sampling conditions, whereas for ANSA method, it was found 1.0 as against the reported value of 0.93. After applying derived correction factors, both the methods produced almost similar concentration values of NO₂.     

Transportation and fate of cationic surfactant in river water

The degradation, sorption, transportation and material balance of cationic surfactants discharged from domestic waste into river water was studied. Ion-pair solid-phase extraction behavior showed that the sorption of cationic surfactants as an ion-pair with anionic surfactant onto river sediment was so strong that little cationic surfactant was found in the bulk water. Cationic surfactant was found in river sediment at more than 500 times higher concentration than that in the bulk water. The degradation of the cationic surfactant was very slow in river water and much slower in the sediment. A material balance of cationic surfactant was estimated for a river running through Toyama City by measuring the flow rate and the concentration of cationic surfactant in the water at several points. It was found that more than 30% of cationic surfactant introduced to the river was lost during the river running through ca. 3 km in 3 h. This reduction probably comes from a quick transfer of the cationic surfactant from river water to sediment and water weed by means of adsorption or precipitation with suspending solids.     

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.     

A holistic approach combining factor analysis, positive matrix factorization, and chemical mass balance applied to receptor modeling

Rapid urbanization and population growth resulted in severe deterioration of air quality in most of the major cities in India. Therefore, it is essential to ascertain the contribution of various sources of air pollution to enable us to determine effective control policies. The present work focuses on the holistic approach of combining factor analysis (FA), positive matrix factorization (PMF), and chemical mass balance (CMB) for receptor modeling in order to identify the sources and their contributions in air quality studies. Insight from the emission inventory was used to remove subjectivity in source identification. Each approach has its own limitations. Factor analysis can identify qualitatively a minimal set of important factors which can account for the variations in the measured data. This step uses information from emission inventory to qualitatively match source profiles with factor loadings. This signifies the identification of dominant sources through factors. PMF gives source profiles and source contributions from the entire receptor data matrix. The data from FA is applied for rank reduction in PMF. Whenever multiple solutions exist, emission inventory identifies source profiles uniquely, so that they have a physical relevance. CMB identifies the source contributions obtained from FA and PMF. The novel approach proposed here overcomes the limitations of the individual methods in a synergistic way. The adopted methodology is found valid for a synthetic data and also the data of field study.     

Heavy metals contamination in water and three species of most consumed fish sampled from Caspian Sea, 2011

The present study is an attempt to evaluate the heavy metal contamination in the marine environment of the Caspian Sea of Iran. The concentrations of zinc, chromium, cadmium, and lead were measured in water and dorsal muscle of fish sampled from 10 selected stations along Gorgan coast using atomic absorption spectrophotometry during summer of 2011. The average concentrations of lead in water and fish samples (115.29?±?14.78 and 113.23?±?27.01 μg/L) were significantly higher than that of cadmium and chromium (P?<?0.05). The highest heavy metals content was observed in Cyprinus carpio samples. The heavy metals concentrations in the samples were within the acceptable recommended standards. But, elevating levels of heavy metals in water and fish made a serious concern about ecosystem and food chain contamination. It is recommended that protective authorities should carry out a continual assessment on the levels of pollutants in the sea.     

Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924–1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km² (52.5 %), 56,661.1 km² (36.4 %), 51,642.3 km² (33.2 %), 49,773 km² (32 %) and 48,669.4 km² (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 %?year^?1 during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km²) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.     

Delineating shallow saline groundwater zones from Southern India using geophysical indicators

A geophysical survey was conducted over an industrial belt encompassing 80 functional leather factories in Southern India. These factories discharge untreated effluents which pollute shallow groundwater where electrical conductivity (EC) value had a wide range between 545 and 26,600 μS/cm (mean, 3, 901 μS/cm). The ranges of Na⁺ and Cl^? ions were from 46 to 4,850 mg/L (mean, 348 mg/L) and 25 to 10,390 mg/L (mean, 1,079 mg/L), respectively. Geoelectrical layer parameters of 37 vertical electrical soundings were analyzed to demarcate fresh and saline water zones. However, the analysis not did lead to a unique resolution of saline and fresh waters. It was difficult to assign a definitive value to the aquifer resistivity of any area. Thus, geophysical indicators, namely longitudinal unit conductance (S), transverse unit resistance (T), and average longitudinal resistivity (R _s), were calculated for identifying fresh and saline waters. Spatial distributions of S, T, and R _s reflected widely varying ranges for the saline and fresh water zones. Further, the empirical relation of formation factor (F) was established from pore-water resistivity and aquifer resistivity for fresh and saline aquifers, which may be used to estimate local EC values from the aquifer resistivity, where well water is not available.     

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.     

Sediment fluxes and the littoral drift along northeast Andhra Pradesh Coast, India: estimation by remote sensing

The littoral drift regime along the northeastern coast of India was investigated by analyzing coastal drift indicators and shoreline changes based on multitemporal satellite images. The study of offshore turbidity patterns and quantitative estimation of suspended sediments was undertaken to understand the magnitude and direction of movement of sediment fluxes. The study revealed that: (1) the character of coastal landforms and sedimentation processes indicate that the sediment transport is bidirectional and monsoon dependent; (2) multidate, multitemporal analysis of satellite images helps to show the nature of sediment transport along the coast. The dominant net sediment transport is in a NE direction along the eastern coast of India. Finally, this assessment demonstrates the potential of remote sensing technology in understanding the coastal morphometric changes, long-term sediment transport, shoreline changes, and offshore turbidity distribution pattern and the implications for the transport of sediment-associated pollutants.