QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

     

Alternative approaches to dealing with respondent uncertainty in contingent valuation: a comparative analysis

Several strategies have been proposed to deal with response uncertainty in contingent valuation. One approach, often applied to address issues of hypothetical bias, recodes and/or reweights responses according to stated levels of certainty but so far few analyses compare alternative recoding and reweighting strategies. We explore the choice among alternative strategies that exploit a numerical certainty scale obtained from a follow-up to the payment question in a valuation survey about a whale conservation program. Two novel variations of previously followed approaches perform best on our dataset in terms of the efficiency of estimates. The first one uses an exponential transformation of the numerical certainty scale as a weight in the willingness to pay regression. The other one is based on constructing a continuous willingness to pay variable with the highly certain "yes" and "no" original responses to the payment question as extreme values and with mid-point values that correspond to the original "don't know" responses. We find, though, that the effect of using different treatment strategies on mean willingness to pay is rarely statistically significant and we fail to detect a consistent effect on the efficiency of the estimation regardless of the strategy applied.     

Understanding the impact of waste disposal sites on soil quality for agricultural production: A case study of the Kiteezi landfill,Uganda

Leachate from waste disposal sites (WDS) can significantly affect the soil physical, chemical and biological qualities, reducing soil health and agricultural productivity. However, there is a paucity of data on soil quality for understanding soil health impact due to WDS in Uganda. This study's aim was to understand, using the Kiteezi landfill site, how WDS impact soil properties. Soil samples were collected over 4 months in and around the landfill from three locations, and the soil quality data of the nearby area were used as baseline data for assessing the impact on soil due to WDS in the area. There were significant increases in the concentration of all studied parameters at the landfill except total N. The concentrations of ammonium (14.84 ± 1.76 mg/kg), nitrate (127.96 ± 18.36 mg/kg), and pH (7.8) were above the optimum levels. The levels of available P (181.4 ± 28.9 mg/kg) and exchangeable bases; K (2.23 ± 0.24 mg/kg), Na (1.17 ± 0.14 mg/kg), Mg (3.35 ± 0.3 mg/kg) and Ca (14 ± 1.1 mg/kg) cmol (+)/kg were optimal for plant growth. The calculated CEC of 20.75 cmol (+)/kg) showed that soils have a good potential to supply plant nutrients. Heavy metal levels were still below the permissible limit. Hence, the study shows that although WDS may contribute to the increase of soil fertility, they can potentially reduce the overall productivity of soil by increasing nutrient levels beyond optimum levels.     

Vertical characterization of soil contamination using multi-way modeling – A case study

This study describes application of chemometric multi-way modeling approach to analyze the dataset pertaining to soils of industrial area with a view to assess the soil/sub-soil contamination, accumulation pathways and mobility of contaminants in the soil profiles. The three-way (sampling depths, chemical variables, sampling sites) dataset on heavy metals in soil samples collected from three different sites in an industrial area, up to a depth of 60 m each was analyzed using three-way Tucker3 model validated for stability and goodness of fit. A two component Tucker3 model, explaining 66.6% of data variance, allowed interpretation of the data information in all the three modes. The interpretation of core elements revealing interactions among the components of different modes (depth, variables, sites) allowed inferring more realistic information about the contamination pattern of soils both along the horizontal and vertical coordinates, contamination pathways, and mobility of contaminants through soil profiles, as compared to the traditional data analysis techniques. It concluded that soils at site-1 and site-2 are relatively more contaminated with heavy metals of both the natural as well as anthropogenic origins, as compared to the soil of site-3. Moreover, the accumulation pathways of metals for upper shallow layers and deeper layers of soils in the area were differentiated. The information generated would be helpful in developing strategies for remediation of the contaminated soils for reducing the subsequent risk of ground-water contamination in the study region.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

Removal of acutely hazardous pharmaceuticals from water using multi-template imprinted polymer adsorbent

Molecularly imprinted polymer adsorbent has been prepared to remove a group of recalcitrant and acutely hazardous (p-type) chemicals from water and wastewaters. The polymer adsorbent exhibited twofold higher adsorption capacity than the commercially used polystyrene divinylbenzene resin (XAD) and powdered activated carbon adsorbents. Higher adsorption capacity of the polymer adsorbent was explained on the basis of high specific surface area formed during molecular imprinting process. Freundlich isotherms drawn showed that the adsorption of p-type chemicals onto polymer adsorbent was kinetically faster than the other reference adsorbents. Matrix effect on adsorption of p-type chemicals was minimal, and also polymer adsorbent was amenable to regeneration by washing with water/methanol (3:1, v/v) solution. The polymer adsorbent was unaltered in its adsorption capacity up to 10 cycles of adsorption and desorption, which will be more desirable in cost reduction of treatment compared with single-time-use activated carbon.     

Soil nitrous oxide emissions following band-incorporation of fertilizer nitrogen and swine manure

Treatment of liquid swine manure (LSM) offers opportunities to improve manure nutrient management. However, N2O fluxes and cumulative emissions resulting from application of treated LSM are not well documented. Nitrous oxide emissions were monitored following band-incorporation of 100 kg N ha(-1) of either mineral fertilizer, raw LSM, or four pretreated LSMs (anaerobic digestion; anaerobic digestion + flocculation: filtration; decantation) at the four-leaf stage of corn (Zea mays L.). In a clay soil, a larger proportion of applied N was lost as N2O with the mineral fertilizer (average of 6.6%) than with LSMs (3.1-5.0%), whereas in a loam soil, the proportion of applied N lost as N2O was lower with the mineral fertilizer (average of 0.4%) than with LSMs (1.2-2.4%). Emissions were related to soil NO3 intensity in the clay soil, whereas they were related to water-extractable organic C in the loam soil. This suggests that N2O production was N limited in the clay soil and C limited in the loam soil, and would explain the interaction found between N sources and soil type. The large N2O emission coefficients measured in many treatments, and the contradicting responses among N sources depending on soil type, indicate that (i) the Intergovernmental Panel on Climate Change (IPCC) default value (1%) may seriously underestimate N2O emissions from fine-textured soils where fertilizer N and manure are band-incorporated, and (ii) site-specific factors, such as drainage conditions and soil properties (e.g., texture, organic matter content), have a differential influence on emissions depending on N source.