Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches

Groundwater hydrochemistry of an urban industrial region in Indo-Gangetic plains of north India was investigated. Groundwater samples were collected both from the industrial and non-industrial areas of Kanpur. The hydrochemical data were analyzed using various water quality indices and nonparametric statistical methods. Principal components analysis (PCA) was performed to identify the factors responsible for groundwater contamination. Ensemble learning-based decision treeboost (DTB) models were constructed to develop discriminating and regression functions to differentiate the groundwater hydrochemistry of the three different areas, to identify the responsible factors, and to predict the groundwater quality using selected measured variables. The results indicated non-normal distribution and wide variability of water quality variables in all the study areas, suggesting for nonhomogenous distribution of sources in the region. PCA results showed contaminants of industrial origin dominating in the region. DBT classification model identified pH, redox potential, total-Cr, and λ ₂₅₄ as the discriminating variables in water quality of the three areas with the average accuracy of 99.51 % in complete data. The regression model predicted the groundwater chemical oxygen demand values exhibiting high correlation with measured values (0.962 in training; 0.918 in test) and the respective low root mean-squared error of 2.24 and 2.01 in training and test arrays. The statistical and chemometric approaches used here suggest that groundwater hydrochemistry differs in the three areas and is dominated by different variables. The proposed methods can be used as effective tools in groundwater management.     

Factors influencing the variability of SO2 concentrations in Istanbul

The correlation between sulfur dioxide (SO2) concentrations measured at the European and Asian sides of Istanbul and meteorological parameters is investigated using principal component analysis (PCA) and multiple regression analysis techniques. Several meteorological parameters are selected to represent the atmospheric conditions during two winter periods: 1993-1994 and 1994-1995. Six principal components are found to explain the majority of the observed meteorological variability. Surface pressure, 850-mb temperature, and surface zonal (east-west) and meridional (north-south) winds show high loadings on separate factors identified by PCA. We seek dominant meteorological parameters that control the SO2 levels at each monitoring station. Several multiple regression analysis models are fitted to the data from each monitoring station using six principal components and previous-day SO2 concentrations as independent variables. Results suggest that the most important parameters, highly correlated with SO2 concentrations in the Istanbul metropolitan area, are atmospheric pressure and surface zonal and meridional winds. These components have more influence on the determination of the air pollution levels at the Asian side than at the European side.     

Emissions into the Atmosphere Due to the Ceramic Salt Glazing Process

The techniques of Principal Component Analysis (PCA) and subsequent regression analysis were used in an attempt to describe local and upwind chemical and physical factors which affect the variability of SO₄ ^–2 concentrations observed in a rural area of the northeastern U.S. The data used in the analyses included upwind and local O₃ concentrations, temperature, relative humidity and other climatological information, SO₂, and meteorological information associated with backward trajectories. The investigation identified five principal components, three major (eigenvalues >1) and two minor (eigenvalues < one), which accounted for 52% (r = 0.72) of the variability in the SO₄ ^–2 regression model. These components can be described as representing local and upwind photochemistry, droplet growth, SO₂ emissions, and air mass characteristics. The study also indicated that in future studies it will be necessary to a priori select air pollution and meteorological variables for measurement to potentially increase the sensitivity of this type of receptor model.     

Factors Influencing the Variability of SO2 Concentrations in Istanbul

ABSTRACT

The correlation between sulfur dioxide (SO₂) concentrations measured at the European and Asian sides of Istanbul and meteorological parameters is investigated using principal component analysis (PCA) and multiple regression analysis techniques. Several meteorological parameters are selected to represent the atmospheric conditions during two winter periods: 1993–1994 and 1994–1995. Six principal components are found to explain the majority of the observed meteorological variability. Surface pressure, 850-mb temperature, and surface zonal (east-west) and meridional (north-south) winds show high loadings on separate factors identified by PCA. We seek dominant meteorological parameters that control the SO₂ levels at each monitoring station. Several multiple regression analysis models are fitted to the data from each monitoring station using six principal components and previous day SO₂ concentrations as independent variables.

Results suggest that the most important parameters, highly correlated with SO₂ concentrations in the Istanbul metropolitan area, are atmospheric pressure and surface zonal and meridional winds. These components have more influence on the determination of the air pollution levels at the Asian side than at the European side.     

Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N(2)O emissions simulated by the CERES-EGC model

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N₂O emissions were mainly sensitive to the fraction of N₂O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.     

The impact of acidification on macroinvertebrate assemblages in welsh streams: towards an empirical model

The acidification of surface waters has profound ecological consequences. There is a need to predict the effects of possible future patterns of acid deposition on the biological components of fresh waters. This paper describes a model of the relationships between water chemistry and macroinvertebrate assemblages in eighteen streams in the upper Tywi whose catchments are subject to different land uses. Using established statistical techniques on data sets derived from riffle and margin samples taken in spring and summer, the macroinvertebrate assemblages were classified into three groups, which corresponded with streams draining conifer afforested catchments, acidic moorland streams and circumneutral moorland streams. Following principal components analysis to select key environmental variables, the application of multiple discriminant analysis generated two discriminant functions which were related most strongly to mean filterable aluminium concentration and mean total hardness, respectively. The discriminant functions were used to assign site-group membership with 100% success in the case of the spring data set with combined habitats. In addition, multiple regression of the primary ordination axis of each data set on mean aluminium concentration and mean hardness or pH, produced equations which explained 62.0%-87.2% of the variance. We conclude that the methods used here provide an effective analytical and potentially predictive tool for use in the understanding and management of the impact of acidification on freshwater ecosystems.     

Welcome

Abstract

An indicator of solid waste generation potential (SWGP) is proposed as a versatile means to assist the development of integrated solid waste (SW) management plans. The proposed indicator is based on key sodoeconomic variables for the State of Illinois which were found to be highly correlated with variables describing the SW stream of the State. The proposed indicator was derived by applying the principal components analysis (PCA) technique. The technique is used to merge the rank transformed socioeconomic variables into a single variable, the SWGP indicator, while maintaining the regional information of the original variables. An innovative aspect of this indicator approach is the use of the ordinal scale for all these diverse variables. The validity of this approach was assessed and the proposed indicator was found to be directly proportional to a composite variable describing the SW stream for the State of Illinois. The use of Geographic Information Systems (GIS) to depict the spatial distribution of the SWGP will help planners visualize the expected overall refuse generation pattern and to identify critical regions. In addition, the proposed indicator could be used as an instrument to validate the solid waste generation (SWG) quantities reported by counties to state agencies.     

Bioaccessibility of trace elements as affected by soil parameters in smelter-contaminated agricultural soils: a statistical modeling approach

An investigation was undertaken to identify the most significant soil parameters that can be used to predict Cd, Pb, and Zn bioaccessibility in smelter-contaminated agricultural soils. A robust model was established from an extended database of soils by using: (i) a training set of 280 samples to select the main soil parameters, to define the best population to be taken into account for the model elaboration, and to construct multivariate regression models, and (ii) a test set of 110 samples to validate the ability of the regression models. Total carbonate, organic matter, sand, P(2)O(5), free Fe-Mn oxide, and pseudototal Al and trace element (TE) contents appeared as the main variables governing TE bioaccessibility. The statistical modeling approach was reasonably successful, indicating that the main soil factors influencing the bioaccessibility of TEs were taken into account and the predictions could be applicable for further risk evaluation in the studied area.     

Comparison of a rhizosphere-based method with other one-step extraction methods for assessing the bioavailability of soil metals to wheat

There is no method recognized as a universal approach for evaluation of bioavailability of heavy metals in soil. Based on the simulation of the rhizosphere soil conditions and integration of the combined effects of root-soil interactions as a whole, a rhizosphere-based method has been proposed. Wet fresh rhizosphere soil was extracted by low-molecular-weight organic acids (LMWOAs) to fractionate metal fractions of soil pools, which were then correlated with the metal contents of wheat roots and shoots. The rhizosphere-based method was compared with other one-step extraction methods using DTPA, EDTA, CaCl2, and NaNO3 as extractants and the first step of the Community Bureau of Reference (BCR) method. Simple correlation and stepwise multiple regression analysis were used for the comparison. Simple correlation indicated that the extractable Cu, Zn, Cr, and Cd of soils by the rhizosphere-based method were significantly correlated with the metal contents of wheat roots. For DTPA, BCR1 and EDTA methods there was a relatively poor correlation between the extractable Cu, Zn and Cd of soil and metal contents of wheat roots. Stepwise multiple regression analysis revealed that the equation of the rhizosphere-based method was the simplest one, and no soil properties variables needed to be added. In contrast, the equations of other one-step extraction methods were more complicated, and soil properties variables needed to be entered. The most distinct feature of the rhizosphere-based method was that the recommended method was suitable for acidic, neutral and near alkaline soils. However, the DTPA and EDTA extraction methods were suitable for calcareous soils only-or-only for acidic soils. The CaCl2, and NaNO3 extraction methods were only suitable for exchangeable metals. In short, the rhizosphere-based method was the most robust approach for evaluation of bioavailability of heavy metals in soils to wheat.     

Neural networks and periodic components used in air quality forecasting

Forecasting of air quality parameters is one topic of air quality research today due to the health effects caused by airborne pollutants in urban areas. The work presented here aims at comparing two principally different neural network methods that have been considered as potential tools in that area and assessing them in relation to regression with periodic components. Self-organizing maps (SOM) represent a form of competitive learning in which a neural network learns the structure of the data. Multi-layer perceptrons (MLPs) have been shown to be able to learn complex relationships between input and output variables. In addition, the effect of removing periodic components is evaluated with respect to neural networks. The methods were evaluated using hourly time series of NO₂ and basic meteorological variables collected in the city of Stockholm in 1994–1998. The estimated values for forecasting were calculated in three ways: using the periodic components alone, applying neural network methods to the residual values after removing the periodic components, and applying only neural networks to the original data. The results showed that the best forecast estimates can be achieved by directly applying a MLP network to the original data, and thus, that a combination of the periodic regression method and neural algorithms does not give any advantage over a direct application of neural algorithms.     

Height growth of northern red oak in relation to site and atmospheric deposition in Pennsylvania

A forest tree growth-response to atmospheric deposition is expected to arise indirectly through soil chemical changes and would probably be observable only in the long term. We examined this hypothesis by evaluating the relationship between periodic height growth of mature northern red oak (Quercus rubra L.) trees and soil, physiography and atmospheric sulfate deposition along a 170-km west-to-east gradient of decreasing sulfate deposition in north central Pennsylvania, USA. Height increments for three common 20-year periods beginning in 1929, 1949 and 1969 were estimated from exponential-monomolecular growth functions fitted to stem analysis data for each of 45 trees in 13 ecologically analogous stands along the deposition gradient. Canonical analysis was used to identify a statistically manageable subset of the original 48 independent soil, site and tree (age, crown width) variables strongly associated with height growth. Predictive models relating total (60-year) and the three 20-year height increments to the reduced variable set plus estimated average sulfate and nitrate deposition were derived by best subsets multiple regression. An inherent spatial gradient of decreasing height growth from western to eastern sites was apparent in even the earliest (1929-1948) increment. This inferred non-deposition-related spatial growth trend was accounted for in the 1949-1968 growth increment by introduction of the earliest (1929-1948) growth increment as a significant covariate in the regression model. The inherent growth largely disappeared by the 1969-1988 period as a probable consequence of converging growth rates reported to occur in oaks after age 60 years regardless of site quality. The 1969-1988 growth increment was not as strongly correlated with site factors as was growth in preceding periods, nor was early growth or sulfate deposition significantly related to this height increment. Growth effects from sulfate deposition, if any, would most likely occur within the recent (1969-1988) increment coincident with the period of naturally decreasing growth rate, when site differences and possibly environmental factors would have less influence on growth. Our results give no indication that wet sulfate inputs are affecting northern red oak height growth across the atmospheric deposition gradient.     

Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.     

GIS-based source identification and apportionment of diffuse water pollution: perfluorinated compound pollution in the Tokyo Bay basin

To efficiently reduce perfluorinated compound (PFC) pollution, it is important to have an understanding of PFC sources and their contribution to the pollution. In this study, source identification of diffuse water pollution by PFCs was conducted using a GIS-based approach. Major components of the source identification were collection of the monitoring data and preparation of the corresponding geographic information that was extracted from a constructed GIS database. The spatially distributed pollution factors were then explored by multiple linear regression analysis, after which they were visually expressed using GIS. Among the 35 PFC homologues measured in a survey of the Tokyo Bay basin, 18 homologues were analyzed. Pollution by perfluorooctane sulfonate (PFOS) was explained well by the percentage of arterial traffic area in the basin, and the 84% variance of the measured PFOS concentration was explained by two geographic variables, arterial traffic area and population. Source apportionment between point and nonpoint sources was conducted based on the results of the analysis. The contribution of PFOS from nonpoint sources was comparable to that from point sources in several major rivers flowing into Tokyo Bay. Source identification and apportionment using the GIS-based approach was shown to be effective, especially for ubiquitous types of pollution, such as PFC pollution.     

The conversion of chicken manure to biooil by fast pyrolysis I. Analyses of chicken manure, biooils and char by 13C and 1H NMR and FTIR spectrophotometry

Fast pyrolysis of chicken manure produced two biooils (Fractions I and II) and a residual char. All four materials were analyzed by chemical methods, 13C and 1H Nuclear Magnetic Resonance Spectrometry (13C and 1H NMR), and Fourier Transform Infrared Spectrosphotometry (FTIR). The char showed the highest C content and the highest aromaticity. Of the two biooils Fraction II was higher in C, yield and calorific value but lower in N than Fraction I. The S and ash content of the two biooil fractions were low. The Cross Polarization Magic Angle Spinning (CP-MAS) 13C NMR spectrum of the initial chicken manure showed it to be rich in cellulose, which was a major component of sawdust used as bedding material. Nuclear Magnetic Resonance (NMR) spectra of the two biooils indicated that Fraction I was less aromatic than Fraction II. Among the aromatics in the two biooils, we were able to tentatively identify N-heterocyclics like indoles, pyridines, and pyrazines. FTIR spectra were generally in agreement with the NMR data. FTIR spectra of both biooils showed the presence of both primary and secondary amides and primary amines as well as N-heterocyclics such as pyridines, quinolines, and pyrimidines. The FTIR spectrum of the char resembled that of the initial chicken manure except that the concentration of carbohydrates was lower.     

Infant mortality and air pollution: a comprehensive analysis of U.S. data for 1990

This paper uses U.S. linked birth and death records to explore associations between infant mortality and environmental factors, based on spatial relationships. The analysis considers a range of infant mortality end points, regression models, and environmental and socioeconomic variables. The basic analysis involves logistic regression modeling of individuals; the cohort comprises all infants born in the United States in 1990 for whom the required data are available from the matched birth and death records. These individual data include sex, race, month of birth, and birth weight of the infant, and personal data on the mother, including age, adequacy of prenatal care, and smoking and education in most instances. Ecological variables from Census and other sources are matched on the county of usual residence and include ambient air quality, elevation above sea level, climate, number of physicians per capita, median income, racial and ethnic distribution, unemployment, and population density. The air quality variables considered were 1990 annual averages of PM10, CO, SO2, SO4(2-), and "non-sulfate PM10" (NSPM10--obtained by subtracting the estimated SO4(2-) mass from PM10). Because all variables were not available for all counties (especially maternal smoking), it was necessary to consider various subsets of the total cohort. We examined all infant deaths and deaths by age (neonatal and postneonatal), by birth weight (normal and low [< 2500 g]), and by specific causes within these categories. Special attention was given to sudden infant death syndrome (SIDS). For comparable modeling assumptions, the results for PM10 agreed with previously published estimates; however, the associations with PM10 were not specific to probable exposures or causes of death and were not robust to changes in the model and/or the locations considered. Significant negative mortality associations were found for SO4(2-). There was no indication of a role for outdoor PM2.5, but possible contributions from indoor air pollution sources cannot be ruled out, given higher SIDS rates in winter, in the north and west, and outside of large cities.     

The conversion of chicken manure to biooil by fast pyrolysis I. Analyses of chicken manure,biooils and char by 13C and 1H NMR and FTIR spectrophotometry

Fast pyrolysis of chicken manure produced two biooils (Fractions I and II) and a residual char. All four materials were analyzed by chemical methods, ¹³C and 1H Nuclear Magnetic Resonance Spectrometry (¹³C and 1H NMR), and Fourier Transform Infrared Spectrosphotometry (FTIR). The char showed the highest C content and the highest aromaticity. Of the two biooils Fraction II was higher in C, yield and calorific value but lower in N than Fraction I. The S and ash content of the two biooil fractions were low. The Cross Polarization Magic Angle Spinning (CP-MAS) ¹³C NMR spectrum of the initial chicken manure showed it to be rich in cellulose, which was a major component of sawdust used as bedding material. Nuclear Magnetic Resonance (NMR) spectra of the two biooils indicated that Fraction I was less aromatic than Fraction II. Among the aromatics in the two biooils, we were able to tentatively identify N-heterocyclics like indoles, pyridines, and pyrazines. FTIR spectra were generally in agreement with the NMR data. FTIR spectra of both biooils showed the presence of both primary and secondary amides and primary amines as well as N-heterocyclics such as pyridines, quinolines, and pyrimidines. The FTIR spectrum of the char resembled that of the initial chicken manure except that the concentration of carbohydrates was lower.     

Spatial modeling of factor analysis scores

The use of mosses as biomonitors operates as an indicator of their concentration in the environment, becoming a methodology which provides a significant interpretation in terms of environmental quality. The different types of pollution are variables that can not be measured directly in the environment - latent variables. Therefore, we propose the use of factor analysis to estimate these variables in order to use them for spatial modelling. On the contrary, the main aim of the commonly used principal components analysis method is to explain the variability of observed variables and it does not permit to explicitly identify the different types of environmental contamination. We propose to model the concentration of each heavy metal as a linear combination of its main sources of pollution, similar to the case of multiple regression where these latent variables are identified as covariates, though these not being observed. Moreover, through the use of geostatistical methodologies, we suggest to obtain maps of predicted values for the different sources of pollution. With this, we summarize the information acquired from the concentration measurements of the various heavy metals, and make possible to easily determine the locations that suffer from a particular source of pollution.