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Abstract: In this study, linear and nonlinear modeling was performed to predict the urban air quality of the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial regression (MPR), and artificial neural network (ANN) approach-based models were constructed to predict the respirable suspended particulate matter (RSPM), SO2, and NO2 in the air using the meteorological (air temperature, relative humidity, wind speed) and air quality monitoring data (SPM, NO2, SO2) of five years (2005–2009). Three different ANN models, viz. multilayer perceptron network (MLPN), radial-basis function network (RBFN), and generalized regression neural network (GRNN) were developed. All the five different models were compared for their generalization and prediction abilities using statistical criteria parameters, viz. correlation coefficient (R), standard error of prediction (SEP), mean absolute error (MAE), root mean squared error (RMSE), bias, accuracy factor (Af), and Nash–Sutcliffe coefficient of efficiency (Ef). Nonlinear models (MPR, ANNs) performed relatively better than the linear PLSR models, whereas, performance of the ANN models was better than the low-order nonlinear MPR models. Although, performance of all the three ANN models were comparable, the GRNN over performed the other two variants. The optimal GRNN models for RSPM, NO2, and SO2 yielded high correlation (between measured and model predicted values) of 0.933, 0.893, and 0.885; 0.833, 0.602, and 0.596; and 0.932, 0.768 and 0.729, respectively for the training, validation and test sets. The sensitivity analysis performed to evaluate the importance of the input variables in optimal GRNN revealed that SO2 was the most influencing parameter in RSPM model, whereas, SPM was the most important input variable in other two models. The ANN models may be useful tools in the air quality predictions. [Copyright &y& Elsevier]

The spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs) was investigated in Gomti River, a major tributary of the Ganga river (India). A total of 96 samples (water and sediments) were collected from eight different sites over a period of 2 years and analysed for 16 PAHs. The total concentrations of 16 PAHs in water and bed sediments ranged between 0.06 and 84.21 μg/L (average ( n = 48), 10.33 ± 19.94 μg/L) and 5.24-3,722.87 ng/g dw [average ( n = 48): 697.25 ± 1,005.23 ng/g dw], respectively. In water, two- and three-ring PAHs and, in sediments, the three- and four-ring PAHs were the dominant species. The ratios of anthracene (An)/An + phenenthrene and fluoranthene (Fla)/Fla + pyrene were calculated to evaluate the possible sources of PAHs. These ratios reflected a pattern of pyrolytic input as a major source of PAHs in the river. Principal component analysis, further, separated the PAHs sources in the river sediments, suggesting that both the pyrolytic and petrogenic sources are contributing to the PAHs burden. The threat to biota of the river due to PAHs contamination was assessed using effect range low and effect range median values, and the results suggested that sediment at some occasions may pose biological impairment. [ABSTRACT FROM AUTHOR]

This study reports source apportionment of polycyclic aromatic hydrocarbons (PAHs) in particulate depositions on vegetation foliages near highway in the urban environment of Lucknow city (India) using the principal components analysis/absolute principal components scores (PCA/APCS) receptor modeling approach. The multivariate method enables identification of major PAHs sources along with their quantitative contributions with respect to individual PAH. The PCA identified three major sources of PAHs viz. combustion, vehicular emissions, and diesel based activities. The PCA/APCS receptor modeling approach revealed that the combustion sources (natural gas, wood, coal/coke, biomass) contributed 19–97% of various PAHs, vehicular emissions 0–70%, diesel based sources 0–81% and other miscellaneous sources 0–20% of different PAHs. The contributions of major pyrolytic and petrogenic sources to the total PAHs were 56 and 42%, respectively. Further, the combustion related sources contribute major fraction of the carcinogenic PAHs in the study area. High correlation coefficient ( R 2 > 0.75 for most PAHs) between the measured and predicted concentrations of PAHs suggests for the applicability of the PCA/APCS receptor modeling approach for estimation of source contribution to the PAHs in particulates. [ABSTRACT FROM AUTHOR]

This study explores the extent and possible sources of heavy metal (Cd, Cr, Cu, Fe, Mn, Pb, Zn and Ni) contamination in the bed sediments of the Gomti River performing principal component analysis on the five years (Jan. 1994–Dec. 1998) data set obtained through continuous monitoring of the river water and bed sediments at eight selected sites and water/wastewater of its tributaries/drains. Influence of anthropogenic activities on metal contamination of the bed sediments was evaluated through computing the geoaccumulation index for various metals at studied sites. PCA performed on combined (river bed sediment, water, suspended solids, water/wastewater from tributaries/drains) data set extracted two significant factors explaining more than 58% of total variance. Factor loadings suggested the presence of both natural as well as anthropogenic sources for all these metals in the river bed sediments. Among all the sites, the sites 4 and 5 are more contaminated with Cd, Cu, Cr and Pb, which was supported by the geoaccumulation indices computed for metals. Factor scores revealed presence of seasonal (monsoon-related) differences in metals profiles for river water and suspended solids and absence of seasonal differences for bed sediment and wastewater. Further, the metal contamination of the bed sediment was also evaluated using biological thresholds. Results suggested that the river bed sediments are contaminated with heavy metals, which may contribute to sediment toxicity to the freshwater ecosystem of the Gomti River. [ABSTRACT FROM AUTHOR]

This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994–1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources. [Copyright &y& Elsevier]

This study reports the concentration levels and distribution pattern of the persistent organochlorine pesticide (OCPs) residues in the water and bed-sediments of the Gomti River collected seasonally over a period of 2 years. The water and bed-sediment samples were collected from eight different sites and analyzed for aldrin, dieldrin, endrin, HCB, HCH isomers, DDT isomers/metabolites, endosulfan isomers (alpha and beta), endosulfan sulfate, heptachlor and its metabolites, alpha-chlordane, gamma-chlordane and methoxychlor. In the river water and sediments SigmaOCPs residues ranged between 2.16 and 567.49 ng l(-1) and 0.92 and 813.59 ng g(-1), respectively. The results, further, suggested that source of DDT contamination is from the aged and weathered agricultural soils with signature of recently used DDT in the river catchments. To assess any adverse effect of OCPs contamination on river's biological component, the threshold effect level (TEL) was used. The results revealed that bed-sediments of the Gomti River are contaminated with lindane, endrin, heptachlor epoxides and DDT, particularly at site-4 and may contribute to sediment toxicity in the freshwater ecosystem of the river.

Rain water samples were collected to study the chemical composition of wet atmospheric precipitation (first event) over the Lucknow city in the northern Indo-gangetic alluvial plains. The samples were collected in the month of July, 2005 from different sites. The wet precipitation samples were analyzed for pH, EC, major ions (HCO(-3), Cl(-), SO4(2-), NO(-3), PO4(-3), F(-), Na(+), K(+), Ca(2+), Mg(2+), NH4+) and heavy metals (As, Cd, Al, Co, Cr, Cu, Fe, Mn, Mo, Ni, Hg, Pb, Se, Sn, Ti, V, Zn). The pH values of wet precipitation samples ranged between 6.5 and 8.7. The analysis of linear regression applied to the set of studied variables and computation of neutralization factors showed that neutralization occurred in precipitation samples and Ca(2+) had the maximum neutralization capacity. It was found that Cl(-), [Formula: see text], Ca(2+), Mg(2+), Na(+) and K(+) in the precipitation samples originated mainly from crustal/anthropogenic sources in the region. On an average Fe, and Al accounted for >72% of the total concentration of trace metals in the wet precipitation samples followed by Zn (>10%). Enrichment factors calculated for heavy metals over reference background level in seawater and Earth's crust showed relatively higher enrichment of Zn. The principal component analysis (PCA) identified the possible sources of ionic species and heavy metals in the wet precipitation samples.

This study reports the levels and distribution patterns of some organochlorine pesticides (OCPs) in fish samples of the Gomti river, India, collected from three sites. In the fish muscles Sigma OCPs ranged between 2.58-22.56 ng g(-1) (mean value: 9.66 +/- 5.60 ng g(-1)). Neither spatial nor temporal trends could be observed in distribution of the OCPs. Aldrin was the predominant OCP, whereas, HCB and methoxychlor could not be detected. alpha-HCH and beta-HCH among the isomers of HCH and pp-DDE among the metabolites of DDT were the most frequently detected OCPs. The results revealed that the fish of the Gomti river are contaminated with various OCPs.

A 10 years surface water quality data set pertaining to a polluted river was analyzed using partial least squares (PLS) regression models. Both the unfold-PLS and N-PLS (tri-PLS and quadri-PLS) models were calibrated through leave-one out cross-validation method. These were applied to the multivariate, multi-way data array with a view to assess and compare their predictive capabilities for biochemical oxygen demand (BOD) of river water in terms of their relative mean squares error of cross-validation, prediction and variance captured. The sum of squares of residuals and leverages were computed and analyzed to identify the sites, variables, years and months which may have influence on the constructed model. Both the tri- and quadri-PLS models yielded relatively low validation error as compared to unfold-PLS and captured high variance in model. Moreover, both of these methods produced acceptable model precision and accuracy. In case of tri-PLS the root mean squares errors were 1.65 and 2.17 for calibration and prediction, respectively; whereas these were 2.58 and 1.09 for quadri-PLS. At a preliminary level it seems that BOD can be predicted but a different data arrangement is needed. Moreover, analysis of the scores and loadings plots of the N-PLS models could provide information on time evolution of the river water quality.

This study reports the concentration levels and distribution pattern of the organochlorine pesticide (OCPs) residues in the soil and surface water samples collected from the northern Indo-Gangetic alluvial plains. A total of 31 soil and 23 surface water samples were collected from the study region in Unnao district covering an area of 2150 km(2) and analyzed for aldrin, dieldrin, endrin, HCB, HCH isomers, DDT isomers/metabolites, endosulfan isomers (alpha and beta), endosulfan sulfate, heptachlor and its metabolites, alpha-chlordane, gamma-chlordane and methoxychlor. In both the soil and surface water samples beta- and delta-isomers of HCH were detected most frequently, whereas, methoxychlor was the least detected pesticide. The results showed contamination of soil and surface water of the region with several persistent organic pesticides. The total OCPs level ranged from 0.36-104.50 ng g(-1) and 2.63-3.72 microg L(-1) in soil and surface water samples, respectively.

The groundwater samples collected from the shallow and deep groundwater aquifers of an industrial area of the Kanpur city (Uttar Pradesh, India) were analyzed for the concentration levels and distribution pattern of nitrogenous species, such as nitrate-nitrogen (NO(3)-N), nitrite-nitrogen (NO(2)-N), ammonical-nitrogen (NH(4)-N), organic-nitrogen (Org-N) and total Kjeldahl-nitrogen (TKN) to identify the possible contamination source. Geo-statistical approach was adopted to determine the distribution and extent of the contaminant plume. In the groundwater aquifers NO(3)-N, NO(2)-N, NH(4)-N, TKN, Org-N and Total-N ranged from 0.10 to 64.10, BDL (below detection limit)-6.57, BDL-39.00, 7.84-202.16, 1.39-198.97 and 8.89-219.43 mg l(-1), respectively. About 42% and 26% of the groundwater samples of the shallow and deep groundwater aquifers, respectively, exceeded the BIS (Bureau of Indian Standards) guideline value of 10 mg l(-1) for NO(3)-N and may pose serious health hazards to the people of the area. The results of the study revealed that the groundwater aquifers of the study area are highly contaminated with the nitrate and indicates point source pollution of nitrate in the study area.

The concentrations of cadmium, chromium, copper, iron, lead, manganese, nickel, and zinc in water and bed sediments of river Gomti have been studied in a fairly long stretch of 500 km from Neemsar to Jaunpur. Grab samples of water (October 2002-March 2003) and bed sediments (December 2002 and March 2003) were collected from 10 different locations following the standard methods. The river water and sediment samples were processed and analyzed for heavy metals viz., Cd, Cr, Cu, Fe, Pb, Mn, Ni, and Zn, and using ICP-AES. The heavy metals found in the river water were in the range: Cd (0.0001-0.0005 mg/L); Cr (0.0015-0.0688 mg/L); Cu (0.0013-0.0.0043 mg/L); Fe (0.0791-0.3190 mg/L); Mn (0.0038-0.0.0973 mg/L); Ni (0.0066-0.011 mg/L); Pb (0.0158-0.0276 mg/L); and Zn (0.0144-0.0298 mg/L) respectively. In the sediments the same were found in the range: Cd (0.70-7.90 /microg/g); Cr (6.105-20.595 microg/g); Cu (3.735-35.68 microg/g); Fe (5051.485-8291.485 micorg/g); Mn (134.915-320.45 microg/g); Ni (13.905-37.370 microg/g); Pb (21.25-92.15 microg/g); and Zn (1 5.72-99.35 microg/g) of dry weight respectively. Some physico-chemical parameters viz., pH, total solids, total dissolved solids, total suspended solids, dissolved oxygen, biological oxygen demand, chemical oxygen demand, hardness etc. were estimated as these have direct or indirect influence on the incidence, transport and speciation of the heavy metals. Based on the geoaccumulation indices, the Gomti river sediments from Neemsar to Jaunpur are considered to be unpolluted with respect to Cr, Cu, Fe, Mn, and Zn. It is unpolluted to moderately polluted with Pb. In case of Cd it varies from moderately polluted to highly polluted. As far as Ni is concerned the sediment is very highly polluted at Barabanki and Jaunpur D/s. No correlation was found between enrichment factor and geoaccumulation index.