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In the present study, the quality of the surface water of the Gomti river (Lucknow, India) was investigated. Lucknow is situated in the centre of Uttar Pradesh, which is most the populated state in India. The locality has experienced rapid, unregulated development activities and population growth in recent decades, both of which have had a negative impact on its ecosystem and environment. Continuous monitoring is required to maintain the ecosystem at the desired level. Nine samples of river water were collected from the Gomti River in Lucknow, and they were analysed for a total of nine different characteristics, including pH, turbidity (Tur), dissolved oxygen (DO), total dissolved solids (TDSs), chemical oxygen demand (COD), chloride ion (Cl-) concentration, temperature (T), biochemical oxygen demand (BOD5) and total hardness (TH). The observed data were analysed using multivariate statistical methods. A cluster analysis (CA) was used to sort the sampling locations into different groups, and a principal component analysis (PCA) was used to find the different sources of pollution. Using a cluster analysis, all the water quality parameters were divided into three groups. Cluster 1 represented the less polluted sites, cluster 2 represented the moderately polluted sites and cluster 3 represented the highly polluted sites. Sampling sites SS8, SS4, S99 and SS7 were highly polluted because of nearby pollution sources such as domestic wastewater and runoff storm water. The principal component analysis yielded two meaningful components that explained 82.4% of the total variation in the data. The first factor and second factor explained 59.022 and 23.363 percentages of the total variance, respectively. It was noticed that major sources of pollution for the Gomti river are storm water runoff and the release of domestic and industrial wastewater from residents and industries, respectively. This study will help policy makers to ensure sustainable practices and reduce negative impacts on the availability and quality of water, allowing for the most efficient use of the Gomti River. [ABSTRACT FROM AUTHOR]

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 PM concentration (μg/m) in Lucknow city at 4 locations in three different seasons ranged between 148.6-210.8 (avg. 187.2 ± 17.1) during summer, 111.8-187.6 (avg. 155.7 ± 22.7) during monsoon and 199.3-308.8 (avg. 269.3 ± 42.9) during winter while PM ranged between 32.4-67.2 (avg. 45.6 ± 10.9), 25.6-68.9 (avg. 39.8 ± 4.6) and 99.3-299.3 (avg. 212.4 ± 55.0) during respective seasons. The mass fraction ratio of PM ranged between 0.22-0.92 (avg. 0.42 ± 0.26) and was significantly high during winter season indicating their composition. [ABSTRACT FROM AUTHOR]

The rainwater samples collected from the Lucknow city (India) were analyzed for selected organochlorine pesticides (OCPs) and polycyclic aromatic hydrocarbons (PAHs). HCH-isomers contributed most to the ΣOCPs with the highest levels of β-HCH isomer. The OCPs, which are currently banned in the country, were also observed. The residue levels of ΣOCPs in rainwater samples ranged between BDL and 447.17 ng L−1. Endrin, β-endosulfan, heptachlor epoxide B and methoxychlor could not be detected in any of the sample. The levels of ΣPAHs in rainwater samples ranged between 19.32 and 11,112.09 ng L−1 and the most abundant hydrocarbon was acenaphthylene. Further, the lower molecular weight PAHs dominated over the high molecular weight compounds. [ABSTRACT FROM AUTHOR]