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Karnāl, India

Chakraborty S.,Ramakrishna Mission Vivekananda University | Weindorf D.C.,Texas Tech University | Li B.,Louisiana State University | Ali M.N.,Ramakrishna Mission Vivekananda University | And 2 more authors.
Environmental Pollution | Year: 2014

This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r2 = 0.87, RMSE = 0.580 log10 mg kg-1, and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r2 = 0.65 and RMSE 0.261 log10 mg kg-1) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane. ©2014 Elsevier Ltd. All rights reserved.

Chakraborty S.,Ramakrishna Mission Vivekananda University | Weindorf D.C.,Texas Tech University | Paul S.,Ramakrishna Mission Vivekananda University | Ghosh B.,Ramakrishna Mission Vivekananda University | And 5 more authors.
Geoderma Regional | Year: 2015

Soil lead (Pb) contamination by anthropogenic and industrial activities is a problem of global concern. In this research the possibility to adapt mid infrared-diffuse reflectance infrared Fourier transform spectroscopy (MIR-DRIFTS) approach for the quantitative estimation of Pb in polluted soils was explored. One hundred soil samples were collected from an urban landfill agricultural site and scanned by MIR-DRIFTS. The raw reflectance spectra were preprocessed using four spectral transformations for predicting soil Pb contamination using three multivariate algorithms. Partial least squares regression using Savitzky-Golay (SG) first derivative spectra (RPD = 3.05) outperformed principal component regression models. The artificial neural networks-SG model using an independent validation set produced satisfactory generalization capability (RPD = 2.01). Thus, the combination of MIR-DRIFTS and multivariate models can reduce chemical analysis frequency for soil pollution monitoring, substantially reducing labor and analytical cost. © 2015 Elsevier B.V. All rights reserved.

Chakraborty S.,Ramakrishna Mission Vivekananda University | Das B.S.,Indian Institute of Technology Kharagpur | Nasim Ali M.,Ramakrishna Mission Vivekananda University | Li B.,Louisiana State University | And 3 more authors.
Waste Management | Year: 2014

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r2=0.91 and RMSE=13.38μgg-1h-1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky-Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity. © 2013 Elsevier Ltd.

Chandna P.,International Rice Research Institute | Chandna P.,Jawaharlal Nehru University | Khurana M.L.,Soil Testing Laboratory | Ladha J.K.,International Rice Research Institute | And 3 more authors.
Environmental Monitoring and Assessment | Year: 2011

Increased use of nitrogenous fertilizers in the intensively cultivated rice (Oryza sativa)-wheat (Triticum aestivum) cropping system (covers a 13.5-ha m area in South Asia) has led to the concentration of nitrates (NO3-N) in the groundwater (GW) in Haryana State of India. Six districts from the freshwater zone were selected to identify factors affecting NO3-N enrichment in GW. Water and soil samples were collected from 1,580 locations and analyzed for their chemical properties. About 3% (26,796, and 10,588 ha) of the area was estimated to be under moderately high (7.5-10 mg l-1) and high (>10 mg l-1) risk categories, respectively. The results revealed that NO3-N was 10-50% higher during the pre-monsoon season than in the monsoon season. Nitrate-N decreased with the increase in aquifer depth (r 2=0.99). Spatial and proximity analyses using ArcGIS (9.2) revealed that (1) clay material in surface and sub-surface texture restricts N leaching, (2) piedmont and rolling plains act as an N sink, and (3) perennial rivers bring a dilution effect whereas seasonal rivers provide favorable conditions for NO3 - enrichment. The study concludes that chemical N fertilizers applied in agro-ecosystems are not the sole factor determining the NO3 in groundwater; rather, it is an integrated process governed by several other factors including physical and chemical properties of soils, proximity and type of river, and geomorphologic and geographical aspects. Therefore, future studies should adopt larger area (at least watershed scale) to understand the mechanistic pathways of NO3 enrichment in groundwater and interactive role of the natural drainage system and surrounding physical features. In addition, the study also presents a conceptual framework to describe the process of nitrate formation and leaching in piedmont plains and its transportation to the mid-plain zone. © 2010 Springer Science+Business Media B.V.

Ghosh A.,Indian Agricultural Research Institute | Das A.,Indian Agricultural Research Institute | Lepcha R.,Indian Agricultural Research Institute | Majumdar K.,Soil Testing Laboratory | Baranwal V.K.,Indian Agricultural Research Institute
Journal of Asia-Pacific Entomology | Year: 2015

Darjeeling hills and Dooars of West Bengal (India) are well known for production of mandarin orange and lime. Citrus tristeza virus (CTV) is wide spread in this region. Role of insect vectors in spreading CTV in this region has not been studied so far. Therefore, a study on identification of insect vectors along with their temporal and spatial distribution was undertaken. Five aphid species were identified from citrus orchards of Darjeeling and Dooars viz. Toxoptera citricida, T. aurantii, Aphis gossypii, Myzus persicae and Brachycaudus helichrysi. T. citricida was found predominant in the orchards of lower altitude and was responsible for maximum spread of CTV. T. aurantii was dominant in the citrus orchards at high altitude (>. 500 m). Incidence of CTV was higher in the orchards where T. citricida was present either alone or with other species. Under caged conditions, T. citricida was more efficient to transmit CTV than the other aphid species. Occurrences of all aphid species were highly influenced by the advent of new flushes. © 2015 Korean Society of Applied Entomology, Taiwan Entomological Society and Malaysian Plant Protection Society.

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