Sheo Prasad Shukla

Linear and nonlinear modeling approaches for urban air quality prediction.

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), SO(2), and NO(2) in the air using the meteorological (air temperature, relative humidity, wind speed) and air quality monitoring data (SPM, NO(2), SO(2)) 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 (A(f)), and Nash-Sutcliffe coefficient of efficiency (E(f)). 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, NO(2), and SO(2) 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 SO(2) 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.

Soil quality index for evaluation of reclaimed coal mine spoil.

Success in the remediation of mine spoil depends largely on the selection of appropriate tree species. The impacts of remediation on mine soil quality cannot be sufficiently assessed by individual soil properties. However, combination of soil properties into an integrated soil quality index provides a more holistic status of reclamation potentials of tree species. Remediation potentials of four tree species (Acacia auriculiformis, Cassia siamea, Dalbergia sissoo, and Leucaena leucocephala) were studied on reclaimed coal mine overburden dumps of Jharia coalfield, Dhanbad, India. Soil samples were collected under the canopies of the tree species. Comparative studies on the properties of soils in the reclaimed and the reference sites showed improvements in soil quality parameters of the reclaimed site: coarse fraction (-20.4%), bulk density (-12.8%), water holding capacity (+0.92%), pH (+25.4%), EC (+2.9%), cation exchange capacity (+46.6%), organic carbon (+91.5%), N (+60.6%), P (+113%), K (+19.9%), Ca (+49.6%), Mg (+12.2%), Na (+19.6%), S (+46.7%), total polycyclic aromatic hydrocarbons (-71.4%), dehydrogenase activity (+197%), and microbial biomass carbon (+115%). Principal component analysis (PCA) was used to identify key mine soil quality indicators to develop a soil quality index (SQI). Selected indicators include: coarse fraction, pH, EC, soil organic carbon, P, Ca, S, and dehydrogenase activity. The indicator values were converted into a unitless score (0-1.00) and integrated into SQI. The calculated SQI was significantly (P<0.001) correlated with tree biomass and canopy cover. Reclaimed site has 52-93% higher SQI compared to the reference site. Higher SQI values were obtained for sites reclaimed with D.sissoo (+93.1%) and C.siamea (+86.4%).

Neutralization of rainwater acidity at Kanpur, India

Particulate matter (PM) levels show significant seasonal variability and this can influence the neutralization of rainwater acidity. Months were grouped in two periods: monsoon (July to October) and non-monsoon (November to June) for studying the seasonal variability in PM and rainwater composition. To clearly establish the cause effect relationship of acid rain neutralization, a two tier model was proposed involving source apportionment of particulates at two levels: (i) ambient air and (ii) rainwater particulate interaction. For modelling purpose, PM10 (n = 100), soil (n = 4) and rainwater (n = 83) samples were collected at Kanpur, India during 2000–2002. The collected samples were analysed for metals and water soluble ion composition to employ factor analysis for source identification. Knowledge of statistical correlation and chemistry fundamentals were combined to estimate the sources for acid rain neutralization. NH4 + was a dominating ion responsible for neutralizing the acidity of rainwater in monsoon period and Ca2+ was dominating in non-monsoon period. Components of secondary particles (SO42− and NO3−) showed affinity with NH4+, signifying the major role that ammonia can play if present in excess of stoichiometric requirements.

Modified Cenospheres as an Adsorbent for the Removal of Disperse Dyes

The main objective of this investigation was to use modified cenospheres for the removal of disperse blue 79:1 (DB) and disperse orange 25 (DO) dyes from aqueous solution by batch adsorption process under different conditions (pH, adsorbent dose, adsorbate concentration, agitation speed, contact time, and temperature). Modified cenosphere was capable of removing up to 78% of DB and 81% of DO dyes from aqueous solutions of 40 mg/L dyes concentration. The investigated data was explained by the Langmuir isotherm. The experimental data were found to follow the pseudo-second-order kinetic model. The results of this study suggested that modified cenospheres could be used as a low-cost alternative to expensive adsorbents like activated carbon in wastewater treatment for the removal of disperse dyes.

Development of GIS-aided Emission Inventory of Air Pollutants for an Urban Environment

Understanding types of pollutants and their emission rates is essential for control of air pollution. The emission rates, along with the rates of various chemical reactions those take place in the atmosphere, the prevailing meteorological conditions and topographical factors determine the extent of pollutions experienced by various environmental components. An emission inventory is a quantitative detailed repository of air pollutants emitted (along with their sources) into the atmosphere from a given area for a specified time period (Behera et al., 2011). Present and future inventories are critical components of air quality planning and modeling. The ultimate goal of the pollution control planning process is to identify and achieve emission patterns that do not result in violations of ambient air quality standards. An emission inventory should be able to provide: (i) a consistent estimate of total emissions of different pollutants, (ii) the spatial and temporal distribution of pollutants, (iii) the time-specific evolution of emissions and their distributions, (iv) the identification and characterization of sources, (v) tracking progress towards attainment of National Ambient Air Quality Standards (NAAQS) and emission reduction plans, (vi) serve as the basis in modeling for prediction of pollutant concentrations in ambient air, and (vii) compliance records with allowable emission rates established by facilities and regulatory agencies. The broad source categories of air pollution in an urban area include; (i) transport (motor vehicles and railways), (ii) commercial establishments, (iii) industrial, (iv) domestic cooking/heating, (v) fugitive dust and (vi) biomass burning. There could also be some unique or specific sources in a particular area. Procedures and reliability of emission inventory for regular point, area and line sources are well-established. However, identification and quantification of fugitive/non-point emission sources (emissions not released through stacks, vents, ducts or pipes) are quite challenging. For these sources, emission factors (EFs) (EF: a ratio that relates the emission of a pollutant to an activity level) are either not developed or, if developed, are associated with high amounts of uncertainty. The important non-point sources include: traffic related non-exhaust (NE) emissions (tire, road and brake wear, road dust), evaporative emissions, raw material handling, open crushing, and biomass and agricultural residue burning. The development of a better emission inventory poses a challenge to air quality engineers, as it requires systematic in situ surveys to collect activity data and proper management

Adsorptive capacity of sawdust for the adsorption of MB dye and designing of two-stage batch adsorber

Abstract The use of low-cost locally available adsorbent, sawdust for the removal of methylene blue (MB) dye in a batch adsorber system has been investigated. The experimental data fitted best in Langmuir isotherm as compared to Freundlich and Temkin isotherms, showing maximum adsorption capacity of 76.92 mg/g. The study revealed that the adsorption of MB dye onto sawdust follows pseudo-second-order kinetic model and the same has been used in design of a two-stage batch adsorber by minimizing total contact time to attain a fixed percentage of MB dye removal. The minimum contact time required for the removal of MB dye with 99% efficiency has been found as 37.54 min.

An Efficient Removal of Disperse Dye from Wastewater Using Zeolite Synthesized from Cenospheres

AbstractIn the present work, cenospheres were modified to synthesize zeolite in order to improve their adsorption capacity. The synthesized zeolite was characterized by attenuated total reflectance...

An approach for evaluation of proposed air pollution control strategy to reduce levels of nitrogen oxides in an urban environment

In this study, an atmospheric dispersion model along with a systematic emission inventory was used to explore the possible control strategy to reduce ambient levels of nitrogen oxides (NOx) in Kanpur city, India. A GIS based emission inventory of NOx was developed for the base, 5th and 10th years. It was observed that the 5th and 10th years will experience increased emissions by factors of 1.7 and 2.5 of the base year if no control policy is implemented. Seventeen control options (i.e. introduction of Euro 6 to vehicles, banning of 15-year old private vehicles etc.) were considered for evaluation through the dispersion modelling. A control scenario comprising the following control options (1) implementation of Euro 6 for vehicles, (2) compressed natural gas (CNG) for commercial and public vehicles, etc. was found to be most effective in reducing the ambient NOx levels and attaining a 24-hour average air quality standard.