A.K. Dikshit

Neural Network Models for Air Quality Prediction: A Comparative Study

The present paper aims to find neural network based air quality predictors, which can work with limited number of data sets and are robust enough to handle data with noise and errors. A number of available variations of neural network models such as Recurrent Network Model (RNM), Change Point detection Model with RNM (CPDM), Sequential Network Construction Model (SNCM), and Self Organizing Feature Maps (SOFM) are implemented for predicting air quality. Developed models are applied to simulate and forecast based on the long-term (annual) and short-term (daily) data. The models, in general, could predict air quality patterns with modest accuracy. However, SOFM model performed extremely well in comparison to other models for predicting long-term (annual) data as well as short-term (daily) data.

Removal mechanism of endosulfan sorption onto wood charcoal

Endosulfan is among the most widely used pesticides in developing countries and other parts of the world and has been found to contaminate various parts of the environment, including drinking water sources. In an earlier study to find a suitable adsorbent to remove endosulfan, wood charcoal was found to give promising results. In the present study, the process controlling the rate of endosulfan sorption onto wood charcoal and the mechanism of removal were examined using various methodologies. Both film and pore diffusion coefficients were determined, and the linearity of the rate constants of adsorption with initial endosulfan concentrations revealed the process to be controlled by film diffusion. This was supported by the linear fit of the rate constants with the inverse of the diameter of adsorbent particles and the change in adsorption rates with agitation speed. Multiple interruption tests also revealed that endosulfan sorption onto wood charcoal is controlled by film diffusion. The increase in reaction rate constant with temperature and isosteric heat of adsorption in the range of -2.655 to 5.185 kcal/mol implied that the endosulfan removal process was endothermic in nature. The activation energy of 2.33 kcal/mol, which was less than 12 kcal/mol, revealed that the removal mechanism could be attributed to physisorption with a major contribution of van der Waals and electrostatic forces.

Applications of the deep-shaft activated sludge process in wastewater treatment

The deep-shaft activated sludge process is a unique modification of the activated sludge system. The main objective of this system is to increase the amount of dissolved oxygen available for biological activity. This can be achieved by increasing the rate of oxygen transfer from the gas phase to the liquid phase. The deep-shaft configuration increases the partial pressure of oxygen, thereby causing a high saturation concentration in the reactor. In the deep-shaft process, owing to high oxygen availability, a higher organic loading can be accommodated with a comparatively low air supply. This reduces the energy and area requirements and lowers the overall cost of treatment. This technology has been successfully applied for the high-rate treatment of strongly polluted wastewater, as well as for the treatment of wastewater containing toxic or slowly biodegradable pollutants. This paper presents different applications of the deep-shaft activated sludge process, along with their relative performances.

Studies of air quality predictors based on neural networks

In recent years, urban air pollution has emerged as an acute problem because of its negative effect on health and living conditions. Regional air quality problems, in general, are linked to violations of specified air quality standards. The current study aims to find neural network based air quality predictors, which can work with a limited number of datasets and are robust enough to handle data with noise and errors. A number of available variations of neural network models, such as the Recurrent Network Model (RNM), the Change Point Detection Model with RNM (CPDM), the Sequential Network Construction Model (SNCM), the Self Organising Feature Model (SOFM), and the Moving Window Model (MWM), were implemented using MATLAB software for predicting air quality. Developed models were run to simulate and forecast based on the annual average data for 15 years from 1985 to 1999 for seven parameters, viz. VOC, NOx, CO, SO2, PM10, PM2.5 and NH3 for one county of California, USA. The models were fitted with first nine years of data to predict data for remaining six years. The models, in general, could predict air quality patterns with modest accuracy. However, the SOFM model performed extremely well in comparison with the other models for predicting long-term (annual) data.

Competitive Sorption of Pesticides onto Treated Wood Charcoal and the Effect of Organic and Inorganic Parameters on Adsorption Capacity

This paper presents competitive sorption of coexisting pesticides onto treated wood charcoal and describes the effect of various water quality parameters, viz., pH, ionic strength, chloride concentration, presence of calcium and magnesium, fertilizers, humic acid, polyacrylic acid, and also the effect of coexisting pesticides on the sorption of endosulfan onto treated wood charcoal. The coexisting pesticides were found to hinder the performance of wood charcoal in removing endosulfan. Competitive uptake study revealed that endosulfan occupies more sites followed by atrazine and monocrotophos. Solubility in water could be one of the major reasons for this preferential order. The presence of humic acid was found to show much more significant influence on the performance of wood charcoal than the presence of polyacrylic acid. Among fertilizers, single superphosphate was found more influential. Most of it, among the other reasons, could be due to the competition of the coexisting molecules for the available a...

Hybrid reactor system for wastewater treatment – application and approach of modelling

Wastewater treatment by the hybrid reactor system has become wide-spread as it provides advantages of both the suspended and attached growth phase at the same time. It may be used to treat some rate-limiting substrate, priority pollutants, volatile organic compounds etc. as well as for nitrification. This versatile nature of hybrid reactor demands for a detailed investigation on the mechanism, mode of operation, different applications and major configurations available. The present article is devoted to explore these issues with respect to previous background and successive development in this area. Apart from the laboratory and pilot-scale study, some industrial applications have been overviewed to understand the performance of hybrid reactor in the concerned field. The approach of modelling for the hybrid reactor system is also demonstrated with the hypothetical data set. A comprehensive details about major hybrid processes is presented along with their schematic diagrams. The review on hybrid process revealed that it would be economic for upgradation of existing activated sludge system, ensuring carbonaceous oxidation and nitrification in a single reactor and treatment of slowly bio-degradable substances also.

Removal of endosulfan using aerobic mixed bacterial culture

With the increasing use of pesticides in modern agriculture, increased evidence of their disastrous effects on the environment has been noticed. Pesticides applied in various modes and places contaminate various parts of the environment, including groundwater sources. As pesticide problems are greater in the rural areas, the authors have developed a successful low-cost technology for rural areas with wood charcoal treated with nitric acid. As pesticides are classified as hazardous waste, the sludge resulting from their treatment has to be disposed off safely. This paper describes the removal of pesticides at a higher concentration of 24 mg/l, using a mixed culture of aerobic bacteria, and also a study of the inhibiting action of endosulfan on bacterial cells. It was found that bacteria without acclimatisation could remove 89.7% of endosulfan, and with prior acclimatisation the efficiency was 96%. It was found that removal in the initial phase is because of the hydrophobic nature of endosulfan and its affinity to sediments. The adsorbed endosulfan subsequently undergoes biotransformation, which has been confirmed by monitoring endosulfan concentrations in the bacterial sludge. Transformation was found to be significant in the acclimatised culture system. The fluctuation in bacterial performance was greater at lower concentrations of endosulfan, and overall inhibition was greater at higher concentrations.

Kinetic Study of Sorption of 2,4‐D and Atrazine on Rubber Granules

For the removal of 2,4‐D (2,4‐dichloro‐phenoxy‐acetic acid) and atrazine (2‐chloro‐4‐ethyalamino‐6‐isopropylamino‐s‐traiazine) from water environment, batch studies were performed to study the kinetics of 2,4‐D and atrazine sorption by rubber granules. Experiments were conducted at various initial concentrations of 7.5 mg/L, 4 mg/L, and 0.5 mg/L of 2,4‐D and atrazine with 18 g/L sorbent dose. The equilibrium time was found to be 120 minutes for both 2,4‐D and atrazine. The regression analysis for the experimental data showed that the rapid stage zone of kinetics profiles followed a first order reversible kinetics for all initial concentrations of sorbate. The various thermodynamic properties such as enthalpy and entropy activation values (ΔH and ΔS) also show that the sorption is spontaneous. This article will provide information toward understanding the sorption phenomena of 2, 4‐D and atrazine on waste tyre rubber granules. It also is valuable to us in finding out the rate constant of sorption of 2,4‐D and atrazine.

Study of Sorption of 2,4‐D on Outer Peristaltic Part of Waste Tire Rubber Granules

From this study it was evident that outer peristaltic parts of waste tire granules gave the highest removal. Film and pore diffusions are the major factors controlling rates of sorption from solution by porous adsorbents. For sorption of 2,4‐D on waste tire rubber granules, the sorption rate coefficient of second‐order kinetic equation was utilized indirectly to determine the rate‐limiting step. The diffusion coefficient lies in the scale of 10−8 cm2/s, and the pore diffusion coefficient is in the range of 10−9–10−10 cm2/s. So both film and pore diffusion are rate limiting. Considering external mass transfer from fluid to particle, using the effect of initial concentration, and using the effect of adsorbent size, no conclusion was reached regarding rate‐controlling steps. It is apparent from the study that external mass transfer (film diffusion) as well as intra‐particle diffusion (pore diffusion) play significant roles in the sorption process for 2,4‐D removal from water onto rubber granules.

AIR QUALITY PREDICTION: AN OPPORTUNISTIC NEURO-ENSEMBLE APPROACH

The present article discusses the development of neural-network-based air quality prediction models which can work with a limited number of data sets and are robust enough to handle data with noise. Five different variations of neural network models (partial recurrent network (PRNM), sequential network construction (SNCM), self-organizing feature maps (SOFM), moving window (MWM), and integrated normalized autoregressive moving average-self-organized feature maps models (NARMA-SOFM)), were implemented in a WINDOWS environment using MATLAB software. Developed models were run to simulate and forecast the daily average data for three parameters: RPM (respirable particulate matter), SO2 (sulphur dioxide), and NO2 (nitrogen dioxide) for the Ashram Chowk location in New Delhi, India. The implemented models were found to predict air quality patterns with modest accuracy. To improve the models’ performance, an innovative approach using an opportunistic ensemble of the first four developed neural network models (OEM) was proposed for predicting the same short-term data. The ensemble approach indeed demonstrated an improvement on earlier models. However, the NARMA-SOFM model performed the best.