Isolation and molecular identification of extracellular polymeric substances (EPS) producing bacterial strains for sludge settling and dewatering.

One of the major problems in overall wastewater treatment process is sludge settling and dewatering. In general, sludge settling and dewatering is carried out using conventional physico-chemical methods that are known to be expensive, and these processes further increase the sludge volume and ultimate disposal costs. To overcome this problem, a suitable alternative could be the use of bioflocculants for sludge settling and dewatering. To achieve bioflocculation, extracellular polymeric substances (EPS) producing bacterial strains were isolated from the complex microbial community of wastewater sludge. Crude EPS produced in the form of bacterial broth was used to test kaolin flocculation activity. Three out of 10 bacterial strains (B2, B8 and B9) were pre-selected for sludge settling. Based on sludge settling and dewatering results, B8 possessed better flocculating property than other bacterial strains. These sludge microorganisms were identified based on their 16S rDNA sequences and bacterial strain B8 was identified as Serratia sps.

Long-term land application of biosolids-a case study.

Impact of long-term land application of biosolids on groundwater and soil quality of an application site, which had been operated for 8-15 years, was evaluated in this study. During and after the biosolids application, biosolids-amended soil, groundwater, and background soil samples were collected mainly for pathogen, nitrogen, phosphorus, and heavy metal analyses. Soil test data showed that there was no heavy metal accumulation in the biosolids-amended soil even after 10 years of biosolids application. Similar results were also observed from the groundwater samples in which the heavy metal concentrations in all groundwater samples were well below the maximum contamination levels of the drinking water standards. In addition, bacteriological levels of the soil and groundwater samples were close to the background level and below the permissible limits, respectively, thereby showing no pathogen contamination. However, nitrate-nitrogen contamination of the groundwater was occasionally observed probably due to an excess loading of the biosolids in the past. This problem can be alleviated by applying biosolids at agronomic rates so that no excess nitrogen is available for leaching down to the groundwater.

Optimization of ambient air quality monitoring networks : (Part I).

A method has been developed to obtain a joint solution to the problem of optimum number and configuration of ambient air quality monitors, on the principles of spatial correlation analysis and the minimum spanning tree. The interest in this case is to represent the patterns of regional air quality, at a minimum of an overlap of information. This methodology is extended to account for the uncertainties in air quality simulations and also to incorporate the probabilities of occurrence. As an illustration to these methodologies, an example of Taipei City, Taiwan has been considered.

Optimization of ambient air quality monitoring networks : (Part II).

Minimum Spanning Tree (MST) algorithm developed by Modak and Lohani (1984a) has been extended to consider multiple objectives for the optimum siting of ambient air monitors. Two approaches have been proposed, namely one based on the utility function and another based on the principles of sequential interactive compromise. The sequential interactive approach is heuristic but perhaps best suited to consider several objectives at a time, and particularly when professional judgements are also involved. The utility function approach may be normally restricted to two objectives at a time, but could be extended to consider a number of pollutants in the optimum design. For the purpose of illustration, the case of Taipei City, Taiwan has been considered.

Optimization of ambient Air Quality Monitoring Networks : (Part III).

The methodologies presented in Parts I and II (refer Modak and Lohani, 1984a and b) are essentially for deciding the best number and configuration for a single pollutant monitor. In practical situations however, Air Quality Monitoring Networks (AQMN) are expected to measure more than one pollutant and therefore simultaneous consideration of different types of pollutants must be made.In this paper, two new approaches have been developed for the multipollutant AQMN design. The first method makes use of the index theory and the other makes use of the principles of Pareto optimality. As an illustration of these methodologies, an example from Taipei City, Taiwan is considered.