Performance evaluation and prediction for a pilot two-stage on-site constructed wetland system employing dewatered alum sludge as main substrate
Research highlights
► Two-stage alum sludge-based wetland system can effectively treat agricultural wastewater. ► Regression analysis can be used to develop models for treatment performance. ► Such models can provide a cheap alternative for process control and management.
Introduction
Constructed wetland (CW) systems are becoming increasingly popular in response to increasing environmental issues and growing importance of natural and sustainable wastewater treatment systems. They are regarded as cost-effective and eco-friendly treatment systems with low maintenance and comparatively less energy consumption (Tomenko et al., 2007). They are also fast becoming the system of choice for wastewater treatment especially in rural or isolated areas where conventional systems are not as feasible because of cost effectiveness. It is well known that wastewater treatment in CW systems involve complex physical, chemical and biological processes.
Consequently, CW systems are often seen as complex “black box” systems and the processes within them are difficult to model due to the complexity of the relationships between most water quality variables (Gernaey et al., 2004, Lee and Scholz, 2006). On the other hand, the appropriate design, operation and evaluation of CW systems are crucial as well as contingent on a good understanding of the internal treatment processes and mechanisms. Regression analysis has been found to be useful for simplified description and analysis of CW systems performance as they provide a means of understanding their treatment process/mechanism (Tomenko et al., 2007, Murray-Gulde et al., 2008, Tang et al., 2009). Although, there are many more approaches with stronger capabilities that could be used to model CW systems performance such as artificial neural networks and multi-component reactive transport module (CW2D) (Langergraber, 2008, Akratos et al., 2009), the use of these complex approaches has been limited and yet to be proven.
The CW system described in the present study employs dewatered alum sludge as the main substrate in line with recent trends aimed at using natural and by-products as substrates in CW systems, e.g. limestone pellets (Tao and Wang, 2008) and filtralite (Albuquerque et al., 2009). Alum sludge is a by-product of drinking water treatment plants utilizing aluminum salts as coagulants. It is the most widely generated drinking water treatment residual and it is mostly landfilled, being perceived as a by-product of limited reuse value (Babatunde and Zhao, 2007). Therefore, the beneficial reuse of alum sludge in CW systems, hitherto considered as a waste by-product for wastewater treatment, may present an innovative approach of using waste for wastewater treatment. Extensive laboratory scale studies on the novel reuse of alum sludge in CW systems have been conducted in the authors research group (Zhao et al., 2009). Trials of several CW systems using the alum sludge as substrate are currently being conducted as pilot field-scale demonstrations to treat wastewater emanating from an animal research farm (Zhao et al., 2010). It is expected that these novel CW systems will offer a sustainable and cost-effective solution to the treatment of agricultural wastewaters, which is a wide-spread challenge particularly across the European Union. Until now, the prevalent practice on many farms is to store dirty water and spray it onto fields during the dry season and this has been found to cause degradation of surface and groundwaters (Wood et al., 2007). However, before the application of the novel CW systems on full scale, it is imperative to analyse their performance as a pilot field-scale model first. Therefore, this study presents performance analysis of a two-stage on-site CW system utilizing alum sludge as main substrate and explores the newly developed model for predicting final effluent concentrations. The key issues addressed are analysis of the CW system performance, identification of correlations among the water quality variables and the development of statistical models for predicting final effluent concentrations.
Section snippets
Design and operation of the system
The pilot field-scale CW system was constructed on an animal research farm in Newcastle, Co. Dublin, Ireland to treat wastewater (after settlement) emanating from the farm. The system consists of two stages operated with a hydraulic loading rate of 0.56 m3/m2 d and a hydraulic retention time of 4 h in each stage. The system is configured (from the top) with 20 mm gravel as distribution layer in the 0–10 cm depth range and this is followed 10–75 cm of dewatered alum sludge cakes as the main substrate
Treatment performance
The characteristics of the source wastewater varied greatly over time in concert with seasonal changes and farming operations. With regards to the characteristics of the influent wastewater to the CW system, the range of pollutant concentration were BOD5 (31–968 mg/L), COD (124–1634 mg/L), PO4P (2.8–60 mg-P/L), TN (16–273 mg-N/L) and SS (25–633 mg/L). Fig. 1 shows the trend of pH, temperature and ORP in the influent and effluent wastewater. pH between the influent and effluent varied very little
Impacts, implications and limitations of the study
Results have demonstrated the efficiency of a two-stage alum sludge-based CW system in removing pollutants from wastewater. One of the wider benefits of the study could be in its application by farmers to reduce the pollution load of wastewater emanating from their practice. On the other hand, the study also has an impact on drinking water treatment plants that are constantly faced with the quagmire of disposing residual sludge from the treatment processes in a cost-effective and sustainable
Conclusions
Concurrent high removal rate of COD, ammonia, and phosphorous can be obtained in a two-stage constructed wetland system, demonstrating its potential use for cost-effective reduction of pollution load of agricultural wastewaters.
Model developed for predicting effluent biochemical oxygen demand and chemical oxygen demand given as BOD5 = 54.66 pH – 0.03 ORP +8.29 Temp – 464.31 and COD = 49.25 pH – 0.34 ORP – 39.36 Temp +314.39 respectively, proved strong. This can enable data obtained in real-time to
Acknowledgements
Authors wish to acknowledge financial support received for this field study from: (1) Enterprise Ireland under the Proof of Concept Scheme (project No. PC/2007/0308); (2) the Irish state Department of Agriculture, Fisheries and Food under the Research Stimulus Fund (project No. RSF 07-528). The UCD farms, Lyons Estate, Newcastle, Co. Dublin, Dr Edward Jordan and Mr Michael Hegarthy are all sincerely thanked for their support for the field work.
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Present address: Discipline of Civil Engineering, School of Computing, Science and Engineering, University of Salford, Salford, M5 4WT, Greater Manchester, U.K.