A Closer Look on Spatiotemporal Variations of Dissolved Oxygen in Waste Stabilization Ponds Using Mixed Models

Abstract

Dissolved oxygen is an essential controlling factor in the performance of facultative and maturation ponds since both take many advantages of algal photosynthetic oxygenation. The rate of this photosynthesis strongly depends on the time during the day and the location in a pond system, whose roles have been overlooked in previous guidelines of pond operation and maintenance (O&M). To elucidate these influences, a linear mixed effect model (LMM) was built on the data collected from three intensive sampling campaigns in a waste stabilization pond in Cuenca, Ecuador. Within two parallel lines of facultative and maturation ponds, nine locations were sampled at two depths in each pond. In general, the output of the mixed model indicated high spatial autocorrelations of data and wide spatiotemporal variations of the oxygen level among and within the ponds. Particularly, different ponds showed different patterns of oxygen dynamics, which were associated with many factors including flow behavior, sludge accumulation, algal distribution, influent fluctuation, and pond function. Moreover, a substantial temporal change in the oxygen level between day and night, from zero to above 20 mg O 2 ·L −1 , was observed. Algal photosynthetic activity appeared to be the main reason for these variations in the model, as it was facilitated by intensive solar radiation at high altitude. Since these diurnal and spatial patterns can supply a large amount of useful information on pond performance, insightful recommendations on dissolved oxygen (DO) monitoring and regulations were delivered. More importantly, as a mixed model showed high predictive performance, i.e., high goodness-of-fit (R 2 of 0.94), low values of mean absolute error, we recommended this advanced statistical technique as an effective tool for dealing with high autocorrelation of data in pond systems.

Generalised Linear Models for Prediction of Dissolved Oxygen in a Waste Stabilisation Pond

“…Although repeated samplings were not done at the same times of the day, the average concentration of chlorophyll a and DO near the surface were always higher than that near the bottom. This could be associated with the photosynthesis of microalgae that occurred stronger at the surface of the ponds because of the sunlight [33]. However, it was also interesting to note that the maximum concentration observed in the WSP was 500 µg L −1 , which was considered to be low, according to Mara [1], as the chlorophyll a concentration in "healthy" WSPs is usually in the range of 500-2000 µg L −1 .…”

Advances in Ecological Water System Modeling: Integration and Leanification as a Basis for Application in Environmental Management

“…Integrated ecological models can support environmental management and policy development in various ways. These models provide insights into water-related disease control [4], assessments of environmental impact of wastewater and combined sewer overflows [5], wastewater treatment selection [6] and improvement [7], ecosystem services analysis [8,9], effects of land use on water quality [10] and aquatic community composition [11], as well as ecological water quality [12], determination of habitat restoration projects [13,14], distribution prediction and control of invasive species [15], integration of ecological insights into flood mitigation [16], and flow control related to reservoir management [17]. Moreover, models can be used for exploration purposes [18] and trade-off analysis [19], and environmental impact assessment [20] to inspire stakeholders and to support decision-making for policy developers.…”

Statistically-Based Comparison of the Removal Efficiencies and Resilience Capacities between Conventional and Natural Wastewater Treatment Systems: A Peak Load Scenario

“…On the one hand, higher temperature and intensive solar radiation in tropical countries can substantially increase the removal efficiency of both WSPs and CWs. On the other hand, the daily and seasonally variation of these meteorological variables can lead to the fluctuation in the performance of the natural systems [51,52]. This factor should also be considered in choosing the optimal treatment systems.…”