Abstract
The current estimation of greenhouse gas emissions in municipal wastewater treatment plants
(WWTPs), based on the Intergovernmental Panel on Climate Change (IPCC) method, faces
limitations in formulating targeted emission reduction strategies at the individual unit level. This
research endeavours to enhance predictive accuracy for carbon dioxide equivalent (๐ถ๐!")
emissions - a metric used to express the combined impact of multiple greenhouse gases, including
methane (๐ถ๐ป#), nitrous oxide (๐!๐) and carbon dioxide (๐ถ๐!), in terms of their warming potential
relative to carbon dioxide (๐ถ๐!) over a specific time horizon. The study focuses on Northern
Wastewater Treatment Plant (WWTP) in Durban by leveraging advanced predictive tools,
specifically the GPS-X and Bridle models. These models are employed in conjunction with
measurements conducted following IPCC guidelines.
Significant variations were observed in the magnitude of methane, carbon dioxide and nitrous
oxide emitted in response to variations in component units' operating parameters and influent
characteristics like influent flow rate, COD and temperature. The examined treatment system used
a primary clarifier, anoxic tank, aerobic digester, secondary clarifier, thickener, belt presser and
anaerobic digester along with chemical disinfection unit.
The IPCC Northern wastewater treatment baseline estimate emission for the examined week was
611.29 kg๐ถ๐!". The Bridle model estimated an average daily emission of 8309.55 kg๐ถ๐!" with
the sludge treatment process contributing between 68.70% and 70.8% of the total emissions. The
average daily prediction differs from the actual IPCC emission with over 7698.26 kg๐ถ๐!". The
GPS-X software simulation indicated an average daily emission of 2121.58 kg๐ถ๐!" with an
anaerobic digester as the main emission component unit. The GPS-X daily average prediction was
247.06 % more than the actual IPCC daily average emission.
The GPS-X model showed a stronger correlation with observed emissions (Rยฒ = 70.5%) compared
to the Bridle model (Rยฒ = 47.95%). The 70.5% correlation indicates that the GPS-X model has a
higher explanatory power, making it more effective in predicting the variability observed in the
actual IPCC emission values than the Bridle model. This demonstrates that the GPS-X model offers
a more accurate representation of emission patterns. The study examined the temporal dynamics of
VI
emissions, with a particular focus on daily variations over the seven-day data simulation period.
Greenhouse gas production and emissions are driven by non-linear processes, which are influenced
by daily fluctuations and varying operational conditions.
GPS-X operates as a mechanistic model, simulating emissions on a component-specific unit basis
by incorporating biochemical reaction, kinetics and mass balance principles. This assumes that
emissions are directly influenced by microbial activity, reactor design, and operational conditions.
The Bridle model adopts an empirical process-based approach, estimating emissions based on
predefined treatment stages and historical emission factors, assuming a more generalized
relationship between treatment processes and greenhouse gas outputs. This characteristic renders
GPS-X a superior predictive model, particularly well-suited for formulating technical strategies
aimed at emission control. Furthermore, the research explored the factors contributing to the
variability emissions, with a specific emphasis on the influence of operational parameters,
environmental conditions, and the unique characteristics of the influent. The sensitivity analysis
revealed that the system's performance is highly responsive to changes in influent flow rate, with a
sensitivity of 10%. A reduction in influent had a proportional impact on the emissions, and the
system responded in a somewhat linear manner to changes in this parameter.
The study quantifies direct emissions from wastewater treatment facilities and highlights the
potential of biogas utilization to offset these emissions. By capturing and converting biogas from
sludge treatment into renewable energy, the findings demonstrate how this process can
substantially reduce greenhouse gas emissions. The analysis explores biogas systems as a
sustainable alternative to traditional energy practices, with the capacity to meet internal energy
needs and contribute to broader emission reduction goals.
Additionally, the study identifies key operational factors influencing emission variability, such as
biogas capture efficiency, energy recovery system scale, and sludge treatment management.
Optimizing these factors enhances emission reductions. The findings provide data-driven insights
for wastewater treatment facilities to implement targeted mitigation strategies, promoting
sustainability within the sector.
VI
The study highlights the critical role of local environmental conditions in Durban, such as
temperature, humidity, and precipitation patterns, which can significantly influence the efficiency
of biogas production and emissions at wastewater treatment facilities. These unique climatic factors
can lead to variations in the microbial processes involved in sludge treatment, potentially resulting
in higher or lower emissions compared to regions with more temperate climates. Understanding
these local environmental dynamics is essential for developing region-specific strategies to
optimize biogas recovery and minimize emissions, ensuring more accurate and effective mitigation
measures tailored to Durban's conditions.
Future research could focus on refining predictive models using real-time data, exploring advanced
treatment technologies for further emission reductions, and conducting comparative studies across
diverse climatic regions to enhance the frameworkโs applicability.