AlSayed, Ahmed

Alsayed, Ahmed

Ahmed Mahmoud is with Northwestern University.

Titles from this speaker

Description: Continuous Monitoring of Fugitive Methane in Wastewater Treatment Plants Using...

Continuous Monitoring of Fugitive Methane in Wastewater Treatment Plants Using Ground Sensors

Abstract

Introduction Greenhouse gas (GHG) emissions, primarily comprising carbon dioxide (CO2), methane (CH), and nitrous oxide (N2O), are a major contributor to climate change, driving global warming and environmental instability (Gautam et al., 2021). These gases originate from various human activities, including energy production, transportation, agriculture, oil and gas industry, wastewater treatment, and waste management (Varon et al., 2021). Among these sources, methane is particularly potent, having a global warming potential significantly higher than CO2 over a 20-year period. This makes methane emissions a critical target for mitigation efforts, as reducing them can have an immediate impact on curbing climate change and its negative influence on the environment (Liu et al., 2019). Wastewater treatment plants (WWTPs) are considered an active source of GHG emissions, especially methane, which is generated during the biological processes that involve breaking down the organic matter during the anaerobic digestion processes (Khalil et al., 2024). There are multiple key emission hotspots within WWTPs including the sludge treatment areas, anaerobic digesters, and equalizer tanks, where anaerobic conditions can lead to significant methane release. In addition, there might be additional unexpected sources of GHG emissions that may contribute to the overall emissions from these WWTPs such as aeration tanks. As these emissions can be episodic and location-specific, continuous monitoring is essential for capturing real-time data on emission patterns and variabilities. Despite its significance, a major limitation in the wastewater sector is developing an affordable and continuous methane monitoring systems. Therefore, the use of advanced sensing technologies, such as ground and drone-mounted sensors, has been gaining greater attention during recent years. These technologies enable more precise and continuous monitoring, allowing for effective detection of fugitive methane emissions and identification of the hotspots that might otherwise go unnoticed. Continuous monitoring not only provides critical insights into emission dynamics but also supports timely interventions, helping to reduce the carbon footprint of WWTPs and contribute to broader climate goals. Objectives The main objectives of the current study are to: (1) install a group of ground sensors to isolate the methane emissions from different wastewater treatment processes; (2) identify the hotspots of methane emissions within the wastewater treatment facility based on continuous timeseries of fugitive methane emissions from different sources; and (3) compare the methane emissions from the ground sensors with other sensing technologies such as drone-based techniques. Methods and Materials In the current study, 16 ground sensors were installed in a local wastewater treatment facility (WWTF) in Ontario, Canada. The WWTF comprises the typical treatment processes including the primary and secondary treatment, anaerobic digesters, and solids management facilities. In addition, this WWTF receives high organic loading rates which can contribute to the fugitive methane emissions. The ground sensors were distributed around the treatment processes and building to continuously monitor the fugitive methane from these processes. For example, four ground sensors were installed around the aeration tanks to continuously monitor the methane concentrations and emissions from the basins. It should be noted that the observation period started in June 2024 where the ground sensors observed the methane concentrations on a 15-minute interval. Besides the ground sensors, a drone and Optical Gas Imaging (OGI) sensing campaigns were carried out in the WWTF in Summer 2024 to compare the methane concentrations and emissions from different monitoring levels including the ground and aerial sensing techniques. Results According to the preliminary insights from the time-series of methane concentrations using the ground sensors, it was observed that there are multiple hotspots for methane emissions within the WWTF. The average methane concentration at each ground sensor varied according to the location of sensor, the isolated process, and wind properties that play a crucial role in conveying the methane plume. For example, the average methane concentration over four months (i.e., from beginning of June until the end of September) ranged between 7.2 to 11.4 ppm for the sensors surrounding the aeration tanks (Fig. 1), indicating that these basins are a potential source of methane emissions. In addition, the average methane concentration was approximately 10.3 ppm near the primary clarifiers (Fig. 2a). Moreover, the range of the average methane concentration of the sensors installed around the sludge treatment facilities was 5.7 -- 11.2 ppm. Based on these observations, it can be emphasized that most of the treatment processes are considered hotspots for fugitive methane emissions with the WWTF. These findings were validated using the drone and OGI sensing approaches where the drone flights and OGI camera confirmed the locations of the hotspots for emitting methane within the treatment processes (Fig. 2). Conclusions and summary The deployment of ground-based sensors for the quantification and detection of fugitive methane emissions at the WWTF provided valuable insights into the spatial and temporal distribution of emissions. The sensors, strategically placed near the expected and unexpected emitters such as sludge handling units, anaerobic digesters, and aeration tanks, captured real-time data on methane concentrations, enabling the identification of persistent emission sources. Over the monitoring period, fluctuations in emission levels were observed that were linked to the operational activities and environmental conditions. This data underscored the variability of methane emissions and emphasized the importance of continuous monitoring to capture short-term spikes and long-term trends. Ultimately, the ground sensor campaign highlighted the potential for such technology to complement aerial sensing, providing granular, location-specific data critical for effective emission mitigation strategies in wastewater treatment operations.

This paper was presented at the WEF Residuals & Biosolids and Innovations in Treatment Technology Joint Conference, May 6-9, 2025.

SpeakerElsayed, Ahmed

Presentation time

09:30:00

09:50:00

Session time

08:30:00

10:00:00

SessionDrones, Satelites, Sensors, Oh My!: Advances in Fugitive Methane Monitoring

Session number11

Session locationBaltimore Convention Center, Baltimore, Maryland, USA

TopicBiosolids To Energy, Climate Change Mitigation, Energy and Carbon Neutrality, Greenhouse Gases, Odor/Air Emissions, Regulatory Requirements, Sensors, wastewater

Author(s)

Alsayed, Ahmed, Elsayed, Ahmed, Abdelrahman, Omar, Khalil, Mostafa, Zaghloul, Mohamed, Kakar, Farokh, Bell, Katherine, Le, Trung, Willis, John, Elbeshbishy, Elsayed

Author(s)A. Elsayed1, A. Alsayed2, O. Abdelrahman1, M. Khalil1, M. Zaghloul3, F. Kakar4, K. Bell4, T. Le4, J. Willis4, E. Elbeshbishy1

Author affiliation(s)Toronto Metropolitan University, 1Northwestern University, 2United Arab Emirates University, 3Brown and Caldwell, 4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date May 2025

DOI10.2175/193864718825159805

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count13

Description: Drone-Based Imaging and Sensing: Quantification of Fugitive Methane Emissions from...

Drone-Based Imaging and Sensing: Quantification of Fugitive Methane Emissions from Full-Scale Wastewater Treatment Facility

Abstract

Introduction Greenhouse gas (GHG), such as methane (CH4), nitrous oxide (N2O) and hydrofluorocarbons (HFCs), emissions are considered a major contributor to global climate change which has a substantial impact on the public and environmental health (Hopwood et al., 2008; Gautam et al., 2021). Multiple sources, including the Intergovernmental Panel on Climate Change (IPCC), highlighted that global temperatures have increased between 0.3 - 0.74 degrees Celsius in the past century due to the natural and anthropogenic sources including the GHG emissions (Solomon et al, 2010; Liu et al., 2019). Methane as a GHG has been reported to have 27-29 times higher global warming potential compared to carbon dioxide. According to EPA's 2022 report, the wastewater sector is the fifth largest methane source. For example, the methane emissions from domestic sewage in WWTPs can reach up to 25 Tg-CH4/yr (Wallington et al. 2019). Such estimations were developed based on top-down approaches where average emission factor is used to estimate such emissions. However, a major contributor to such emissions is direct process and fugitive methane emissions such as anaerobic digesters and waste gas burners. Such emissions may vary significantly according to operational conditions and infrastructure integrity. Moreover, there might be additional unexpected sources of GHG emissions that may contribute to the overall emissions from WWTPs. Therefore, the application of sensing technologies can aid in accurate quantification of the methane emissions from the WWTPs where these emissions can be correlated to specific treatment processes. Objectives In the current study, the main objectives are to: (1) utilize a multi-platform sensor approach to detect and quantify the fugitive methane emissions from a WWTP using drone and Optical Gas Imaging (OGI) sensing; (2) identify the hotspots of methane emissions within the wastewater treatment facility based on these different sensing technologies; and (3) determine the overall methane emissions from the treatment facility. Methods and Materials A drone and OGI sensing campaigns were carried out in a local WWTP in Ontario, Canada during the Summer of 2024. The WWTP contains the typical treatment processes including the primary treatment, secondary treatment, anaerobic digesters, biosolids loading facilities, and sludge settling tanks. Worth noting, this WWTP receives high organic loading rates which can significantly increase the methane emissions from the WWTP. During the campaign, the drone was flown to isolate each treatment process through creating flight paths upwind and downwind each process and building. These flight paths are known as the wind curtains where they are selected to be perpendicular on the wind direction at different altitudes around the potential process sources. To effectively capture the methane plumes from emission sources, the wind curtains were flown between 30 to 150 ft upwind and downwind the potential sources and treatment processes. The drone sensing approach generally features a quantification sensor (i.e., open-path laser spectrometer (OPLS)) that can detect methane levels as low as 10 parts per billion (ppb). In general, the flight conditions of the drone require a wind speed range of 4 and 20 mph while maintaining a consistent drone speed approximately 3.0 to 4.0 mph for optimal performance. In addition, an OGI camera was mounted on the drone for detecting the potential leakage within the treatment processes. A hand-held OGI camera comprising a cooled infrared sensor was used to access the areas that cannot be assessed using the drone including the indoor spaces and chambers. The OGI camera detects the methane using three factors: the infrared absorption of the gas aligning with the camera's spectral response, the temperature difference between the gas and background, and the gas concentration meeting the camera's sensitivity threshold. The OGI camera is connected with a gimbal which enables the OGI camera to rotate freely without restrictions. Results According to the outcomes of the drone and OGI campaigns, it was found that there are multiple hotspots for methane emissions within the WWTP. For example, the primary treatment process emitted approximately 8.0 kg-CH4/hr. This can be referred to the high organic loads received in such a treatment plant and mixing primary sludge with thickened waste activated sludge. In addition, the anaerobic digesters, gas burner facility, and sludge handling facilities yielded high emissions that ranged from 2.0 to 19.8 kg-CH4/hr. based on the sludge treatment stages and wind characteristics. For example, the anaerobic digesters had an emission rate of approximately 8.9 kg-CH4/hr. (Fig. 1a). Interestingly, considerable methane emissions were observed around the aeration tanks where the methane emission rate was approximately 3.8 kg-CH4/hr. (Fig. 1b). The activated sludge process in this plant does not entail any anoxic or anaerobic zones. Hence, the high emissions from the aeration basins can be attributed to unideal aeration and mixing conditions (coarse bubble aeration used for mixing and aeration), and/or the circulation of the centrate which is anticipated to be saturated with dissolved methane. A major methane emission spot was found to be the sludge holding tanks. Methane concentrations from such tanks reached more than 60 ppm, highlighting a major fugitive methane emission in the facility. However, there were some challenges in estimating the actual methane emission rates due to low wind speed at this area which ranged between 0.7 to 2.0 m/s that were lower than the recommended values of wind speed for accurate quantification of methane emissions using drone-based sensing techniques. In general, these hotspots were also surveilled using either the drone-based or hand-held OGI camera where the methane plumes were detected and recognized (Fig. 2). Conclusions and summary The current study aimed to accurately quantify the methane emissions from a WWTP using drone-based sensing approach to assist in calculating the methane emissions rates from various treatment processes. The methane emission quantification campaign at the WWTP demonstrated the efficacy of drone-based sensing combined with OGI cameras for detecting and measuring fugitive methane emissions where it provided important insights into the interplay between the treatment processes and methane emissions. The results showed significant methane hotspots near the primary tanks, aeration basins, sludge processing and anaerobic digestion units. This encompasses both anticipated and unexpected emissions sources which can enhance our understanding about the sources of fugitive methane emissions in WWTPs. The advantages of the aerial drone, coupled with the OGI camera, allowed for rapid and comprehensive coverage of the facility, providing accurate spatial emission data. Overall, the campaign underscored the potential of drone and OGI technology as a powerful tool for enhancing fugitive methane monitoring and supporting emission reduction strategies in wastewater operations.

This paper was presented at the WEF Residuals & Biosolids and Innovations in Treatment Technology Joint Conference, May 6-9, 2025.

SpeakerAbdelrahman, Omar

Presentation time

08:50:00

09:10:00

Session time

08:30:00

10:00:00

SessionDrones, Satelites, Sensors, Oh My!: Advances in Fugitive Methane Monitoring

Session number11

Session locationBaltimore Convention Center, Baltimore, Maryland, USA

TopicBiosolids To Energy, Climate Change Mitigation, Energy and Carbon Neutrality, Greenhouse Gases, Odor/Air Emissions, Regulatory Requirements, Sensors, wastewater

Author(s)

Abdelrahman, Omar, Elsayed, Ahmed, Alsayed, Ahmed, Khalil, Mostafa, Zaghloul, Mohamed, Kakar, Farokh, Bell, Katherine, Le, Trung, Willis, John, Elbeshbishy, Elsayed

Author(s)O. Abdelrahman1, A. Elsayed1, A. Alsayed 2, M. Khalil1, M. Zaghloul3, F. Kakar4, K. Bell4, T. Le4, J. Willis4, E. Elbeshbishy1

Author affiliation(s)Toronto Metropolitan University, 1Northwestern University, 2United Arab Emirates University, 3Brown and Caldwell, 4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date May 2025

DOI10.2175/193864718825159804

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count15

Description: Enhancing Post-Treatment Efficiency of Temperature-Phased Anaerobic Digestate...

Enhancing Post-Treatment Efficiency of Temperature-Phased Anaerobic Digestate Through Combined Chemical Conditioning for "Class A" Biosolids Production

Abstract

A post-treatment process is required to reduce the volume temperature-phased anaerobic digestate (TPAD), so that it can be more easily transported and disposed of. The digestate liquid fraction contains a lot of nutrients as well and biosolids might contain pathogen. Traditional dewatering process that uses expensive polymers, separates solid and liquid parts to produce condensed biosolids. Polymer conditioning focuses only on volume reduction. TPAD has a high solid content which makes dewatering difficult. Other challenges associated to digestate dewatering such as nutrient and resource recovery, odour control pathogen contamination in biosolids is not addressed in traditional polymer conditioning. It is hypothesised that metal salts as coagulant and oxidants are added along with polymer to enhance the post-treatment of TPAD that would enhance dewatering, nutrient and resource recovery and pathogen elimination in dewatered biosolids. Through the combination of chemicals, the current research seeks to increase TPAD post-treatment efficiency by improving the removal of P in the liquid fraction while producing a Phosphorus (P) rich and pathogen free class A biosolids. To compare the effects of polymer conditioning alone, conditioning with polymer with ferric chloride (FeCl3), polymer, FeCl3 along with hydrogen peroxide (H2O2) before centrifugal dewatering, cationic polymer alone, combined polymer with FeCl3, and cationic polymer with FeCl3, H2O2 in a pH-controlled environment. The best combination of chemicals was 2.5 kg/t DS polymer, 2.1 kg/t dry solids (DS) FeCl3 and 600 mg/l H2O2 at pH 8.0 with the lowest turbidity 11 NTU, specific resistance to filtration (SRF) 0.08 Tm/kg, capillary suction time (CST) 11.5s. The combined chemical dose shows a 94 to 99% improvement in dewatering indices such as CST, turbidity and SRF than raw TPAD. Around 88 to 90% decrease in centrate, TS2- and PN/PS ratio was also observed that contributed to odour causing potential and increased dewatering efficiency. 100 % P removal was achieved with no P in the centrate and P rich biosolids was produced with 40% cake solid content after combined chemical conditioning. However, the fecal coliform level of raw TPAD cake from the full-scale plant exceeds the regulatory limit. The dewatered cake turned into class A biosolids with just 57 MPN/g DS fecal coliform content after combined chemical conditioning in the lab scale study. In comparison with untreated TPAD cake, pathogens were reduced 100%. Combined chemical application reduces polymer dose by 40% as well as associated cost. The novel combined chemical treatment with cationic polymer, FeCl3, and H2O2 at adjusted pH are used to enhance digestate efficiency as well as reduce costs, storage requirements, and address nutrient imbalance, pathogens, and odors that offered more sustainable management. Wastewater treatment plants are thereby recommended to adopt the novel combined chemical conditioning the synergistic chemical combination of polymer, FeCl3 and H2O2, adjusting pH at 8.0 with Ca(OH)2 for TPAD post-treatment while assessing the full scale adaptability through pilot studies.

This paper was presented at the WEF Residuals and Biosolids Conference, June 18-21, 2024.

SpeakerHyder, Umme Sharmeen

Presentation time

16:15:00

16:45:00

Session time

13:30:00

16:45:00

SessionDewatering

Session number05

Session locationOklahoma City Convention Center, Oklahoma City, Oklahoma

TopicClass A biosolids, Digestate Post-treatment, Phosphorus Removal and Recovery

Author(s)

Hyder, Umme Sharmeen

Author(s)U. Hyder1, 2, A. Alsayed3, E. Elbeshbishy2, J. McPhee2, R. Misir2

Author affiliation(s)Stantech1; Toronto Metropolitan University 2; Northwestern University 3; Toronto Metropolitan University 2; Toronto Metropolitan University 2; Toronto Metropolitan University 2;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Jun 2024

DOI10.2175/193864718825159457

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count17

Description: Harnessing Machine Learning for Enhanced A-Stage Process Efficiency: A Pioneering...

Harnessing Machine Learning for Enhanced A-Stage Process Efficiency: A Pioneering Full-Scale Study

Abstract

Introduction The shift towards energy- and resource-efficient WWTPs is no longer a privilege. For this reason, the A/B scheme is attracting substantial industrial and scientific interest in which the A-stage role is to maximize carbon diversion to the recovery streams and maintain stable low carbon influent to the B-stage. This is attained by operating the process in a very dynamic manner where high mass and volumetric loading rates are applied. As a result, a key challenge in the A-stage process is sludge settleability and the associated maintenance of effluent quality, particularly effluent suspended solids (ESS) [1]. In an operational-related context, it was reported that return streams can have a noticeable effect on A-stage performance [5]. It was found that bio-augmenting the A-stage process with waste nitrification sludge (WNS) may positively influence process performance, whereas supernatant return flow from solids processing (SPB) may negatively influence process stability [5]. However, this was never quantified or transformed into a control strategy for the return streams This is particularly of operational significance because such variables (WNS and SPB flows) are the key manipulated variables, given that solids wastage in Blue Plains is highly dependent on the capacity of the solids processing facilities rather than process performance. Hence, there is a need to develop a robust control strategy for ESS minimization, based on WNS and SPB. The bottleneck for implementing such a control strategy is to develop a model that can overcom the complex and dynamic nature of A-stage process. To this end, this study presents two data-driven modelling approaches to predict ESS of the full scale HiCS system operating at Blue Plains WWTP. This is the first machine learning (ML) model to be developed for an A-stage process. Methodology Modeling are based on a 2-year dedicated data effort from the WEST train of full-scale HiCS system at Blue Plains advanced WWTP. In addition to primary effluent, HiCS handles return flows such as SPB and WNS. Process parameters that are routinely measured include suspended solids, COD fractions, environmental and operational conditions (i.e., SRT, SOR, SLR, MLSS), advanced settleability measurements (i.e., TOF, and EPS TKN). Such variables represent possible model features (inputs), whereas the output (target) is process ESS, which was consistently monitored. Findings The data considered for this project were collected from Online sensors, DC Water Lab, and Research team. As shown in Figure 1, the obtained features have significant variation in frequency (how frequent these features are measured) and consistency (consistency in measuring features at the suggested frequency). This can be referred to the complexity of the process, which necessitates the consideration of sophisticated parameters like TOF, and LOSS [1]. In addition, sensing associated challenges have a key role to play [3]. To address challenges with the data, two modelling approaches are presented: (1) high frequency features classification (HF-C) model, and (2) all features regression (AF-C) model. In the HF-C model, the problem was approached as a classification problem where ESS was classified into three classes (good: <25 mg/L, action needed: >25 mg/L & 35 mg/L <, and bad: >35 mg/L). In this model, only features with high frequency were considered. Such features are mostly the flow-based features such influent, return streams, and wastage flow rates. AUC-ROC and F1 score along with accuracy were used to assess model performance which considered the effect of the less represented classes. Among different algorithms tested, the performance of four algorithms stands out, as shown in Figure 2a. When further evaluated based on the recall of the minor classes and extent of overfitting, it can be seen from Figure 2b that the support vector machine with radial basis function (SVM-rbf) is the best performing model. For the AF-R, the framework, demonstrated in Figure 3, was developed to circumvent the challenges of predicting dynamic and highly non-linear targets, significant variations in features availability, A-stage complexity, and data imbalance. A three-step data imputation approach where adopted to alleviate the effects of the high missingness in the data. Figure 4 shows the resulting outcome of the target and other features after outlier removal, and imputation. Subsequently, the model was developed in a multi-step fashion where the 7-day and 3-day moving average (MA) were first estimated and used as inputs to predict the Raw ESS. Figure 5 shows the effect of optimizing model architecture. These optimizations included the utilization of a weighted loss function to count for data imbalance and stratified cross validation to equally sample from different periods over the 866 days. These modifications resulted in an R2 of 0.72 and 48% reduction in RMSE (in comparison with average) using RF. This is compared to R2 of 0.2 to 0.3 for the model before optimization. Finally, it was observed that the integration of settling features (TOF, LOSS, EPS COD, and SVI) and operational parameters (SRT, WAS solids, and MLSS), and WNS characteristics (WNS COD fractions) significantly improved model performance. Figure 6 shows an example of the results when such features are introduced to the model where R2 above 0.85 was observed with more than 65% reduction in RMSE.

We explore the use of machine learning (ML) to enhance the efficiency of High-rate Contact Stabilization in the A-stage process of Blue Plains Advanced WWTP. By analyzing 2 years of operational data, research develops ML models to predict effluent suspended solids, incorporating high-frequency flow-based features & advanced settleability parameters. Optimized models significantly improve predictive accuracy, highlighting ML's potential for process control, stabilization, & resource recovery.

SpeakerAlSayed, Ahmed

Presentation time

16:00:00

16:30:00

Session time

15:30:00

17:00:00

SessionApplication of Machine Learning and Modeling in Carbon Diversion Technologies

Session number419

Session locationRoom 243

TopicAdvanced Level, Facility Operations and Maintenance, Intelligent Water

Author(s)

AlSayed, Ahmed, Ngo, Nam, Khan, Usman, De Clippeleir, Haydee, Wells, George

Author(s)A. AlSayed1, K. Nam Ngo2 U. Khan3, H. De Clippeleir, 4, G. Wells5

Author affiliation(s)1, 5Department of Civil and Environmental Engineering, McCormick School of Engineering, Northwestern University, <sup2, 4> District of Columbia Water and Sewer Authority 3Department of Civil Engineering, Lassonde School of Engineering, York University; District of Columbia Water and Sewer Authority; Department of Civil and Environmental Engineering, McCormick School of Engineering

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159678

Volume / Issue

Content sourceWEFTEC

Word count13

Description: Not Every Utility is Equal: How Operational Patterns, Influent Characteristics, and...

Not Every Utility is Equal: How Operational Patterns, Influent Characteristics, and Compliance Limits Shape Fugitive GHG Emission Variability in Wastewater Treatment Plants

Abstract

Introduction Amid the accelerating impacts of climate change, the urgent need to reduce greenhouse gas (GHG) emissions is more critical than ever, yielding commitments from developing countries aimed at achieving net-zero emissions by 2050. While energy sector decarbonization is underway, less attention has been given to direct GHG emissions from essential infrastructure. To this end, the wastewater sector alone contributes approximately 5% of global non-CO2‚ GHG emissions, largely in the form of methane (CH) and nitrous oxide (N2O), which have global warming potentials of 28 and 298 times higher than CO2, respectively [1]. As a prerequisite to mitigation, there has been growing academic and industrial interest in the quantification of the direct, or scope 1, emissions from wastewater treatment plants (WWTPs) -- emissions generated through bioprocesses [2]. The standard method for estimating emissions relies on the current Intergovernmental Panel on Climate Change (IPCC) guidelines, which use a simplified global emission factor (EF). However, this approach lacks the granularity required to capture the actual dynamics of GHG emissions from WWTPs [3]. Recent studies document substantial discrepancies, with observed CH emissions reaching up to 100 times the IPCC's estimated EF [4], and N2O emissions varying over six orders of magnitude [5]. These findings underscore the critical need for site-specific investigations to better understand emissions behavior, identify the factors driving variability, and pinpoint the specific sources contributing to these emissions. Therefore, in this research, we present findings from an ongoing Canadian project monitoring greenhouse gas (GHG) emissions from three full-scale wastewater treatment plants (WWTPs) across Canada. Our multi-level approach combines various advanced measurement techniques, including gas/liquid sensors, optical gas imaging, drone-based sensing, aircraft imaging, and satellite imaging. While previous studies have largely examined emissions variability in relation to treatment technology [5] and plant's size [4], this research attempts to link plant's GHG emissions with influent characteristics, compliance limits, and operational patterns. Treatment plant description and measurements campaign The Canadian wastewater treatment plant featured in this study operates with a typical treatment configuration. The plant comprises primary treatment, followed by the activated sludge (AS) process and, finally, disinfection. Given that there are no nitrogen compliance requirements, the AS process does not incorporate anaerobic and anoxic zones. Secondary sludge undergoes thickening, which is then combined with primary sludge for anaerobic digestion. The digested sludge is subsequently dewatered, and the centrate is returned to the AS process. To enhance settling efficiency, thickened waste activated sludge (TWAS) is mixed with primary sludge (PS) before digestion. To monitor nitrous oxide, two Unisense® sensors were used and located strategically to capture possible variation across the aeration tank. Methane (CH) emissions are quantified using 16 ground-based sensors, complemented by a 3-day intensive measurement campaign employing drone-based and optical gas imaging techniques. Results and discussion For over six months, liquid N2O concentrations were monitored at two locations in the aeration basin: the first and third quarters. As illustrated in Figure 1, median daily N2O emissions were recorded at 2.22 KgN-N2O/day and 2.91 KgN-N2O/day, with only occasional instances exceeding 10 KgN-N2O/day. This corresponds to an N2O emission factor (EF) of 0.005±0.0012 kg N2O-N per kg TN which is substantially lower than the IPCC EF of 0.016 kg N2O-N per kg. This can be referred to the limited nitrogen removal in the plant, Figure 1. This is because the plant does not have ammonia or total nitrogen compliance limit. While prior studies reported higher N2O emissions with incomplete nitrification [6], the present plant was operated at sludge age (3.34±0.65 day) close to the washout of nitrifying biomass which resulted in lower EF. Results of the methane emissions quantification are shown in Figure 2. The average methane emissions from the plant equal to 1293±807 kgCH4/day. This is equivalent to 3.5-4% of gCH4/m3 treated and 2.9-3.1% of the influent COD load. These values are relatively high but are within the typical range reported in literature [4]. Yet, emissions distribution within the plant is interesting. As seen in Figure 2, the highest contributors to methane emissions are the aeration tanks and primary clarifiers which combined contributed to almost 60% of total plant's methane emissions, whereas Digesters and gas burners contributed only 25% of such emissions. The low emissions from the digesters were confirmed by the handheld OGI camera which indicated no detected leakage either from covers or structural cracks. The high emissions from aeration tanks can be referred to several potential reasons. One possible reason can be unideal aeration and mixing, given that coarse bubble aeration is used to do both. This is supported by the low DO levels observed at the first quarter of the tank (below 0.5 mg/L). Another potential source is the centrate return after digesters which can contain high dissolved methane concentrations. Similar observation were reported in prior studies [7]. The high emissions from the primary clarifiers can be potentially explained by (i) high methane concentrations in the received influent, (ii) high TSS concentrations in the influent, (iii) the return of the TWAS and associated high sludge blankets in the primary sludge. The resulting total daily GHG emissions of the plant are estimated to be equivalent to 43.57 metric tonne of CO2. Interestingly, as shown in Figure 3, methane constituted more than 80% of the plant's emission. This is opposing the typical results in the literature where N2O contributes to more than 50% of plant's GHG emissions [3,8]. This is due to (i) compliance limits that do not mandate ammonia removal, (ii) operational decisions such as centrate return, operating at short sludge age washing out nitrifiers, TWAS return, and (iii) received influent that contained high dissolved methane concentrations and high TSS concentrations. Conclusion In conclusion, this study highlights how influent characteristics, operational patterns, and compliance limits can influence GHG emissions from wastewater treatment plant processes. Methane accounted for over 80% of the total emissions, contrasting with typical findings where Nâ‚‚O dominates. These findings emphasize the need for site-specific GHG monitoring and targeted mitigation strategies in wastewater treatment.

This paper was presented at the WEF Residuals & Biosolids and Innovations in Treatment Technology Joint Conference, May 6-9, 2025.

SpeakerAlsayed, Ahmed

Presentation time

10:45:00

11:05:00

Session time

10:45:00

11:45:00

SessionAcademic Advancements in Digestion and Fugitive Greenhouse Gas Emissions

Session number2

Session locationBaltimore Convention Center, Baltimore, Maryland, USA

TopicAnaerobic co-digestion, Biomethane, Lipids/proteins/carbohydrates ratio, TWAS, Manure, SSO, Anaerobic Digestion, Biogas, Biogas Production, Energy recovery, food waste, Greenhouse Gases, Membrane bioreactor, Nitrous oxide, Resource Recovery, Sustainability And Social Responsibility

Author(s)

Alsayed, Ahmed, Elsayed, Ahmed, Khalil, Mostafa, Zaghloul, Mohamed, Kakar, Farokh, Bell, Katherine, Willis, John, Elbeshbishy, Elsayed

Author(s)A. Alsayed1, A. Elsayed2, M. Khalil2, M. Zaghloul3, F. Kakar5, K. Bell5, J. Willis5, E. Elbeshbishy2

Author affiliation(s)Northwestern University, 1Toronto Metropolitan University, 2United Arab Emirates University, 3Brown and Caldwell, 4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date May 2025

DOI10.2175/193864718825159802

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count23

Description: Understanding the AAA: A Promising Alternative A-stage Process for Organics Recovery...

Understanding the AAA: A Promising Alternative A-stage Process for Organics Recovery from Wastewater

Abstract

Recently, the achievement of energy neutrality and resources recovery in wastewater treatment plants has become inventible. Accordingly, the interest in the A-stage of the adsorption/bio-oxidation (A/B) for carbon management has considerably increased. To this end, high-rate activated sludge and high-rate contact stabilization systems are employed. Induced by its flexible and compact design, the Alternating Activated Adsorption (AAA) system was recently proposed and patented as an alternative A-stage system. Yet, literature on such a system is scarce. Hence, in this article for the first time, the performance of the novel AAA system is evaluated. Two systems were operated to assess AAA as a primary settler replacement (AAA-1) or to complement the primary settler (AAA-2). The goal of this study is to assess and position the AAA system among other A-stage systems. As settling and aeration are performed in the same reactor, AAA maintained high MLSS (2121±293 mg/L for AAA-1 and 806±116 mg/L for AAA-2) compared to the literature at such a very low aerobic SRT (< 6 hours). Moreover, AAA-1 has high COD removal efficiency for soluble (67±8%) and particulates (62±14%) as well as COD redirection (47±7%). In addition, it is demonstrated that the bottom feeding/top discharging regime adds unique capacity for pCOD capture in AAA. Whereas further enhancements are needed to improve the AAA-2’s pCOD removal and TCOD redirection. Owing to the oxygen supply pattern and short SRT, AAA attains low mineralization regardless wastewater strength (16-17%). Compared with other systems, it can be concluded that, with further optimizations, AAA system has the potential to outcompete other A-stage systems. As such, improving sludge settleability is found to be the bottleneck, especially when treating low strength wastewater.

Induced by its flexible and compact design, the Alternating Activated Adsorption (AAA) system was recently proposed as an alternative A-stage system. Yet, literature on such a system is scarce. In this article, two systems were operated to assess AAA as a primary settler replacement (AAA-1) or to complement the primary settler (AAA-2). AAA-1 shows high sCOD (67±8%) and pCOD (62±14%) removal efficiency, and COD redirection (47±7%). Whereas, further enhancements are needed to improve the AAA-2’s pCOD removal and TCOD redirection. It can be concluded that AAA system has the potential to outcompete other A-stage systems. For EPS and PHA extraction and measurement are detailed elsewhere (Meerburg et al., 2016; Salem, 2020).

SpeakerAlSayed, Ahmed

Presentation time

11:40:00

12:00:00

Session time

11:00:00

12:00:00

SessionAdvancements in Primary Treatment: Going Beyond the Conventional

Session number204

TopicAdvanced Level, Facility Operations and Maintenance, Municipal Wastewater Treatment Design

Author(s)

Ahmed AlSayed