Abrera, John

John has more than 35-years' experience specializing in the application of Information Technology for utilities. He is currently supporting projects...

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About Abrera, John

John has more than 35-years' experience specializing in the application of Information Technology for utilities. He is currently supporting projects using new technologies in data platform, predictive analytics, machine-to-machine (M2M) and Internet-of-Things (IoT) and their application to utilities. John works in Stantec's Digital Practice as the Operational Analytics Leader, where his focus in on the application of smart sensors, data platforms and analytics to solve specific problems. John is a registered Engineer, Plant Operator, Collection System Operator and Pretreatment Inspector.

Abrera, John

Titles from this speaker

City of Memphis Implementation of a Smart Wastewater Monitoring Network

Abstract

The quantification of SARS-CoV-2 in wastewater relies on concentrating and assessing the number of viral particles in wastewater using SARS-CoV-2 specific genes (nucleocapsid, N1 and N2). Recent work evaluated the performance of several concentration methods, including ultrafiltration, electronegative membranes, and polyethylene glycol (PEG) precipitation (1). The PEG method demonstrated high yield and consistent recovery. In the same study, for quantification, the SARS-CoV-2 N1 was measured at a slightly higher but significant value compared to N2.

The following conference paper was presented at WEFTEC 2021, October 16-20, 2021. To read the full abstract, see "Abstract" tab below.

SpeakerAbrera, John

Presentation time

14:10:00

14:25:00

Session time

13:30:00

15:00:00

SessionDigital Transformation: Leveraging Smart Infrastructure and Data Analytics to Optimize Performance

Session number508

TopicAsset Management, Intelligent Water, Urban Systems, Utility Management and Leadership

Author(s)

John Abrera

Author(s)J. Abrera2; S.C. Morgan1; R. Knecht1; T. King1; J.D. Balentine2; K.Y. Bell2;

Author affiliation(s)City of Memphis, Memphis, TN1Brown and Caldwell 2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2021

DOI10.2175/193864718825158145

Volume / Issue

Content sourceWEFTEC

Word count11

Description: That's Smart: Using Low-Cost Internet-of-Things Sensors to Characterize Water...

That's Smart: Using Low-Cost Internet-of-Things Sensors to Characterize Water Quality in a City of Atlanta Combined Sewer

Abstract

Agencies often look for efficient and inexpensive ways to lower the risk of unauthorized discharges into their combined sewer system, specifically pertaining to water quality monitoring, and finding point sources of pollution. The City of Atlanta is addressing issues with identifying sources of specific pollutants (heavy metals and Total Dissolved Solids) in the stormwater runoff entering the City's combined sewer system. The traditional approach would be to deploy manual samplers at multiple sites, based on potential pollutant sources, such as proximity to an Interstate Highway or to an industrial site, to try to capture samples right after a rain event. Though automation exists allowing for collection of grab samples, often they may not be collected at the correct time and require significant logistics around frequent sample collection. This could lead to an unknown probability of success in understanding if and where there is truly a problem. The City of Atlanta is taking an innovative approach to identifying point sources of specific pollutants in stormwater runoff entering the City's sewers. The City and Stantec worked on a solution to preliminarily characterize the sites prior to a more expensive, and expansive deployment of samplers. This low-cost solution involved the utilization of Internet-of-Things sensors to capture Electrical Conductivity and Total Dissolved Solids water quality readings. These inexpensive sensors continuously capture water quality every 15 minutes. Their findings will help to deploy samplers only in areas that truly warrant further investigation. Sensor data was further analyzed by Stantec's Altitude Operational Services Platform to provide additional insights, including correlating the data with rain events. This presentation will share our multi-pronged approach to early identification, sensor logistics, system calibration, and how the technology behind the platform is helping to improve the combined sewer system 1.2 million people depend on daily. We will also share what's next in our machine learning journey — how predicting pollutant concentrations for future rain events will help us confirm point sources of pollution and learn how the system reacts during first flush rain events. Understanding this will ultimately help to better understand impacts to City systems. This is an excellent example of how our joint teams are using machine learning to find the right balance of smart sensors to build on the City's solution to stormwater, and effectively, wet weather water quality fluctuations.

This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.

SpeakerAbrera, John

Presentation time

09:30:00

10:00:00

Session time

08:30:00

10:00:00

SessionInnovations in Stormwater Data Collection and Management

Session number26

Session locationConnecticut Convention Center, Hartford, Connecticut

TopicClimate Change Adaptation, Combined Sewer Overflow, Flooding, IDDE, Information Technology, Innovative Technology, Long Term Control Planning, Machine Learning, Sensors, Stormwater Management Design And Analysis, Water Quality, Wet Weather

Author(s)

Askew, Cornelius

Author(s)C. Askew1, J. Abrera2, C. Gunn

Author affiliation(s)City of Atlanta Department of Watershed Management 1, Stantec 2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2024

DOI10.2175/193864718825159413

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count18

Base text size -

Abrera, John

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