Today, the street sweeping industry features fully-digital and all-electric sweeper trucks, a variety of off-the-shelf integrated business platforms with full-scope ERPs, including sophisticated CRMs, networked field devices, advanced telecom systems, and more, all adapted to better manage the delivery and performance of street sweeping services. So, the profession is ready for this next-level advancement: Enter Andrew Sheerin, Ph.D. in Environmental Engineering, with emphasis in data analytics software design. Dr. Sheerin’s dissertation focused on building prescriptive systems for custom street sweeping frequency management to protect local waterbodies through stormwater pollution reduction.
Sheerin’s current study entails the development of a readily replicable platform that integrates sweeping service tracking data, service route characteristics, and weather prediction data, into a system for flagging key areas for sweeping to complement weather conditions for maximal effectiveness in improving water quality from sweeping based on changing conditions.
Environmental Engineering – Analytical Systems Design
Sheerin started his academic journey in Systems Engineering at George Washington University before beginning his graduate studies in Civil and Environmental Engineering at the University of Rhode Island (URI). The graduate program, funded by the Transportation Infrastructure Durability Center, is sponsored by the University of Rhode Island, through a USDOT grant. The Department was looking to bring in a curriculum around enhanced non-structural Best Management Practices (BMPs). It was supposed to be a 2-year project, and the plan was for Andrew to complete a Master’s degree in that discipline. However, as he explains, things evolved and grew, and with a little convincing from his advisor, Dr. Vinka Oyanedel-Craver, he decided to go for the full Ph.D.:
“My dissertation started with the evaluation of non-structural BMPs, the current State approach, and determining how that could be enhanced. Looking through all the available data on street sweeping across the country, there have been many great studies. We chose Warwick, Rhode Island as our research base, to focus on the effects of sweeping in urban areas.
We picked 8 sites, and did street debris collection and water collection for examination of physical and chemical characteristics. I took more classes and furthered aspects of the study through those. I was fairly proficient with Python. Using Geographic Information Systems (GIS), I recognized the layering potential for geographic data sets and started to figure out methods for correlating GIS data with factors like accumulation rates, pollutant concentrations, and a range of others.
This is not new. People have linked land usage to heavy metals and nutrients in water before, but I wanted to incorporate the specifics of it into the analytical system to generate a clear picture of the compound impacts and the mitigation effects.
I created a map from existing modification solutions, to defend enhanced street sweeping as a primary cause of reduced pollutants in water. There is a widely used model from the EPA. But it is limited in its ability to reflect sweeping routines, i.e., to program dynamic sweeping schedules based on irregular rainfall events.
Innovative Geographic Information Systems
The message is that urban sweeping shouldn’t be managed with a static approach, such as on a weekly schedule that doesn’t consider when it last rained and doesn’t maximize efforts to remove pollution that will otherwise go into waterways via storm drains. I endeavored to create a model to support my research, which took the form of the Storm Water Pollution Tracker (SWPT) software application.
The SWPT app functions with respect to weather forecasting data and a granular assessment of other environmental factors, such as adopted urban activity data sets. For example, land usage, tree cover, traffic volume, watershed characteristics, road slope, road roughness, curbing, and so on.
Initially, I developed the model based on local field data and did not plan to scale it out to other regions. It’s a big data model, encompassing broad categories of inputs, prioritizing, and optimizing these:
- Simulation: We conduct simulations of the transporting of pollutants through rain events, and through sweeping, so we can track how much has been remediated by sweeping. The simulation module can produce metrics for summarizing street sweeping effectiveness, including stormwater pollution reductions.
- Prioritization: The system determines how we prioritize routes based on geographic considerations. The determination can’t be to dispatch sweepers to go out based on rain predictions alone. There must be logic to identify which factors should be included in the real-time decisional analysis, like residential or industrial locations, roadway usage levels at various times, trees, and a range of other inclusions. This way we can target specific areas and pollutants of concern.
A priority score is calculated based on combined factors to assess high to low priority roads, depending on particular circumstances, including the kind and characteristics of the predicted weather event.
- Optimization: The model system optimizes routes to maximize efficiency in collection of dirt and debris, in other words, to pick up as much as possible without wasting resources.
- Python Programming: Python language enables programming for a vast range of purposes, including aligning timeframes and GIS data to improve analytical and interpretive efficiency.
Street Sweeping App for Environmental Protection
When I wrapped up my dissertation, I started to realize the commercial application potential for this. Every kind of operation in the country with environmental policies is always looking for relevant new enhanced BMPs, including state and municipal agencies, sweeping contractors, etc.
The URI Research Foundation sponsors the URI RISE-UP program. The goal of that program is to get ideas out of the lab and into the market. Its Patents2Products is an invaluable public service and support structure for inventors. I am fortunate to have been a Patents2Products Fellow. They trained me through the entrepreneurial process to understand what it takes to start a business to serve the needs of communities who can benefit from the newly developed SWPT platform.
Environmental Management Program Design Team
I’ve been fortunate to connect with Seth Brown and Greg McPartlin, colleagues who had been working together on standardizing a nationwide sweeping program. The work is still in the early stages. The initiative is called Clean Streets = Cleaner Water (CS=CW). The goal of the initiative is to provide a regulatory framework for municipal, county, and state use to develop enhanced street sweeping management plans. The objective is to grow a nationwide program that can prove street sweeping is a cost-efficient and effective means of reducing stormwater pollution.
To meet the goal of the CS=CW initiative, the work is being done to prove that street sweeping is a performative best practice and facilitates meeting regulatory requirements. I’m the Data Engineer of the project. I bring data together, connect the dots, and model the effectiveness of street sweeping in improving water quality. I’m working with organizations in the industry to help them research the exact benefits to water quality in relation to sweeping and frequency.
CS=CW is currently supported by 1-800-SWEEPER, NAPSA, NMSA, Schwarze, Stewart Amos, Elgin, and Tymco. The initiative model is very new. CS=CW officially launched at the Sweeper Summit on November 5th, in Las Vegas. There are a few different paths we’re taking to get people on board. My part is harnessing and standardizing the sweeping data. All can contribute to that. 1-800-SWEEPER has operating data generated by their contractors, and the manufacturers have efficiency data from their equipment users.
We improve the modeling capabilities by plugging in the various data types with existing peer-reviewed studies and operating data sets. That yields more accurate data analysis for SWPT to develop into more of a decision support package, with front-end tracking operators helping decide where and when to sweep and how to get regulatory credits. The SWPT website explains how the platform brings together weather data, sweeping performance data, and GIS data to trigger sweeping route service orders.
Inspired Sweeping Route Tracking App by A. Sheerin, Ph.D.
My home state, Rhode Island, is the second-densest state in population that is heavily centered on land-to-ocean interaction. I was born and raised in Newport, Rhode Island. It’s considered the sailing capital of the US. I’ve spent most of my life on a sailboat.
I’ve been pretty intensely into sail boating and racing since I was 6 years old. I spent most of my childhood in competitive sailing, in grade school, high school, and college. I’ve sailed on many dirty water bodies. So, the issue has always been extremely transparent to me.
When I see a body of water impaired and am completely surrounded by it, it’s always there in my head. That experience and my passion for sustainability have inspired me to pursue this path to help improve environmental conditions on the water.
The Future of SWPT and the Power Sweeping Industry
From his start in Systems Engineering as a humble undergrad to a doctorate in Environmental Engineering, Andrew’s approach has been utilizing his research to solve problems. His passion for building things and solving challenging problems led him to the connection between urban street sweeping and water quality. His project progressed naturally to become the SWPT program. He says he’s simply now seeing how far he can go to help improve the industry and make a difference. He is using computers and science to make that difference.
So, by virtue of this incoming Ph.D. to the field, it’s fair to say that, technologically, the street sweeping profession has arrived. Let any who may view the ever-advancing power sweeping industry as unevolving or stuck in old patterns take notice. The ambition of the field has been defined by relentless expansion, rapid tech growth, and intense commitment to solutions for improving sweeping performance quality.
However, there has been, as Andrew notes, “little scientific methodology” in terms of fine planning for environmentally optimized sweeping schedules. Further, in the street sweeping component of the International Stormwater BMPs Database, a central repository for all BMPs and their impact on water quality, street sweeping is treated as a maintenance activity and not as a potential stormwater pollution control method. To get it included in that base would be very significant. Dozens of studies over the past 50 years support that inclusion.
Andrew Sheerin emphasizes the objective of standardizing that data to produce a cohesive body of knowledge. He further sums up his environmental work as an effort to help manage water quality through keeping urban roads cleaner by using data and science to tell the story and answer the questions.
For more information about the SWPT program, you can email Andrew at asheerin@fathomsolutions.dev, or visit the website. For information about CS=CW, you can email Seth Brown at seth.brown@nationalstormwateralliance.org or Greg McPartlin at greg@urbanquarries.com.