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The Growing List of LCRR Commonly Asked Questions

April 1, 2022
5
Min Read
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The Lead & Copper Rule Revisions (LCRR) has prompted many questions. Each week, we will feature a new question and will add it here to create a library of the most commonly asked questions. Follow the hashtag #LCRRQuestions on the Trinnex LinkedIn and Twitter accounts each Friday to catch the newest question. Want to submit your own question? Send it to marketing@trinnex.io with the subject line “LCRR Questions”.

LCRR commonly asked questions

  1. What's the typical success rate utilities can expect to see with self-reporting of service lines for LCRR?
  2. What falls under the "unknown - not lead" LCRR category?
  3. When it comes to LCRR reporting, does city responsibility end outside the house, meaning interior plumbing is excluded?
  4. If we don't have lead, do we automatically meet LCRR compliance?
  5. Is there a benefit to using Artificial Intelligence in the LCRR compliance process?
  6. When is the optimal time to start a service line inventory?
  7. Our GIS does not have service laterals or customer points. What’s the best way to even start assembling an inventory?
  8. How do you determine the level of effort to achieve Lead & Copper Rule compliance if there’s limited access to clean data?
  9. Which cities or states have the highest amount of lead pipes?
  10. Is there a set number of field verifications necessary to make a machine learning model accurate?
  11. How can machine learning become an acceptable method for service line inventory development?
  12. How can small utilities reduce the amount of time and resources spent on their inventory development?
  13. Has the EPA released any guidance on service line inventory development?

Question 1: What's the typical success rate utilities can expect to see with self-reporting of service lines for LCRR?

Answer: Based on similar projects such as the Newark Lead Service Line Replacement Program, utilities can expect to see a 5 to 10% response rate with self-reporting. Customers that do submit a response are typically 95% accurate in reporting copper or plastic. We don’t have enough data yet on lead and galvanized pipes to say what the typical accuracy rate is with self-reporting. By the way, DC water has a great website helping customers on how to identify lines.

Overall, self-reporting provides a low-cost way of getting information. You can put the form on your website, mail out postcards, or use door hangers with the QR code that will take them to the form. There will be some back and forth like with some homeowners taking a picture of their gas line rather than the water line. But some inventory data collection systems have workflows built in to address common issues like this, with the ability to notify homeowners when a new photo is needed. While it's not as accurate as physically checking the pipe itself, self-reporting is a lot less costly and time-consuming method that ultimately contributes to the material identification process and makes your community feel involved with the process.

Question 2: What falls under the "unknown - not lead" LCRR category?

Answer: Under the LCRR, unknown material is considered guilty until proven innocent. However, service lines installed or updated after the 1990s are likely safe to submit under the "unknown - not lead" category, as long as records exist in the system justifying that lead is not used and that other material such as copper or PVC is instead in use.

But best practice is to identify as many service lines as you can, especially before LCRR 2024 deadline. Not only will you stay on path with maintaining LCRR compliance with services lines identified, but you can also take advantage of potential funding opportunities to help remove lead service lines. Water utilities have a few options for obtaining data to help identify materials and develop a service line inventory before jumping into a more invasive and expensive investigation. Several top information sources include paper records, historic municipal building codes, and and several other options as shown here.

Question 3: When it comes to LCRR reporting, does city responsibility end outside the house, meaning interior plumbing is excluded?

Yes, in most cases, city responsibility for reporting ends with the customer side up to the foundation. But with self-reporting, we're talking about where the pipe enters at the foundation before the meter. Depending on the region (such as in the South), you might not have access to the pipe coming in through the foundation to be able to identify and verify its material type but once it hits the foundation, you don't have to report on it. It is important to check with your state primacy agency on internal plumbing, some states and municipalities are requiring that information.

Question 4: If we don't have lead, do we automatically meet LCRR compliance?                

This is actually a trick question. Part of meeting LCRR compliance involves showing proof that service lines do not contain lead. The LCRR assumes service line owners are guilty until proven innocent and requires the submittal of a service line inventory (with materials clearly marked) to the Environmental Protection Agency by October 2024. Some states have earlier requirement dates. Information sources such as a GIS database, historical records, or physical inspections can help identify material types.

Depending on population size, you'll have to do more than just submit an inventory. The inventory must be publicly-accessible, with municipalities serving 50,000 or more required to publish online.

Question 5: Is there a benefit to using Artificial Intelligence in the LCRR compliance process?

Yes, artificial intelligence or AI can help streamline the LCRR compliance process by helping to identify and verify service line material. AI models can help predict the probability of lead on a small to large scale, from individual homes to an entire neighborhood block and pinpoint where to undertake physical inspections, which results in significant cost savings.

A recent blog post discusses the cost saving advantages available through business intelligence and AI tools. For LCRR activities in particular, utilities can save up to 10% of total inspections needed by using an AI model for service line material predictions.

lcrr compliance

Question 6: When is the optimal time to start a service line inventory?

As soon as possible–with the clock already ticking down to October 2024, water service providers should have a plan in place to collect the necessary information on material types now to develop a service line inventory. A full inventory can be extremely time-intensive, and getting started now will save you time and effort in generating communications to ratepayers who have lead or unidentified material types on either the municipal or private side of the lateral.

Question 7: Our GIS does not have service laterals or customer points. What’s the best way to even start assembling an inventory?

Several sources can help provide a good starting point to gather data needed to assemble a service line inventory. For example, billing system data, meter data, and parcels can provide some direction to conduct a desktop study. Other top information sources include service cards, historical building records, CMMS data, or service cards. Integrating your GIS with all the data collected from inventory development efforts will make it easier to update and maintain for regulatory compliance and stakeholder communications.

Machine Learning Webinar replay

Question 8: How do you determine the level of effort to achieve Lead & Copper Rule compliance if there’s limited access to clean data?

When little clean data is available, many utilities have considered machine learning to help streamline material identification and verification. When used for pipe prediction, machine learning helps reduce end-user costs for verifications​, can be updated to EPA/State criteria​, and provides high-accuracy predictions.

For example, Sandy Kutzing from CDM Smith suggests that if a past contractor used or has never used lead pipe material, machine learning can help connect patterns such as spotting all the locations where the same contractor completed work, which might indicate its likelihood for lead.

Question 9: Which cities or states have the highest amount of lead pipes?

According to the Natural Resources Defense Council (NRDC), eight cities have the highest amounts of lead in tap water: Baltimore, Chicago, Detroit, Milwaukee, Newark, New York, Pittsburgh, and Washington, D.C. However, many of these cities are already well on their way to getting the lead out, including Newark. The NRDC also states that the true amount of lead pipes might be underestimated across the United States.

Question 10: Is there a set number of field verifications necessary to make a machine learning model accurate?

No magic number of field verifications exists to properly stabilize a machine learning model and make it more accurate. It depends on several factors such as how your water system was built out (whether it was built in the same way), and if the same materials were used in certain areas and time periods. Some models might require as little as 200 verifications to stabilize the model and prove that more verifications won't provide additional benefits. Others might require a lot more.

Question 11: How can machine learning become an acceptable method for service line inventory development?

Machine learning has come into question as an acceptable method for service line inventory. The EPA allows machine learning to collect verifications, but no guidelines exist yet on accuracy criteria or the number of verifications needed. Some states have adopted their own machine learning guidelines, such as Michigan, which provides details about field verification data collection.

For instance, if less than 1,500 unknowns exist, at least 20% of those locations will require physical verification. If more than 1,500 unknowns exist, you will have to physically verify that enough of those lines will reach 95% confidence.

Question 12: How can small utilities reduce the amount of time and resources spent on their inventory development?

Small utilities can reduce time and resources spent on developing an inventory by expanding data collection efforts through self-reporting. Self-reporting minimizes the burden by using homeowners to help collect enough data to indicate where lead material might be and which areas to prioritize for verification.

When combined with a data management system or dynamic central repository, you have the advantage of having a tracking mechanism in place as data starts flowing in. Some tools have self-reporting forms that feed right into the database for live data collection.

Question 13 (Updated): Has the EPA released any guidance on service line inventory development?

The EPA released new guidance materials on August 4, 2022. The EPA originally submitted documents for review by the Office of Management and Budget or OMB on June 7th, 2022. The OMB typically doesn't review compliance documents but the goal is this guidance will help utilities as they implement Lead and Copper Rule Revisions  inventory requirements. The Association of State Drinking Water Administrators or ASDWA had originally reported the initial update.    

The OMB typically assists the President in evaluating the effectiveness of  agency problems, policies, and procedures.

Looking for more LCRR guidance?

We've collected several resources related to LCRR compliance.

Understanding LCRR compliance

The LCRR will become effective in October 2024, with many water utilities seeking resources to help understand the regulatory requirements. A common misconception is that not all water utilities will have to comply with the 2024 regulation. Here are a few resources on meeting compliance.

Service line inventory development

All water utilities must provide a materials inventory of their service lines, regardless if they have lead or not. The new rule has sparked some confusion as the definition of a lead service line varies from state to state and the identification process can be costly. Some communities have tossed around machine learning/artificial intelligence methods to help predict the probability of lead. The following resources cover this topic more in depth.  

Lead service line replacement and progress tracking

Part of the LCRR 2024 regulation includes keeping a publicly accessible inventory. Communities who have identified lead service lines will also have to create a lead service line replacement plan to meet the LCRR 2024 compliance deadline. Homeowners with confirmed lead service lines will be notified and kept abreast of replacement plan progress.  

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Andrea Lebron
Marketing Professional
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Andrea is Trinnex’s demand generation marketer with a passion for writing about technology and digital-first resiliency.

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