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The Toxics Release Inventory – Made Easy Web-based (TRI-MEweb) Application for Reporting Year 2009 is Now Available. - The TRI-MEweb application allows facilities across the U.S. to file, via the Internet, a paperless report, significantly reducing data errors, and receiving instant receipt confirmation of their submissions to US EPA and their State. The TRI-MEweb application is now available on the US EPA TRI Web site for facilities to report RY 2009 chemical release data in compliance with EPCRA reporting requirements. The deadline to report chemical releases for RY 2009 is midnight July 1, 2010. Learn more here.
TRI Reporting Aids and Assistance Materials
By law, if your facility is in a covered industry, you must file your Toxics Release Inventory (TRI) reports by July 1 of each year. The TRI Compliance Assistance web page contains access to the resources necessary for industrial facilities to report their releases and transfers of certain toxic chemicals to meet EPCRA Section 313 requirements. Please note that the TRI Program is no longer offering in-person TRI training workshops.
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ECOS is interested hearing your suggestions on topics for an upcoming TRI Webinar.
ECOS is interested hearing your suggestions on topics for an upcoming Webinar and additional topics for future dates. The first Webinar will be held in late October/early November 2009 and be open to the public and all stakeholders. The main purpose of the Webinars is to present and discuss specific TRI-related topics of interest to stakeholders.
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Topic for TRI Webinar
"The considerations necessary when using TRI data in scientific studies (and suggested approaches for researchers)".
Individuals often discuss individual data limitations, but never as a complete tool kit and seldom with approaches to address the limitations when using TRI data. I have seen where researchers well respected in their fields make common mistakes by making the wrong assumption as to what a specific data element means or how it may have been reported to the EPA. Typically the mistakes become apparent when certain data trends are applied at too fine a scale or an assumed exactness is attributed to the data.