Water Quality

57th Annual Yankee Conference

The 57th Annual Yankee Conference on Environmental Health will be held from September 11-13, 2019 in Plymouth Massachusetts at Hotel 1620, Plymouth Harbor, 180 Water Street, Plymouth MA 0260.

The Conference is the annual conference of the NEHA New England Affiliates: Connecticut, Massachusetts, Northern New England (Maine New Hampshire, Vermont) and Rhode Island. The theme of this year's conference is “Hands on Environmental Health”. 

EHTER Operations Course

The Federal Emergency Management Agency’s (FEMA) Center for Domestic Preparedness (CDP) will be holding an Environmental Health Training in Emergency Response (EHTER) Operations course on November 10 - 15, 2019 at its training facilities in Anniston, AL. 

Groundwater in Fractured Bedrock

This one-day course focuses on practical aspects of groundwater in the fractured sedimentary bedrock of the Newark Group (Brunswick Aquifer), where thousands of sites have been impacted by industrial contaminants. Remedial activities at such sites are usually impaired by oversimplified notions regarding groundwater flow and contaminant migration, as well as by the use of inadequate hydrogeologic characterization methods.

Decentralized Wastewater Webinar

Natural disasters are on the rise. With at least one in five homes and many businesses in the U.S. dependent on septic systems and other decentralized wastewater systems, what are the implications regarding their vulnerabilities, and how do affected communities respond? This webinar will cover new resources developed by the National Environmental Health Association (NEHA), promoting key messages to populations dependent on decentralized systems across the U.S.

NAS Workshop on Artificial Intelligence

Artificial Intelligence is being called the new electricity—a technological invention that promises to transform our lives and the world. The resurgence of investment and enthusiasm for artificial intelligence, or the ability of machines to carry out “smart” tasks, is driven largely by advancements in the subfield of machine learning. Machine learning algorithms can analyze large volumes of complex data to find patterns and make predictions, often exceeding the accuracy and efficiency of people who are attempting the same task.

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