All posts in “ASI Blog”

Air Toxics: Designing an Affordable Monitoring Network

The state of Oregon has proposed a new set of rules, currently in the draft state, for a risk-based air toxics permitting program; the program is named “Cleaner Air Oregon.” The stated goal of Cleaner Air Oregon is to reduce health risks from existing industrial facilities to below 100 in 1 million by 2030. To accomplish this goal, the state of Oregon will require emissions reductions from facilities for which the aggregate health risk, based on predicted health risk from 260 chemicals with known health-protective levels, is above the 100 in 1 million threshold.
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Air Sciences Inc. Receives the Professional Services Contractor of the Year Award  

During the Los Angeles Department of Water and Power (LADWP) 2017 vendor Exhibit, themed “Transforming Connectivity,” Air Sciences was presented with the Professional Services Contractor of the Year award. The event was organized by LADWP to help promote economic development, diversity in contracting, and excellence in water and power services to the agency and residents of Los Angeles. Dr. Mark Schaaf (Air Sciences), as well as supporting subcontractor team member Dr. John Dickey (PlanTierra) and Mica Heilmann (Land IQ), accepted the award on the team’s behalf from Commissioner Noonan who praised Air Sciences for supporting “LADWP’s goal of providing opportunities to small-, minority-, and women-owned firms by effectively including and utilizing SBE, MBE, and WBE firms as its contractors.”

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Visualizing Data from Meteorological Station Networks

Meteorological station data remains one of the best tools for informing avalanche safety decisions. For recreationists and practitioners alike, everyone requires information specific to their plan for the day. But parsing through large station networks is often tedious, and information overload can actually limit the amount of information we internalize.  Additionally, few recreationists understand the complexities at each station and looking at less than ideal data can be misleading.

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