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Real-Time Monitoring Solutions
What is Near-Reference Data?
The term “Near-Reference” quality data is tied to established air monitoring standards and methods. This data from these monitors is of sufficient accuracy and quality to complement existing air pollution monitors and networks, and the equipment is less expensive to own and operate. Oftentimes, the monitoring stations are located far away from the actual sources of pollution, and may not accurately report short-term (seconds to minutes vs. 24-hour average), localized high concentration events which the public is exposed to.
To further support the need for more local monitoring, the U.S. Environmental Protection Agency (EPA) has published the Air Sensor Guidebook , which defines a tiered approach and how an instrument performs in comparison to Federal Reference Method (FRM) or Federal Equivalent Method (FEM). To complement these FRM and FEM sites, supplemental monitoring and source identification monitoring tiers have been defined to have errors less than 20% and 30%, respectively. The European Union has also similarly defined PM monitoring data quality as highlighted in the 2008/50/EC directive for both Reference and Indicative measurements, with total expanded uncertainties of 25% and 50%, respectively.
The range of applications for TSI’s DustTrak™ Environmental Monitor includes supplementary monitoring, air quality studies, and industrial compliance monitoring, with the ability to add third-party sensors that measure VOCs, gases, wind speed and more. Additionally, there is a focus on monitoring PM during natural disasters such as wildfires, where rapid deployment, real–time data and comparability to FEM data are key attributes the DustTrak Environmental Monitor provides. Below is a comparison of data from the DustTrak Environmental Monitor and a similar air monitoring device.
In October 2015, California Office of Emergency Services (OES) Sacramento conducted a controlled burn event in Orleans, California, to compare the performance of a DustTrak Environmental Monitor (model EDT-DRX) and a Met One E-BAM, in support of wildfire monitoring. Both instruments were located within five feet of each other and operated according to the manufacturers’ standard operating procedures. The PM 2.5 24-hour average data from the EDT-DRX is shown to compare extremely well with the E-BAM (Slope of 0.993 and R^2 of 0.99), as supported by the correlation plots below. The hourly average data from the E-BAM highlights the increased negative concentration data from a filter-based measurement, due to volatile losses and the filter hysteresis that occurs with this type of method of detection (filter tape sampler / monitor for PM).