63rd Cement Industry Technical Conference – Virtual
May 24 - May 28
The largest conference in the cement industry will be held virtually this year beginning May 24th and lasting through May 28th. The Conference will feature virtual exhibitor booths, technical paper presentations, industry updates, regulatory status updates, and more. CleanAir’s Ali Lashgari will be presenting on the “Use of Next Generation Emission Measurement Sensors and Data Analysis in Industrial Applications.”
Here’s a sneak peek of the presentation:
The industrial, private, and regulatory sectors each have an interest in better assessing the impact of industrial sources on surrounding communities. In recent years, there has been considerable interest in the development of Next Generation Emission Measurement (NGEM) sensor technology to help in these efforts. As technology improves, NGEM sensors are beginning to be sensitive enough to criteria and other hazardous air pollutants (HAP) to allow such sensors to be used in comprehensive ambient air quality monitoring. This allows air quality data to be generated at much higher spatial and temporal resolution and considerably lower cost than those generated by traditional State or Local Air Monitoring Station (SLAMS) networks. However, lower cost often comes with the tradeoff of lower data quality, which is not acceptable in applications where the results of the measurements are ultimately used to show compliance with National Ambient Air Quality Standards (NAAQS). However, there are other applications where a less rigorous approach would be acceptable. Such applications can involve source identification, back-trajectory modeling, and source apportionment, which are all vital parts of the U.S. Environmental Protection Agency’s NGEM efforts. These efforts represent the confluence of measurement with data analytics, aiming to positively identify sources and quantify their impact using emerging technologies deployed in dense networks around or within facility boundaries. This paper introduces NGEM concepts and discusses the influence of project objectives on application data quality needs. Source identification approaches will be presented along with challenges in the quality assurance of these systems.