Roger Cudmore Member Name
Principal Environmental Consultant
Cameron McNaughton Member Name
Principal Air Quality Consultant
Clean air is one of the most basic and fundamental expectations for quality of life. Regardless of where we live, we all want to be confident that the air we breathe isn’t going to harm our health, or the health of the living things in our environment.
For industrial businesses operating close to or within urban areas, managing air quality is an important part of gaining and retaining community acceptance, or a social license to operate. Simply meeting the regulatory standards is not enough to demonstrate a commitment to the health of the community as regulations tend to lag developments in health science. Businesses must therefore be able to show that they’re keeping up with scientific recommendations.
Certainly, as scientific understanding advances, we’re likely to see increasingly stringent regulations for oxides of nitrogen (NOx) and fine particulate matter, which are both major components of the health risks associated with air pollution. When we think about managing air quality in industrial settings, it is more important than ever to understand the actual impacts and changes to the surrounding air. To do that, we need data, and lots of it. Data that is local and specific, not broadly regional. We also need to properly interpret that data in order to separate the key effects from the background noise.
Gathering ambient air quality data
In Australia and New Zealand, air quality standards are based on cumulative air contaminant levels from all sources – near and far. Business owners need to determine what their facility is contributing to the total picture and understand what parts of the total come from other sources, either natural or anthropogenic, that are not under their control.
In the absence of detailed, local data, we’re left with guesswork about background contributions, and guesswork does not satisfy a permit requirement or gain trust from a community or tribunal.
How can ambient air quality data be gathered in a cost-effective and reliable way? The most important objective of any air sampling process is to gather a large representative set of samples for a full range of ambient conditions. The continuous monitoring methods for achieving this are rapidly improving.
Industrial stack emissions testing
To assess the potential effects of industrial air discharges, a common method is to measure contaminant emissions from an industrial stack and model how those emissions disperse in air. However, for fine particulate, this comes with high costs and often with a high level of inaccuracy. It also entails safety risks due to working at heights.
It involves manually sampling the particulate emitted from the process stack onto a filter (e.g. a biomass energy plant), and analysing it at an off-site laboratory. The test result gives a snapshot of particulate emissions at the time of sampling, but it is not always clear how representative this result is of the long-term operation of the facility. For this, continuous particulate emission instrumentation is available but relies on manual methods for calibration.
The manual stack emission monitoring methods have become significantly more expensive as authorities move towards requiring the use of modern U.S. Environmental Protection Agency (EPA) methods for measuring the discharge of fine particulate matter smaller than 2.5 micrometers in aerodynamic diameter size (PM2.5). Exposure to NOx and PM2.5 (along with ground-level ozone) is strongly linked to environmental and human health effects.
Due to the expense of these advanced manual stack testing methods, there is a tendency to apply them infrequently, perhaps only once or twice per year. This will provide only small snapshots rather than a broader picture of a facility’s particulate emissions over time. Conservatively testing at “maximum production levels” also has the potential to significantly overstate the true rate of emissions, which therefore exaggerates the significance of a facility’s impact on ambient NOx and PM2.5 levels.
An alternative to stack testing with set limits for emission compliance is the less commonly used approach of monitoring ambient air quality and specifying ambient limits for protection of human health. Ambient monitoring methods are becoming less expensive and real-time monitoring of NOx and PM2.5 using state-of-the-art techniques has improved the accuracy and repeatability of these measurements. Of course, verifying the accuracy and precision of the measurements require more detailed and frequent instrument calibrations.
The benefits of the ambient monitoring approach over intermittent stack testing campaigns are significant. Parallel continuous monitoring of NOx and PM2.5 levels alongside real-time meteorological data at strategic off-site locations provides very powerful datasets that enable differentiation of pollutant levels arising from background versus local sources. This can cost-effectively determine the facility’s specific contribution to total cumulative NOx and PM2.5.
The large datasets can be uploaded and permanently archived in the cloud and be viewed in near real time (although the process does require a time lag for data quality assurance). The information collected can also be used to optimise process operations, based on a solid understanding of the actual effects on air quality of different process settings.
This ambient monitoring approach can provide accurate, reliable results and publicly accessible information that will help facilities and the community to understand the effects of a facility’s emissions on ambient NOx and PM2.5 concentrations where the background atmosphere is already impacted. This makes it possible to confirm compliance with key health guidelines and to submit credible and robust permit/licence applications which have greater chances of being approved by regulators. Facilities that follow this method will also be in a better position to build and maintain a strong social licence and robust environmental reputation.