Emissions of particulate matter (PM), reactive gases and greenhouse gases (GHGs) from industry, transport and domestic activities, degrade air quality in cities. PM remains suspended in air where it can be inhaled, increasing the chances of developing heart-related illnesses and lung-related diseases.
Actions to mitigate PM require information on emissions sources, which have traditionally been identified using bottom-up accounting exercises. Our goal is to develop and trial a new way to derive maps of air pollution sources in cities – this new method is called MAPM (Mapping Air Pollution eMissions).
A state-of-the-art model experiment will be used to investigate the best combination of measurements from a range of different instrument types and platforms to improve the accuracy of our maps of air pollution sources . Measurements explored will include surface observations from fixed sites across a range of sensor types. Our system will be adaptable enough to be implemented at a wide range of cities and run operationally to provide near real time maps of air pollution sources.
How does MAPM work?
Emissions maps are a key input to decision making involving millions of dollars, yet methods currently available to create emissions maps are expensive, time-consuming, have poor temporal resolution (e.g. average week-day vs. weekend emissions), are commissioned sporadically (every few years at best), are difficult to update and are unvalidated. Errors, or gaps in coverage, could lead to millions of dollars being spent ineffectively. This project capitalizes on low-cost distributed monitoring networks, which have not been used for inverse modelling in this way before, thus adding to the value proposition of such networks.
There are a range of air pollution models that can take a prescribed emissions map for a city and simulate what the pollution levels around the city would be as a result of those emissions. While knowing the level of pollution is useful, knowing where that pollution came from is far more valuable since city officials can then act to close down, or mitigate, those sources.
We will use an approach called inverse modelling that effectively “runs the model backwards” so that it takes atmospheric measurements of pollution as input to infer what the emissions must have been. The inverse modelling also generates uncertainties on those derived emissions. While inverse modelling has been applied to similar problems elsewhere, it has not been applied to infer city-scale PM emissions fields from measured PM concentrations, operationally, to meet direct needs of city officials. Therefore, the design of MAPM has been conducted cognizant of stakeholder needs.
2019 Measurement Campaign
In addition to building the inverse model, this project will address several methodological choices associated with inverse modelling. The choices made will be validated using measurements made during a field campaign in Christchurch.
For more details, visit our Campaign Page
The MAPM Team
Our assembled team members from Bodeker Scientific (Project Lead), National Institute of Water and Atmospheric Research (NIWA), Environment Canterbury, University of Canterbury, University of Otago, and the Karlsruher Institute of Technology (KIT) will leverage existing state-of-the-art knowledge and facilities. Our overseas collaborators will ensure that we are following best practice in our modelling approach and that the IP developed is tailored to meet the needs of services that are fit for purpose.
The Future for MAPM
The goal over the 2-year course of the project is to develop the intellectual property (IP) that underpins the MAPM method and, once done, vest that IP in a new agency that will then commercialize the IP as a service to city officials in offshore countries.
Our hope is that access to air pollution source maps by city officials will reduce sources of pollution with subsequent improvements in urban air quality and improvements in human health. Developing and testing such a capability in New Zealand, and then deploying it as service also offshore, should provide a source of offshore revenue – a weightless export for New Zealand.