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Particulate matter emissions maps for cities

Emissions of particulate matter (PM), reactive gases and greenhouse gases (GHGs) from industry, transport and domestic activities, degrade air quality in cities. In New Zealand, it is primarily emissions of PM from burning wood or fossil fuels that are of concern. PM remains suspended in the air where it can be inhaled, increasing the chances of developing heart-related illnesses and lung-related diseases. In addition to PM, emissions of gases such as carbon monoxide (CO), nitrogen oxides (NOx), and volatile organic compounds (VOCs), contribute to poor air quality. Furthermore, CO and VOCs react with NOx to form tropospheric ozone, which is a GHG and an air pollutant. While most New Zealand towns and cities fortunately do not experience photochemical smog, for many megacities internationally, smog is a serious health hazard urgently requiring remediation.
 
Actions to mitigate PM and gaseous emissions require information on emissions sources, which have traditionally been identified using bottom-up accounting exercises. Through this project we are developing a superior approach to inferring PM emission maps at high spatial and temporal resolution that adds value to bottom-up approaches by capitalising on surface observations and a mesoscale atmospheric model enhanced to simulate aerosol microphysics. The system is called MAPM (Mapping Air Pollution eMissions).

The mesoscale model at the core of MAPM transforms a prescribed time varying two-dimensional surface PM emissions field into a time varying three-dimensional atmospheric PM concentration distribution. The model requires a description of the meteorology - primarily the winds that transport PM from their emissions sources to where they are measured. The model also requires (i) a description of atmospheric temperature to capture the effects of inversion layers that trap PM close to the ground, (ii) humidity, which affects aerosol microphysics, and (iii) turbulence, which drives mixing of air masses. Here, however, we measure the concentration field and infer the emissions field. Our plan is to use a method known as inverse modelling to ‘run the model backwards’ to infer emissions fields from concentrations measured at point sources or in vertical profiles, in the atmosphere of polluted cities. 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 policy needs. Therefore, the design thinking for MAPM has been conducted cognizant of stakeholder needs; a primary New Zealand user of this service, Environment Canterbury, has been included in the development of this proposal and will be an active participant in the execution of the project.

In addition to building the inverse model for deployment and operation in the cloud, this project will address several methodological choices associated with inverse modelling. The choices made will be validated through a field campaign in Timaru in 2019. Once tested and verified, the IP developed through this project will be vested in a new commercial entity that will deploy the service internationally to city officials seeking to identify PM sources. The same technique can, and is, being applied to infer emissions of GHGs and gases participating in photochemical smog; combined retrieval of PM and these other trace gases has co-benefits. Therefore, while the primary focus of this project is on PM emissions maps retrievals, a stretch goal will be to incorporate retrievals of CO, NOx, and VOCs, and, through additional future funding, to extend the capability to include GHGs.


A bit more detail...
Answers to questions posed by the NZ Herald

What’s the background to this study? What understanding are we building off (and on), and what questions are we really trying to ask and answer?

Emissions of particulate matter (PM), reactive gases and greenhouse gases (GHGs) from industry, transport and domestic activities, degrade air quality in cities. In New Zealand, it is primarily emissions of PM from burning wood or fossil fuels that are of concern. PM remains suspended in the air where it can be inhaled, increasing the chances of developing heart-related illnesses and lung-related diseases. In addition to PM, emissions of gases such as carbon monoxide (CO), nitrogen oxides (NOx), and volatile organic compounds (VOCs), contribute to poor air quality. Furthermore, CO and VOCs react with NOx to form tropospheric ozone, which is a GHG and an air pollutant. While most New Zealand towns and cities fortunately do not experience photochemical smog, for many megacities internationally, smog is a serious health hazard urgently requiring remediation.
Actions to mitigate PM and gaseous emissions require information on emissions sources, which have traditionally been identified using bottom-up accounting exercises. Through this project we will develop a superior approach to infer PM emission maps at high spatial and temporal resolution that adds value to bottom-up approaches by capitalising on surface observations and a mesoscale atmospheric model enhanced to simulate aerosol microphysics.

A walk-through of how we’ll carry the study out. Who and what will it involve?

There are a range of air pollution computer models that can take a prescribed emissions map for a city and simulate what the resultant pollution levels around the city would be as a result of those emissions. But that doesn’t help us in the quest described above. We measure the pollution levels around a city and want to figure out what the emissions map, e.g. over the previous 24 hours, must have been to produce those pollution levels. 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 real-world measurements of pollution and infers what the pollution map must have looked like, including the uncertainties on those maps. The goal over the 2-year course of the project is to figure out exactly how to do this and, once done, vest that intellectual property (IP) in a new company that will then commercialize the capability as a service to city officials in offshore countries.

Project partners include: Bodeker Scientific (the lead organisation), the National Institute of Water and Atmospheric Research (NIWA), Environment Canterbury, Canterbury University, University of Otago, Sigma Space (a company in Washington DC), and Karlsruher Institute of Technology (KIT). 

What do we envisage will present the biggest challenges?

This project has some tough challenges. We need to figure out what combination of measurements of atmospheric pollution levels leads to the best quality inferred emissions maps, where and what meteorological measurements around a city are required to constrain the model, and what model formulations will lead to the highest quality emissions maps. Small uncertainties in transport pathways of air parcels from their sources to where they are measured can result in large uncertainties in the inferred pollution emissions fields. Figuring how to mitigate those so-called non-linear effects, will be a huge challenge. This is the first time that this inverse modelling approach has been applied at a city scale and the first time that the goal has been to develop an operational capability.

What do we expect – or hope – will result from it?

Our hope is that access to 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.

What benefits or real-world applications do we believe this project might deliver?

The goal of this research is to develop a real-world capability to generate air pollution source maps.

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