Current Projects‎ > ‎

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.