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Projects

How does land use impact on global air quality and climate: a combined satellite and model study

Background and Motivation

Future pressures driven by food security and population growth result in anthropogenic land use change. Land use change impacts air quality by changing natural emissions both of gaseous and aerosol species, and impacts climate through radiative forcing. This occurs because land-use change alters atmosphere-biosphere interactions that influence radiation, moisture and carbon budgets. Vegetation also emits biogenic volatile organic compounds (BVOCs) that form secondary organic aerosols (SOA) that modify levels of major air pollutants particulate matter (PM2.5) and ozone and influence the abundance short-lived climate forcers (SLCFs). In addition, land use change is often brought about by fires and biomass burning that too cause poor air quality and impact climate. Understanding how current and future land-use change, such as large-scale deforestation, impacts atmospheric composition and radiation is important for global efforts to tackle poor air quality and mitigate climate change. A key uncertainty is our limited understanding of how land-use change influences air quality and climate through changes in BVOC emissions and SOA formation –this will be addressed in this project.

Aim and Objectives

This project will use satellite measurements combined with new and existing multi-model simulations to quantify the key processes and impacts of land use change on air quality and on climate. Our objectives are: to study emissions and atmospheric composition over major areas of deforestation; to assess multi-model simulations of land-use change impacts on air quality and improvements in model simulations with more detailed process representation; and to quantify how the combination of future changes in land use, climate and anthropogenic emissions will further impact air quality and climate.

Methodology

Fig. 1: An example -spring OMI tropospheric column NO2 levels over Amazonia. Pope et al. (2019)

A wealth of satellite measurements, using a range of remote sensing techniques and spectral information (e.g. UV, visible and IR wavelengths), enable us to monitor both land use change (e.g. NDVI, LAI , soil moisture, LST ) and atmospheric composition change (i.e. total columns or profiles)(Reddington et al. 2015; Pope et al. 2019). Here, long-term global records e.g. from GOME/GOME II, OMI provide crucial spatial information unavailable from other measurement types. Alongside unparalleled new high resolution measurements from the ESA TROPOMI instrument, we therefore have the opportunity to examine how both emissions of air pollutant precursors (methane, formaldehyde- a VOC oxidation product) and concentrations of air pollutants (ozone, nitrogen dioxide and aerosol optical depth (AOD) as a proxy for PM2.5) change with different land-use classifications and throughout the satellite sensing period.

For the latest IPCC 6th assessment (AR6) we have performed a set of model simulations with the UKESM1 Earth System model to examine the role of land-use on atmospheric composition– making this project timely. The project will utilise model simulations to analyse the impacts of land-use on air quality based on changing BVOC emissions and also to assess inter-model sensitivities. In addition, recently we have implemented a process-based BVOC emission model in UKESM1 that includes: new BVOC species, more realistic VOC chemical oxidation pathways and new biogenic-mediated particle formation (Scott et al. 2014; Kelly et al. 2019). New model simulations will also be performed to investigate the impacts of these new SOA sources and processes on land use emissions and on atmospheric composition and radiative forcing. Satellite measurements described above will enable comprehensive model evaluation. Further model simulations will assess the relative role and importance of future land-use change when combined with future anthropogenic emissions and climate. Depending on student interests, we can also use UKESM1 with the INFERNO fire model and satellite trace gas and AOD measurements that detect fires/biomass burning.

Kelly, J. M. et al. Geosci. Model Dev. https://doi.org/10.5194/gmd-12-2539-2019, 2019.

Pope et al. https://doi.org/10.1029/2019GL084143, 2019

Reddington et al. https://doi.org/10.1038/ngeo2535,

Scott, C. E., et al. https://doi.org/10.5194/acp-14-447-2014, 2014.