Large role of anthropogenic climate change in driving wildfires and smoke exposure across the western US
Wildfire activity has increased dramatically in western United States (US) over the last three decades, having a significant impact on air quality and human health. However, quantifying the drivers of trends in wildfires and subsequent smoke exposure is challenging, as both natural variability and anthropogenic climate change (ACC) play important roles. Using a machine learning model (Gaussian Processing Regression), we build relationships between observed meteorology, vegetation, and the burned areas of lightning-caused and human-caused fires in ecoregions across the western US from 1992 to 2020. We determine the long-term ACC signals in meteorology, using simulations from the Coupled Model Intercomparison Project, phase 6. A natural climate scenario is constructed by subtracting the ACC signals from observed meteorological variables; using the machine learning model, we then predict the expected burned area under this scenario. Our results show that ACC contributes 33-82% to observed total burned area in the western US, depending on the ecoregion, yielding 65% of total fire emissions of organic carbon on average across the West from 1992 to 2020. To quantify the contributions of ACC and natural variability to surface concentrations of fire-related fine particulate matter (smoke PM2.5), we rely on GEOS-Chem, a chemical transport model. For the 1997-2020 timeframe, we find that on average, ACC contributes 49% to smoke PM2.5 concentrations across the West, and can also explain 58% of the increase in smoke PM2.5 from 2010 to 2020 in this region. Our work points to the large role of ACC in degrading US air quality.
Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights
Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from MERRA-2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM2.5 in several key cities near the wildfire source regions, with smoke PM2.5 accounting for 5 %–52 % of total PM2.5 during fire seasons from 2009 to 2020.
Impacts of Ship Emissions on Air Quality in Southern China
In early 2020, two unique events perturbed ship emissions of pollutants around Southern China, proffering insights into the impacts of ship emissions on regional air quality: the decline of ship activities due to COVID-19 and the global enforcement of low-sulfur (<0.5%) fuel oil for ships. In early 2020, estimated ship emissions of NOx, SO2, and primary PM2.5 over Southern China dropped by 19, 71, and 58%, respectively, relative to the same period in 2019. Ship emission reductions in early 2020 drove 16 to 18% decreases in surface NO2 levels but 3.8 to 4.9% increases in surface ozone over Southern China. We estimated that ship emissions contributed 40% of surface NO2 concentrations over Guangdong in winter. Our results indicated that future abatements of ship emissions should be implemented synergistically with reductions of land-borne anthropogenic emissions of non-methane volatile organic compounds to effectively alleviate regional ozone pollution.
Development of WRF-GC: online two-way coupling of WRF and GEOS-Chem
We developed the WRF-GC model, an online two-way coupling of the Weather Research Forecasting (WRF) meteorological model and GEOS-Chem chemical transport model, for regional atmospheric chemistry and air quality modeling. WRF-GC offers regional modelers access to the latest GEOS-Chem chemical module, which is state-of-the-science and actively developed by a large international user base. At the same time, WRF-GC enables GEOS-Chem users to perform high-resolution forecasts and hindcasts for any region and time of interest. WRF-GC includes aerosol-radiation-cloud interactions, allowing users to investigate the feedbacks between meteorology and atmospheric chemistry. WRF-GC is open source and freely available from here.
Neural network predictions of pollutant emissions from open burning of crop residues
Open burning of crop residues is a strong seasonal source of air pollutants in many parts of China, but the large day-to-day variability of the associated emissions pose a great challenge for air quality forecasts. Here we developed back-propagation neural network (BPNN) ensembles to forecast the daily fire pixel counts in Southern China. The BPNN ensembles were trained using daily assimilated surface meteorological data and daily fire pixel observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) during the years 2003–2012. We showed that the BPNN ensembles successfully forecasted the day-to-day variability and the interannual variability of fire pixel counts over Southern China of the years 2013–2015. We used the forecasted daily fire pixel counts to scale the climatological biomass burning emissions from the Fire Inventory from NCAR (FINN) and applied the resulting forecasted daily biomass burning emissions to drive the WRF-Chem air quality model. The use of BPNN-ensemble-forecasted daily biomass burning pollutant emissions led to significant improvements in the daily forecasts of PM2.5 concentrations in Southern China.