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Sci Total Environ ; 722: 137755, 2020 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-32199359


PKU-FUEL is a recently developed gridded global emission inventory for multiple air pollutants that uses a bottom-up approach. The inventory includes data collected monthly for the period of 1960 to 2014 and at a 0.1° × 0.1° latitude/longitude resolution. In an effort to evaluate and improve this emission inventory, the PKU-FUEL Sulfur Dioxide (SO2) emission inventory was compared to other currently available and widely used global SO2 emission inventories constructed based on bottom-up and top-down approaches, including CEDS and OMI-HTAP. While PKU-FUEL is capable of capturing SO2 emissions across the globe and particularly in Asia, it misses 41 industrial point sources globally, accounting for 9.3% of Ozone Monitoring Instrument (OMI) remote sensing-measured industrial point sources. Most of these missing point sources are identified in Latin America, the Middle East (~60%), and some remote places. To improve the PKU-FUEL SO2 inventory, we applied OMI-measured emissions to sources missing from PKU-FUEL. GEOS-Chem model simulations were performed to evaluate original and improved PKU-FUEL SO2 inventories against measured SO2 concentrations across the world. Results were further compared to GEOS-Chem modeled SO2 concentrations using the CEDS inventory. We show that the modeled SO2 concentrations determined using both CEDS and improved PKU-FUEL inventories to a large extent corroborate sampled data and that the improved PKU-FUEL performs better for those regions lacking monitoring data.

Environ Pollut ; 261: 114186, 2020 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-32092627


Polycyclic aromatic hydrocarbons (PAHs) are formed by the incomplete combustion of fossil fuels and forest or biomass burning. PAHs undergo long-range atmospheric transport, as evidenced by in situ observations across the Arctic. However, monitored atmospheric concentrations of PAHs indicate that ambient PAH levels in the Arctic do not follow the declining trend of worldwide anthropogenic PAH emissions since the 2000s, suggesting missing sources of PAHs in the Arctic or other places across the Northern Hemisphere. To trace origins and causes for the increasing trend of PAHs in the Arctic, the present study reconstructed PAH emissions from forest fires in the northern boreal forest derived by combining forest carbon stocks and MODIS burned area. We examined the statistical relationships of forest biomass, MODIS burned area, emission factors, and combustion efficiency with different PAH congeners. These relationships were then employed to construct PAH emission inventories from forest biomass burning. We show that for some PAH congeners, for example, benzo[a]pyrene (BaP)-the forest-fire-induced air emissions are almost one order of magnitude higher than previous emission inventories in the Arctic. A global-scale atmospheric chemistry model, GEOS-Chem, was used to simulate air concentrations of BaP, a representative PAH congener primarily emitted from biomass burning, and to quantify the response of BaP to wildfires in the northern boreal forest. The results showed that BaP emissions from wildfires across the northern boreal forest region played a significant role in the contamination and interannual fluctuations of BaP in Arctic air. A source-tagging technique was applied in tracking the origins of BaP pollution from different northern boreal forest regions. We also show that the response of BaP pollution at different Arctic monitoring sites depends on the intensity of human activities.

Environ Sci Technol ; 53(22): 13238-13245, 2019 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-31633339


Given the lack of understanding of the complex physiochemical and environmental processes of persistent organic pollutants (POPs) in the Arctic and around the globe, atmospheric models often yield large errors in the predicted atmospheric concentrations of POPs. Here, we developed a recurrent neural network (RNN) method based on nonparametric deep learning algorithms. The RNN model was implemented to predict monthly air concentrations of polycyclic aromatic hydrocarbons (PAHs) at the high Arctic monitoring station Alert. To train the RNN system, we used MODIS satellite remotely sensed forest fire data, air emissions, meteorological data, sea ice cover area, and sampled PAH concentration data from 1996 to 2012. The system was applied to forecast monthly PAH concentrations from 2012 to 2014 at the Alert station. The results were compared with monitored PAHs and an atmospheric transport model (CanMETOP) for POPs. We show that the RNN significantly improved PHE and BaP predictions from 2012 to 2014 by 62.5 and 91.1%, respectively, compared to CanMETOP predictions. The sensitivity analysis using the Shapley value reveals that air emissions determined the magnitude of PAH levels in the high Arctic, whereas forest fires played a significant role in the changes in PAH concentrations in the high Arctic, followed by air temperature and meridional wind fields.

Poluentes Atmosféricos , Hidrocarbonetos Policíclicos Aromáticos , Regiões Árticas , Aprendizado Profundo , Monitoramento Ambiental