Organic carbon dry deposition outpaces atmospheric processing with unaccounted implications for air quality and freshwater ecosystems

Science Advances, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 3, 2025

Dry deposition is an important yet poorly constrained process that removes reactive organic carbon from the atmosphere, making it unavailable for airborne chemical reactions and transferring to other environmental systems. Using aircraft-based measurement method, we provide large-scale estimates of total gas-phase rates fluxes. Observed downwind unconventional oil operations reached up 100 tC hour −1 , with fluxes exceeding 0.1 gC m −2 . The observed lifetimes (τ dep ) were short enough (i.e., 4 ± 2 hours) compete oxidation processes affect fate atmospheric carbon. Yet, much this deposited cannot be accounted using traditional algorithms used in regional air quality models, signifying underrepresented, but influential, chemical-physical surface properties processes. Furthermore, these represent a major unaccounted contribution freshwater ecosystems outweigh terrestrial sources, necessitating inclusion dry aquatic balances models.

Язык: Английский

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SOURCES OF IONIZING RADIATION

Опубликована: Дек. 11, 2024

I welcome all participants of the scientific forum "Radiation Safety in Modem World"The security situation world is surprisingly not stable 21st century and humanity exposed to unexpected challenges that affect each us.Chemical radioactive substances are wonderful helpers a great result human progress.Although they supportive, on other hand, can be very dangerous, event accidents or deliberate misuse.Therefore, it important organize conferences this type, where we, scientists, participate promoting safety.

Язык: Английский

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Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 138, С. 104470 - 104470

Опубликована: Март 11, 2025

Язык: Английский

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The Global Forest Fire Emissions Prediction System version 1.0

Опубликована: Март 6, 2024

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in real time for global air-quality forecasting. uses bottom-up approach, based on remotely-sensed hotspot locations and databases linking burned area per to ecosystem-type classification at 1-km resolution. Unlike other forest fire emissions models, GFFEPS provides dynamic of fuel consumption behaviour the Canadian Danger Rating System. Combining forecasts daily weather hourly meteorological conditions with land classification, produces emission predictions 3-hour steps (in contrast non-dynamic models use fixed rates require collection make post-burn emissions). has been designed near-real-time forecasting applications as well historical simulations which data are available. A study was conducted running through six-year period (2015–2020). Regional annual total smoke emissions, unit predicted by were generated assess performance over multiple years regions. distinguished grass-dominated regions from forested, while also showed high variability affected El Niño deforestation. carbon then compared wildfire including GFAS, GFED4.1s FINN1.5/2.5. estimated values lower than GFAS/GFED (80 %/74 %), similar FINN1.5 (97 %). This largely due impact moisture captured modelling. An effort underway validate model, further developments improvements expected.

Язык: Английский

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The Global Forest Fire Emissions Prediction System version 1.0

Geoscientific model development, Год журнала: 2024, Номер 17(21), С. 7713 - 7749

Опубликована: Ноя. 5, 2024

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. uses bottom-up approach, based on remotely sensed hotspot locations, and databases linking burned area per to ecosystem-type classification at 1 km resolution. Unlike other fire emissions models, GFFEPS provides dynamic of fuel consumption, behaviour growth the Canadian Danger Rating System, plant phenology as calculated from daily weather burned-area using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots historical statistics. Combining forecasts hourly meteorological conditions with land classification, produces consumption emission predictions 3 h steps (in contrast non-dynamic models use fixed rates require collection make post-burn emissions). has been designed operational forecasting applications well simulations which data are available. A study was conducted showing through 6-year period (2015–2020). Regional annual total smoke emissions, unit predicted by were generated assess performance over multiple years regions. model's results clearly distinguished regions dominated grassland (Africa) those forests (boreal regions) showed high variability affected El Niño deforestation. carbon then compared wildfire including Assimilation (GFAS), Database (GFED4.1s) INventory NCAR (FINN 1.5 2.5). estimated values lower than GFAS GFED (80 % 74 %) had similar FINN (97 %). This largely due impact moisture captured modelling. Model evaluation efforts date described – an ongoing effort underway further validate model, developments improvements expected future.

Язык: Английский

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A VEHICLE WITH ENHANCED CROSS-COUNTRY ABILITY TO EVACUATE PEOPLE FROM DANGEROUS AREAS

Опубликована: Дек. 11, 2024

I welcome all participants of the scientific forum "Radiation Safety in Modem World"The security situation world is surprisingly not stable 21st century and humanity exposed to unexpected challenges that affect each us.Chemical radioactive substances are wonderful helpers a great result human progress.Although they supportive, on other hand, can be very dangerous, event accidents or deliberate misuse.Therefore, it important organize conferences this type, where we, scientists, participate promoting safety.

Язык: Английский

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Comment on gmd-2024-31

Опубликована: Апрель 14, 2024

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in real time for global air-quality forecasting. uses bottom-up approach, based on remotely-sensed hotspot locations and databases linking burned area per to ecosystem-type classification at 1-km resolution. Unlike other forest fire emissions models, GFFEPS provides dynamic of fuel consumption behaviour the Canadian Danger Rating System. Combining forecasts daily weather hourly meteorological conditions with land classification, produces emission predictions 3-hour steps (in contrast non-dynamic models use fixed rates require collection make post-burn emissions). has been designed near-real-time forecasting applications as well historical simulations which data are available. A study was conducted running through six-year period (2015–2020). Regional annual total smoke emissions, unit predicted by were generated assess performance over multiple years regions. distinguished grass-dominated regions from forested, while also showed high variability affected El Niño deforestation. carbon then compared wildfire including GFAS, GFED4.1s FINN1.5/2.5. estimated values lower than GFAS/GFED (80 %/74 %), similar FINN1.5 (97 %). This largely due impact moisture captured modelling. An effort underway validate model, further developments improvements expected.

Язык: Английский

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Comment on gmd-2024-31

Опубликована: Апрель 25, 2024

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in real time for global air-quality forecasting. uses bottom-up approach, based on remotely-sensed hotspot locations and databases linking burned area per to ecosystem-type classification at 1-km resolution. Unlike other forest fire emissions models, GFFEPS provides dynamic of fuel consumption behaviour the Canadian Danger Rating System. Combining forecasts daily weather hourly meteorological conditions with land classification, produces emission predictions 3-hour steps (in contrast non-dynamic models use fixed rates require collection make post-burn emissions). has been designed near-real-time forecasting applications as well historical simulations which data are available. A study was conducted running through six-year period (2015–2020). Regional annual total smoke emissions, unit predicted by were generated assess performance over multiple years regions. distinguished grass-dominated regions from forested, while also showed high variability affected El Niño deforestation. carbon then compared wildfire including GFAS, GFED4.1s FINN1.5/2.5. estimated values lower than GFAS/GFED (80 %/74 %), similar FINN1.5 (97 %). This largely due impact moisture captured modelling. An effort underway validate model, further developments improvements expected.

Язык: Английский

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Reply on RC1

Опубликована: Май 29, 2024

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in real time for global air-quality forecasting. uses bottom-up approach, based on remotely-sensed hotspot locations and databases linking burned area per to ecosystem-type classification at 1-km resolution. Unlike other forest fire emissions models, GFFEPS provides dynamic of fuel consumption behaviour the Canadian Danger Rating System. Combining forecasts daily weather hourly meteorological conditions with land classification, produces emission predictions 3-hour steps (in contrast non-dynamic models use fixed rates require collection make post-burn emissions). has been designed near-real-time forecasting applications as well historical simulations which data are available. A study was conducted running through six-year period (2015–2020). Regional annual total smoke emissions, unit predicted by were generated assess performance over multiple years regions. distinguished grass-dominated regions from forested, while also showed high variability affected El Niño deforestation. carbon then compared wildfire including GFAS, GFED4.1s FINN1.5/2.5. estimated values lower than GFAS/GFED (80 %/74 %), similar FINN1.5 (97 %). This largely due impact moisture captured modelling. An effort underway validate model, further developments improvements expected.

Язык: Английский

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Reply on RC2

Опубликована: Май 29, 2024

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in real time for global air-quality forecasting. uses bottom-up approach, based on remotely-sensed hotspot locations and databases linking burned area per to ecosystem-type classification at 1-km resolution. Unlike other forest fire emissions models, GFFEPS provides dynamic of fuel consumption behaviour the Canadian Danger Rating System. Combining forecasts daily weather hourly meteorological conditions with land classification, produces emission predictions 3-hour steps (in contrast non-dynamic models use fixed rates require collection make post-burn emissions). has been designed near-real-time forecasting applications as well historical simulations which data are available. A study was conducted running through six-year period (2015–2020). Regional annual total smoke emissions, unit predicted by were generated assess performance over multiple years regions. distinguished grass-dominated regions from forested, while also showed high variability affected El Niño deforestation. carbon then compared wildfire including GFAS, GFED4.1s FINN1.5/2.5. estimated values lower than GFAS/GFED (80 %/74 %), similar FINN1.5 (97 %). This largely due impact moisture captured modelling. An effort underway validate model, further developments improvements expected.

Язык: Английский

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