An official website of the United States government
Abstract Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national‐level alert‐day identification relies predominately on surface monitor networks and forecasters. Satellite‐based estimates of surface air quality have rapidly advanced and have the capability to inform exposure‐reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite‐based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5 > 35 μg m−3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person‐alerts (alert days × population) in 2020 over polar‐orbiting satellite data. We apply pre‐existing estimates of PM2.5exposure reduction by individual behavior modification and find these additional person‐alerts may lead to 1,200 (800–1,500) or 54% more averted PM2.5‐attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8–17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.
more »
« less
Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder
Abstract Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model’s effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R2value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth’s atmosphere and support decision-making concerning air quality and public health.
more »
« less
- PAR ID:
- 10477094
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near‐real‐time for both cloudy and clear sky conditions at a five‐minute resolution. We compared two machine learning (ML) models, Long Short‐Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top‐of‐atmosphere observations from the Advanced Baseline Imager (ABI) on the GOES‐16 satellite against observations from hundreds of observation sites for a five‐year period. Long Short‐Term Memory outperformed GBR, especially at coarser resolutions and under challenging conditions, with a clear sky R2of 0.96 (RMSE 2.31K) and a cloudy sky R2of 0.83 (RMSE 4.10K) across CONUS, based on 10‐repeat Leave‐One‐Out Cross‐Validation (LOOCV). GBR maintained high accuracy and ran 5.3 times faster, with only a 0.01–0.02 R2drop. Feature importance revealed infrared bands were key in both models, with LSTM adapting dynamically to atmospheric changes, while GBR utilized more time information in cloudy conditions. A comparative analysis against the physically based ABILSTproduct showed strong agreement in winter, particularly under clear sky conditions, while also highlighting the challenges of summer LST estimation due to increased thermal variability. This study underscores the strengths and limitations of data‐driven models for LST estimation and suggests potential pathways for integrating ML models to enhance the accuracy and coverage of LST products.more » « less
-
Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2(NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks withR2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks.more » « less
-
Abstract Global economic development and urbanization during the past two decades have driven the increases in demand of personal and commercial vehicle fleets, especially in developing countries, which has likely resulted in changes in year-to-year vehicle tailpipe emissions associated with aerosols and trace gases. However, long-term trends of impacts of global gasoline and diesel emissions on air quality and human health are not clear. In this study, we employ the Community Earth System Model in conjunction with the newly developed Community Emissions Data System as anthropogenic emission inventory to quantify the long-term trends of impacts of global gasoline and diesel emissions on ambient air quality and human health for the period of 2000–2015. Global gasoline and diesel emissions contributed to regional increases in annual mean surface PM2.5(particulate matter with aerodynamic diameters ⩽2.5μm) concentrations by up to 17.5 and 13.7µg m−3, and surface ozone (O3) concentrations by up to 7.1 and 7.2 ppbv, respectively, for 2000–2015. However, we also found substantial declines of surface PM2.5and O3concentrations over Europe, the US, Canada, and China for the same period, which suggested the co-benefits of air quality and human health from improving gasoline and diesel fuel quality and tightening vehicle emissions standards. Globally, we estimate the mean annual total PM2.5- and O3-induced premature deaths are 139 700–170 700 for gasoline and 205 200–309 300 for diesel, with the corresponding years of life lost of 2.74–3.47 and 4.56–6.52 million years, respectively. Diesel and gasoline emissions create health-effect disparities between the developed and developing countries, which are likely to aggravate afterwards.more » « less
-
Abstract Bangkok, Thailand is a tropical Asian megacity with high aerosol concentrations and frequent thunderstorm activity. This investigation examines the covariation between thermodynamics, aerosols, and thunderstorms, using lightning stroke counts as a measure of intensity, for a five-year period (2016–2020). The investigation incorporates data from the aerosol robotic network (AERONET), ERA-5 reanalysis, ground-based air quality stations, and total lighting data from Vaisala Inc.’s GLD360 network to examine the aerosol-thermodynamic interrelationships within thunderstorm initiation environments. Results indicate that aerosol impacts on thunderstorms are robust and, when examined in concert with instability, can augment lightning. Thermodynamic instability is also positively correlated with stroke counts in thunderstorms. Particulate matter greater than 10 µg m-3(PM10) concentration is significantly higher in thunderstorms containing more than 100 strokes, supporting the potential role of aerosols in promoting the non-inductive charge process. The emergence of a “boomerang” or threshold effect is also evident as aerosol optical depth (AOD) increases. Evidence suggests increasing AOD initially promotes, then limits, instability and thunderstorm intensity. Finally, there exists a positive relationship between aerosol concentration and particle size in thunderstorm initiation environments.more » « less
