Air Quality Monitoring and Forecasting
External reference: https://openalex.org/T12120
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FuXi-Air forecasts six pollutants with multimodal data FuXi-Air combines meteorological, emission, and observational data for high-precision air quality forecasting at scale, generating 72-hour predictions across multiple sites in seconds.
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Chicago air-quality monitors are unevenly distributed across the city Study reveals severe spatial disparities in Chicago's air quality monitoring, with affluent areas over-monitored and minority communities facing undetected PM2.5 pollution hotspots.
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Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning Survey of uncertainty quantification methods for satellite-based climate variables derived from deep learning, addressing aleatoric and epistemic uncertainties in ECV estimation.
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Ozone formation shifted by season in Guanzhong Basin Analysis of warm-season ozone and secondary aerosol formation in the Guanzhong Basin reveals sub-seasonal regime shifts, with traffic and industrial emissions driving both pollutants.
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XGBoost outperformed ARIMA and Prophet for TB forecasting XGBoost machine learning model demonstrates superior accuracy for monthly tuberculosis forecasting in coastal urban environments compared to traditional ARIMA and Prophet approaches.