Impact of decreased aerosols during the COVID-19 pandemic on winter wheat gross primary productivity and water use efficiency

Rapid and comprehensive lockdowns to contain the coronavirus 2019 (COVID-19) pandemic reduced anthropogenic emissions and, thereby, decreased the aerosol optical depth (AOD) in Xiangyang, Hubei Province. However, their complicated interactions make quantifying the contribution of decreased aerosols to crop growth challenging. Here, we explored the indirect effects of decreased aerosol concentrations on the gross primary productivity (GPP) and water use efficiency (WUE) of winter wheat by quantifying the contributions of key environmental factors. Our results showed high temporal and spatial associations between aerosols (represented by AOD), GPP, and WUE before, during, and after the COVID-19 pandemic. AOD decreased by 23.8% +/- 10.1%, whereas GPP and WUE increased by 16.5% +/- 5.8% and 17.0% +/- 15.3%, respectively. The GeoDetector model revealed that photosynthetically active radiation (PAR) had a major impact on GPP and WUE, followed by precipitation, surface soil moisture, subsurface soil moisture, and surface temperature. Moreover, causality analysis showed a causal relationship between AOD and the dominant factors (PAR and precipitation) during the lockdown, thereby indicating a positive effect of decreased aerosols on GPP and WUE changes of winter wheat. Our findings assist in understanding the mechanisms causing GPP and WUE changes, given the environmental factors that changed significantly during the pandemic. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

Traffic Data-Empowered XGBoost-LSTM Framework for Infectious Disease Prediction

Large-scale infectious diseases pose a tremendous risk to humans, with global outbreaks of COVID-19 causing millions of deaths and trillions of dollars in economic losses. To minimize the damage caused by large-scale infectious diseases, it is necessary to develop infectious disease prediction models to provide assistance for prevention. In this paper, we propose an XGBoost-LSTM mixed framework that predicts the spread of infectious diseases in multiple cities and regions. According to big traffic data, it was found that population flow is closely related to the spread of infectious diseases. Clustering and dividing cities according to population flow can significantly improve prediction accuracy. Meanwhile, an XGBoost is used to predict the transmission trend based on the key features of infection. An LSTM is used to predict the transmission fluctuation based on infection-related multiple time series features. The mixed model combines transmission trends and fluctuations to predict infections accurately. The proposed method is evaluated on a dataset of highly pathogenic infectious disease transmission published by Baidu and compared with other advanced methods. The results show that the model has an excellent predictive effect and practical value for large-scale infectious disease prediction.