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Fei He

Publications and source records attributed to Fei He.

2 recordsLinked to original sources

Seasonal increases in global dryland gross primary production are modulated by root soil moisture and temperature

Dryland ecosystems, which are highly sensitive to environmental variability across space and through time, play a critical role in the global carbon cycle. To understand the carbon sink role of drylands, this study used different sources of global dryland gross primary productivity (GPP) and evaluated the spatiotemporal variations in seasonal GPP in response to climatic and soil water conditions from 1982 to 2018. Root-zone soil moisture consistently exerted a predominant positive influence on dryland GPP across all seasons, while the effect of surface soil moisture was less influential. Maximum temperature (Tmax) ranked as the second most influential factor on GPP, switching from a notable positive effect during cooler seasons to a negative impact during warmer seasons. Similarly, during cool seasons, vapor pressure deficit exhibited a notable positive effect on GPP, but this influence became negative in warmer seasons. Tmax indirectly regulated GPP by modulating the influence of other meteorological factors. During the warm season, Tmax negatively affected GPP via VPD, while in the cold season, it positively affected GPP through VPD. This study explicitly identifies the pivotal role of root-zone soil moisture in determining GPP and highlights the substantial seasonal differences and regulatory role of temperature in how other environmental variables influence GPP. These findings provide a novel perspective for understanding the responses of dryland ecosystems to climate change.

Global and Planetary Change

Assessing environmental oil spill based on fluorescence images of water samples and deep learning

Measuring oil concentration in the aquatic environment is essential for determining the potential exposure, risk, or injury for oil spill response and natural resource damage assessment. Conventional analytical chemistry methods require samples to be collected in the field, shipped, and processed in the laboratory, which is also rather time-consuming, laborious, and costly. For rapid field response immediately after a spill, there is a need to estimate oil concentration in near real time. To make the oil analysis more portable, fast, and cost effective, we developed a plug-and-play device and a deep learning model to assess oil levels in water using fluorescent images of water samples. We constructed a 3D-printed device to collect fluorescent images of solvent-extracted water samples using an iPhone. We prepared approximately 1,300 samples of oil at different concentrations to train and test the deep learning model. The model comprises a convolutional neural network and a novel module of histogram bottleneck block with an attention mechanism to exploit the spectral features found in low-contrast images. This model predicts the oil concentration in weight per volume based on fluorescence image. We devised a confidence interval estimator by combining gradient boosting and polymodal regressor to provide a confidence assessment of our results. Our model achieved sufficient accuracy to predict oil levels for most environmental applications. We plan to improve the device and iPhone application as a near-real-time tool for oil spill responders to measure oil in water.

Journal of Environmental Informatics