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Tropospheric ozone (O3) is a greenhouse gas which can absorb heat and make the weather even hotter during extreme heatwaves. Besides, it is an influential ground-level air pollutant which can severely damage the environment. Thus evaluating the importance of various factors related to the O3 formation process is essential. However, O3 simulated by the available climate models exhibits large variance in different places, indicating the insufficiency of models in explaining the O3 formation process correctly. In this paper, we aim to identify and understand the impact of various factors on O3 formation and predict the O3 concentrations under different pollution-reduced and climate change scenarios. We employ six supervised methods to estimate the observed O3 using fourteen meteorological and chemical variables. We find that the deep neural network (DNN) and long short-term memory (LSTM) based models can predict O3 concentrations accurately. We also demonstrate the importance of several variables in this prediction task. The results suggest that while Nitrogen Oxides negatively contributes to predicting O3, solar radiation makes a significantly positive contribution. Furthermore, we apply our two best models on O3 prediction under different global warming and pollution reduction scenarios to improve the policy-making decisions in the O3 reduction.
The end-Permian mass extinction is the most severe known from the fossil record. The most likely cause is massive volcanic activity associated with the formation of the Permo-Triassic Siberian flood basalts. A proposed mechanism for extinction due to
Bursts of gamma ray showers have been observed in coincidence with downward propagating negative leaders in lightning flashes by the Telescope Array Surface Detector (TASD). The TASD is a 700~square kilometer cosmic ray observatory located in southwe
Ozone (O$_{3}$) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O$_{3}$ have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surfac
In its first 2 years of operation, the ground-based Terrestrial gamma ray flash and Energetic Thunderstorm Rooftop Array(TETRA)-II array of gamma ray detectors has recorded 22 bursts of gamma rays of millisecond-scale duration associated with lightni
Crowdsourcing information constitutes an important aspect of human-in-the-loop learning for researchers across multiple disciplines such as AI, HCI, and social science. While using crowdsourced data for subjective tasks is not new, eliciting useful i