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Register a new userInvestigating Ground-level Ozone Formation: A Case Study in Taiwan
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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.
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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 this volcanic activity is depletion of stratospheric ozone, leading to increased penetration of biologically damaging Solar ultraviolet-B (UVB) radiation to Earths surface. Previous work has modeled the atmospheric chemistry effects of volcanic emission at the end-Permian. Here we use those results as input for detailed radiative transfer simulations to investigate changes in surface-level Solar irradiance in the ultraviolet-B, ultraviolet-A and photosynthetically available (visible light) wave bands. We then evaluate the potential biological effects using biological weighting functions. In addition to changes in ozone column density we also include gaseous sulfur dioxide (SO2) and sulfate aerosols. Ours is the first such study to include these factors and we find they have a significant impact on transmission of Solar radiation through the atmosphere. Inclusion of SO2 and aerosols greatly reduces the transmission of radiation across the ultraviolet and visible wavelengths, with subsequent reduction in biological impacts by UVB. We conclude that claims of a UVB mechanism for this extinction are likely overstated.
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 southwestern Utah, U.S.A. In data collected between 2014 and 2016, correlated observations showing the structure and temporal development of three shower-producing flashes were obtained with a 3D lightning mapping array, and electric field change measurements were obtained for an additional seven flashes, in both cases co-located with the TASD. National Lightning Detection Network (NLDN) information was also used throughout. The showers arrived in a sequence of 2--5 short-duration ($le$10~$mu$s) bursts over time intervals of several hundred microseconds, and originated at an altitude of $simeq$3--5 kilometers above ground level during the first 1--2 ms of downward negative leader breakdown at the beginning of cloud-to-ground lightning flashes. The shower footprints, associated waveforms and the effect of atmospheric propagation indicate that the showers consist primarily of downward-beamed gamma radiation. This has been supported by GEANT simulation studies, which indicate primary source fluxes of $simeq$$10^{12}$--$10^{14}$ photons for $16^{circ}$ half-angle beams. We conclude that the showers are terrestrial gamma-ray flashes (TGFs), similar to those observed by satellites, but that the ground-based observations are more representative of the temporal source activity and are also more sensitive than satellite observations, which detect only the most powerful TGFs.
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 surface O$_{3}$ across China for periods before and during the lockdown. We find that daytime O$_{3}$ decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O$_{3}$ changes, with a larger impact from the former especially in central China. The plunge in nitrogen oxide (NO$_{x}$) emission contributed to O$_{3}$ increases in populated regions, whereas the reduction in volatile organic compounds (VOC) contributed to O$_{3}$ decreases across the country. Due to a decreasing level of NO$_{x}$ saturation from north to south, the emission reduction in NO$_{x}$ (46%) and VOC (32%) contributed to net O$_{3}$ increases in north China; the opposite effects of NO$_{x}$ decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in these two precursors contributed to net O$_{3}$ declines in south China. Our study highlights the complex dependence of O$_{3}$ on its precursors and the importance of meteorology in the short-term O$_{3}$ variability.
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 lightning. In this study, we present the TETRA-II observations detected at the three TETRA-II ground-level sites in Louisiana, Puerto Rico, and Panama together with the simultaneous radio frequency signals from the VAISALA Global Lightning Data set, VAISALA National Lightning Detection Network, Earth Networks Total Lightning Network, and World Wide Lightning Location Network. The relative timing between the gamma ray events and the lightning activity is a key parameter for understanding the production mechanism(s) of the bursts. The gamma ray time profiles and their correlation with radio sferics suggest that the gamma ray events are initiated by lightning leader activity and are produced near the last stage of lightning leader channel development prior to the lightning return stroke.
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 insights from such data remains challenging due to a variety of factors such as difficulty of the task, personal prejudices of the human evaluators, lack of question clarity, etc. In this paper, we consider one such subjective evaluation task, namely that of estimating experienced emotions of distressed individuals who are conversing with a human listener in an online coaching platform. We explore strategies to aggregate the evaluators choices, and show that a simple voting consensus is as effective as an optimum aggregation method for the task considered. Intrigued by how an objective assessment would compare to the subjective evaluation of evaluators, we also designed a machine learning algorithm to perform the same task. Interestingly, we observed a machine learning algorithm that is not explicitly modeled to characterize evaluators subjectivity is as reliable as the human evaluation in terms of assessing the most dominant experienced emotions.
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