Abstract. In the summer of 2017, heavy ozone pollution swamped most of the North China
Plain (NCP), with the maximum regional average of daily maximum 8 h ozone
concentration (MDA8) reaching almost 120 ppbv. In light of the continuing
reduction of anthropogenic emissions in China, the underlying mechanisms for
the occurrences of these regional extreme ozone episodes are elucidated from
two perspectives: meteorology and biogenic emissions. The significant
positive correlation between MDA8 ozone and temperature, which is amplified
during heat waves concomitant with stagnant air and no precipitation,
supports the crucial role of meteorology in driving high ozone
concentrations. We also find that biogenic emissions are enhanced due to
factors previously not considered. During the heavy ozone pollution episodes
in June 2017, biogenic emissions driven by high vapor pressure deficit
(VPD), land cover change and urban landscape yield an extra mean MDA8 ozone
of 3.08, 2.79 and 4.74 ppbv, respectively, over the NCP, which together
contribute as much to MDA8 ozone as biogenic emissions simulated using the
land cover of 2003 and ignoring VPD and urban landscape. In Beijing, the
biogenic emission increase due to urban landscape has a comparable effect on
MDA8 ozone to the combined effect of high VPD and land cover change between
2003 and 2016. In light of the large effect of urban landscape on biogenic
emission and the subsequent ozone formation, the types of trees may be
cautiously selected to take into account of the biogenic volatile organic compound (BVOC) emission during the afforestation of cities. This study highlights the vital contributions of
heat waves, land cover change and urbanization to the occurrence of extreme
ozone episodes, with significant implications for ozone pollution control in
a future when heat wave frequency and intensity are projected to increase
under global warming.
To solve the invalidation problem of Dempster-Shafer theory of evidence (DS) with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs) to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs) but also the divergence degree of the hypothesis that two BBAs support. Finally, the weighting factors used to reassign conflicts on BBAs are developed and Dempster’s rule is chosen to combine the discounted sources. Simple numerical examples are employed to demonstrate the merit of the proposed method. Through analysis and comparison of the results, the new combination approach can effectively solve the problem of conflict management with better convergence performance and robustness.
A common assumption in multimodal learning is the completeness of training data, i.e., full modalities are available in all training examples. Although there exists research endeavor in developing novel methods to tackle the incompleteness of testing data, e.g., modalities are partially missing in testing examples, few of them can handle incomplete training modalities. The problem becomes even more challenging if considering the case of severely missing, e.g., ninety percent of training examples may have incomplete modalities. For the first time in the literature, this paper formally studies multimodal learning with missing modality in terms of flexibility (missing modalities in training, testing, or both) and efficiency (most training data have incomplete modality). Technically, we propose a new method named SMIL that leverages Bayesian meta-learning in uniformly achieving both objectives. To validate our idea, we conduct a series of experiments on three popular benchmarks: MM-IMDb, CMU-MOSI, and avMNIST. The results prove the state-of-the-art performance of SMIL over existing methods and generative baselines including autoencoders and generative adversarial networks.
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