Surface dust from degraded lands is a major global aerosol source, mobilized by meteorological events like sandstorms. Microplastics (MPs) in dust can be enriched in the atmosphere and transported over long distances to sensitive regions during sandstorms. This study was conducted in a megacity frequently impacted by sandstorms in spring, exploring the influx, characteristics, enrichment mechanism, and transport pathway of sandstorm-derived MPs. The deposition rate of these MPs reached 1823.65 ± 892.53 items·m-2·d-1, predominantly consisting of low-density polymers and those mainly used in synthetic fiber, with an average size of 60.75 µm. Compared to MPs in annual atmospheric deposition, these MPs were smaller and contained a higher proportion of potentially harmful polymers. These factors could increase exposure risks for residents from sandstorm-derived MPs, along with distinct meteorological and ecological effects. Backward trajectory analysis suggested the observed sandstorms originated from the Mongolian Plateau, over 1000 km away. Comparisons of MPs from surface-collected dust on the Mongolian Plateau with sandstorms-delivered MPs revealed the transport was determined by MP shape, size, and density. This study highlights the critical role of sandstorms in the MP atmospheric cycling, emphasizing the extensive impacts of MPs and the need for coordinated mitigation efforts across regions.
We propose a simple iterative (SI) algorithm for the maxcut problem through fully using an equivalent continuous formulation. It does not need rounding at all and has advantages that all subproblems have explicit analytic solutions, the cut values are monotonically up- dated and the iteration points converge to a local optima in finite steps via an appropriate subgradient selection. Numerical experiments on G-set demonstrate the performance. In particular, the ratios between the best cut values achieved by SI and those by some ad- vanced combinatorial algorithms in [Ann. Oper. Res., 248 (2017), 365–403] are at least 0.986 and can be further improved to at least 0.997 by a preliminary attempt to break out of local optima.
Harmful algal blooms pose tremendous threats to ecological safety and human health. In this study, simulated solar light (SSL) irradiation was used to activate periodate (PI) for the inactivation of Microcystis aeruginosa and degradation of microcystin-LR (MC-LR). We found that PI-SSL system could effectively inactivate 5 × 106 cells·mL−1 algal cells below the limit of detection within 180 min. ·OH and iodine (IO3· and IO4·) radicals generated in PI-SSL system could rupture cell membranes, releasing intracellular substances including MC-LR into the reaction system. However, the released MC-LR could be degraded into non-toxic small molecules via hydroxylation and ring cleavage processes in PI-SSL system, reducing their environmental risks. High algae inactivation performance of PI-SSL system in solution with a wide pH range (3–9), with the coexisting anions (Cl−, NO3− and SO42−) and the copresence of natural organic matters (humic acid and fulvic acid), real water (lake water and river water), as well as in continuous-flow reactor (14 h) were also achieved. In addition, under natural sunlight irradiation, effective algae inactivation could also be achieved in an enlarged reactor (1 L). Overall, our study showed that PI-SSL system could avoid the inference by the background substances and could be employed as a feasible technique to treat algal bloom water.
Direction of arrival (DoA) estimation in complex environments is a challenging task. The traditional methods suffer from invalidity under low signal-to-noise ratio (SNR) and reverberation conditions, and the data-driven methods lack of generalization to unseen data types. In this paper we propose a robust DoA estimation approach by combining the two methods above. To focus on spatial information modeling, the proposed method directly uses the compressed covariance matrix of the first-order ambisonics (FOA) signal as input, while only white noise is used during training. To adapt to different characteristics of FOA signals in different frequency bands, our method estimates DoA in different frequency bands by particular models, and the subband results are finally integrated together. Experiments are carried out on both simulated and measured datasets, and the results show the superiority of the proposed method than existing baselines under complex conditions and the scalability for unseen data types.
