In recent years, increasing concern has surrounded the consequences of improper electric and electronic waste (e-waste) disposal. In this paper, Phthalate esters (PAEs) including dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butyl phthalate (DBP), Di-(2-ethylhexyl) phthalate (DEHP) and di-n-octyl phthalate (DnOP) in the e-waste soils were collected and analyzed from sites Fengjiang, Nanshan and Meishu in Taizhou city. The result showed that the total PAEs concentrations ranged from 12.566 to 46.669 mg/kg in these three sites. DEHP, DBP and DEP were the major phthalates accounting for more than 94% of total phthalates studied. Comparing to the results from other studies, the e-waste soils from Taizhou city were severely contaminated with PAEs.

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Air quality forecasting is of great importance in environmental protection, government decision-making, people's daily health, etc. Existing research methods have failed to effectively modeling long-term and complex relationships in time series PM2.5 data and exhibited low precision in long-term prediction. To address this issue, in this paper a new lightweight deep learning model using sparse attention-based Transformer networks (STN) consisting of encoder and decoder layers, in which a multi-head sparse attention mechanism is adopted to reduce the time complexity, is proposed to learn long-term dependencies and complex relationships from time series PM2.5 data for modeling air quality forecasting. Extensive experiments on two real-world datasets in China, i.e., Beijing PM2.5 dataset and Taizhou PM2.5 dataset, show that our proposed method not only has relatively small time complexity, but also outperforms state-of-the-art methods, demonstrating the effectiveness of the proposed STN method on both short-term and long-term air quality prediction tasks. In particular, on singe-step PM2.5 forecasting tasks our proposed method achieves R2 of 0.937 and reduces RMSE to 19.04 µg/m3 and MAE to 11.13 µg/m3 on Beijing PM2.5 dataset. Also, our proposed method obtains R2 of 0.924 and reduces RMSE to 5.79 µg/m3 and MAE to 3.76 µg/m3 on Taizhou PM2.5 dataset. For long-term time step prediction, our proposed method still performs best among all used methods on multi-step PM2.5 forecasting results for the next 6, 12, 24, and 48 h on two real-world datasets.

Transition metal complexes of 2 (1 (carboxymethyl) 2 methyl 1H benzimidazol 3 ium 3 yl)acetate (HL), namely [Co(L) 2 (H 2 O) 4 ] ⋅ 6H 2 O (I) and [Cu(L) 2 (H 2 O) 2 ] ⋅ 4H 2 O (II), have been synthesized by a hydrothermal procedure and characterized by X ray crystallography, CIF files CCDC nos. 1007524 (I), 1007525 (II). Both I and II are mononuclear molecules. In I, the Co 2+ ion is in octahedral coordiantion envi ronment and surrounded by four O atoms from water molecules and two carboxylate O atoms of two depro tonated ligand (L -) occupied six culmination. While in II, the Cu 2+ ion is located in a square planar geom etry, bounded to two aqua O atoms and two carboxylate O atoms from L -.