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Surface treatment waste (HW17) contains a large amount of toxic and corrosive components, which may be harmful to the public health if improper disposal. To understand its metabolism is a prerequisite for the management optimization. In this study, the metabolic network for HW17 in Chengdu city (Sichuan Province, Southwestern China) is constructed by fusion of administrative data and web textual data to identify the associated issues through an investigation of the waste flow and direction. On such basis, a dual-objective mixed-integer linear programming model, with three scenarios (cost minimization, risk minimization, both the cost and risk minimization), is established to optimize the metabolic network for enhancing the management efficiency. The results show that the total HW17 in 2019 is 32,846.14 tons in Chengdu city. Of this, 0.4% of HW17 does not have downstream metabolic paths, and only 23.51% of it is completely disposed. About 78.08% of the generated HW17 is transported outside the metropolitan area of Chengdu city for disposal, highlighted by an open-loop feature. After optimization, the network cost reduces by 3.91% among the three scenarios, and risk decreases by 87.85%. In view of these results, this study further discusses the capacities of the recycling centers and disposal centers, as well as the limitations and uncertainty. Managerial implications are proposed, to lay a foundation for the improvement of waste management sustainability. [Display omitted] • Surface treatment waste is an environmental hazard with expensive recycling. • A metabolic network was constructed for surface treatment waste flow in Chengdu. • Dual-objective mixed-integer linear programming model for cost/risk minimization. • Delicate balance among cost/risk, spatial agglomeration & infrastructure capacity. • Potential to improve efficiency and sustainability of waste management. [ABSTRACT FROM AUTHOR]

In order to investigate the effects of the Coronavirus Disease 2019 (COVID-19) lockdown on air quality in cities in southwest China, a single particle aerosol mass spectrometer (SPAMS) and other online equipments were used to measure the air pollution in Chengdu, one of the megacities in this area, before and during the lockdown period. It was found that the concentrations of fine particulate matter (PM 2.5), nitric oxide (NO), nitrogen dioxide (NO 2), sulfur dioxide (SO 2) and carbon monoxide (CO) decreased by 38.6%, 77.5%, 47.0%, 35.1% and 14.1%, respectively, while the concentration of ozone (O 3) increased by 57.5% from the time before to the time during lockdown. All particles collected during the study period could be divided into eight categories: biomass burning (BB), coal combustion (CC), vehicle emissions (VE), cooking emissions (CE), Dust, K-nitrate (K–NO 3), K-sulfate (K–SO 4) and K-sulfate-nitrate (K-SN) particles, and their contributions changed significantly after the beginning of lockdown. Compared to before lockdown, the contribution of VE particles experienced the largest reduction (by 14.9%), whereas the contributions of BB and CE particles increased by 7.0% and 7.3%, respectively, during the lockdown period. Regional transmission was critical for pollution formation before lockdown, whereas the pollution that occurred during the lockdown period was caused mainly by locally emitted particles (such as VE, CE and BB particles). Weighted potential source contribution function (WPSCF) analysis further verified and emphasized the difference in the contribution of regional transmission for pollution formation before and during lockdown. In addition, the potential source area and intensity of the particles emitted from different sources or formation mechanisms were quite different. • Single aerosol particles during the COVID-19 lockdown were measured based on SPAMS. • The lockdown caused reductions in PM 2.5 , NO, NO 2 , SO 2 and CO, while increase in O 3. • Vehicle emission particles decreased the most (by 14.9%) during lockdown period. • Pollution formation mechanism before and during lockdown was completely different. [ABSTRACT FROM AUTHOR]