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The COVID-19 epidemic has substantially limited human activities and affected anthropogenic emissions. In this work, daily NO x emissions are inferred using a regional data assimilation system and hourly surface NO 2 measurement over China. The results show that because of the coronavirus outbreak, NO x emissions across the whole mainland China dropped sharply after 31 January, began to rise slightly in certain areas after 10 February, and gradually recover across the country after 20 February. Compared with the emissions before the outbreak, NO x emissions fell by more than 60% and~30% in many large cities and most small to medium cities, respectively. Overall, NO x emissions were reduced by 36% over China, which were mainly contributed by transportation. Evaluations show that the inverted changes over eastern China are credible, whereas those in western China might be underestimated. These findings are of great significance for exploring the reduction potential of NO x emissions in China. Plain Language Summary In this study, we quantitatively estimate the impact of the COVID-19 lockdown on NO x emissions over China based on nationwide surface hourly NO 2 monitoring data. We find that NO x emissions dropped sharply after 31 January and began to gradually recover after 20 February across the country; NO x emissions fell by more than 60% in many large cities and decreased by~30% in most small to medium cities. Across the whole mainland China, NO x emissions were reduced by 36% due to the COVID-19 lockdown. Generally, a "bottom-up" method of emission inventory technology (Zhang et al., 2019) is adopted to quantify the emission changes, which depends on sector-specific emissions factors and activity levels. Due to the temporal resolution and the lag in release of statistical data (i.e., activity level) as well as the large uncertainties in emission factors and statistical data, it is difficult to use the "bottom-up" method to quantify shortterm and nationwide emission changes (Ding et al., 2015). Data assimilation (DA) is a "top-down" method that can improve emissions estimates by combining observations and background fields. For example, Zhang et al. (2016) applied 4D-VAR DA to optimize daily aerosol primary and precursor emissions over North China during the APEC 2014. Feng et al. (2020) inferred the CO emissions changes over China during the "Action Plan" using surface CO observations. Chu et al. (2018) and Ding et al. (2015) estimated PM 2.5 and NO x emission changes during the 2015 China Victory Day parade and the 2014 Youth Olympic Games by assimilating surface PM 2.5 and OMI retrievals, respectively.

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It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as Nu) under axial compression. Taking the square CFST short column as an example, a mass of experimental data is obtained through axial compression tests. Combined with support vector machine (SVM) and particle swarm optimization (PSO), this paper presents a new method termed PSVM (SVM optimized by PSO) for Nu value prediction. The nonlinear relationship in Nu value prediction is efficiently represented by SVM, and PSO is used to select the model parameters of SVM. The experimental dataset is utilized to verify the reliability of the PSVM model, and the prediction performance of PSVM is compared with that of traditional design methods and other benchmark models. The proposed PSVM model provides a better prediction of the ultimate axial capacity of square CFST short columns. As such, PSVM is an efficient alternative method other than empirical and theoretical formulas.

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The Vehicle Routing Problem with Time Windows (VRPTW) has drawn considerable attention in the last decades. The objective of VRPTW is to find the optimal set of routes for a fleet of vehicles in order to serve a given set of customers within capacity and time window constraints. As a combinatorial optimization problem, VRPTW is proved NP-hard and is best solved by heuristics. In this paper, a hybrid swarm intelligence algorithm by hybridizing Ant Colony System (ACS) and Brain Storm Optimization (BSO) algorithm is proposed, to solve VRPTW with the objective of minimizing the total distance. In the BSO procedure, both inter-route and intra-route improvement heuristics are introduced. Experiments are conducted on Solomon's 56 instances with 100 customers benchmark, the results show that 42 out of 56 optimal solutions (18 best and 24 competitive solutions) are obtained, which illustrates the effectiveness of the proposed algorithm. INDEX TERMS Ant colony system, brain storm optimization, heuristics, swarm intelligence, vehicle routing problem with time windows.