Surface ozone (O₃), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O₃ datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O₃ influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O₃ concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O₃ on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R²) between O₃ concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R² (0.86) and lowest validation RMSE (13.74 μg/m³) in estimating O₃ concentrations, followed by support vector machine (SVM) (R² = 0.75, RMSE = 18.39 μg/m³), backpropagation neural network (BP) (R² = 0.74, RMSE = 19.26 μg/m³), and multiple linear regression (MLR) (R² = 0.52, RMSE = 25.99 μg/m³). Our China High-Resolution O₃ Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O₃ concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O₃ was mainly affected by human activities in higher urbanization regions, while O₃ was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O₃ formation and improving the quality of the O₃ dataset.

The mixing-layer analogy is due to Raupach, Finnigan & Brunet (Boundary-Layer Meteorol., vol. 25, 1996, pp. 351–382). In the analogy, the flow in the roughness sublayer of a homogeneous deep vegetation canopy boundary layer is analogous to a plane mixing layer rather than a surface layer. Evidence for the analogy includes the inflected velocity profile, which resembles the velocity profile in a plane mixing layer, and, most notably, the following estimate as a result of the Kelvin–Helmholtz instability: $\varLambda _x=8.1L_s$ , where $\varLambda _x$ is the spacing of the large-scale eddies, and $L_s$ is the shear length. The mixing-layer analogy has been very successful in vegetation canopy flow research, but has received only limited support in urban-canopy flow research. This work revisits Raupach et al.'s mixing-layer analogy, and we present the evidence for the mixing-layer analogy in urban-canopy flows: the exponential velocity profile in the canopy layer, i.e. $(U-U_c)/(U_h-U_c)=\exp (z/L_m)$ , and $L_m\sim [(U_h/U_c-1)(U_h/U_c+3)]^{-1}$ . Here, $z$ is the vertical coordinate, $L_m$ is the attenuation length and is a measure of the largest eddy in the canopy layer, $U_h$ is the wind speed at the canopy crest and $U_c$ is the velocity in the inactive layer. We conduct direct numerical simulations of various deep homogeneous urban-canopy flows and test the above two scalings. We also discuss why Raupach et al.'s analogy has not seen as many successes in urban-canopy flows as in vegetation canopy flows.

Metal additive manufacturing has enabled geometrically complex internal cooling channels for turbine and heat exchanger applications, but the process gives rise to large-scale roughness whose size is comparable to the channel height (which is 500 $\mathrm {\mu }$ m). These super-rough channels pose previously unseen challenges for experimental measurements, data interpretation and roughness modelling. First, it is not clear if measurements at a particular streamwise and spanwise location still provide accurate representation of the mean (time- and plane-averaged) flow. Second, we do not know if the logarithmic layer survives. Third, it is unknown how well previously developed rough-wall models work for these large-scale roughnesses. To answer the above practical questions, we conduct direct numerical simulations of flow in additively manufactured super-rough channels. Three rough surfaces are considered, all of which are obtained from computed tomography scans of additively manufactured surfaces. The roughness’ trough to peak sizes are 0.1 $h$ , 0.3 $h$ and 0.8 $h$ , respectively, where $h$ is the intended half-channel height. Each rough surface is placed opposite a smooth wall and the other two rough surfaces, leading to six rough-wall channel configurations. Two Reynolds numbers are considered, namely $Re_\tau =180$ and $Re_\tau =395$ . We show first that measurements at one streamwise and spanwise location are insufficient due to strong mean flow inhomogeneity across the entire channel, second that the logarithmic law of the wall survives despite the mean flow inhomogeneity and third that the established roughness sheltering model remains accurate.

A strategy of the combination of Computational Fluid Dynamics (CFD) and Building Information Model (BIM) technology was proposed to evaluate the wind environment around a building with complex façades for green building assessment. The Science and Education Building located at the Emerald Lake Campus of the Hefei University of Technology in China was selected as a case study. Two modeling methods, refined modeling by BIM software (BIM model) and traditional simplified modeling by computer aided design software (simplified model, referred to as SM model), were considered. The differences between the CFD results from the two geometry models were carefully examined. Three typical outdoor activity areas were selected to study the wind velocity difference, including the central channel, roof garden, and overhead corridor. The pressure difference between the CFD simulation results of the BIM model and the SM model at the exterior windows of the high-rise building was compared and analyzed. The results show that when a narrow channel is formed between two buildings, the wind velocity of the BIM model at the channel will be significantly lower than that of the SM model. The large “wall-type” components of the building roof will enhance the vortex area. The average pressure difference is greater than 0.5 Pa at the external window of building façades, accounting for up to 39.6%. The maximum pressure difference is as high as 10.13 Pa. The wind velocity difference is as high as 2.65 m/s at the central channel, 4 m/s at the roof garden, and 3.69 m/s at the overhead corridor. These differences will significantly affect the evaluation of the outdoor wind environment in green building assessment.

Numerical simulations are performed for the flow around an inclined 5:2 prolate spheroid in a uniform freestream. The Reynolds number (Re = 300, 500, 700, and 1000) and incidence angle (α = 0°–90°) are considered as significant parameters affecting the wake transitions, where α = 0° indicates flow parallel to the major axis of the prolate spheroid, and the Re is based on the inflow velocity U₀ and the volume-equivalent sphere diameter D_e of the spheroid. In the range considered of Re and α, eight flow regimes are identified: (i) steady axisymmetric (SA) flow regime; (ii) steady planar symmetric flow regime; (iii) steady asymmetric (SAS) flow regime; (iv) periodic planar symmetric flow regime with non-zero mean lift or “Zig-zig-like” (Zz-like) mode; (v) periodic asymmetric flow regime with double-sided vortex shedding; (vi) multi-periodic asymmetric flow regime with double-sided vortex shedding and low frequency modulation (MPADL); (vii) multi-periodic asymmetric flow regime with single-sided vortex shedding and low frequency modulation (MPASL); and (viii) weakly chaotic state. Three of them are new and first reported, i.e., SAS, MPADL, and MPASL modes. The wake structure of the Zz-like mode is different from that of the zig-zig mode in the sphere/disk wake with a pair of streamwise vortices extending to the near wake. It is found that the elongated body can delay the onset of unsteadiness at small incidence angles. A flow regime map in the considered (Re, α) space is then provided. Finally, the physical mechanisms of the low-frequency phenomena observed at different wake modes are explored.