Severe ozone (O₃) pollution has been recorded in China in recent years. The key precursor, volatile organic compounds (VOCs), is still not well understood in Nanning, which is a less developed city compared to other megacities in China. In this study, a year-long measurement of VOCs was conducted from 1 October 2020 to 30 September 2021, to characterize the ambient variations and apportion the source contributions of VOCs. The daily-averaged concentration of VOCs was measured to be 26.4 ppb, ranging from 3.2 ppb to 136.2 ppb across the whole year. Alkanes and oxygenated VOCs (OVOCs) were major species, contributing 46.9% and 25.2% of total VOC concentrations, respectively. Propane, ethane, and ethanol were the most abundant in Nanning, which differed from the other significant species, such as toluene (3.7 ppb) in Guangzhou, ethylene (3.8 ppb) in Nanjing, and isopentane (5.5 ppb), in Chengdu. The positive matrix factorization (PMF) model resolved six source factors, including vehicular emission (contributing 33% of total VOCs), NG and LPG combustion (19%), fuel burning (17%), solvent use (16%), industry emission (10%), and biogenic emission (5%). This indicated that Nanning was less affected by industrial emission compared with other megacities of China, with industry contributing 12–50%. Ethylene, m/p-xylene, butane, propylene, and isoprene were key species determined by ozone formation potential (OFP) analysis, which should be priority-controlled. The variations in estimated OFP and observed O₃ concentrations were significantly different, suggesting that VOC reactivity-based strategies as well as meteorological and NO_x effects should be considered collectively in controlling O₃ pollution. This study presents a year-long dataset of VOC measurements in Nanning, which gives valuable implications for VOC control in terms of key sources and reactive species and is also beneficial to the formulation of effective ozone control strategies in other less developed regions of China.

The momentum transport and scale-dependent motion characteristics within vegetation canopies play a crucial role in shaping near-surface turbulent structures and exchange processes, yet the interactions among different turbulent scales and their statistical representations remain insufficiently understood. Based on a series of controlled wind tunnel experiments, this study identifies coherent turbulent structures using a phase-space algorithm constructed from streamwise velocity fluctuation u′, acceleration a, and jerk j, and compares transport efficiency (exuberance η). This study uses scale-wise (cut-off frequency) momentum flux contribution analysis, natural visibility graph (NVG), and large–small-scale amplitude modulation to examine transport and multiscale behaviors across different canopy densities, array layouts, and inflow conditions. Results show that canopy density (different C_d drag coefficient) is a primary factor governing transport efficiency. Under low-wind staggered configurations, increasing canopy density strengthens the contribution of low-frequency large-scale motions to total momentum flux. In contrast, high-wind aligned configurations intensify canopy-top shear, enhancing small-scale motions and thereby reducing the relative contribution of large-scale motions. NVG analysis further reveals that in high-density canopies, large-scale acceleration and deceleration events tend toward equilibrium, whereas deceleration events dominate consistently in low- and medium-density cases. Amplitude modulation results indicate that high-density cases exhibit highly consistent modulation behavior, followed by low-density cases, while medium-density cases display a pronounced height-dependent variation, characterized by a distinct modulation critical point. This study proposes a unified analytical framework integrating coherent structure detection, graph-theoretic analysis, multiscale transport characterization, and large–small-scale modulation, providing a comprehensive description of momentum transport and scale motions within canopy flows, and it offers new insight into the mechanisms governing complex vegetation canopy turbulence.

The urban drag coefficient (Cd​) is a key parameter in urban climate modeling, influencing airflow, pollutant dispersion, and energy exchange. This study aims to map the spatial distribution of Cd​ in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) by combining computational fluid dynamics (CFD) simulations and machine learning techniques. We employ the Reynolds-Averaged Navier-Stokes (RANS) turbulence model to simulate airflow over idealized urban surfaces with varying building density, average height, and height variability. Two groups of urban configurations are designed—one with uniform building heights and another with heterogeneous heights—considering multiple plan area fractions (λp​) and standard deviation of building heights (σH​). Machine learning models, particularly Random Forest, are trained on CFD-derived Cd​ values to predict spatially distributed drag coefficients at a 1-km resolution. SHapley Additive exPlanations (SHAP) analysis reveals that λpand σH​ are the dominant factors influencing Cd​. The resulting drag coefficient map captures the spatial variability of urban aerodynamic resistance, providing a refined parameterization scheme for mesoscale climate models. This study enhances the representation of urban canopy processes and can be extended to other metropolitan regions, improving the accuracy of urban climate simulations.