Verifying the pattern of toxic gas dispersion simulations under mountainous conditions is vital for emergency response and rescue. In this study, a comparative analysis is conducted between CALPUFF (California Puff Model) and CFD (Computational Fluid Dynamics) gas dispersion modeling focusing on the range of Semi-Lethal Concentration (LC₅₀) and Immediate Danger to Life and Health Concentration (IDLH). To identify general dispersion patterns, a hypothetical pipeline breakout accident in a mountainous area is simulated and thirteen groups of simulation conditions are set up for the experiments, including calm wind (velocity less than 0.5 m/s) and winds from the east (E), south (S), west (W), and north (N) at velocities of 1, 2, and 3 m/s with a 1 arc-second degree SRTM data as terrain data. Comparative experiments show the diffusion patterns of the two models are essentially consistent, and the overall dispersion range deviation between two methods is within 266 m. The evaluation of CALPUFF’s adaptability for microscale mountainous environments indicates its potential use for high-sulfur gas fields and gas dispersion simulations in emergency scenarios.

The calculation of the three-dimensional atmospheric dispersion model is often time-consuming, which makes the model difficult to apply to the emergency field. With the aim of addressing this problem, we propose a parallel computing algorithm for the CALPUFF atmospheric dispersion model. Existing methods for parallelizing the atmospheric dispersion model can be divided into two categories, with one using the parallel computing interface to rewrite the source code and the other directly dividing the repetitive elements in the computation task. This paper proposes an improved method based on the latter approach. Specifically, the method of spatial division with buffers is adopted to parallelize the wind field module of the CALPUFF model system, and the method for receptor layering is adopted to parallelize the dispersion module. In addition, the message queue software RabbitMQ is used as the communication middleware. A performance test is conducted on nine computing nodes on the Alibaba Cloud Computing Platform for a single-source continuous emergency leak case. The results show that the division method with a buffer of ten cells is most suitable for the case above in order to maintain the balance between computation speed and accuracy. This reduces the computation time of the model to about one-sixth, which is of great significance for extending the atmospheric dispersion model to the emergency field.

Real-time simulation of toxic gas dispersion is critical for emergency management in high-sulphur gas fields. The CALPUFF (California Puff model) gas dispersion model, a Lagrangian Gaussian Puff model, is often used in mountainous environments due to its ability to handle complex terrain and weather conditions. Verifying the accuracy of toxic gas dispersion simulations under mountainous conditions is vital for emergency response and rescue. In this article, CALPUFF and Computational Fluid Dynamics (CFD) gas dispersion modeling are compared, studying emissions from a hypothetical pipeline breakout accident in a mountainous area, focusing on range of Semi-Lethal Concentration (LC$_{50}$), Immediate Danger to Life and Health Concentration(IDLH). Thirteen groups of simulation conditions are set up for the experiment, including static wind and winds from the east(E), south(S), west(W), and north(N) at speeds of 1, 2, and 3$m/s$. Comparative experiments between CALPUFF and CFD models show consistent results, confirming the feasibility of CALPUFF for near-field moutainous applications. The overall dispersion range deviation between two methods under calm or light wind of N, E and W is about 300$m$. However,the simulation results of the CALPUFF model and the CFD model show significant differences in S wind. In CALPUFF simulation that the toxic gases are channeled by the valleys, while in the CFD model the toxic gases climb over the mountain top and continue to spread and extend towards the northern valley. The evaluation of CALPUFF’s suitability for applications in near-field mountainous environments indicates its potential use for high-sulfur gas fields and gas dispersion simulations in emergency scenarios.

Inclusive and socially resilient cities depend on streetscapes that work for diverse residents, yet mainstream planning still optimizes for an ”average” user, implicitly treating perception as demographically uniform. The open problem, however, is not whether perceptual divergences exist, but that they have lacked a comparable, city-scale measure to locate and prioritize them. To address this, we construct a Heterogeneity Index that quantifies demographic perceptual divergence across 22 streetscape attributes, using Contrastive Language–Image Pretraining (CLIP)-derived representations within a supervised preference model. Trained on 400,000 pairwise evaluations from 38,525 Japanese participants stratified by gender and age, the proposed approach estimates these attributes. The models are applied to a large-scale case study of 241,912 geo-tagged street-view images from Tokyo’s Setagaya Ward, while inter-group differences are evaluated using the Heterogeneity Index based on correlation, absolute deviation, and data distribution. Divergence coordinate scatter plots visualize patterns of consensus and divergence. The models achieved a mean accuracy of 0.8983. Evaluated by the index and validated against a model-free human baseline (Spearman ρ = 0.761), results indicate that perceptions of ”open” and ”greenery” are consistent across groups, while ”desirable for living,” ”friendly,” and ”interesting” diverge considerably. Regression analysis indicates these gaps concentrate in commercial and quasi-residential contexts. By quantifying these divergences, we present a tiered framework to distinguish streetscape qualities suitable for universal standards from those requiring targeted interventions. By making these divergences measurable, this approach provides an empirical basis for identifying where average-user assumptions fail, informing inclusive urban design and social resilience.