Susceptibility and exposure risk to airborne aerosols in intra-urban microclimate: Evidence from subway system of mega-cities.

The health of intra-urban population in modern megacities relies largely on the biosafety within the microclimate of subway system, which can be vulnerable to epidemical challenges brought by virus-laden bioaerosols under varying factors. The literature has yet to address the association between the exposure risks to infectious pathogens and the dynamic changes of boundary conditions in this densely populated microclimate. This study aims at characterizing the bioaerosol dispersion, evaluating the exposure risks under various train arrival scenarios and hazard releasing positions in a real-world double-decker subway station. The results provide the evidence for the dominating airflow pattern, bioaerosols dispersion behaviors, exposure risk, and evacuation guidance in a representative microclimate of mega-cities. The tunnel effects of nearby pedestrian passageways are found to be dominating the airflow pattern, leading to the discharging of airborne bioaerosols. At least 60 % increasing of discharging rate of bioaerosol is attributed to the arrival of one or two trains at the subway platform compared with the scenario with no train arriving. Results from risk assessment with improved Wells-Riley model estimate 57.62 % of maximum infectivity probability with no train arriving. Large areas near the source at the platform floor still cannot be considered safe within 20 min. For the other two scenarios where trains arrive at the platform, the maximum probability of infection is below 5 %. Moreover, the majority of train carriages can be regarded as safe zones, as the ventilation across the screen door are mostly directed towards the platform. Additionally, releasing the bioaerosols at the platform floor poses the most severe threats to human health, and the corresponding evacuation strategies are suggested. These findings offer practical guidance for the design of the intra-urban microclimate, reinforcing the need for exposure reduction device or contingency plans, and providing potential evacuation strategy towards improved health outcomes.

Check dam storage capacity calculation based on high-resolution topogrammetry: Case study of the Cutou Gully, Wenchuan County, China.

Debris flows are a common natural trigger of disasters in mountainous areas, and check dams are standard structural measures for controlling debris flows. Despite their prevalence in debris flow-prone areas worldwide, the capacity of check dams is still calculated using empirical formulas, which lead to large calculation errors. This paper proposes a new method that uses GIS to calculating the design storage capacity of a check dam in the debris flow-prone Cutou Gully in Wenchuan County, China. Large-scale digital surface models derived from unmanned aerial vehicle imagery and ground surveys identify local topographic changes in the debris flow path and develop appropriate maintenance plans for check dams. The measured storage capacity of the check dam is determined by analyzing the DEM differences. This study uses the newly proposed method to calculate the design storage capacity of the check dam. The accuracy of the calculation results was evaluated using the checkpoint method, and the results showed that the design and measured siltation surface errors ranged from -1.16-2.96 m, with a root mean square error of 0.93 m. The design capacity of the check dam is 33.6× 104 m3, and the actual capacity is 36.7× 104 m3, with an absolute error of 3.1× 104 m3 and relative error of 8.6%. The results prove the validity of the proposed calculation method; moreover, this study shows that the new method is accurate, easy to operate, and highly efficient for visualizing the spatial distribution of the siltation depth behind the check dam. This work will help improve future engineering decisions, design strategies, and find optimal design solutions to minimize the risk of debris flow hazards.

Author Correction: Respective influence of vertical mountain differentiation on debris flow occurrence in the Upper Min River, China.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Respective influence of vertical mountain differentiation on debris flow occurrence in the Upper Min River, China.

The generation, formation, and development of debris flow are closely related to the vertical climate, vegetation, soil, lithology and topography of the mountain area. Taking in the upper reaches of Min River (the Upper Min River) as the study area, combined with GIS and RS technology, the Geo-detector (GEO) method was used to quantitatively analyze the respective influence of 9 factors on debris flow occurrence. We identify from a list of 5 variables that explain 53.92%% of the total variance. Maximum daily rainfall and slope are recognized as the primary driver (39.56%) of the spatiotemporal variability of debris flow activity. Interaction detector indicates that the interaction between the vertical differentiation factors of the mountainous areas in the study area is nonlinear enhancement. Risk detector shows that the debris flow accumulation area and propagation area in the Upper Min River are mainly distributed in the arid valleys of subtropical and warm temperate zones. The study results of this paper will enrich the scientific basis of prevention and reduction of debris flow hazards.

Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.

A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. The temporal dynamics of the segmentation and of the Poisson intensities are modeled with exponential correlation in time, implemented in the form of a first-order autoregressive model for uniformly sampled discrete data, and via a Gaussian process with an exponential kernel for general temporal sampling. We consider and compare two different inference techniques: a Markov chain Monte Carlo sampler, which has relatively high computational complexity; and an approximate and efficient variational Bayesian analysis. The model is demonstrated with a simulated example and a real example of space-time crime events in Cincinnati, Ohio, USA.