Seismic vulnerability assessment of urban buildings using the rough set theory and weighted linear combination.

Seismic vulnerability assessment of urban buildings is among the most crucial procedures to post-disaster response and recovery of infrastructure systems. The present study proceeds to estimate the seismic vulnerability of urban buildings and proposes a new framework training on the two objectives. First, a comprehensive interpretation of the effective parameters of this phenomenon including physical and human factors is done. Second, the Rough Set theory is used to reduce the integration uncertainties, as there are numerous quantitative and qualitative data. Both objectives were conducted on seven distinct earthquake scenarios with different intensities based on distance from the fault line and the epicenter. The proposed method was implemented by measuring seismic vulnerability for the seven specified seismic scenarios. The final results indicated that among the entire studied buildings, 71.5% were highly vulnerable as concerning the highest earthquake scenario (intensity=7MM and acceleration calculated based on the epicenter), while in the lowest earthquake scenario (intensity=5MM), the percentage of vulnerable buildings decreased to approximately 57%. Also, the findings proved that the distance from the fault line rather than the earthquake center (epicenter) has a significant effect on the seismic vulnerability of urban buildings. The model was evaluated by comparing the results with the weighted linear combination (WLC) method. The accuracy of the proposed model was substantiated according to evaluation reports. Vulnerability assessment based on the distance from the epicenter and its comparison with the distance from the fault shows significant reliable results.

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method.

In order to realize the simulation and evaluation of road traffic noise among urban buildings, a spatial subdivision-based beam-tracing method is proposed in this study. First, the road traffic source is divided into sets of point sources and described with the help of vehicle emission model. Next, for each pair of source and receiver, spatial subdivision-based beam-tracing method is used in noise paths generation. At last, noise distribution can be got by noise calculation of all receivers considering the complex transmission among urban buildings. A measurement experiment with a point source is carried out to validate the accuracy of the method; the 0.8 m height and 2.5-m height average errors are about 0.9 dB and 1.2 dB, respectively. Moreover, traffic noise analysis under different building layouts and heights are presented by case applications and conclusions can be reached: (1) Different patterns result in different noise distributions and patterns designed as self-protective can lead to an obvious noise abatement for rear buildings. Noise differences between the front and rear buildings are about 7-12 dB with different patterns. (2) Noise value might not show a linear variation along with the height as shielding of different layers is various in reality.

Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings.

Household air pollution is ranked the 9(th) largest Global Burden of Disease risk (Forouzanfar et al., The Lancet 2015). People, particularly urban dwellers, typically spend over 90% of their daily time indoors, where levels of air pollution often surpass those of outdoor environments. Indoor air quality (IAQ) standards and approaches for assessment and control of indoor air require measurements of pollutant concentrations and thermal comfort using conventional instruments. However, the outcomes of such measurements are usually averages over long integrated time periods, which become available after the exposure has already occurred. Moreover, conventional monitoring is generally incapable of addressing temporal and spatial heterogeneity of indoor air pollution, or providing information on peak exposures that occur when specific indoor sources are in operation. This article provides a review of new air pollution sensing methods to determine IAQ and discusses how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide. We also show that besides the opportunities, challenges still remain in terms of maturing technologies, or data mining and their interpretation. Moreover, we discuss further research and essential development needed to close gaps between what is available today and needed tomorrow. In particular, we demonstrate that awareness of IAQ risks and availability of appropriate regulation are lagging behind the technologies.

Culturable fungi associated with urban stone surfaces in Mexico City

Background: Urban surface stones in Mexico City are exposed to a temperate climate and a range of atmospheric conditions ranging from mildly impacted to heavily polluted areas. In this study, we focused on the characterization of the cultivable fungal component of selected biological patinas in the surrounding area of Chapultepec castle, a historic monument in Mexico City. Thirty four representative fungal isolates selected based on distinctive differential macroscopic characteristics out of a total of 300 fungi, were characterized using morphological and molecular approaches. Results: This identification strategy based on the combination of phenotypic- and molecular-based methodologies allowed us to discriminate the fungal community in some cases down to the species level. Conclusions: The characterization of this mycoflora revealed the presence of a complex fungal community mainly represented by filamentous fungi belonging to the genera Fusarium, Trichoderma, Aspergillus, Cladosporium, Alternaria, Mucor, Penicillium, Pestalotiopsis, and the dimorphic fungus Aureobasidium, along with the yeast Rhodotorula. A specific distribution of fungi could be observed based on the type of biological patina analyzed.