The coronavirus (COVID-19) pandemic has brought immense challenges to the natural and built environment to develop an antivirus-enabled model for reducing potential risks of spreading the virus at varied scales such as buildings, neighborhoods, and cities. Spatial configurations of structures may hinder or assist the spread of viruses in the built environment. In this study, we have hypothesized that suitable air ventilation in historic buildings may enhance the built environment to combat the spreading of infectious viruses. To provide such quantitative shreds of evidence, we have generated and estimated an integrated model to summarize obtained information by considering natural ventilation, wind speed, inflow and outflow, wind direction, and forecasting the associated risks of airborne disease transmission in a historical building (i.e., the Hazzazi House in particular). Intrinsically, the results have demonstrated that the effectiveness of natural ventilation has directly influenced reducing the risks of transmitting airborne infectious viruses for the selected heritage building in Jeddah (Saudi Arabia). The adopted methods in this research may be useful to understand the potentials of conserving old heritage buildings. Consequently, the results demonstrate that natural air ventilation systems are critical to combat the spread of infectious diseases in the pandemic.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037546PMC
http://dx.doi.org/10.3390/ijerph18073601DOI Listing

Publication Analysis

Top Keywords

built environment
12
risks airborne
8
hazzazi house
8
jeddah saudi
8
saudi arabia
8
air ventilation
8
infectious viruses
8
natural ventilation
8
historical buildings
4
buildings adaptive
4

Similar Publications

Fishers and First Responders: Oil Spill Safety Workshop Design and Evaluation.

J Agromedicine

January 2025

Post-Graduate Program in Health, Environment, and Labor, School of Medicine, Federal University of Bahia, Salvador, Brazil.

Objectives: This paper describes the design and evaluation of a workshop created to develop safer disaster response strategies for fishing communities, using the 2019 Northeast Brazil Oil Spill as a starting point for community-engaged education.

Methods: The 3-day pilot workshop included presentations, structured discussions, and interactive activities with small-scale fishers (SSFs), university researchers, and representatives of local government agencies. The workshop was evaluated through a mixed-method approach that considered qualitative data from discussion groups, collectively built products, and content retention.

View Article and Find Full Text PDF

Objective: To investigate the distribution of snails in different water systems in Anqing City from 2016 to 2022, so as to provide insights into snail control in the city.

Methods: Snail survey data and distribution of water systems in snail-infested environments were collected from schistosomiasis-endemic areas of Anqing City from 2016 to 2022. The vector maps of towns and water systems in Anqing City were downloaded from National Geomatics Center of China.

View Article and Find Full Text PDF

Model compression for real-time object detection using rigorous gradation pruning.

iScience

January 2025

Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia.

Achieving lightweight real-time object detection necessitates balancing model compression with detection accuracy, a difficulty exacerbated by low redundancy and uneven contributions from convolutional layers. As an alternative to traditional methods, we propose Rigorous Gradation Pruning (RGP), which uses a desensitized first-order Taylor approximation to assess filter importance, enabling precise pruning of redundant kernels. This approach includes the iterative reassessment of layer significance to protect essential layers, ensuring effective detection performance.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!