ReviewMicrobial risk analysis2016
Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock - A review to inform risk assessment studies.
Review in Microbial risk analysis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic knowledge synthesis on the spatial dimensions of Q fever epidemics.Zoonoses and public health · 2019Pooled it
- A Direct DNA Extraction Workflow for Metabarcoding Fungal Bioaerosols From Adhesive Samplers.Environmental microbiology · 2026Article
- Opportunistic bacterial pathogens in bioaerosols emitted at municipal wastewater treatment plants, South Africa.Scientific reports · 2025Article
- Bacterial diversity and biomarkers screening of station and carriage surface in Shanghai metro system, China.Current research in microbial sciences · 2025Article
- Decay of Airborne Bacteria from Cattle Farm Under A-Band Ultraviolet Radiation.Animals : an open access journal from MDPI · 2024Article
- A Clean and Health-Care-Focused Way to Reduce Indoor Airborne Bacteria in Calf House with Long-Wave Ultraviolet.Microorganisms · 2024Article
- Quantitative Risk Assessment of Wind-Supported Transmission of Highly Pathogenic Avian Influenza Virus to Dutch Poultry Farms via Fecal Particles from Infected Wild Birds in the Environment.Pathogens (Basel, Switzerland) · 2024Article
- Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review.Environmental health perspectives · 2024Review
- Distribution characteristics and potential risks of bioaerosols during scattered farming.iScience · 2023Article
- Article
- Bioaerosol dispersion and environmental risk simulation: Method and a case study for a biopharmaceutical plant of Gansu province, China.The Science of the total environment · 2023Article
- The Impact of Harsh Stratospheric Conditions on Survival and Antibiotic Resistance Profile of Non-Spore Forming Multidrug Resistant Human Pathogenic Bacteria Causing Hospital-Associated Infections.International journal of environmental research and public health · 2023Article
- Article
- Assessment of rotavirus and norovirus emitted from water spray park: QMRA, diseases burden and sensitivity analysis.Heliyon · 2022Article
- Disaster Microbiology-a New Field of Study.mBio · 2022Review
- Article
- Antimicrobial Resistance in the Environment: Towards Elucidating the Roles of Bioaerosols in Transmission and Detection of Antibacterial Resistance Genes.Antibiotics (Basel, Switzerland) · 2022Article
- Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review.Sustainable cities and society · 2022Review
- A Review of Current Bacterial Resistance to Antibiotics in Food Animals.Frontiers in microbiology · 2022Review
- Characteristics and health effects of potentially pathogenic bacterial aerosols from a municipal solid waste landfill site in Hamadan, Iran.Journal of environmental health science & engineering · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this review we discuss studies that applied atmospheric dispersion models (ADM) to bioaerosols that are pathogenic to humans and livestock in the context of risk assessment studies. Traditionally, ADMs have been developed to describe the atmospheric transport of chemical pollutants, radioactive matter, dust, and particulate matter. However, they have also enabled researchers to simulate bioaerosol dispersion. To inform risk assessment, the aims of this review were fourfold, namely (1) to describe the most important physical processes related to ADMs and pathogen transport, (2) to discuss studies that focused on the application of ADMs to pathogenic bioaerosols, (3) to discuss emission and inactivation rate parameterisations, and (4) to discuss methods for conversion of concentrations to infection probabilities (concerning quantitative microbial risk assessment). The studies included human, livestock, and industrial sources. Important factors for dispersion included wind speed, atmospheric stability, topographic effects, and deposition. Inactivation was mainly governed by humidity, temperature, and ultraviolet radiation. A majority of the reviewed studies, however, lacked quantitative analyses and application of full quantitative microbial risk assessments (QMRA). Qualitative conclusions based on geographical dispersion maps and threshold doses were encountered frequently. Thus, to improve risk assessment for future outbreaks and releases, we recommended determining well-quantified emission and inactivation rates and applying dosimetry and dose-response models to estimate infection probabilities in the population at risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.