TY - JOUR A1 - Maloukh, Lina A1 - Nazzal, Yousef A1 - Kumarappan, Alagappan A1 - Howari, Fares A1 - Ambika, Lakshmi Kesari A1 - Yahmadi, Rihab A1 - Sharma, Manish A1 - Iqbal, Jibran A1 - Al-Taani, Ahmed A. A1 - Salem, Imen Ben A1 - Xavier, Cijo M. A1 - Naseem, Muhamad T1 - Metagenomic analysis of the outdoor dust microbiomes: a case study from Abu Dhabi, UAE JF - Atmosphere N2 - Outdoor dust covers a shattered range of microbial agents from land over transportation, human microbial flora, which includes pathogen and commensals, and airborne from the environment. Dust aerosols are rich in bacterial communities that have a major impact on human health and living environments. In this study, outdoor samples from roadside barricades, safety walls, and fences (18 samples) were collected from Abu Dhabi, UAE and bacterial diversity was assessed through a 16S rRNA amplicon next generation sequencing approach. Clean data from HiSeq produced 1,099,892 total reads pairs for 18 samples. For all samples, taxonomic classifications were assigned to the OTUs (operational taxonomic units) representative sequence using the Ribosomal Database Project database. Analysis such as alpha diversity, beta diversity, differential species analysis, and species relative abundance were performed in the clustering of samples and a functional profile heat map was obtained from the OTUs by using bioinformatics tools. A total of 2814 OTUs were identified from those samples with a coverage of more than 99%. In the phylum, all 18 samples had most of the bacterial groups such as Actinobacteria, Proteobacteria, Firmicutes, and Bacteroidetes. Twelve samples had Propionibacteria acnes and were mainly found in RD16 and RD3. Major bacteria species such as Propionibacteria acnes, Bacillus persicus, and Staphylococcus captis were found in all samples. Most of the samples had Streptococcus mitis, Staphylococcus capitis. and Nafulsella turpanensis and Enhydrobacter aerosaccus was part of the normal microbes of the skin. Salinimicrobium sp., Bacillus alkalisediminis, and Bacillus persicus are halophilic bacteria found in sediments. The heat map clustered the samples and species in vertical and horizontal classification, which represents the relationship between the samples and bacterial diversity. The heat map for the functional profile had high properties of amino acids, carbohydrate, and cofactor and vitamin metabolisms of all bacterial species from all samples. Taken together, our analyses are very relevant from the perspective of out-door air quality, airborne diseases, and epidemics, with broader implications for health safety and monitoring. KW - dust microbiomes KW - metagenomics KW - microbial diversity KW - pollution KW - GIS Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304391 SN - 2073-4433 VL - 14 IS - 2 ER - TY - JOUR A1 - Asare-Kyei, Daniel A1 - Forkuor, Gerald A1 - Venus, Valentijn T1 - Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches JF - Water N2 - Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized. KW - climate change KW - rational model KW - community KW - flood hazard index KW - West Africa KW - GIS KW - vulnerability KW - performance KW - impact KW - risk KW - mapping KW - runoff Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151581 VL - 7 SP - 3531 EP - 3564 ER -