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Limited data are currently available on the concentrations of airborne bacteria, fungi, and endotoxins in indoor environments. The levels of aerial bacteria and fungi were measured at several microenvironments within a well-ventilated residential apartment in Singapore including the living room, kitchen, bedroom, toilet, and at a workplace environment by sampling indoor air onto culture medium plates using the 6-stage Andersen sampler. Total microbial counts were determined by collecting the air samples in water with the Andersen sampler, staining the resultant extracts with a fluorescent dye, acridine orange, and counting the microbes using a fluorescent microscope. The levels of airborne endotoxins were also determined by sampling the airborne microorganisms onto 0.4 lm polycarbonate membrane filter using the MiniVol sampler at 5 l/min for 20 h with a PM 2.5 cut-off device. The aerial bacterial and fungal concentrations were found to be in the ranges of 117-2,873 CFU/m 3 and 160-1,897 CFU/m 3 , respectively. The total microbial levels ranged from 49,000 to 218,000 microbes/m 3 . The predominant fungi occurring in the apartment were Aspergillus and Penicillium while the predominant bacterial strains appeared to be Staphylococcus and Micrococcus. The average indoor endotoxin level was detectable in the range of 6-39 EU/m 3 . The amount of ventilation and the types of human activities carried out in the indoor environment appeared to be important factors affecting the level of these airborne biological contaminants.

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

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We report an investigation of microbially induced carbonate precipitation by seven indigenous bacteria isolated from a landfill in China. Bacterial strains were cultured in a medium supplemented with 25 mmol/L calcium chloride and 333 mmol/L urea. The experiments were carried out at 30 °C for 7 days with agitation by a shaking table at 130 r/min. Scanning electron microscopic and X-ray diffraction analyses showed variations in calcium carbonate polymorphs and mineral composition induced by all bacterial strains. The amount of carbonate precipitation was quantified by titration. The amount of carbonate precipitated in the medium varied among isolates, with the lowest being Bacillus aerius rawirorabr15 (LC092833) precipitating around 1.5 times more carbonate per unit volume than the abiotic (blank) solution. Pseudomonas nitroreducens szh_asesj15 (LC090854) was found to be the most efficient, precipitating 3.2 times more carbonate than the abiotic solution. Our results indicate that bacterial carbonate precipitation occurred through ureolysis and suggest that variations in carbonate crystal polymorphs and rates of precipitation were driven by strain-specific differences in urease expression and response to the alkaline environment. These results and the method applied provide benchmarking and screening data for assessing the bioremediation potential of indigenous bacteria for containment of contaminants in landfills.

The impact of contaminated leachate on groundwater from landfills is well known, but the specific effects on bacterial consortia are less well-studied. Bacterial communities in a landfill and an urban site located in Suzhou, China, were studied using Illumina high-throughput sequencing. A total of 153 944 good-quality reads were produced and sequences assigned to 6388 operational taxonomic units. Bacterial consortia consisted of up to 16 phyla, including Proteobacteria (31.9%-94.9% at landfill, 25.1%-43.3% at urban sites), Actinobacteria (0%-28.7% at landfill, 9.9%-34.3% at urban sites), Bacteroidetes (1.4%-25.6% at landfill, 5.6%-7.8% at urban sites), Chloroflexi (0.4%-26.5% at urban sites only), and unclassified bacteria. Pseudomonas was the dominant (67%-93%) genus in landfill leachate. Arsenic concentrations in landfill raw leachate (RL) (1.11 × 10 μg/L) and fresh leachate (FL2) (1.78 × 10 μg/L) and mercury concentrations in RL (10.9 μg/L) and FL2 (7.37 μg/L) exceeded Chinese State Environmental Protection Administration standards for leachate in landfills. The Shannon diversity index and Chao1 richness estimate showed RL and FL2 lacked richness and diversity when compared with other samples. This is consistent with stresses imposed by elevated arsenic and mercury and has implications for ecological site remediation by bioremediation or natural attenuation.