Spatiotemporal variation in urban wastewater pollution impacts on river microbiomes and associated hazards in the Akaki catchment, Addis Ababa, Ethiopia.

In Addis Ababa and its environs, most urban wastewater is discharged into rivers without treatment. This study related urban wastewater characteristics to the prevalence of faecal, antibiotic resistant, and potentially pathogenic bacteria in rivers of the Akaki catchment across six locations, for the dry and wet season. Spatiotemporal variation in bacterial hazards across the catchment was up to 6 log10 units. Cooccurrence of sewage pollution marker gene HF183 in all river samples testing positive for the Vibrio cholerae marker gene ompW, and high levels of these two genes in untreated wastewater, identified human sewage as the likely source of Vibrio cholerae hazards in the catchment. Levels of the marker genes rodA for E. coli, HF183 for human host associated Bacteroides, ciaB for Arcobacter, and ompW for Vibrio cholerae were all higher in the dry season than in the wet season. Marker gene gyrB for Pseudomonas aeruginosa was not detected in the samples. From the sequencing data, notable bacterial genera in the dry season included wastewater pollution indicators Arcobacter and Aeromonas, whereas soil erosion may explain the greater prominence of Legionella, Vicinamibacter, and Sphingomonas during the wet season. Except for the most upstream location, all faecal coliform (FC) counts exceeded WHO standards of 1000 CFU/100 mL for unrestricted irrigation. Concerningly, 0.6-20% of FC had ESBL producing antimicrobial resistance traits. In conclusion, multiple bacterial hazards were of concern for river water users in the Akaki catchment, and elevated in the dry season, when the river water is being used for irrigation of vegetable fields that supply the markets of Addis Ababa. This reflects inadequate treatment and limited dilution of urban wastewater by the natural river flows during periods of low rainfall.

Metagenomic water quality monitoring with a portable laboratory.

We describe the technical feasibility of metagenomic water quality analysis using only portable equipment, for example mini-vacuum pumps and filtration units, mini-centrifuges, mini-PCR machines and the memory-stick sized MinION of Oxford Nanopore Technologies, for the library preparation and sequencing of 16S rRNA gene amplicons. Using this portable toolbox on site, we successfully characterized the microbiome of water samples collected from Birtley Sewage Treatment Plant, UK, and its environs. We also demonstrated the applicability of the portable metagenomics toolbox in a low-income country by surveying water samples from the Akaki River around Addis Ababa, Ethiopia. The 16S rRNA gene sequencing workflow, including DNA extraction, PCR amplification, sequencing library preparation, and sequencing was accomplished within one working day. The metagenomic data became available within 24-72 h, depending on internet speed. Metagenomic analysis clearly distinguished the microbiome of pristine samples from sewage influenced water samples. Metagenomic analysis identified the potential role of two bacterial genera not conventionally monitored, Arcobacter and Aeromonas, as predominant faecal pollution indicators/waterborne hazards. Subsequent quantitative PCR analysis validated the high Arcobacter butzleri abundances observed in the urban influenced Akaki River water samples by portable next generation sequencing with the MinION device. Overall, our field deployable metagenomics toolbox advances the capability of scientists to comprehensively monitor microbiomes anywhere in the world, including in the water, food and drinks industries, the health services, agriculture and beyond.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.