Emmanuel Appoh

A Low-cost sensors for particulate matter can provide high spatiotemporal resolution monitoring of air quality, especially in much of the Global South, and sub-Saharan Africa (SSA) in particular, where reference-grade instrumentation is often not available. However, ensuring high-quality data from low-cost sensor (LCS) platforms is essential. Until now, LCS required calibration by collocation with a reference-grade monitor to be used for more than qualitative studies of air quality, but reference-grade monitors are not available in many countries of the Global South. Since a key artifact in optical PM sensors is aerosol hygroscopic growth, we explore the viability of an alternative LCS calibration method: a hygroscopic growth correction factor using particle composition data from the MERRA-2 reanalysis dataset. We compare 3 different LCS located in 3 different areas of SSA – Kenya, Ghana, and South Africa - with 3 different calibration techniques: traditional linear calibrations with a reference-grade monitor, a κ-Köhler-derived correction with MERRA-2 data, and a random forest machine learning regression utilizing MERRA-2 and the regulatory-grade monitor. Random forest regressions using MERRA-2 particle composition data and collocation with a reference-grade monitor improve sensor performance to near that of regulatory-grade monitors. But even without collocation, a hygroscopic growth correction based on MERRA-2 particle composition alone can improve LCS PM2.5 performance by reducing mean-normalized bias to near-zero and reducing error by up to 40%.

Air pollution is one of the major environmental issues due to increasing anthropogenic activities and its negative impact on human health. Accurate forecasting is crucial in reducing exposure to poor air quality as well as mitigating against the effects of air pollution. For this reason, the performance of available models need to be evaluated to enhance the development of effective early warning systems. The Goddard Earth Observing System composition forecast (GEOS-CF) system is an operational product from NASA's Global Modeling and Assimilation Office (GMAO).The system provides near-real time forecast of atmospheric composites. This study evaluates the GEOS-CF representation of PM2.5 concentrations over Accra against the BAM-1020 ground station measurements. The performance of the model was described using index of agreement (IOA), coefficient of determination (R2) and fraction of prediction with a factor of 2 (FAC2) while the error was assessed with the normalized mean bias (NMB) and root mean square error (RMSE).The results showed good agreement between the GEOS-CF and surface observation with IOA, R2 and FAC2 recording 0.58, 0.52 and 0.81 respectively. The NMB and RMSE were found to be -0.03 and 15.82 respectively. The model skill improved after the application of the XGBoost regression model with an R2 of 0.65 and RMSE of 6.13. The end goal of this study is to apply a bias-correction factor to the model output for air quality forecasting over Ghana.

Antimicrobial resistant (AMR) bacteria in effluents from seafood processing facilities can contribute to the spread of AMR in the natural environment. In this study conducted in Tema, Ghana, a total of 38 effluent samples from two seafood processing facilities were collected during 2021 and 2022, as part of a pilot surveillance project to ascertain the bacterial load, bacterial species and their resistance to 15 antibiotics belonging to the WHO AWaRe group of antibiotics. The bacterial load in the effluent samples ranged from 13–1800 most probable number (MPN)/100 mL. We identified the following bacterial species: E. coli in 31 (82%) samples, K. pneumoniae in 15 (39%) samples, Proteus spp. in 6 (16%) samples, P. aeruginosa in 2 (5%) samples and A. baumannii in 2 (5%) samples. The highest levels of antibiotic resistance (100%) were recorded for ampicillin and cefuroxime among Enterobacteriaceae. The WHO priority pathogens—E. coli (resistant to cefotaxime, ceftazidime and carbapenem) and K.pneumoniae (resistant to ceftriaxone)—were found in 5 (13%) effluent samples. These findings highlight the need for enhanced surveillance to identify the source of AMR and multi-drug resistant bacteria and an adoption of best practices to eliminate these bacteria in the ecosystem of the seafood processing facilities.

Inappropriate use of antibiotics has led to the presence of antibiotic-resistant bacteria in ambient air. There is no published information about the presence and resistance profiles of bacteria in ambient air in Ghana. We evaluated the presence and antibiotic resistance profiles of selected bacterial, environmental and meteorological characteristics and airborne bacterial counts in 12 active air quality monitoring sites (seven roadside, two industrial and three residential) in Accra in February 2020. Roadside sites had the highest median temperature, relative humidity, wind speed and PM₁₀ concentrations, and median airborne bacterial counts in roadside sites (115,000 CFU/m³) were higher compared with industrial (35,150 CFU/m³) and residential sites (1210 CFU/m³). Bacillus species were isolated in all samples and none were antibiotic resistant. There were, however, Pseudomonas aeruginosa, Escherichia coli, Pseudomonas species, non-hemolytic Streptococci, Coliforms and Staphylococci species, of which six (50%) showed mono-resistance or multidrug resistance to four antibiotics (penicillin, ampicillin, ciprofloxacin and ceftriaxone). There was a positive correlation between PM₁₀ concentrations and airborne bacterial counts (r_s = 0.72), but no correlations were found between PM₁₀ concentrations and the pathogenic bacteria nor their antibiotic resistance. We call for the expansion of surveillance of ambient air to other cities of Ghana to obtain nationally representative information.