Analysis of nighttime aerosols and relation to covariates over a highly polluted sub-Saharan site using Mann-Kendall and wavelet coherence approach.
Ali KöseSalman TariqBanu Numan UyalMuhammad KhanHusam RjoubUsman MehmoodPublished in: Journal of environmental quality (2024)
High emissions of aerosols and trace gases during nighttime can cause serious air quality, climate, and health issues, particularly in extremely polluted cities. In this paper, an effort has been made to examine the variations in aerosols and trace gases over a sub-Saharan city of Ilorin (Nigeria) during nighttime. We have used Aerosol Robotic Network data of aerosol optical depth (AOD) at 500 nm, Angstrom exponent (AE) (440/870), and precipitable water (WVC). Both AE and WVC showed a decreasing trend of -0.0012% and -0.0010% per year, respectively. We also analyzed nighttime data of carbon monoxide (CO), methane (CH 4 ) , and ozone (O 3 ) from Atmospheric Infrared Sounder and aerosol subtypes from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation). AOD, AE, and WVC average values are found to be 0.64 ± 0.33, 0.74 ± 0.24, and 3.40 ± 0.97, respectively. As a result of northeasterly winds carrying Saharan dust during the dry season, the greatest value of AOD (1.29) was observed in February. Desert dust aerosols (37.63%) were the most prevalent type, followed by mixed aerosols (44.15%). Winds at a height of 1500 m above ground level were likely transporting Saharan dust to Ilorin. CALIPSO images revealed that Ilorin's atmosphere contained dust, polluted continental, clean maritime, and polluted dust on high AOD days. The National Oceanic and Atmospheric Administration's vertical sounding profiles showed that the presence of high AOD values was caused by the inversion layer trapping aerosol pollution. Average nighttime concentrations of CO, O 3 , and CH 4 were measured to be 127 ± 18, 29.7 ± 2.1, and 1822.6 ± 12.7 ppbv, respectively. The wavelet coherence spectra exhibited significant quasi-biannual and quasi-annual oscillations at statistically significant levels.
Keyphrases
- heavy metals
- water soluble
- health risk assessment
- health risk
- human health
- risk assessment
- particulate matter
- sewage sludge
- convolutional neural network
- electronic health record
- polycyclic aromatic hydrocarbons
- healthcare
- drinking water
- public health
- mental health
- optical coherence tomography
- climate change
- body mass index
- big data
- hydrogen peroxide
- deep learning
- room temperature
- single cell
- magnetic resonance
- quality improvement
- robot assisted
- anaerobic digestion