Air Pollution in Addis Ababa: The Predicament of “Chile Sefer”

2001 ◽  
pp. 177-202
Author(s):  
Edlam Aberra
Keyword(s):  
Air Pollution ◽  
Addis Ababa

Indoor air pollution in slum neighbourhoods of Addis Ababa, Ethiopia

2014 ◽  
Vol 89 ◽  
pp. 230-234 ◽  
Author(s):  
Habtamu Sanbata ◽  
Araya Asfaw ◽  
Abera Kumie
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Addis Ababa

scholarly journals Source Apportionment of Fine Organic Particulate Matter (PM2.5) in Central Addis Ababa, Ethiopia

2021 ◽  
Vol 18 (21) ◽  
pp. 11608
Author(s):  
Worku Tefera ◽  
Abera Kumie ◽  
Kiros Berhane ◽  
Frank Gilliland ◽  
Alexandra Lai ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Source Apportionment ◽  
Biomass Burning ◽  
Addis Ababa ◽  
Atmospheric Particulate Matter ◽  
Motor Vehicles ◽  
Soil Dust ◽  
Rain Season ◽  
The Impact

The development of infrastructure, a rapidly increasing population, and urbanization has resulted in increasing air pollution levels in the African city of Addis Ababa. Prior investigations into air pollution have not yet sufficiently addressed the sources of atmospheric particulate matter. This study aims to identify the major sources of fine particulate matter (PM2.5) and its seasonal contribution in Addis Ababa, Ethiopia. Twenty-four-hour average PM2.5 mass samples were collected every 6th day, from November 2015 through November 2016. Chemical species were measured in samples and source apportionment was conducted using a chemical mass balance (CMB) receptor model that uses particle-phase organic tracer concentrations to estimate source contributions to PM2.5 organic carbon (OC) and the overall PM2.5 mass. Vehicular sources (28%), biomass burning (18.3%), plus soil dust (17.4%) comprise about two-thirds of the PM2.5 mass, followed by sulfate (6.5%). The sources of air pollution vary seasonally, particularly during the main wet season (June–September) and short rain season (February–April): From motor vehicles, (31.0 ± 2.6%) vs. (24.7 ± 1.2%); biomass burning, (21.5 ± 5%) vs. (14 ± 2%); and soil dust, (11 ± 6.4%) vs. (22.7 ± 8.4%), respectively, are amongst the three principal sources of ambient PM2.5 mass in the city. We suggest policy measures focusing on transportation, cleaner fuel or energy, waste management, and increasing awareness on the impact of air pollution on the public’s health.

scholarly journals Evaluation of WRF-CHIMERE coupled models for the simulation of PM<sub>2.5</sub> in large East African urban conurbations

2022 ◽  
Author(s):  
Andrea Mazzeo ◽  
Michael Burrow ◽  
Andrew Quinn ◽  
Eloise A. Marais ◽  
Ajit Singh ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Transport Model ◽  
Addis Ababa ◽  
East African ◽  
Coupled Models ◽  
Meteorological Model ◽  
High Resolution Data ◽  
Air Contamination ◽  
Rural Environments

Abstract. Urban conurbations of East Africa are affected by harmful levels of air pollution. The paucity of local air quality networks and the absence of capacity to forecast air quality make it difficult to quantify the real level of air pollution in this area. The chemistry-transport model CHIMERE has been coupled with the meteorological model WRF and used to simulate hourly concentrations of Particulate Matter PM2.5 for three East African urban conurbations: Addis Ababa in Ethiopia, Nairobi in Kenya and Kampala in Uganda. Two existing emission inventories were combined to test the performance of CHIMERE as an air quality tool for a target monthly period of 2017 and the results compared against observed data from urban and rural sites. The results show that the model is able to reproduce hourly and daily temporal variability of aerosol concentrations close to observations both in urban and rural environments. CHIMERE’s performance as a tool for managing air quality was also assessed. The analysis demonstrated that despite the absence of high-resolution data and up-to-date biogenic and anthropogenic emissions, the model was able to reproduce 66–99 % of the daily PM2.5 exceedances above the WHO 24-hour mean PM2.5 guideline (25 µg m−3) in the three cities. An analysis of the 24-hour mean levels of PM2.5 was also carried out for 17 constituencies in the vicinity of Nairobi. This showed that 47 % of the constituencies in the area exhibited a low air quality index for PM2.5 in the unhealthy category for human health exposing between 10000 to 30000 people/km2 to harmful levels of air contamination.

scholarly journals Magnitude and variation of traffic air pollution as measured by CO in the City of Addis Ababa, Ethiopia

2011 ◽  
Vol 24 (3) ◽  
Author(s):  
A Kume ◽  
K Charles ◽  
Y Berehane ◽  
E Anders ◽  
A Ali
Keyword(s):  
Air Pollution ◽  
Addis Ababa ◽  
The City

scholarly journals Chemical Characterization and Seasonality of Ambient Particles (PM2.5) in the City Centre of Addis Ababa

2020 ◽  
Vol 17 (19) ◽  
pp. 6998
Author(s):  
Worku Tefera ◽  
Abera Kumie ◽  
Kiros Berhane ◽  
Frank Gilliland ◽  
Alexandra Lai ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Vehicle Emissions ◽  
Addis Ababa ◽  
Ambient Air ◽  
World Health ◽  
Health Concern ◽  
City Centre ◽  
Rain Season ◽  
The City

Ambient air pollution is a growing public health concern in major African cities, including Addis Ababa (Ethiopia), where little information is available on fine particulate matter (PM2.5, with aerodynamic diameter <2.5 µm) pollution. This paper aims to characterize annual PM2.5, including bulk composition and seasonal patterns, in Addis Ababa. We collected 24-h PM2.5 samples in the central city every 6 days from November 2015 to November 2016. The mean (±SD) daily PM2.5 concentration was 53.8 (±25.0) µg/m3, with 90% of sampled days exceeding the World Health Organization’s guidelines. Principal components were organic matter (OM, 44.5%), elemental carbon (EC, 25.4%), soil dust (13.5%), and SNA (sulfate, nitrate, and ammonium ions, 8.2%). Higher PM2.5 concentrations were observed during the heavy rain season, while crustal dust concentrations ranged from 2.9 to 37.6%, with higher levels during dry months. Meteorological variables, vehicle emissions, biomass fuels, unpaved roads, and construction activity contribute to poor air quality. Compared to the Air Quality Index (AQI), 31% and 36% of observed days were unhealthy for everyone and unhealthy for sensitive groups, respectively. We recommend adopting effective prevention strategies and pursuing research on vehicle emissions, biomass burning, and dust control to curb air pollution in the city.

Sign in / Sign up

Export Citation Format

Share Document