Air pollution measurements and land-use regression in urban sub-saharan Africa using low-cost sensors—possibilities and pitfalls
Abera, Asmamaw ; Mattisson, Kristoffer LU
; Eriksson, Axel
LU
; Ahlberg, Erik
LU
; Sahilu, Geremew
; Mengistie, Bezatu
; Bayih, Abebe Genetu
; Aseffaa, Abraham
; Malmqvist, Ebba
LU
and Isaxon, Christina
LU
(2020)
In Atmosphere
11(12).
- Abstract
Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables... (More)
Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city’s meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m3), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area.
(Less)
- author
- Abera, Asmamaw
; Mattisson, Kristoffer
LU
; Eriksson, Axel
LU
; Ahlberg, Erik
LU
; Sahilu, Geremew
; Mengistie, Bezatu
; Bayih, Abebe Genetu
; Aseffaa, Abraham
; Malmqvist, Ebba
LU
and Isaxon, Christina
LU
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Alphasense, PM2.5, Purple Air, Urban air pollution
- in
- Atmosphere
- volume
- 11
- issue
- 12
- article number
- 1357
- publisher
- MDPI AG
- external identifiers
-
- scopus:85098077057
- ISSN
- 2073-4433
- DOI
- 10.3390/atmos11121357
- language
- English
- LU publication?
- yes
- id
- f4bdf8ff-061a-4e2a-a1b5-47582185649a
- date added to LUP
- 2021-01-07 10:19:16
- date last changed
- 2023-11-20 19:06:11
@article{f4bdf8ff-061a-4e2a-a1b5-47582185649a,
abstract = {{<p>Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city’s meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m<sup>3</sup>), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area.</p>}},
author = {{Abera, Asmamaw and Mattisson, Kristoffer and Eriksson, Axel and Ahlberg, Erik and Sahilu, Geremew and Mengistie, Bezatu and Bayih, Abebe Genetu and Aseffaa, Abraham and Malmqvist, Ebba and Isaxon, Christina}},
issn = {{2073-4433}},
keywords = {{Alphasense; PM2.5; Purple Air; Urban air pollution}},
language = {{eng}},
number = {{12}},
publisher = {{MDPI AG}},
series = {{Atmosphere}},
title = {{Air pollution measurements and land-use regression in urban sub-saharan Africa using low-cost sensors—possibilities and pitfalls}},
url = {{http://dx.doi.org/10.3390/atmos11121357}},
doi = {{10.3390/atmos11121357}},
volume = {{11}},
year = {{2020}},
}