Addressing Air Quality and Public Health using Earth Observation in Africa - Gregory S. Jenkins1, S. Freire, M. Gueye, D. Niang, M Drame, M ...
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Addressing Air Quality and Public
Health using Earth Observation in
Africa
Gregory S. Jenkins1, S. Freire, M. Gueye, D.
Niang, M Drame, M. Doumbia, E. Toure, B.
Diop, M. Camara, T. Ogunro, INMG, CGQA
1. Director, AESEDA, Professor of Meteorology
1
Postponement 6 HLD in Philadelphia –
Spring 2020
6th HIGH-LEVEL INDUSTRY-SCIENCE-GOVERNMENT DIALOGUE ON
ATLANTIC INTERACTIONS
Oct 5-7 2020
Penn State at the Navy Yard
Philadelphia, Pennsylvania, USA
ALL-ATLANTIC SUMMIT ON INNOVATION FOR
SUSTAINABLE MARINE DEVELOPMENT AND THE
BLUE ECONOMY: FROM EARTH OBSERVATION TO
SOCIOECONOMIC BENEFIT
High Level Dialogue: Oct 5
Technical Sessions: Oct 6,7
3
Air pollution can be a mix of the three
drivers leading to short unexpected and
dangerous health outcomes
Human
Drivers
Natural Climate
Hazards Change
Unexpected
and
dangerous
environmental
outcomes
Finding solutions to pollution requires
an understanding of its connections
Environment
innovation Health
Community
Energy
Engagement
Policy
Numerous Sources of Particulate
Matter at different spatial/temporal
scales (Natural and human hazard)
Squall line in
Kaffrine, Senegal
6
Biomass Burning in West Africa as
source of pollution
Fire Location -- Black
Seasonal Biomass burning Carbon Aerosol and
gases (CO, O3, CH4)
21 October 2019 21 Nov. 2019
21 Dec. 2019 21 Jan 2020
7
Saharan Dust Sources
The Sahara Desert (Mauritania)
Location of Saharan Air and dust
Status of Air Quality efforts in West
Africa
• Green- Action is
Monitoring
sufficient
• Yellow – Work is in
Action to reduce
progress
health impacts prediction
• Orange – some progress
but more more needed.
• Red – deficient and
Health Linkages
Communication
to public
action require
immediately
11Potential Impacts on health
(Zhang et al. 2016)
12Contribution of PM2.5 to infant Mortality
(Heft-Neal, 2018)
Estimated 780, 000 premature
Deaths, most from desert dust
(Bauer et al. 2019)
13Project: DUSTRISK - A risk index for health effects of mineral dust and associated microbes
Execution dates: 01-05-2020 to 30-04-2023 (36 Months):
Coordinator: Khanneh Wadinga Fomba (E-mail: fomba@tropos.de )
Participating Institutions: TROPOS and Leibniz ((Microbiology, DSMZ); (Biophysics, FZB, );
(Toxicology, IUF)) /Cape Verde: ((Chemist, Epidemiologist, UniCV); INMG; (Pulmonologist,
Hospitals); DNA; INSP)
Funding Entity: Leibniz Institute for Tropospheric Research e.V
Funding line: Leibniz collaborative excellence
Main Scientific Goal: Develop a dust-health-risk-index by evaluating the health effects of dust
and associated microbes on respiratory diseases.
Added-values:
(1)Interdisciplinary network of Leibniz and international experts collaborating to establish scientific tools
that can improve on public health
(2)Consolidates existing Leibniz topic “Infections 21”
(3)The risk-index is a good example of transfer of scientific outcomes to useful societal products
(4)Provides opportunity for capacity building and new transdisciplinary research in the fields of public
health, toxicology and atmospheric sciences
(5)Strengthen international collaboration providing new insights into health effects of dust and
associated microbes
14(a)
(c)
Percentage of bacteria in dust( %) M Percentage of bacteria in dust( %)
6
8
0
1
2
3
4
5
7
9
.lu
M te
.n M u
6
8
0
2
4
10
12
14
ish .v s
in ari
B.cepacia om an
iy s
ae
V.metschnikori M n s
.ro is
P.luteola se
O.anthropi M us
K. .ag
W.virosa se
de ilis
n
S.paucimibilis K. tar
i
B. sch us
R.radiobacter th ro
ur ete
E.hoshinae in r
B. gie i
m ns
S.ficaria eg i
at s
S.plymuthica er
iu
B m
S.maltophilia B. .br
sp evi
B.gladioli ha s
er
P.aeruginosa
Co i
ry B cu
ne .pu s
Brucella spp ba m
microbiological spatula sampling
ct i
er lis
E.cloacae iu
m
Percent of Gram-Positive isolates from
P.pseudomallei S. sp
microbiological spatula sampling
au
A.hydrophila re
us
Percent of Gram-Negative isolates from
S.rubidaea
Percentage of bacteria Percentage of bacteria in
(d))
(b))
in dust( %) dust( %)
S.
m
al
0
2
4
6
8
to
p
0.5
1.5
2.5
0
1
2
B. hili
gl
ad
i
P.l oli
ut
e
B. ola
P.a epa c
er c
ug ia
M.varians
Br ino
uc s
el a
la
E. spp
c
QuickTake® 30 sampling
QuickTake® 30 sampling
P.p loa
se cae
ud
A. om
Percent of Gram-Positive Isolates from
a
Percent of Gram-Negative isolates from
Senegal (Marone et al. 2020)
hy
dr l
op
S.
ru hil
bi
M.sedentarius
da
ea
Biological Activity on dust from Dakar,
15Age distribution of respiratory related
disease in Senegal (Toure et al. 2019)
(a)
Age Distribution of Asthma and Bronchitis (b) Age Distribution of ARI
30000
in Senegal for 2015 and 2016 400000 in Senegal for 2015 and 2016
350000
25000
Asthma
Bronchitis
300000
ARI
20000
250000
# of Cases
# of Cases
15000 200000
150000
10000
100000
5000
50000
0 0 12-59 months
0-11 months
12-59 months
0-11 months
15-25 yrs
26-49 yrs
50-59 yrs
>= 60 yrs
5-14 yrs
15-25 yrs
26-49 yrs
50-59 yrs
>= 60 yrs
Age ND
5-14 yrs
Age ND
Age Group Age Group
16Senegalese Females carry burden of
ARI with age(Toure et al. 2019) ACUTE RESPIRATORY INFECTION
PERCENTAGES SHIFT WITH AND
AGE AND GENDER
Precentage of ARI Cases by Gender Precentage of ARI Cases by Gender Precentage of ARI Cases by Gender
2015-2016 Ages 0-14 years 2015-2016 Ages 15-60 years 2015-2016 > 60 years
male
female
48.4% 51.6% 41.6% 54.7% 45.3%
58.4%
Increasing Age
17Senegalese Females carry burden of
Asthma with age (Toure et al. 2019)
ASTHMA PERCENTAGES SHIFT
WITH AND AGE AND GENDER
Precentage of Asthma Cases by Gender Precentage of Asthma Cases by Gender Precentage of Asthma Cases by Gender
2015-2016 Agest 0-14 years 2015-2016 Ages 15-60 years 2015-2016 > 60 years
male
female
47.1% 52.9% 54.5% 45.5% 48.5% 51.5%
Increasing Age
18How to observe pollution (PM and
gases)
• Remotely – Satellites or ground based Sun
photometers (AERONET)
• In situ
19Why Space and Satellites for Pollution
monitoring ?
• Continuous temporal sampling;
• Continuous spatial sampling;
• Global coverage to initialize and evaluate
predictive models;
• Long-time series (back to 1978) for Aerosols;
• Identify trends and patterns
(globally,regionally);
• Could be used to drive policy.
20December 4, 2015 Space and Cape Verde
21Satellite Aerosol Optical Depth –
identifies lots of dust particles
22Satellite Limitations and particulate
matter observations
• Cloud coverage
• Night conditions
• Temporal coverage (satellite overpass time)-
does it pass over when there is pollution.
• Spatial coverage (urban pollution)
• What are the surface PM concentrations?
23Satellite Problem of night and clouds
24December 4, 2015 Space and Cape Verde
25Cape Verde (December 4, Dec 7, 2015)
26Why we need real-time networks of
particulate matter to quantify AQ
• Fast growing megacities;
• Localized sources of pollution (e.g.waste sites);
• Limited access to health care in some locations
(rural zones);
• Provides information at local to regional scales
for decision-makers ;
• Evaluation of satellites and PM forecasts;
• Poor Air Quality is natural hazard and level of the
threat should be communicated to the public.
27Real-time Insitu PM measurements can
support satellites but limited in Africa
28Air Quality Alerts by CGQA in Dakar, Senegal
29EO needed to evaluate Use of Real-time PM2.5
concentration forecast for West African cities
30AQUA AOD March 2019 event
7 March 2019 9 March 2019
12 March 2019 17 March 2019Forecasted PM10 Spatial Distribution
6-14 March, 2019
0-54.5 (Healthy) 0-54.5 (Healthy)
0-54.5 (Healthy)
54.6-154.5 (Moderate) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate)
154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups)
254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy)
354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy)
425+ (Hazardous) 425+ (Hazardous) 425+ (Hazardous)
Light Gray Canvas Base Light Gray Canvas Base Light Gray Canvas Base
Saint Louis Saint Louis Saint Louis
6,7,8 March Louga
Matam
Louga
Matam
Louga
Matam
DakarThies Diourbel DakarThies Diourbel DakarThies Diourbel
Kaffrine Kaffrine Kaffrine
Kaolack Kaolack Kaolack
Fatick Tambacounda Fatick Tambacounda Fatick Tambacounda
Kolda Kolda Kolda
Sedhiou Kedougou Sedhiou Kedougou Sedhiou
Ziguinchor Ziguinchor Ziguinchor Kedougou
0 40 80 160 240 320 0 40 80 160 240 320 0
Miles Miles 40 80 160 320240 Miles
0-54.5 (Healthy)
54.6-154.5 (Moderate) 0-54.5 (Healthy)
0-54.5 (Healthy)
154.6-254.5 (Unhealthy for Sensitive Groups) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate)
254.6-354.5 (Unhealthy) 154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups)
354.6-425 (Very Unhealthy) 254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy)
425+ (Hazardous) 354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy)
Light Gray Canvas Base 425+ (Hazardous) 425+ (Hazardous)
Saint Louis Light Gray Canvas Base Light Gray Canvas Base
Saint Louis Saint Louis
9,10,11 March Louga
Matam
Louga
Matam
Louga
Matam
DakarThies Diourbel
DakarThies Diourbel DakarThies Diourbel
Kaffrine
Kaffrine Kaffrine
Kaolack
Fatick Tambacounda Kaolack
Kaolack Fatick Tambacounda
Fatick Tambacounda
Kolda
Sedhiou Kedougou Kolda
Ziguinchor Kolda Sedhiou
Sedhiou Kedougou Ziguinchor Kedougou
Ziguinchor
0 40 80 160 240 320
Miles 0 40 80 160 240 320 0 40 80 160 240 320
Miles Miles
0-54.5 (Healthy) 0-54.5 (Healthy) 0-54.5 (Healthy)
54.6-154.5 (Moderate) 54.6-154.5 (Moderate) 54.6-154.5 (Moderate)
154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups) 154.6-254.5 (Unhealthy for Sensitive Groups)
254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy) 254.6-354.5 (Unhealthy)
354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy) 354.6-425 (Very Unhealthy)
425+ (Hazardous) 425+ (Hazardous) 425+ (Hazardous)
Light Gray Canvas Base Light Gray Canvas Base Light Gray Canvas Base
Saint Louis Saint Louis Saint Louis
Louga Louga Louga
12,13,14 March DakarThies Diourbel
Matam
DakarThies Diourbel
Matam
DakarThies Diourbel
Matam
Kaffrine Kaffrine Kaffrine
Kaolack Kaolack Kaolack
Fatick Tambacounda Fatick Tambacounda Fatick Tambacounda
Kolda Kolda Kolda
Sedhiou Kedougou Sedhiou Kedougou Sedhiou Kedougou
Ziguinchor Ziguinchor Ziguinchor
0 40 80 160 240 320 0 40 80 160 240 320 0 40 80 160 240 320
Miles Miles MilesEstimated WRF Unhealthy PM10 concentrations
(>255 µg-m-3)
March 4-14, 2019
# days of exposure Total population % Children under 5%
percentage of districts with 100 100
>1day
percentage of districts with 100 100
>3 days
percentage of districts with 82 79
>5 days
percentage of districts with 73 71
>7daysWhy we need real-time networks of
particulate matter to quantify AQ
Hospital
s/clinics
Air
Public Research
Quality
Policy
34Prototype low-cost sensor network
with Universities and government
• Senegal
– Cheikh Anta Diop University (Dakar)
– CGQA (Dakar)
– USSEIN (Kaolack and Kaffrine campuses)
– Gaston Berger (Saint Louis)
– Village Madina Ndiabete (NE)
– National Children’s Hospital (Diadiamdio)
– High school (Poder) –
– High School (Popenguine) –
• Nigeria
– Ibidan (Lead City University)
• Cabo Verde
– Institute for Meteorology and Geophysics (INMG)
– University of Cabo Verde (UNICV)
• Ivory Coast
– (University of Felix Houphouët-Boigny) UHFB
• Angola
– UKBLow-Cost Sensors ($200-750)
https://www.purpleair.com/map
June 11, 2019 Purple air Jan 22, 2020
https://openmap.clarity.io/ Jan 22, 2020 Clarity
37Typical drivers of large dust events
Typical dust event
H
Azores
high
Bodele
Depression
38Anomalous dust conditions in 2020
WINTER 2020
H
Azores
high
Bodele
Depression
39US Air Quality Index (µg/m )
3
AQI PM2.5 PM10 Air quality
0-50 0-12 0-54.5 Good
51-100 12-35.4 55-154.5 Moderate
101-150 35.5-55.4 155-254.5 Unhealthy for
Sensitive Groups
151-200 55.5-150.4 255-354.5 Unhealthy
201-300 150.5-250.4 355-424.5 Very Unhealthy
301-500 > 250 >425 Hazardous
40Dust Event 1-5 January 2020
(a) Visible Image (2 Jan, 2020) (b) Hourly PM2.5 Sal, and Praia CV (Purple Air )
150 1-5 Jan 2020
Sal
Praia
100
-3
Unhealthy
µ g-m
50
0
2 Jan
5 Jan
1 Jan
11
10
12
14
16
18
20
22
10
12
14
15
17
19
21
23
13
15
17
19
21
23
10
12
14
16
18
20
22
10
12
14
16
18
20
22
2
4
6
8
2
4
6
8
1
3
5
7
9
1
3
5
7
8
2
4
6
8
date/hour
(c) (d) Percentage of Air Quality (120 hours)
Percentage of Air Quality (120 hours) 1 -5 Jan 2020 Praia, Cabo Verde
0%
0%
1 -5 Jan 2020 Sal, Cabo Verde
10.1%
11.7%
good
moderate
Unhealthy sensitive sensitive
Unhealthy
Very Unhealthy
25.8% 31.9%
hazardous 58%
62.5%
41Air Quality by hour (1-20, 2020)- Large
Cities
# of hours of varying Air Quality % of hours of varying Air Quality % hours of varying Air Quality
in Dakar, Senegal (total 445 hours) in Abidjan, Ivory Coast (total 440 hours) in Praia, Senegal (total 440 hours)
3.15%0% 0%
1.83%
0%
16%
18.9% 25.6%
34.8%
43%
50.6% 16%
27.4%
29.5%
33.2%
good
moderate
Unhealthy sensitive
Unhealthy
Very Unhealthy
42Air Quality networks require
collaboration across Disciplines and
space
• Regional networks: (Senegal, the Gambia,
Cabo Verde); (Nigeria, Niger, Burkina Faso,
Ghana, Ivory Coast) are ideal to develop
regional health-pollution partnerships;
– Doctors, researchers, students, community
participatory research and partnership;
– Coordinated Public campaigns (national asthma
week, national pollution week)COVID-19 and Air Pollution
• Air quality remains a factor in driving co-
morbidities such as respiratory and
cardiovascular disease in Africa and the Southern
Hemisphere over the next few months:
– Levels of Air quality over the last few months.
– Biomass burning Southern Hemisphere;
– Long range transport of Saharan dust to the
Caribbean May-September.
– Wet season disease in West Africa (Vector and water
borne disease, Asthma and Acute respiratory disease)
44Purple Air Network
45% of hours by category for West
African cities 1 Feb-15 March 2020
% hourly of PM Concentration
2.5
1 Feb-15 Mar 2020
2.50% 5.22% 0.514%
0%
10.7%
13.3%
22.8%
77.2%
70.3%
good
moderate
Unhealthy sensitive sensitive
Sal, Cabo Verde Dakar, Senegal
Unhealthy
Very Unhealthy
0.226%
0%
1.81% 3.83% 0%
10.8%
hazardous 8.26%
19.5%
22.5%
53.5%
25%
54.7%
Abidjan, CI Ibadan, Nigeria
46% of hours by category for Senegalese
cities
% hourly of PM Concentration
2.5
1 Feb-15 Mar 2020
0.0851%
0%
1.87%1.7% 4.89% 0.334%
0.111%
5.22% 0.514%
0%
10.7%
6.45%
13.3%
31.1% 37.4%
65.2%
50.8%
70.3%
Saint Louis, Senegal Dakar, Senegal Ziguinchor, Senegal
good
moderate
Unhealthy sensitive sensitive
Unhealthy
Very Unhealthy
47
hazardousFires and Thermal Anomalies in 2019
for Africa and Brazil
May 22, 2019
June 22,
2019
Time
July 22,
2019
Aug 22, 2019
48Monthly Total 2015-2016 Adult acute
respiratory Infection Reports
Monthly 2015 and 2016 Total ARI Adult
Senegal Reports (Toure et al. 2019)
40000
35000
30000
25000
# of Reports
20000
15000
10000
5000
0
J F M A M J J A S O N D
Month
49Ideas for AIR network to address to be
done
• Expansion of PM monitoring network across Africa; - megacities,
pollution zones;
• Capacity Building – workshops and pilot projects using Earth
observations (satellite and public health);
• Evaluation of predictive tools and other tools for analysis;
• Develop framework for communicating hazards to public and
across disciplines in real-time via social networks and mobile phone
apps;
• Support linkages between environment and health for
cardiovascular, NC and Communicable respiratory and infectious
disease;
• Support workshops to develop policy – energy usage, air quality as
natural hazards, health impacts, community partnership.
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