Addressing Air Quality and Public Health Using Earth Observation in Africa

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 sufficient Monitoring • Yellow – Work is in progress Action to reduce health impacts prediction • Orange – some progress but more more needed. • Red – deficient and

Communication Health Linkages to public action require immediately

11 Potential Impacts on health (Zhang et al. 2016)

12 Contribution of PM2.5 to infant Mortality (Heft-Neal, 2018)

Estimated 780, 000 premature Deaths, most from desert dust (Bauer et al. 2019)

13 Project: 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: [email protected] ) 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 Biological Activity on dust from Dakar, Senegal (Marone et al. 2020)

(a) (b)) Percent of Gram-Positive isolates from Percent of Gram-Positive Isolates from QuickTake® 30 sampling %) microbiological spatula sampling

t( 9 n dus i

8 n i a

7 i a 6 r 8 e eri

t t 5 ) c c 6 a

4 a b %

3 4 f t( of 2 o e s

1 u 2 e tag d 0 g n a e 0 t c r s i s s i s r s p n us ns us ius i m us s us a ili u e e Pe evi te i ens se r ete ns ri r ric mili m r a c i lu a a o .ag t o ie te e u iu ur li l a a a p e l il a . v r n g a .a r i l i a a . . M e hr n B.b h r S io o c s p c h e M miy M d c i ga p B.p e h e a o s p a M o e s s cte p d t n a m d s P a p a o o o i in K. hur me B. ba to l lu e i l l d r b K. t B. e l g . .c g .c u d u ish B. n a P u cel e y r n ry m B. B er u E s .h . . . a r .p S M Co S P. B P A

(c) Percent of Gram-Negative isolates from (d)) Percent of Gram-Negative isolates from microbiological spatula sampling QuickTake® 30 sampling t( %)

a i dus

14 r n e i

12 t a ) 10 c a eri 2.5 % t

b c 8

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u of 4 e e d g 1.5 2 a tag n t i n 0 e n

c i i r s e e a a a a a e li r a r a i a a a e a s pp il o l c r s ic ili op ili i llei c s o ci n Pe cte o h r a o b ko i h

a 0.5 h da i n t r a i hr i te e ad oa ic pa t l op u m b sh vi g u op .f r . oba n P cl hn l o i ce . lt cimi d S c cell do

ru 0 ru h .a W P . y a s E. B.g u lym u e B. ad S h ru O e E. r m .p .a . s B .pa P S A. met

S M.varians M.sedentarius R. . .p S P V 15 Age distribution of respiratory related disease in Senegal (Toure et al. 2019)

(a) Age Distribution of Asthma and Bronchitis (b) Age Distribution of ARI in Senegal for 2015 and 2016 30000 in Senegal for 2015 and 2016 400000

350000 25000 Asthma Bronchitis 300000

ARI 20000 250000

s s e e s s 200000

15000 Ca Ca f f o o

# # 150000 10000

100000

5000 50000

0 0 s s s h s t h h s s s s t t h s s s s s t D s yr yr yr yr D on

yr yr yr yr yr on N on

on N

m m 25 49 59 60

14 25 49 59 60 - - - ge

14 - - - - ge - 59 A 5 11 59 - A 15 26 50 >= 5 11 - - 15 26 50 >= - 0 12 0 12 Age Group Age Group

16 Senegalese Females carry burden of

ARI with age(ACUTE RESPIRATORYToure INFECTION et al. 2019) 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% 58.4% 41.6% 54.7% 45.3%

Increasing Age 17 Senegalese 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

18 How to observe pollution (PM and gases) • Remotely – Satellites or ground based Sun photometers (AERONET) • In situ

19 Why 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.

20 December 4, 2015 Space and Cape Verde

21 Satellite Aerosol Optical Depth – identifies lots of dust particles

22 Satellite 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?

23 Satellite Problem of night and clouds

24 December 4, 2015 Space and Cape Verde

25 Cape Verde (December 4, Dec 7, 2015)

26 Why 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.

27 Real-time Insitu PM measurements can support satellites but limited in Africa

28 Air Quality Alerts by CGQA in Dakar, Senegal

29 EO needed to evaluate Use of Real-time PM2.5 concentration forecast for West African cities

30 AQUA AOD March 2019 event

7 March 2019 9 March 2019

12 March 2019 17 March 2019 Forecasted 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 Louga Louga Matam Matam Matam Thies Thies Dakar Diourbel Dakar Diourbel DakarThies Diourbel

Kaffrine Kaffrine Kaffrine Kaolack Kaolack Kaolack Fatick Tambacounda Fatick Tambacounda Fatick Tambacounda

Kolda Kolda Kolda Sedhiou Sedhiou Sedhiou Ziguinchor Kedougou Ziguinchor Kedougou 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

Louga Louga Louga Matam 9,10,11 March Matam Matam Thies Dakar Diourbel Thies DakarThies Diourbel Dakar Diourbel

Kaffrine Kaffrine Kaffrine Kaolack Tambacounda Fatick Kaolack Kaolack Fatick Tambacounda Fatick Tambacounda

Kolda Sedhiou Kedougou Kolda Kolda Ziguinchor 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

Louga Louga Louga 12,13,14 March Matam Matam Matam DakarThies Diourbel DakarThies Diourbel DakarThies Diourbel

Kaffrine Kaffrine Kaffrine Kaolack Kaolack Kaolack Fatick Tambacounda Fatick Tambacounda Fatick Tambacounda

Kolda Kolda Kolda Sedhiou Sedhiou Sedhiou Ziguinchor Kedougou Ziguinchor Kedougou Ziguinchor Kedougou

0 40 80 160 240 320 0 40 80 160 240 320 0 40 80 160 240 320 Miles Miles Miles Estimated 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 >7days Why we need real-time networks of particulate matter to quantify AQ

Hospital s/clinics

Air Public Research Quality

Policy

34 Prototype 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 – UKB Low-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

37 Typical drivers of large dust events

Typical dust event

H Azores high

Bodele Depression

38 Anomalous dust conditions in 2020

WINTER 2020

H Azores high

Bodele Depression

39 US Air Quality Index (µg/m3)

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

40 Dust 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 m - g µ

n n a a 1 0 2 4 6 8 0 2 0 2 4 5 7 9 1 3 3 5 7 9 1 3 0 2 4 6 8 0 2 0 2 4 6 8 0 2 an 2 4 6 8 2 4 6 8 1 3 5 7 9 1 3 5 7 8 2 4 6 8 J J J 1 1 1 1 1 2 2 1 1 1 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 1 2 2

2 5 1 date/hour

(c) (d) Percentage of Air Quality (120 hours) Percentage of Air Quality (120 hours) 1 -5 Jan 2020 Praia, Cabo Verde 0% 1 -5 Jan 2020 Sal, Cabo Verde 0% 10.1% 11.7% good

moderate Unhealthy sensitive sensitive

Unhealthy Very Unhealthy 25.8% 31.9% hazardous 58% 62.5%

41 Air 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% 1.830%% 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

42 Air 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)

44 Purple 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% 0.5140%% 10.7%

13.3%

22.8%

77.2%

70.3% good

moderate Unhealthy sensitive sensitive Dakar, Senegal Sal, Cabo Verde Unhealthy Very Unhealthy 0.22601.81%% % 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

1.70.0851%0% % 0.3340.111%% 1.87% 5.22% 0.5140%% 4.89% 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 hazardous Fires and Thermal Anomalies in 2019 for Africa and Brazil

May 22, 2019

June 22, 2019 Time Time

July 22, 2019

Aug 22, 2019

48 Monthly 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

20000 # # of Reports 15000

10000

5000

0 J F M A M J J A S O N D

Month

49 Ideas 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.