Air Quality Management
Sally Benson
WESTERN CAPE
HUMAN HEALTH RISK ASSESSMENT STUDY
October 2017
© Western Cape Government 2012 |
Human Health Risks Assessment
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Phase 1
[2013-2016]
Assess HHRA Status in
selected areas in the Western Cape
1 2013 - 2023
2
Project designed with a 10 year vision, and
Phase 1 planned for 3 years (2013-2016).
Phase 2
[2017 – 2019]
Focus and implement air
quality measures to inform HHR in the Western Cape
2
Phase 3
[2020– 2022]
Assess and report on air quality measures implemented
3
Phase 4
[2023]
Monitoring & Evaluation of outcomes
4
Air Quality Measures
© Western Cape Government 2012 |
3
A team from the Nova Institute, the CSIR, e-Science Associates, the University
of Cape Town and Prime Africa Consultants was appointed to assess the
human health risk of individuals living in selected areas of the Western Cape.
The work focused on 5 major work packages:
Phase 1
[2013-2016]
1
© Western Cape Government 2012 | WCG-PPT Slide Gallery-01112012.pptx
To conduct
comprehensive
Human Health
Risk Assessment
studies in
identified areas
of the Western
Cape Province.
Cross-Sectional Epidemiological Survey
human health status of communities and linking this understanding to
environmental factors in the areas surveyed 3
HHRA Aim Work Packages
Human Health Risk Assessment – Desktop Study
determine the current health status of the population in identified
areas of the Western Cape as baseline information for air quality
planning and management in the Province 2
1
4
Epidemiological Cohort Study: Children and Adults
effect of air pollution and airborne pollen on asthma in children and
cardiopulmonary health status of adults 4 Economic Valuation of Air Pollution
quantify the costs of health outcomes associated with air pollution in
the Western Cape Province 5
Emissions and Air Quality Modelling
collate and verify air quality data for use in the HHRA project, as well
as develop emissions inventory for modelling and exposure studies
Phase 1
[2013-2016]
1
© Western Cape Government 2012 |
Methodology
6
CAMx CAMx
AQ
Database
CalMET to model 3-
d meteorology for
input into CalPUFF
CalPUFF to model
pollution dispersion
and visibility
CAMx to model
atmospheric
chemical
interactions related
to air pollution
CalPUFF
CalMET
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© Western Cape Government 2012 |
Methodology
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CAMx
AQ
Database
Emissions Inventory
Database:
• Industrial sources [APPA &
AEL]
• Household [fuel type, census]
• Vehicle [traffic count, fleet,
e-factors / e-rates]
• Biomass burning [area burnt,
fuel load]
• Biogenic & marine aerosols
[BVOCs in the presence of
NOx, acts as precursor for
O3]
CalPUFF
CalMET
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Study Areas | Model Domains
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Blouberg (Table View);
Bluedowns;
Elsies River;
Fisantekraal;
Milnerton;
Nyanga;
Khayelitsha;
Grabouw;
Philippi;
Paarl;
Wellington;
Mossel Bay;
St Helena bay;
Oudtshoorn
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Needs Analysis
© Western Cape Government 2012 |
Data collected during 2011 – 2013 at the following stations were used:
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Station Owner Purpose
Athlone CCT Traffic
Khayelitsha WCG | CCT Monitoring Residential & Traffic emissions
Bothasig CCT Monitor industrial and traffic emissions
Table View CCT Monitoring Traffic & Residential emissions
Goodwood CCT Monitor traffic emissions
Wallacedene CCT Monitor agriculture
Oudtshoorn WCG Tannery, abattoir and municipal sewage
works close to residential area.
Danabaai WCG Traffic and domestic pollution; close to
refinery.
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Emissions Inventory
Emissions inventories were created for industrial emitters, household, vehicles
and biomass burning. Emissions were estimated from information submitted
directly by emitters, and the APPA registration data.
Household emission rate at peak hour (g/s) for PM10 and SO2
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EXAMPLE:
© Western Cape Government 2012 |
Traffic emission
rates from major
roads around
City of Cape
Town:
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Emissions Inventory
EXAMPLE:
all roads NOx and PM10 (top 2 figures)
modelled roads
NOx and PM10 (bottom 2 figures)
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Emissions Inventory
EXAMPLE:
Emission rates (g/s) from biomass burning for January 2011 for NOx & PM10
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Results of hourly SO2
concentrations showed
an over-prediction
when compared to the
ambient measurements
Example: Tableview.
Timeseries of measured
SO2 at Tableview (2011),
after correction for:
a) all industries as per
the AEL;
b) all industries with
updated emissions
information for a
facility near the
Tableview station.
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Industrial Sources of SO2
a)
b)
© Western Cape Government 2012 |
Concentrations of NOx
compare well to the
ambient data, although
emissions of PM10 and NOx
at the Foreshore was likely
influenced by the harbour,
and in Tableview by a
nearby industry.
Modelled PM10
concentrations were low, as
model input values were
primarily from industrial
emission inventories and
thus the higher measured
values were most likely due
to a contribution from a
background source that is
not quantified in the model. 15
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Vehicular Sources of PM10
and NOx
NOx
PM10
© Western Cape Government 2012 |
Regional background PM10 oscillates between 0-40 µg/m3. This cycle was evident at
three separate monitoring stations, Khayelitsha, Goodwood and Tableview.
The oscillation may be due to regional influx of marine air and associated aerosols,
or an increase in humidity which can result in increased PM10 through condensation.
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Background PM10
Running minimum 24 hour PM10 concentrations
© Western Cape Government 2012 |
Package 1 information used to inform Packages 2 - 5:
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Package 1: Summary
3 2
4 5
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HHRA
desk EPI
study
EPI cohort
COST
health
Dispersion modelling of SO2 and NOX compared favourably to ambient
concentrations, attributed to industries and
vehicles.
After taking background PM10 into
consideration, PM10 temporal trends compared well.
Spatial distribution of O3 showed high concentrations in the False Bay area, and
extended to the City of Cape Town.
“worse case scenario”, as based on emission
inventory data.
© Western Cape Government 2012 | 19
Area SO2 NO2 O3 PM10 PM2.5
1. Bluedowns and Elsies River
Modelled1 Modelled2 Modelled2 Monitored (Khayelitsha station) Modelled2
2. Fisantekraal, Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
3. Table View, Bothasig & Richwood
Modelled1 Monitored (Table View station)
Modelled2 Modelled1 Modelled2
4. Mossel Bay Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
5. St Helena Bay Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
6. Saldanha Bay Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
7. Grabouw Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
8. Paarl and Wellington
Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
9. Khayelitsha Modelled1 Modelled2 Modelled2 Monitored (Khayelitsha station) Modelled2
10. Oudtshoorn Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
11. Greater Milnerton Modelled1 Modelled2 Modelled2 Modelled1 Modelled2
1= CalPUFF modeling system used 2= CAMx modeling system used
Study Areas, where modelled & monitored data used 2 HH
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© Western Cape Government 2012 | 20
1= CalPUFF modeling system used 2= CAMx modeling system used
Modelling Domains
City of Cape Town
St Helena Bay
Mossel Bay
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© Western Cape Government 2012 | 21
Methodology
US-EPA Human Health Risk Assessment Framework (US-EPA, 2014): relates to the
physical and/or chemical properties of air pollutants and their concentrations.
The framework comprises the following steps:
Hazard
Identification
Exposure
Assessment
Dose-Response
Assessment
Risk Estimation
determine emissions, pathways and rate of movement of a substance, and its transformation and degradation in the environment
estimate the concentration to which individuals are
/ may be exposed, via routes of inhalation, ingestion or dermal contact.
monitored / modelled air quality data to estimate pollutant exposure or dose that individuals are likely to receive.
determine if exposure to a substance may result in detrimental human
health effects
a literature review on the pollutants of concern was conducted to form the basis of characterising the possible detrimental health effects
determine relationship between exposure (body in contact with an agent) or dose (dose of agent inside the body) and the response (incidence and severity of an effect, i.e. the human response to the dose).
the SA-AAQ Standards used as benchmark for O3, SO2, NO2, PM2.5 and PM10.
the WHO guideline benchmark values were used for H2S.
determine the potential for a
detrimental health effect.
the concentrations and benchmark values
were used to determine a hazard quotient (HQ) and hazard indices (HI).
HQ: potential for developing adverse health
effects from exposure to a chemical, biological or physical agent
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Sub-Place
(SPs)
SO2 24h
HQ
NO2 1h
HQ
O3
8hmax
HQ
PM10 24h
HQ
1 0,82 0,15 0,24 0,14
2 1,27 0,16 0,24 0,11
3 0,26 0,32 0,45 0,22
4 0,86 0,17 0,24 0,09
5 0,91 0,17 0,24 0,09
6 0,73 0,17 0,24 0,11
7 0,70 0,18 0,24 0,19
8 1,52 0,29 0,48 0,29
9 0,32 0,25 0,46 0,71
10 0,30 0,23 0,48 0,71
11 0,61 0,27 0,50 0,71
12 0,27 0,23 0,50 0,71
13 0,32 0,25 0,50 0,71
14 0,37 0,25 0,48 0,71
15 2,52 0,05 0,24 0,04
16 1,49 0,03 0,24 0,03
17 2,41 0,03 0,24 0,04
18 4,48 0,03 0,24 0,07
19 6,81 0,03 0,24 0,10
20 0,49 0,25 0,54 3,16
21 0,69 0,17 0,48 0,85
22 0,57 0,08 0,51 0,04
23 0,42 0,10 0,51 0,06
Hazard Quotient: Khayelitsha | Milnerton | Oudtshoorn 2 HH
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Potential for developing health
effects:
HQ = <0.1, no risk exists;
HQ = 0.1 – 1.0, risk is low;
HQ = 1.1 – 10, risk is moderate;
HQ = >10, risk is high
NO2 | O3 | PM10
HQ = 0.1 – 1.0 | risk is low
SO2
HQ = 0.1 – 1.0 | risk is low
HQ = 1.1 – 10 | risk is moderate
© Western Cape Government 2012 | 23
Sub-Place
(SP)
HAZARD
INDEX
1 7.00
2 4.69
3 2.72
4 2.61
5 2.29
6 1.93
7 1.70
8 1.57
9 1.37
10 1.35
11 1.33
12 1.28
13 1.21
14 1.18
15 1.17
Sub-Place
(SP)
HAZARD
INDEX
16 1.17
17 1.15
18 1.11
19 1.09
20 1.06
21 1.04
22 1.03
23 1.02
24 1.01
25 1.01
26 1.00
27 1.00
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HI = SO2 + NO2 + O3
Similar modes of
action in the body.
HI may indicate a risk of adverse effects.
Hazard Index: Khayelitsha | Milnerton | Oudtshoorn
HI = SO2 + NO2 + O3
Similar modes of
action in the body.
HI = 1.1 – 10 risk is moderate
© Western Cape Government 2012 | WCG-PPT Slide Gallery-01112012.pptx 24
prevalence of illness and diseases:
Acute respiratory: bronchitis, ear
infection, hay fever, sinusitus
Chronic respiratory: asthma,
chronic bronchitis, tuberculosis (TB).
Lifestyle diseases: heart failure, high
blood pressure, diabetes, high
cholesterol, HIV.
socio-economic factors as it influences
health of individuals:
Income (no income, salary,
pension, child grant)
Employment level
Housing (type of structure,
possession of assets, type of energy
used, water and sanitation).
Cross-sectional epidemiological surveys to assess:
Health outcomes were
grouped into the following
categories:
Lower Respiratory Illness
(LRI) – conditions of the lung, e.g. bronchitis,
chronic bronchitits,
asthma, TB, pneumonia.
Upper Respiratory Illness
(URI) – ear infection, hayfever, sinusitis.
Lifestyle-related diseases – hypertension, diabetes,
high cholesterol.
3 EP
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STU
DY
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Surveys conducted by 5
field workers / area,
trained over 5 days.
Number of Households
targeted:
• 464 – Khayelitsha
(large informal
settlement)
• 271 – Milnerton /
Dunoon (residential
close to industry)
• 451 – Oudtshoorn
(large rural town)
Approach 3 EP
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STU
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3 EP
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Poverty appears to be
linked to increased risk of
lower respiratory illness.
Areas where acute poverty
is present should be
prioritised for health and
environmental interventions.
Survey outcome:
diagnosed asthma in
Milnerton, and
asthma-like symptoms in
Dunoon.
This result must be validated
and further investigated
with the aim of designing
effective interventions.
Summary of findings
© Western Cape Government 2012 | WCG-PPT Slide Gallery-01112012.pptx
Study 1: Primary school learners
(Grade 4, n = 600)
School children: asthma
Study 2: Adults - parents of learners
(n = 600)
Adults: cardiopulmonary outcomes
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4 EP
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OH
OR
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Epidemiological Cohort Study
Participants, ambient air exposure
and the health outcomes were
monitored over time.
Cohort Study Study Areas
Khayelitsha
Milnerton / Dunoon
Oudtshoorn
Noordhoek (control)
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4 EP
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OH
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Methodology | Sampling Schedule
The cohort study comprised of a baseline study and 2 follow-up studies
© Western Cape Government 2012 |
Instruments: Lung function testing
Instruments: Serial Peak Flow Meter
Adult Questionnaires
Normal Inflamed
• Airway Inflammation
• Lung Volumes: Peak and Forced Expiratory Volumes
(PEV & FEV)
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Methodology | Apparatus | Questionnaires
© Western Cape Government 2012 |
Children | Respiratory Effects
Airway Inflammation:
• assessed using fractional
exhaled nitric oxide (FeNO)
levels
Lung Function:
• assessed using a Spirometer
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4 EP
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OH
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Epidemiological Cohort Study
Adults | Cardio-Pulmonary Effects
Cough:
• known asthma-like symptom /
woken up with cough in the last 12
months
Chest pain:
• crude proxy for angina
Breathlessness:
• crude proxy for cardio-pulmonary
disease
Recommendations:
Respiratory effects - Milnerton and
Khayelitsha
Recommendations:
Cardio-pulmonary - adults from
Khayelitsha
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• Expand and enhance air quality measurements, including pollen;
• promote asthma awareness and asthma education in schooling
communities;
• Promote location of schools away from busy roads, reduction in vehicular
emissions, more enforcement to ensure regular vehicle testing and
vehicle exhaust emissions tests.
• follow-up of study participants from when the initial baseline study was
conducted in 2015 which will enhance the likelihood of detecting health
effects due to long -term low level air pollution exposure.
Recommendations
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Thank you