western cape human health risk assessment study€¦ · 2. fisantekraal, modelledmodelled1...

Air Quality Management

Sally Benson

WESTERN CAPE

HUMAN HEALTH RISK ASSESSMENT STUDY

October 2017

© Western Cape Government 2012 |

Human Health Risks Assessment

WCG-PPT Slide Gallery-01112012.pptx

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


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


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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Methodology

7

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

WCG-PPT Slide Gallery-01112012.pptx 8

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


Data collected during 2011 – 2013 at the following stations were used:


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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© Western Cape Government 2012 | 10

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:


Traffic emission

rates from major

roads around

City of Cape

Town:

11

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.


Emissions and Air Quality Modelling 1 AIR

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Industrial Sources of SO2

a)

b)


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


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


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.


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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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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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


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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© Western Cape Government 2012 | 25 WCG-PPT Slide Gallery-01112012.pptx

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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© Western Cape Government 2012 | 26 WCG-PPT Slide Gallery-01112012.pptx

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

27

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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)

© Western Cape Government 2012 | 28 Go to Insert > Header & Footer > Enter presentation name into footer field

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Methodology | Sampling Schedule

The cohort study comprised of a baseline study and 2 follow-up studies


Instruments: Lung function testing

Instruments: Serial Peak Flow Meter

Adult Questionnaires

Normal Inflamed

• Airway Inflammation

• Lung Volumes: Peak and Forced Expiratory Volumes

(PEV & FEV)

4 EP

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Methodology | Apparatus | Questionnaires


Children | Respiratory Effects

Airway Inflammation:

• assessed using fractional

exhaled nitric oxide (FeNO)

levels

Lung Function:

• assessed using a Spirometer

30

4 EP

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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

© Western Cape Government 2012 | 31 Go to Insert > Header & Footer > Enter presentation name into footer field

• 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

5 CO

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Thank you

western cape human health risk assessment study€¦ · 2. fisantekraal, modelledmodelled1...

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