Radon-based assessment of stability affects on potential ... · Radon-based assessment of stability affects on potential radiological releases Scott Chambers 1, Alastair Williams

Radon-based assessment of stability affects on

potential radiological releases

Scott Chambers1, Alastair Williams1, Dan Galeriu2, Anca Melintescu2 and Marin Duma2

1 ANSTO, NSTLI - Environmental Division, Sydney, NSW, Australia

2 “Horia Hulubei” National Institute for Physics & Nuclear Engineering, Bucharest-Magurele, Romania

H17-001 (Session 8: Modelling air dispersion and exposure to accidental releases)

17th International Conference on Harmonisation within Atmospheric Dispersion Modelling

for Regulatory Purposes

Monday 9th May 2016, Budapest, Hungary

Background and motivation

• Atmospheric mixing volume changes

diurnally, largely as a function of

atmospheric stability

• Wide range of stability classifications

• More accurate techniques (e.g. L, RiBulk)

expensive, labour intensive, derived for

idealised fetch conditions

• Common alternatives (e.g. Pasquill-Gifford) approximate / categorical

• Surface-emitted tracers give a direct measure of mixing intensity / extent;

better than met. proxies in characterising the outcomes of vertical mixing

• Personal risk and exposure directly related to concentration and time

• Concentration of pollutants or accidental releases related to

source strength and mixing volume

• Radon is the only gas in the Uranium-238 decay chain

• Surface-only source

• Mostly from land (unsaturated / unfrozen) not water

• Source function changes relatively little in space & time

• Unreactive / poorly soluble: sole atmospheric sink is radioactive decay

• Short half-life (3.8d) (a) doesn’t accumulate in the atmosphere

(b) large ABL / troposphere gradient

• Rn half-life >> mixing timescale of the ABL (1-hour)

• Over 1 night (10-12h) Rn is an approximately conservative (>90%) tracer

Ideal, versatile and powerful atmospheric tracer

Atmospheric radon (222Rn)

Radon: distribution &

measurement

Variability on many time scales

Seasonal (1-6 months)

Synoptic (2-12 days)

Diurnal (24 hours)

Sub-Diurnal

Fetch, mixing and non-local processes0 30 60 90 120 150 180 210 240 270 300 330 3600

10

20

30

40

50

Ra

do

n (

Bq

m-3)

Day of year

Richmond, NSW, Australia

• Land Ocean, huge source fn.

(2 - 3 orders of magnitude)

• Regional scales (10s 1000s km)

factor 2 - 4 source fn.

• Local scales (≤10s km; nocturnal fetch

for stable conditions) fairly uniform

• Parent (226Ra – half life 1600 y)(little temporal change except for soil moisture)

Griffiths, AD, et al., 2010: A map of radon flux at the Australian

land surface. Atmos. Chem. Phys., 10, 8969-8982.

Diurnal variability - the ABL mixing indicator

2007.34 2007.36 2007.38 2007.40 2007.42 2007.44

0

20

40

Ra

do

n (

Bq

m-3)

Decimal Year

2007.34 2007.36 2007.38 2007.40 2007.42 2007.44

0

20

40

Ra

do

n (

Bq

m-3)

Decimal Year

Fetch changes

Cyclic synoptic variability

Diurnal changes

in mixing depth

• Before Rn can be used as a stability

indicator, need to isolate diurnal signal

• To do this, identify the fetch signal and

subtract it from the orig. time-series

• Fetch signal related to 2-week air mass history

(Rn half-life 3.82 days)

• Remaining variability is driven by mixing

(constant source, changing volume)0 3 6 9 12 15 18 21 24

0

4

8

12

16

Mid-afternoon

Well-mixed convective

boundary-layer

Ra

do

n (

Bq

m-3)

Hour of day

Sunrise

Shallow nocturnal

boundary-layer

Offshore flowOnshore flow

16 18 20 22 0 2 4 6 8 10 12 140

3

6

9

12

Radon (

Bq m

-3)

Sunset

Sunrise

Hour of composite day

Shifted composite of diurnal variability

Accumulation

window

Radon: ~uniform surface source

and ~conservative over 1 night

Therefore, nocturnal accumulation

is directly related to the average

nocturnal stability (mixing depth)

Convective

mixing offConvective

mixing on

Step 1:

Calculate the nocturnal mean

accumulation for each 24-hour

period

Step 2:

Group the resulting values to

devise a stability classification

scheme.

For more information see: Chambers, S.D., et al., 2015: On the use of radon for quantifying the effects of atmospheric stability on

urban emissions. Atmos. Chem. Phys., 15, 1175-1190.

Stability classification example

Cumulative frequency histogram

of average nocturnal radon

accumulation over 5-years

0 5 10 15 20 25 30 350

500

1000

1500

2000

Cu

mu

lative

fre

qu

en

cy

Radon bin (Bq m-3)

0 5 10 15 20 25 30 350

500

1000

1500

2000

Q4

Q3

Q2

Q1

75th percentile

50th percentile

25th percentile

Cu

mu

lative

fre

qu

en

cy

Radon bin (Bq m-3)

Diurnal composite Rn in each stability category

16 18 20 22 0 2 4 6 8 10 12

0

5

10

15

20

25

Radon g

radie

nt (B

q m

-3)


Near Neutral

Weakly stable

Moderately Stable

Stable

About the Stability Categories

• The number of definable nocturnal stability categories dictated by

length of dataset and desired robustness of statistics

• 1 yr, 4 seasons, 4 stab. categories: diurnal composites based on 22 days

5 yr, 4 seasons, 6 stab. categories: diurnal composites based on 76 days

• Categories defined nocturnally over 10-12 hours but can generally be

assigned to whole 24-hour periods (due to atmospheric persistence)

• The most stable nights are usually characterised by:

- Clear skies, calm to light winds (e.g. anticyclonic conditions)

- Usually flanked by the most unstable (convective) days

• The most well-mixed nights are usually characterised by:

- High percentage of cloud cover and moderate to strong winds

- Usually flanked by near-neutral days

• For regulatory dispersion modelling, radon stability categories can be used

like Pasquill-Gifford categories to assign day/night wind speeds and WD to

the 16-point compass on a monthly or quarterly basis

• Categorisation is COMPLETELY INDEPENDENT of site meteorology

Evaluating radon-derived stability categories:

(a) Meteorology

Stable: low nocturnal wind speed, high wind direction variability, large temperature amplitude

Near-neutral: higher, more consistent, wind speed & direction, lower amplitude temp fluctuation

Group met data by Rn-based stability category and form diurnal composites

16 18 20 22 0 2 4 6 8 10 120

1

2

3

Win

d s

pe

ed (

m s

-1)

16 18 20 22 0 2 4 6 8 10 1220

30

40

50

60

70

w

ind

dire

ction

(o)


16 18 20 22 0 2 4 6 8 10 126

12

18

24

Te

mp

era

ture

(C

)

Near neutral

Weakly stable

Moderately stable

Stable


Stable: large amplitude

changes

Near-neutral: small

amplitude changes

NO – primary pollutant,

local surface-based

source (proxy for near-

surface accidental

emission)

Ozone behaviour

supports atmospheric

persistence hypothesis

Evaluating radon-derived stability categories:

(b) urban pollution example

16 18 20 22 0 2 4 6 8 10 120

2

4

6

8

10

12

14

16

NO

(ppb)

16 18 20 22 0 2 4 6 8 10 120

2

4

6

8

10

12

14

NO

2 (

ppb)

16 18 20 22 0 2 4 6 8 10 120

5

10

15

20

25

30

35

Ozo

ne (

ppb)

16 18 20 22 0 2 4 6 8 10 120.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

SO

2 (

ppb)

16 18 20 22 0 2 4 6 8 10 12

2

4

6

8

10

12

PM

2.5

(

g m

-3)


Near neutral

Weakly stable

Moderately stable

Stable


Assign hourly PG cats then

group by Rn-based cats

Stable nocturnal Rn-category

PG: 6 (night), 1-2 (day)

Well-mixed Rn-category

PG: 4-5 (night), 2-4 (day)

Stable nocturnal Rn-categories

Above the critical Richardson

number (RiC=0.25)

Composite nocturnal

Richardson numbers separate

fairly consistently with radon-

derived stability categories.

(for more info.: Williams A.G., S. Chambers

and A. Griffiths. Bulk Mixing and Decoupling

of the Nocturnal Stable Boundary Layer

Characterized Using a Ubiquitous Natural

Tracer. Boundary-Layer Meteorol., 149, 381-

402, 2013)

Comparing radon-derived stability categories

with P-G and RiBulk categorisation

15 17 19 21 23 1 3 5 7 9 11 13

0

1

2

3

4

5

6

7

WinterSummer

Pa

sq

uill

-Giffo

rd (

rad

iatio

n)

iPG near neutral

iPG weakly stable

iPG moderately stable

iPG stable

(a)

15 17 19 21 23 1 3 5 7 9 11 13

0

1

2

3

4

5

6

7

(b)

iPG near neutral

iPG weakly stable

iPG moderately stable

iPG stable

15 17 19 21 23 1 3 5 7 9 11 13-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

(c)

Bu

lk R

ich

ard

so

n N

um

be

r


RiB near neutral

RiB weakly stable

RiB moderately stable

RiB stable

15 17 19 21 23 1 3 5 7 9 11 13-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

RiC

(d)

near neutral

weakly stable

moderately stable

stable

Chambers, S.D., et al., 2016: Atmospheric stability effects on potential radiological releases at a nuclear

research facility in Romania: Characterising the atmospheric mixing state. J. of Environ. Radioact., 154, 68-82.

Characterising diurnal pollutant cycles

Pasquil-Gifford vs Radon-based stability typingPG-turbulence scheme based on WD and mean wind speed

Nocturnal categories: D - neutral, E - moderately stable, F - stable

16 18 20 22 0 2 4 6 8 10 12 140

10

20

30

40

Ozone (

ppb)

16 18 20 22 0 2 4 6 8 10 12 140.0

0.1

0.2

0.3

0.4

SO

2 (

ppb)

16 18 20 22 0 2 4 6 8 10 12 140

2

4

6

8

10

"D" (neutral)

"E" (mod. stable)

"F" (very stable)

PM

2.5 (

g m

-3)

PG

-T

16 18 20 22 0 2 4 6 8 10 120

2

4

6

8

10

12

14

16

NO

(ppb)

16 18 20 22 0 2 4 6 8 10 120

2

4

6

8

10

12

14

NO

2 (

ppb)

16 18 20 22 0 2 4 6 8 10 120

5

10

15

20

25

30

35

Ozo

ne (

ppb)

16 18 20 22 0 2 4 6 8 10 120.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

SO

2 (

ppb)

16 18 20 22 0 2 4 6 8 10 12

2

4

6

8

10

12

PM

2.5

(

g m

-3)


Near neutral

Weakly stable

Moderately stable

Stable

16 18 20 22 0 2 4 6 8 10 120

2

4

6

8

10

12

14

16

NO

(p

pb

)

16 18 20 22 0 2 4 6 8 10 120

2

4

6

8

10

12

14

NO

2 (

pp

b)

16 18 20 22 0 2 4 6 8 10 120

5

10

15

20

25

30

35

Ozo

ne (

pp

b)

16 18 20 22 0 2 4 6 8 10 120.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

SO

2 (

pp

b)

16 18 20 22 0 2 4 6 8 10 12

2

4

6

8

10

12

PM

2.5

(

g m

-3)


Near neutral

Weakly stable

Moderately stable

Stable

Rad

on

PG-T

Distinction between

categories less clear.

Peak concentrations

under stable

conditions

underestimated


Influence of stability on nocturnal mixing depth

The change in radon (C) in the NBL is a balance between

flux (F), decay () and dilution (D).

dC F C Dhdt

Iterative solution for h: equivalent mixing depth (he)

Analytical solution for h: accumulated mixing height (hacc)16 18 20 22 0 2 4 6 8 10 12

0

5

10

15

20

25

Radon g

radie

nt (B

q m

-3)


Near Neutral

Weakly stable

Moderately Stable

Stable

450m

30m

10

100

1000

Stable StableModerately

Stable

Wealky

Stable

Near

Neutral

Near

Neutral

He

ight (m

) agl

Stability / Mixing category

hacc

Wealky

Stable

Moderately

Stable

10

100

1000

he

10th perc.

median

90th per.


F ~ 20 mBq/m2/s

National Institute for Research and Development in

Physics and Nuclear Engineering (IFIN-HH)

IFIN-HH: 10km SW Bucharest, urban-rural landscape, observations from 60m tower, 1 km exclusion

zone, roughness elements 10-15m, challenging fetch for conventional stability typing.

IFIN-HH

Roughness map around IFIN-HH site

Seasonality of potential extreme events

Considering ONLY the most stable

nocturnal atmospheric conditions:

(a) PG scheme reports 20-25%

lower median concs of pollutants

with near-surface sources

(b) BUT - nocturnal mixing depth

under stable conditions 10-20m, and

the stack release height is >40m

0 3 6 9 12 15 180

50

100

150

200

Near

neutral

Weakly

stable

Moderately stable

Cum

ula

tive

fre

qu

en

cy

Mean nocturnal radon (Bq m-3)

Stable

(a)

16 18 20 22 0 2 4 6 8 10 12 140

4

8

12

16

20

24(b)

Rad

on

(B

q m

-3)


Near-neutral

Weakly stable

Moderately stable

StableDespite the non-ideal fetch

conditions, radon-derived stability

categories were easily assigned

16 18 20 22 0 2 4 6 8 10 120

10

20

30

40

50

Win

ter

Ra

do

n (

Bq m

-3)

Su

mm

er

(a) Radon "stable" category

16 18 20 22 0 2 4 6 8 10 120

10

20

30

40

50

(b) PG-R "stable" category

10th percentile

50th percentile

90th percentile

16 18 20 22 0 2 4 6 8 10 120

10

20

30

40

50

(c) Radon "stable" category


16 18 20 22 0 2 4 6 8 10 120

10

20

30

40

50

(d) PG-R "stable" category

Radon-based Pasquil-Gifford

Chambers, S.D., et al., 2016: Atmospheric stability

effects on potential radiological releases at a nuclear

research facility in Romania: Characterising the

atmospheric mixing state. Journal of Environmental

Radioactivity, 154, 68-82.

Conclusions

• Radon is a powerful and comparatively economical tool for

atmospheric stability analysis of pollution / release concs.

• Can be used independently of site meteorology

• Rn-based stability analysis of urban pollution superior to conventional

techniques particularly in conditions of non-ideal fetch

• Day/night (12-hr) Rn-based stability categories can be provided (like

PG classes) for routine dispersion modelling purposes

• Long-term characterisation of pollution by Rn-derived stability

category is also ideal for:

(1) evaluating the efficacy of emission mitigation strategies, and

(2) providing benchmarks for evaluating CTMs

Thank youRecent publications related to this presentation

Chambers, S.D., et al., 2011: Separating remote fetch and local mixing influences on vertical radon measurements in the lower

atmosphere. Tellus 63B, 843-859.

Williams, A.G., et al., 2013. Bulk Mixing and Decoupling of the Nocturnal Stable Boundary Layer Characterized Using a Ubiquitous

Natural Tracer. Bound.-Lay. Meteorol., 149, 381–402.

Chambers, S.D., et al., 2015: On the use of radon for quantifying the effects of atmospheric stability on urban emissions. Atmos.

Chem. Phys., 15, 1175-1190.

Chambers, S.D., et al., 2015: Quantifying the influences of atmospheric stability on air pollution in Lanzhou, China, using a radon-

based stability monitor. Atmos. Environ., 107, 233-243.

Crawford, J., et al., 2016. Assessing the impact of atmospheric stability on primary and secondary aerosols at Richmond, Australia,

using Radon-222. Atmos. Environ., 127, 107-117.

Chambers, S.D., et al., 2016: Atmospheric stability effects on potential radiological releases at a nuclear research facility in Romania:

Characterising the atmospheric mixing state. Journal of Environmental Radioactivity, 154, 68-82.

Williams, A.G., et al., 2016. Radon as a tracer of atmospheric influences on traffic-related air pollution in a small inland city. Tellus B,

submitted January 2016.

Poland (Łódź): urban heat island studies

16 18 20 22 0 2 4 6 8 10 12 14-2

-1

0

1

2

3

4

5

UH

I (U

rban

2m -

Ru

ral 2

m;

oC

)

(a) Summer

16 18 20 22 0 2 4 6 8 10 12 14-2

-1

0

1

2

3

4

5

(b) Autumn Near neutral

Moderately mixed

Weakly mixed

Stable

16 18 20 22 0 2 4 6 8 10 12 14-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

(c) Winter


16 18 20 22 0 2 4 6 8 10 12 14-2

-1

0

1

2

3

4

5

(d) Spring

Stability affect on Urban Heat Island intensity

Urban Heat Island

Intensity depends

strongly on the

regional stability

(derived by radon)

Stability affects on site meteorology

0.0

0.5

1.0

1.5

2.0

Ciosny (Rural)

(a) Summer

0.0

0.5

1.0

1.5

2.0

Lodz (Urban)

(b) Summer

0.0

0.5

1.0

1.5

2.0

2.5

(c) Autumn

2m

win

d s

peed (

m s

-1)

0.0

0.5

1.0

1.5

2.0

2.5

(d) Autumn

1.0

1.2

1.4

1.6

1.8

2.0

2.2

(e) Winter

Near neutral

Moderately mixed

Weakly mixed

Stable

1.0

1.2

1.4

1.6

1.8

2.0

2.2

(f) Winter

16 18 20 22 0 2 4 6 8 10 12 140.0

0.5

1.0

1.5

2.0

2.5

(g) Spring


16 18 20 22 0 2 4 6 8 10 12 140.0

0.5

1.0

1.5

2.0

2.5

(h) Spring

-3

-2

-1

0

1

2

3

4

Lodz (Urban)Ciosny (Rural)

(h) Spring(g) Spring

(f) Winter(e) Winter

(d) Autumn(c) Autumn

(b) Summer(a) Summer

-3

-2

-1

0

1

2

3

4

-2

-1

0

1

2

3

Te

mp

era

ture

gra

die

nt (2

m -

0.2

m;

oC

)

-2

-1

0

1

2

3

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

Near neutral

Moderately mixed

Weakly mixed

Stable

16 18 20 22 0 2 4 6 8 10 12 14-3

-2

-1

0

1

2

3

4


16 18 20 22 0 2 4 6 8 10 12 14-3

-2

-1

0

1

2

3

4

Stability affects on urban meteorology

0.6 0.8 1.0 1.2 1.40

1

2

3

4

5

(b) Summer

UHI = 0.363 U-4.235

UH

I (o

C)

Lodz wind speed (m s-1)

(a) Spring

UHI = 0.9911 U-2.924

0.6 0.8 1.00

1

2

3

4

5

19 20 21 22 23 0 1 2 3 4 5 6 7 8 90

20

40

60

80

100

120

140

Eq

uiv

ale

nt m

ixin

g h

eig

ht (m

)

Hour of day

Moderately Mixed

Weakly Mixed

Stable

(a) Ciosny

19 20 21 22 23 0 1 2 3 4 5 6 7 8 90

20

40

60

80

100

120

140

(b) Lodz

Lodz nocturnal wind direction, SpringLodz

Gentle

slope

Documents

Radon-based assessment of stability affects on potential ... · Radon-based assessment of stability affects on potential radiological releases Scott Chambers 1, Alastair Williams