Application of synoptic typing to an

investigation of nocturnal ozone concentration

at a maritime location, New Zealand

B. A. Khan1, C.R. de Freitas1* and D. Shooter1

1School of Geography, Geology and Environmental Science, The University of Auckland, New Zealand.

*Corresponding author: c.defreitas@auckland.ac.nz

The paper presented at a conference

Resource Management under stormy skies: Water Allocation @ the crossroads?

Held in

Christchurch, New Zealand

20-Nov-2006 to 23-Nov-2006

At University of Canterbury, ChCh

- Ozone (O3) and NO2 are important components of photochemical smog

- Physical and chemical processes at night affect the photochemical oxidant’s concentration the following day

- Meteorological conditions and synoptic situations at night are important with respect to ...

• Dispersion• Destruction• Advection• Chemical reactions

… of pollutants

- Most of research on photochemical oxidants has focused on the day time

processes

BackgroundBackground

NOCTURNAL OZONENOCTURNAL OZONE

Examine relationships between synoptic conditions and nocturnal ozone

ProcedureProcedure

- Define the prevailing weather types in winter at night

- Examine the effect of those weather types on O3 and NOx concentration

- Examine the contribution of background O3 at night time

MAIN AIMMAIN AIM

- Study area: Auckland region

- Monitoring site: • Musick Point; east coast of Auckland region

- Data: • Winter, 19:00 to 06:00 hours for 2004 and 2005

- Variables measured

• Wind speed

• Wind direction

• Relative humidity

• Temperature

• NO

• NO2

• O3

STUDY SITESTUDY SITE

STUDY SITE STUDY SITE Musick PointMusick Point

Musick Point

NN

NN

Deposition

NO

NO2

NO3

N2O5

HNO3

Organic Nitrates

NO, hv

NOx

NO2

O3, RO2

O3

VOC

H2O (Het)

OH∆

hvO O3

O2

Atmospheric Chemistry Atmospheric Chemistry of NOof NOxx and O and O33

NOCTURNAL OZONENOCTURNAL OZONE

Adopted from Brown et al. (2004)

Red arrows indicate reactions that require sunlight while black

arrows indicate reactions that do not

NO + O3 NO2 + O2

NO2 + O3 NO3 + O2

NO2 + NO3 +(M) N2O5 +(M)

N2O5 +H2O(het) 2HNO3

An integrated approach was adopted, addressing a weather condition/type

instead of individual meteorological parameters

Analysis approachAnalysis approach

- Principal Component Analysis (PCA)

- Principal Component Regression

- Weather maps for validation of synoptic regimes

Exploratory analysis methodExploratory analysis method

METHODMETHOD

METHODMETHOD

Daily average night-timedata of winter 2004-2005

Preliminary Analysis

PCA

PCs accounting for highest variation in the

data

Regression AnalysisDominant weather

types

Synoptic Classes

Relationship between synoptic

classes and O3

Var. Transformation

O3 and NOx regressed on PCs

Reg with data allocated to WD

Analysis of PCs score

Verif. weather maps

Variable Minimum Maximum Mean Std. Deviation

O3 (µg m-3) 2.93 95.66 50.64 18.72

NO (µg m-3) .00 66.36 2.60 6.96

NO2 (µg m-3) .33 47.55 12.40 10.47

Air temperature (oC) 6.7 20.10 12.28 2.81

Wind speed (m s-1) 1.00 13.05 3.81 1.96

Relative humidity (%) 62.49 96.34 82.82 6.42

Vapour pressure (hPa) 6.70 21.73 12.07 2.80

Descriptive statisticsDescriptive statistics

PRELIMINARY ANALYSISPRELIMINARY ANALYSIS

Wind frequency in various quadrantsWind frequency in various quadrants

80% wind frequency from W, SW and S (Urban/land winds)

20% wind frequency from N, NE, E (Maritime winds)

PRELIMINARY ANALYSISPRELIMINARY ANALYSIS

Northeast

EastSoutheast

South

Southwest

West

Northwest

WD Quadrants

40.0

50.0

60.0

70.0

Me

an

O3

µg

/m3

Northeast

EastSoutheast

South

Southwest

West

Northwest

WD Quadrants

0.0

5.0

10.0

15.0

20.0

Me

an

NO

2 µ

g/m

3

Ozone (OOzone (O33) and NO) and NO22 concentration in various wind concentration in various wind

quadrantsquadrants

PRELIMINARY ANALYSISPRELIMINARY ANALYSIS

Variable

Component

PC1:Intermediate Cond.

PC2:Stable and

Unstable Cond.

v-component 0.829 0.260

u-component 0.712 -0.070

Relative humidity 0.767 -0.193

Temperature 0.572 0.506

Wind speed -0.121 0.915

Rotated component matrix (loading matrix) Rotated component matrix (loading matrix)

PRINCIPAL COMPONENT ANALYSIS (PCA)PRINCIPAL COMPONENT ANALYSIS (PCA)

Synoptic classReference Weather type

Anticyclonic

1Weak south-westerlies flow with low temperature and moisture content.

2Weak easterly flow with average temperature and moisture content.

Cyclonic

3Strong south-westerlies with average temperature and moisture content.

4Strong north-easterly and easterlies with relatively high temperature and moisture content.

Intermediate

5Moderate southerly, south-westerly and westerly winds with relatively low temperature and moisture content.

6Moderate south-westerlies with average temperature and moisture content.

7Moderate north-easterlies with average temperature and moisture content.

PRINCIPAL COMPONENT ANALYSIS (PCA)PRINCIPAL COMPONENT ANALYSIS (PCA)

Regression Model

Dependent variable R2

Adjusted R2

PC1: Intermediate condition

PC2: Calm/Unstable condition

R2

changeStandardized coefficients β

R2 change

Standardized coefficients β

1 O3 0.257 0.252 0.047 0.217 0.210 0.458

2 NO2 0.457 0.453 0.149 -0.386 0.308 -0.555

Regression models for ORegression models for O33 and NO and NO22

PRINCIPAL COMPONENT REGRESSION (PCR)PRINCIPAL COMPONENT REGRESSION (PCR)

0.0 20.0 40.0 60.0 80.0 100.0

Dependent Variable: O3µg/m3

30.0

40.0

50.0

60.0

70.0

80.0

90.0

Pre

dic

ted

Va

lue

of

O3

µg

/m3

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

Dependent Variable: t.NO2

-2.0

0.0

2.0

4.0

6.0

Pre

dic

ted

Va

lue

of

t.N

O2

Scatter plots of predicted and observed average daily Scatter plots of predicted and observed average daily concentration of Oconcentration of O3 3 and NOand NO22

PRINCIPAL COMPONENT REGRESSION (PCR)PRINCIPAL COMPONENT REGRESSION (PCR)

Synoptic class

Weather type

Winddirection

Wind speed Temp. Humidity O3 NO2 NO

Anticyclonic1 SW Weak L L L H H

2 E Weak M M H L 0

Cyclonic 3 SW Strong M M H L 0

4 NE, E Strong H H H L 0

5 S, SW,W Moderate L L M H M

Intermediate 6 SW Moderate M M M M L

7 NE Moderate M M H L 0

Synoptic classes with corresponding Synoptic classes with corresponding descriptions of seven weather types. descriptions of seven weather types.

H = high or above average; L = low or below average; M = medium or above average. NO = 0 = less than 1μgm-3.

SYNOPTIC TYPINGSYNOPTIC TYPING

- Main process is titration of O3 by NO

- Dry deposition

NONOxx destruction processes destruction processes

Ozone destruction processesOzone destruction processes

Important meteorological variablesImportant meteorological variables

- O3: Wind speed and wind direction

- NOx: Wind speed, wind direction and temperature

- Hydrolysis of N2O5 (dinitrogen pentodxide) by heterogeneous gas phase reaction

SYNOPTIC TYPINGSYNOPTIC TYPING

Sources of human induced O3

- Traffic

- Industry

- Home heating

Sources of background O3

- Maritime winds

- Wind gusts

- Thunderstorms

SOURCES OF OSOURCES OF O33

Study identified three synoptic classes consisting of seven weather types.

Most of the variation in O3 and NOx was observed during cyclonic and anticyclonic conditions.

Ozone was high in maritime wind flows and cyclonic condition while NOx was high in anticyclonic weather types related to urban wind flows

Effect of weather types varied with pollutant: greater effect on NOx than O3.

Background O3 is an important contributor to total O3 in Auckland region

Temperature and humidity had no effect on nocturnal O3 concentration, but contribute to NOx concentration at night

Thunderstorms and wind gusts appears to be important processes for down welling of O3 from upper boundary layer.

CONCLUSIONCONCLUSION

It is difficult to characterise NOx and O3 chemistry using a single

measurement site. A more comprehensive study is required

using data from several sites.

Future WorkFuture Work

CONCLUSIONCONCLUSION

AcknowledgementAcknowledgement

We are grateful to the Auckland Regional Council and

Janet Peterson for providing meteorological and air

pollutants data from Musick Point monitoring station.