1. Application of synoptic typing to an investigation of nocturnal ozone concentration at a maritime location, New Zealand B. A. Khan 1, C.R. de Freitas 1 * and D. Shooter 1 1 School of Geography, Geology and Environmental Science, The University of Auckland, New Zealand. *Corresponding author: c.defreitas@auckland.ac.nz

2. 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

3. - Ozone (O 3 ) and NO 2 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 Background

4. Examine relationships between synoptic conditions and nocturnal ozone Procedure - Define the prevailing weather types in winter at night - Examine the effect of those weather types on O 3 and NO x concentration - Examine the contribution of background O 3 at night time

5. - 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 NO 2 O 3

6. Musick Point

7. Deposition NO NO 2 NO 3 N2O5N2O5 HNO 3 Organic Nitrates NO, hv NO x NO 2 O 3, RO 2 O3O3 VOC H 2 O (Het) OH ∆ hv O  O 3 O2O2 Atmospheric Chemistry of NO x and O 3 Adopted from Brown et al. (2004) Red arrows indicate reactions that require sunlight while black arrows indicate reactions that do not NO + O 3  NO 2 + O 2 NO 2 + O 3  NO 3 + O 2 NO 2 + NO 3 +(M)   N 2 O 5 +(M) N 2 O 5 +H 2 O(het)  2HNO 3

8. An integrated approach was adopted, addressing a weather condition/type instead of individual meteorological parameters Analysis approach - Principal Component Analysis (PCA) - Principal Component Regression - Weather maps for validation of synoptic regimes Exploratory analysis method

9. Daily average night-time data of winter 2004-2005 Preliminary Analysis PCA PCs accounting for highest variation in the data Regression Analysis Dominant weather types Synoptic Classes Relationship between synoptic classes and O 3 Var. Transformation O 3 and NO x regressed on PCs Reg with data allocated to WD Analysis of PCs score Verif. weather maps

10. VariableMinimumMaximumMeanStd. Deviation O 3 (µg m -3 )2.9395.6650.6418.72 NO (µg m -3 ).0066.362.606.96 NO 2 (µg m -3 ).3347.5512.4010.47 Air temperature ( o C)6.720.1012.282.81 Wind speed (m s -1 )1.0013.053.811.96 Relative humidity (%)62.4996.3482.826.42 Vapour pressure (hPa)6.7021.7312.072.80 Descriptive statistics

11. Wind frequency in various quadrants 80% wind frequency from W, SW and S (Urban/land winds) 20% wind frequency from N, NE, E (Maritime winds)

12. Ozone (O 3 ) and NO 2 concentration in various wind quadrants

13. Variable Component PC1: Intermediate Cond. PC2: Stable and Unstable Cond. v-component0.8290.260 u-component0.712-0.070 Relative humidity0.767-0.193 Temperature0.5720.506 Wind speed-0.1210.915 Rotated component matrix (loading matrix)

14. Synoptic class ReferenceWeather type Anticyclonic 1 Weak south-westerlies flow with low temperature and moisture content. 2 Weak easterly flow with average temperature and moisture content. Cyclonic 3 Strong south-westerlies with average temperature and moisture content. 4 Strong north-easterly and easterlies with relatively high temperature and moisture content. Intermediate 5 Moderate southerly, south-westerly and westerly winds with relatively low temperature and moisture content. 6 Moderate south-westerlies with average temperature and moisture content. 7 Moderate north-easterlies with average temperature and moisture content.

15. Regression Model Dependent variableR2R2 Adjusted R 2 PC1: Intermediate condition PC2: Calm/Unstable condition R 2 change Standardized coefficients β R 2 change Standardized coefficients β 1 O3O3 0.2570.2520.0470.2170.2100.458 2NO 2 0.4570.4530.149-0.3860.308-0.555 Regression models for O 3 and NO 2

16. Scatter plots of predicted and observed average daily concentration of O 3 and NO 2

17. Synoptic class Weather type Wind direction Wind speedTemp.HumidityO3O3 NO 2 NO Anticyclonic 1SWWeakLLLHH 2E MMHL0 Cyclonic 3SWStrongMMHL0 4NE, EStrongHHHL0 5S, SW,WModerateLLMHM Intermediate6SWModerateMMMML 7NEModerateMMHL0 Synoptic classes with corresponding 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.

18. - Main process is titration of O 3 by NO - Dry deposition NO x destruction processes Ozone destruction processes Important meteorological variables - O 3 : Wind speed and wind direction - NO x : Wind speed, wind direction and temperature - Hydrolysis of N 2 O 5 (dinitrogen pentodxide) by heterogeneous gas phase reaction

19. Sources of human induced O 3 - Traffic - Industry - Home heating Sources of background O 3 - Maritime winds - Wind gusts - Thunderstorms

20. Study identified three synoptic classes consisting of seven weather types. Most of the variation in O 3 and NO x 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 NO x than O 3. Background O 3 is an important contributor to total O 3 in Auckland region Temperature and humidity had no effect on nocturnal O 3 concentration, but contribute to NO x concentration at night Thunderstorms and wind gusts appears to be important processes for down welling of O 3 from upper boundary layer.

21. It is difficult to characterise NO x and O 3 chemistry using a single measurement site. A more comprehensive study is required using data from several sites. Future Work

22. Acknowledgement We are grateful to the Auckland Regional Council and Janet Peterson for providing meteorological and air pollutants data from Musick Point monitoring station.