Douw Steyn Department of Earth and Ocean Sciences The University of British Columbia Topic 5.2 “Case Study” :International Graduate Institute on Modelling Environmental Space-Time Processes University of Washington, July , 2007 Networks from another perspective OR “Well, that’s what Jim says ”

“As air quality monitoring networks have proliferated over the past, so have doubts arisen as to where, when and what to monitor” (A.C. Stern, 1976). Not much has changed in 30 years!

False Creek, early 1900s. Plumes from lumber mills.

Regional haze over Burnaby, early 1990s.

The GVRD air quality monitoring network

T1: Robson Square

T2: Kitsilano

T9: Rocky Point Park

T24: Kensington Park

Voronoi Analysis of Fixed Monitoring Network

1) Siting criteria for individual stations. 2) Overall Network structure. *********************************** For individual stations, must consider: The area that contributes significantly to concentrations measured at the station The area that the monitoring station can be said to represent

For the entire network, must consider the following network objectives: 1.Capture present conditions and spatio- temporal structures/trends 2.Regulatory control 3.Make short-term predictions 4.Capture effects of land-use strategies 5.Study dose-response relations 6.Provide input data for process based air quality models (Munn, R.E., 1978: The design of air quality monitoring networks. University of Toronto, Pub. # EE-7, 93p.)

Why monitor? JZ Lots of stuff about statistics, then: “address societal concerns” – politicians Then “detect non-compliance with regulatory standards” - regulators “to reduce uncertainty about some aspect of the world” - scientists

Why monitor? JZ (cont) to assess temporal trends –are things getting worse? –is climate changing?

Ozone and Emission Trends in LFV Number of hourly ozone concentrations > 82 ppb (diamonds), Annual calculated VOC emissions (solid line) Annual calculated NOx emissions (dotted line) VOC to NOx emissions ratio (dashed line)

Study of local meteorology during ozone episodes What are mesoscale wind regimes in LFV when peak hourly ozone exceeds NAAQO of 82 ppb? What synoptic flow regimes occur during these mesoscale wind regimes? Have emissions reductions over scale of decades changed ozone patterns for different meteorological regimes? Has population growth in the area significantly changed the spatial distribution of precursor emissions and hence the resulting ozone fields?

Episode selection criteria: At least 1 station in LFV has O3>82 ppb, AND low ( 24.7 deg C. Use statistical clustering techniques to find common circulation regimes. For each regime, study associated meso-scale wind fields, MSLP patterns and ozone footprint. Study Methods

Study Data Sets Hourly wind speed and direction data from YVR and YXX ( ), and BLI ( ). YXX daily maximum temperature and YVR daily total precipitation. Hourly ozone data from GVRD network at 34 different monitoring locations within the LFV. 135 ozone exceedance days meet this criterion in Hourly ozone data from US EPA for lone measuring station in Whatcom Co. Wa.

Plume Centroids To see how emissions reductions and changes in emission patterns may have altered ozone plume on exceedance days, calculate East-West (X c ) and North-South (Y c ) component of centre of mass of observed ozone plume: To see shift in plume, use multiple linear regression with mean wind speed (u or v) and number of days since April 1, 1984 (t):

We analyze only ozone plumes above background levels of 32 or 25 ppb. For all clusters and for both background levels, the Xc linear temporal dependence is always positive (centre of mass has moved eastward). At a 95% confidence level, only for clusters I, II and III is the change significant, independent of background concentration. For Yc, there does not appear to be a consistent trend.

Temporal Variability by Cluster A: Percent of summer days which are fair-weather days. B: Percent of fair-weather days that are exceedance days. (cluster I. pluses, II. Crosses III. triangles, IV. squares)

Conclusions Statistical clustering used to relate spatio-temporal ozone distributions to prevailing meteorological conditions. Observations over 24-hour period used to capture inter- dependence of precursor advection on ozone production. Classification identifies 4 common circulation patterns. Coincident with large drop in precursor emissions, proportion of fair-weather days which have ozone exceedances has decreased for all regimes.

Conclusions (cont.) Plume center of mass has shifted eastward over time Likely network not capturing significant portions of ozone plume Voronoi diagram of network shows station bias with more stations closer to city and fewer away from valley axis A complete description of interplay between emissions, ozone and local circulations regimes will require use of Eulerian grid modeling – presently underway (see next slide).

Modeled Ozone Plume

Then, hidden deep in the text is: “…implying that the optimum design must be regularly revisited” This is called “network review”. It is conducted about every decade, and is presently underway for GVRD network. Define Network Objectives Analyze data from individual stations Analyze network structure (add/remove/move stations)

Correlation matrix for summertime ozone at all GVRD stations

Clusters of GVRD stations based on summertime ozone correlation analysis

First attempts at redesign of GVRD monitoring network, based on ozone data