Air Quality and Climate Variability in the Southwestern United States Erika K. Wise Andrew C. Comrie Department of Geography and Regional Development University of Arizona Air Quality Forum Tucson, AZ December 7, 2004
Project Context CLIMAS Established to assess the impacts of climate variability change on human and natural systems in the Southwest Bring together researchers with individuals and organizations who utilize climate information Goal of the air quality and climate initiative to provide better information on meteorological influences on air quality
Project Goals Local climate and weather conditions often determine whether federal standards are violated in Southwest cities Changes due to emissions are easily masked by meteorological variability; must be separated to understand underlying trends Identify links between tropospheric ozone and particulate matter air quality and meteorological controls in the Southwest Remove the effects of those meteorological controls to examine underlying, emissions-controlled air quality trends Extend current use of KZ filter
Study Area Albuquerque, NM El Paso, TX Las Vegas, NV Phoenix, AZ Tucson, AZ Source: Liverman and Merideth 2002
Previous Studies Ozone Ibarra et al () Milanchus et al () Vingarzan &Taylor (2003) Vukovich and Sherwell (2003) Flaum et al () Tarasora & Karpetchka (2003) Spain Eastern US & LA Vancouver, BC Baltimore-DC Eastern US Russia temp solar radiation temp temp temp temp solar radiation humidity TSUN dew point temp dew point temp humidity wind speed dew point depression wind speed solar radiation humidity wind speed dew point temp temp wind speed wind speed pressure pressure humidity dew point temp wind speed wind direction mixing height PM Triantafyllou et al (2002) Davis and Gay () Greece Grand Canyon wind speed mixing height humidity precipitation solar radiation Ozone: Temp/solar radiation; wind speed; moisture PM: Stagnant conditions, wind speed, moisture
Data Meteorological data National Climatic Data Center Daily values of temperature, wind speed, mixing height, total sunshine, precipitation, relative humidity, and dew point temperature Ozone and PM data Local, state, and federal environmental agencies Ozone: maximum daily 8-hr 8 average concentrations PM: 24-hour average concentrations of PM 10 2 ozone and 2 PM stations for each city Time period: 1990-2003 Background Data and Methods Results Conclusions
Choice of analysis techniques Principal component analysis Fourier transform Morlet wavelet transform Parameter estimation algorithm Monthly anomaly technique Artificial neural network model Kolmogorov-Zurbenko (KZ) filter Background Data and Methods Results Conclusions
Kolmogorov-Zurbenko (KZ) filter Rao-Zurbenko Method (): Time series of an air pollutant can be represented by: O(t) ) = e(t) ) + S(t) ) + W(t) where O(t) ) is the original time series e(t) ) is the long-term trend component - variation due to long-term climate change or policy S(t) ) is seasonal variation - change in solar angle W(t) ) is the short-term term component - change attributable to weather Background Data and Methods Results Conclusions
Kolmogorov-Zurbenko (KZ) filter Low-pass filter - repeated iterations of a moving average Component removed depends on length of window and number of iterations Meteorologically-adjusted adjusted air quality trend produced through regressions on the components of the air pollutant and one or more meteorological variables Widely-used method for ozone air quality studies Background Data and Methods Results Conclusions
Extension of KZ Filter PM Ozone has been focus of method Application Produce results with real numbers Southwest Previous studies focused on Eastern US Different synoptic controls
Ozone Separation by KZ Filter Ozone Separation by KZ Filter 100 90 80 70 60 50 40 30 20 10 0 Original Time Series 40 30 20 10 0-10 -20-30 -40 Weather 1990 2002 2003 1990 2002 2003 Year Year 25 20 15 10 5 0-5 -10-15 -20-25 Seasonal Variation 48 47 46 45 44 43 42 41 Emissions 1990 2002 2003 1990 2002 2003 Year Year Seasonal Ozone Ozone (ppb) Long-Term Ozone Short-Term Ozone
Relative Contributions of the Three Components of the Ozone Time Series to the Total Variation of the Data Set Long-Term Short- Short-Term Seasonal Background Data and Methods Results Conclusions
KZ Filter - Steps 1. Determine meteorological models 2. Separate each air pollutant and meteorological variable time series into its temporal components 3. Run regressions between the baseline and short-term term components of pollutant and meteorological variable Background Data and Methods Results Conclusions
1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 1990 2002 0.6 0.4 0.2 0 25 20 15 10 5 0-5 -10-15 -20-25 -30-0.2-0.4-0.6-0.8 Baseline Component KZ (15,5) Seasonal Ozone Long-Term Ozone Baseline Ozone + -3.02-3.04-3.06-3.08-3.1-3.12-3.14-3.16-3.18-3.2-3.22 = -2-2.5-3 -3.5-4 -4.5 Seasonal Temp Long-Term Temp Baseline Temp 120 86 110 + 85 84 83 82 = 100 90 80 70 60 81 50 80 40
1990 2002 120 110 100 90 80 70 60 50 40 1990 2002 1990 2002 1990 2002-2 -2.5-3 -3.5-4 -4.5 1 0.5 0-0.5-1 -1.5-2 Short-term ozone Short-term temperature 30 20 10 0-10 -20-30 -40 REGRESSION REGRESSION Baseline ozone Baseline temperature Short-Term Residuals + Baseline Residuals Total Residuals ε(t)
KZ (365,3) Total Residuals 0.6 0.4 0.2 0-0.2-0.4-0.6 ε (t) ε (t) Temp ozone Adjusted trend 6 4 2 0-2 -4-6 -0.8 1990 2002 Year -8 Background Data and Methods Results Conclusions
(KZ (365,3) Air Pollutant Data - KZ (365,3) Total Residuals) -3.02-3.04-3.06-3.08-3.1-3.12-3.14-3.16-3.18-3.2-3.22 1990 2002-0.6 ε ε (t) (t) Temp ozone Adjusted trend 0.4 0.2 0-0.2-0.4-0.6-0.8 1990 2002 Year 6 4 2 0-2 -4-6 -8 Background Data and Methods Results Conclusions
Results Meteorological controls on air quality www.bluecorncomics.com
100% Percent Ozone Variance Explained by Meteorology UTEP Station, El Paso, TX 90% 80% 70% 60% Model 4 R2 = 0.46 Model 3 R2 = 0.45 50% 40% 52% 51% 48% 46% 45% Model 2 R2 = 0.44 Model 1 R2 = 0.41 MH Mixing Height Temp Temperature TSUN Solar Radiation Dew Point Dew Point Temperature RH Relative Humidity AWND Wind Speed Precip Precipitation 41% 30% 25% 20% 10% 0% Temp/MH/AWND/ RH Temp/MH/AWND Temp/MH MH Temperature TSUN Dew Point 10% 9% RH AWND 2% Precip
Meteorological Controls - Ozone Albuquerque temperature, mixing height, solar radiation El Paso temperature, mixing height Las Vegas mixing height, temperature Phoenix: temperature, mixing height Tucson mixing height, solar radiation, temperature
Albuquerque: relative humidity, mixing height, wind speed El Paso: relative humidity, wind speed Las Vegas: relative humidity, temperature Phoenix: relative humidity, wind speed Tucson: Meteorological Controls - PM relative humidity, mixing height
Results KZ filter and trends seattlepi.nwsource.com
PM10 (ug/m 3 ) 43 41 39 37 35 33 31 29 27 Modeled and Unadjusted PM 10 Long-Term Trends Orange Grove Station, Tucson, AZ Model 1 ( - RH) Model 2 ( - RH/MH) Model 3 ( - RH/MH/AWND) Model 4 ( - RH/MH/AWND/TSUN) Unadjusted Long-Term PM RH Relative Humidity MH Mixing Height AWND Wind Speed TSUN Solar Radiation 25 Year A LTadj = A LT - ε(t) LT
PM10 (ug/m 3 ) 43 41 39 37 35 33 31 29 Modeled and Unadjusted PM 10 Long-Term Trends Orange Grove Station, Tucson, AZ Model 1 ( - RH) Unadjusted Long-Term PM RH average RH 50 48 46 44 42 40 38 36 34 Relative Humidity (%) 27 32 25 Year 30
2002 2003 Ozone - City Center, Las Vegas, NV 50 40 30 20 10 0-10 -20-30 -40-50 Model 2 ( - MH/Temp) Ozone (ppb) 1990 4 3 2 1 0-1 -2-3 -4 PM - East Craig Station, Las Vegas, NV Model 2 ( - RH/DP) 2002 2003 Year Year 1990 PM10 (ug/m 3 )
Unadjusted Long-Term Ozone Trends 55 Ozone (ppb) 50 45 40 35 Albuquerque 1 (SM) Albuquerque 2 (TW) El Paso 1 (Cham) El Paso 2 (UTEP) Las Vegas 1 (City) Las Vegas 2 (WW) Phoenix 1 (CP) Phoenix 2 (WP) Tucson 1 (CC) Tucson 2 (SNP) 30 Year
Adjusted Ozone Trends 55 Ozone (ppb) 50 45 40 35 Albuquerque 1 (SM) Albuquerque 2 (TW) El Paso 1 (Cham) El Paso 2 (UTEP) Las Vegas 1 (City) Las Vegas 2 (WW) Phoenix 1 (CP) Phoenix 2 (WP) Tucson 1 (CC) Tucson 2 (SNP) 30 Year
Unadjusted Long-Term PM Trends 50 PM (ug/m3) 45 40 35 30 25 20 15 Albuquerque 1 (2ST) Albuquerque 2 (SM) El Paso 1 (RS) El Paso 2 (TM) Las Vegas 1 (EC) Las Vegas 2 (SV) Phoenix 1 (CP) Phoenix 2 (NP) Tucson 1 (DT) Tucson 2 (OG) 10 Year
Adjusted PM 10 Trends 50 PM10 (ug/m 3 ) 45 40 35 30 25 20 15 Albuquerque 1 (2ST) Albuquerque 2 (SM) El Paso 1 (RS) El Paso 2 (TM) Las Vegas 1 (EC) Las Vegas 2 (SV) Phoenix 1 (CP) Phoenix 2 (WP) Tucson 1 (DT) Tucson 2 (OG) 10 Year
Albuquerque Ozone (ppb) 53 48 43 38 San Mateo Station, Albuquerque, NM a) 53 48 43 38 Tramway Station, Albuquerque, NM b) 33 33 2nd St. Station, Albuquerque, NM San Mateo Station, Albuquerque, NM PM µg/m 3 44 39 34 29 24 19 14 a) 44 39 34 29 24 19 14 b)
El Paso El Paso Ozone (ppb) 53 48 43 38 c) Chamizal Station, El Paso, TX 53 48 43 38 d) UTEP station, El Paso, TX 33 33 Tillman Station, El Paso, TX Riverside Station, El Paso, TX PM µg/m 3 44 39 34 29 24 19 14 c) 34 32 30 28 26 24 22 d)
Las Vegas Las Vegas Ozone (ppb) 53 48 43 38 Winterwood Station, Las Vegas, NV e) 53 48 43 38 f) City Center Station, Las Vegas, NV 33 33 PM µg/m 3 44 39 34 29 24 19 14 e) Spring Valley Station, Las Vegas, NV 44 39 34 29 24 19 14 f) East Craig Station, Las Vegas, NV
Phoenix Ozone (ppb) 53 48 43 38 g) Central Phoenix Station, Phoenix, AZ 53 48 43 38 h) West Phoenix Station, Phoenix, AZ 33 33 Central Phoenix Station, Phoenix, AZ North Phoenix Station, Phoenix, AZ 44 39 34 29 24 19 14 g) 44 39 34 29 24 19 14 h) PM µg/m 3
Tucson Ozone (ppb) 53 51 49 47 45 43 41 39 37 35 33 i) Craycroft Station, Tucson, AZ 53 51 49 47 45 43 41 39 37 35 33 j) Saguaro N.P. Station, Tucson, AZ PM µg/m 3 44 39 34 29 24 19 14 i) Downtown Station, Tucson, AZ 44 39 34 29 24 19 14 j) Orange Grove Station, Tucson, AZ
Conclusions Controls and Trends Ozone model: Mixing height, temperature, and solar radiation Temperature dominates in Eastern US Ozone concentrations declining since early 1990s, but trend may be reversing PM model: Moisture variable most important; wind speed and mixing height secondary influences PM has stronger regional correlation High PM years, Steadily increasing over time Background Data and Methods Results Conclusions
Conclusions - Method KZ filter appears to be an appropriate method for both ozone and PM trend separation Ozone adjusted trends show very large meteorological influences PM not as weather-dependant Limitation: Extreme events Background Data and Methods Results Conclusions
Thank you! hikeearizona.com Questions?