Air Quality and Climate Variability in the Southwestern United States

Transcription

1 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

2 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

3 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

4 Study Area Albuquerque, NM El Paso, TX Las Vegas, NV Phoenix, AZ Tucson, AZ Source: Liverman and Merideth 2002

5 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

6 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: Background Data and Methods Results Conclusions

7 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

8 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

9 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

10 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

11 Ozone Separation by KZ Filter Ozone Separation by KZ Filter Original Time Series Weather Year Year Seasonal Variation Emissions Year Year Seasonal Ozone Ozone (ppb) Long-Term Ozone Short-Term Ozone

12 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

13 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

14 Baseline Component KZ (15,5) Seasonal Ozone Long-Term Ozone Baseline Ozone = Seasonal Temp Long-Term Temp Baseline Temp =

15 Short-term ozone Short-term temperature REGRESSION REGRESSION Baseline ozone Baseline temperature Short-Term Residuals + Baseline Residuals Total Residuals ε(t)

16 KZ (365,3) Total Residuals ε (t) ε (t) Temp ozone Adjusted trend Year -8 Background Data and Methods Results Conclusions

17 (KZ (365,3) Air Pollutant Data - KZ (365,3) Total Residuals) ε ε (t) (t) Temp ozone Adjusted trend Year Background Data and Methods Results Conclusions

18 Results Meteorological controls on air quality

19 100% Percent Ozone Variance Explained by Meteorology UTEP Station, El Paso, TX 90% 80% 70% 60% Model 4 R2 = 0.46 Model 3 R2 = % 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

20 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

21 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

22 Results KZ filter and trends seattlepi.nwsource.com

23 PM10 (ug/m 3 ) 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

24 PM10 (ug/m 3 ) Modeled and Unadjusted PM 10 Long-Term Trends Orange Grove Station, Tucson, AZ Model 1 ( - RH) Unadjusted Long-Term PM RH average RH Relative Humidity (%) Year 30

25 Ozone - City Center, Las Vegas, NV Model 2 ( - MH/Temp) Ozone (ppb) PM - East Craig Station, Las Vegas, NV Model 2 ( - RH/DP) Year Year 1990 PM10 (ug/m 3 )

26 Unadjusted Long-Term Ozone Trends 55 Ozone (ppb) 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

27 Adjusted Ozone Trends 55 Ozone (ppb) 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

28 Unadjusted Long-Term PM Trends 50 PM (ug/m3) 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

29 Adjusted PM 10 Trends 50 PM10 (ug/m 3 ) 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

30 Albuquerque Ozone (ppb) San Mateo Station, Albuquerque, NM a) Tramway Station, Albuquerque, NM b) nd St. Station, Albuquerque, NM San Mateo Station, Albuquerque, NM PM µg/m a) b)

31 El Paso El Paso Ozone (ppb) c) Chamizal Station, El Paso, TX d) UTEP station, El Paso, TX Tillman Station, El Paso, TX Riverside Station, El Paso, TX PM µg/m c) d)

32 Las Vegas Las Vegas Ozone (ppb) Winterwood Station, Las Vegas, NV e) f) City Center Station, Las Vegas, NV PM µg/m e) Spring Valley Station, Las Vegas, NV f) East Craig Station, Las Vegas, NV

33 Phoenix Ozone (ppb) g) Central Phoenix Station, Phoenix, AZ h) West Phoenix Station, Phoenix, AZ Central Phoenix Station, Phoenix, AZ North Phoenix Station, Phoenix, AZ g) h) PM µg/m 3

34 Tucson Ozone (ppb) i) Craycroft Station, Tucson, AZ j) Saguaro N.P. Station, Tucson, AZ PM µg/m i) Downtown Station, Tucson, AZ j) Orange Grove Station, Tucson, AZ

35 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

36 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

37 Thank you! hikeearizona.com Questions?