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Section 9 Pollutant Lifecycles and Trends

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1 Section 9 Pollutant Lifecycles and Trends
Definitions and Importance Multi-year (Long-term) Trends Seasonal Trends Short-term Changes

2 THE ATMOSPHERE: OXIDIZING MEDIUM IN GLOBAL BIOGEOCHEMICAL CYCLES
Oxidation Oxidized gas/ aerosol Reduced gas EARTH SURFACE Emission Uptake Reduction

3 Definitions and Importance
Trends are longer-term (multi-year) changes in air pollution caused by population and emissions changes Lifecycles are daily and episodic changes in pollution levels Episodes are several day events when air quality concentrations are high Importance to forecasting Determining how emissions changes affect air quality Knowing which pollutants occur in each season Understanding “typical” day-to-day changes Three time periods Long-term trends Seasonal trends Short-term lifecycles Day/night (diurnal) Day of week Multi-day Section 9 – Pollutant Lifecycles and Trends

4 Section 9 – Pollutant Lifecycles and Trends
Multi-year Trends Multi-year trends – Five or more years Affected by Emissions changes As emissions controls occur, pollutant levels typically decrease Similar weather conditions may not produce the same pollutant concentrations Year-to-year weather changes Multi-year climate changes For example, above normal temperatures typically result in above normal ozone concentrations Monitor environment changes (location, environment) If monitors move or the environment around monitors changes, the resulting air quality conditions will be affected Metric used to evaluate trends can affect trend results Maximum (peak) concentration 90th percentile 4th highest value Days above a threshold Section 9 – Pollutant Lifecycles and Trends

5 Section 9 – Pollutant Lifecycles and Trends

6 Section 9 – Pollutant Lifecycles and Trends
Increase is important from pollution and climate perspectives Section 9 – Pollutant Lifecycles and Trends

7 Multi-year Trends Example (1 of 3)
Long-term ozone trends in Los Angeles, California, USA Section 9 – Pollutant Lifecycles and Trends

8 Multi-year Trends Example (2 of 3)
Number of days with daily maximum 1-hour O3 > 0.10 ppm at any one site in each capital city of Australia, 1991–2001 This was modeled in Splus (and R) using the following formula log(m8max) = ns(tmax,3) + ns(rh,3) + ns(wsam,3) + bs.per.ek(wdam,period=360,4) + ns(wspm,3) + ns(jday,4) + yrf where: m8max = maximum daily 8-hour ozone tmax    = maximum 1-hour temperature rh         = avg relative humidity (10am thru 4 pm) wsam    = scalar average wind speed (7am - 10am) wdam   = vector avg wind direction (7am - 10am) wspm   = vector avg wind speed (1pm - 4pm) jday      =  day of the year (1-365) yrf        =  year expressed as a factor ns (var,df)   = natural spline function of the variable with degrees of freedom = df (number of basis functions in the spline) bs.per.ek  = circular spine function Section 9 – Pollutant Lifecycles and Trends

9 Section 9 – Pollutant Lifecycles and Trends
Seasonal Trends Affected by Season (temperature, precipitation, clouds) Unusual weather conditions may affect severity of episodes For example, above normal temperatures typically result in above normal ozone concentrations Emissions changes (substantial) Reformulated fuel Changes in industrial emissions Other Useful to understand typical season for each air pollutant Determines forecasting season Section 9 – Pollutant Lifecycles and Trends

10 GLOBAL DISTRIBUTION OF CO NOAA/CMDL surface air measurements
Section 9 – Pollutant Lifecycles and Trends

11 Section 9 – Pollutant Lifecycles and Trends
O3 at the surface Seasonal cycle of O3 concentrations at the surface for different rural locations in the United States. From Logan, J. Geophys. Res., , 1999. Surface sites in industrialized regions show an even more pronounced summer-time peak Section 9 – Pollutant Lifecycles and Trends

12 Seasonal Trends Example (1 of 5)
Compare ozone vs. temperature departure from normal Columbus, Ohio, USA Daily 8-hr ozone concentration (AQI) Temperature departure Daily maximum temperature – daily normal temperature 2001 2002 2003 Number of high ozone days Unhealthy for Sensitive Groups on the AQI scale 9 28 6 Number of days with above normal temperature 64 93 40 Section 9 – Pollutant Lifecycles and Trends

13 Seasonal Trends Example (2 of 5)
Temperature departure from normal vs. maximum ozone AQI 2001 Temperature above normal (64) Temperature below normal Unhealthy for SG (9) AQI Moderate Section 9 – Pollutant Lifecycles and Trends

14 Seasonal Trends Example (3 of 5)
Temperature departure from normal vs. maximum ozone AQI 2002 Temperature above normal (93) Temperature below normal Unhealthy (4) Unhealthy for SG (24) AQI Moderate Section 9 – Pollutant Lifecycles and Trends

15 Seasonal Trends Example (4 of 5)
Temperature departure from normal vs. maximum ozone AQI 2003 Temperature above normal (40) Temperature below normal Unhealthy (2) Unhealthy for SG (4) AQI Moderate Section 9 – Pollutant Lifecycles and Trends

16 Seasonal Trends Example (5 of 5)
Days above Air Pollution Index (API) in Shanghai, China, from Section 9 – Pollutant Lifecycles and Trends

17 Short-Term Lifecycles
Largely controlled by weather conditions and emissions events that are predicable Affected by Weather conditions Sunlight Winds Dispersion Other factors Large emissions changes Fires Non-routine emissions events (holidays, etc.) Day-of-week emissions changes Section 9 – Pollutant Lifecycles and Trends

18 Short-Term Changes – Example (1 of 9)
Precursor Accumulation Net Ozone Net Production Peak Destruction Hour (LT) Emissions    Dispersion    Vertical mixing     Sunlight   Transport Removal   Section 9 – Pollutant Lifecycles and Trends

19 Short-Term Changes – Example (2 of 9)
Key diurnal factors Section 9 – Pollutant Lifecycles and Trends

20 Short-Term Changes – Example (3 of 9)
Diurnal Pattern Categories Section 9 – Pollutant Lifecycles and Trends

21 Short-Term Changes – Example (5 of 9)
Diurnal Pattern Categories Section 9 – Pollutant Lifecycles and Trends

22 Short-Term Changes – Example (6 of 9)
Diurnal Pattern Categories Section 9 – Pollutant Lifecycles and Trends

23 Multi-day time series for model predictions at surface sites.
Figure. Comparison of model performance in surface sites, NEI 1999 and NEI 2001 Section 9 – Pollutant Lifecycles and Trends

24 Lifecycles – Multi-day
Combined ozone and PM2.5 Section 9 – Pollutant Lifecycles and Trends

25 Section 9 – Pollutant Lifecycles and Trends
PM 2.5 Variation in Beijing Section 9 – Pollutant Lifecycles and Trends

26 Section 9 – Pollutant Lifecycles and Trends
PM 2.5 variation with: Temperature Dewpoint Wind Speed Section 9 – Pollutant Lifecycles and Trends

27 Section 9 – Pollutant Lifecycles and Trends
Build-up of regional PM 2.5 Section 9 – Pollutant Lifecycles and Trends

28 Section 9 – Pollutant Lifecycles and Trends
Ratio of PM2.5 to PM10 Sulfur-to-Aluminum Ratio Section 9 – Pollutant Lifecycles and Trends

29 Section 9 – Pollutant Lifecycles and Trends
Summary Trends and lifecycle of pollution Long-term – Controlled by changes in emissions and climate Seasonal – Controlled by annual and seasonal weather patterns Short-term – Controlled by weather and non-routine emissions events Section 9 – Pollutant Lifecycles and Trends

30 Short-Term Changes – Example (7 of 9)
GTT – Please provide examples showing the influence of weather, emissions, and chemistry Section 9 – Pollutant Lifecycles and Trends

31 Short-Term Changes – Example (8 of 9)
GTT – Please provide examples showing day of week influence on pollution Section 9 – Pollutant Lifecycles and Trends

32 Short-Term Changes – Example (9 of 9)
GTT - Show multi-day lifecycle of an episode Section 9 – Pollutant Lifecycles and Trends

33 Multi-year Trends Example (3 of 3)
Ozone trends with and without adjusting for meteorology This was modeled in Splus (and R) using the following formula log(m8max) = ns(tmax,3) + ns(rh,3) + ns(wsam,3) + bs.per.ek(wdam,period=360,4) + ns(wspm,3) + ns(jday,4) + yrf where: m8max = maximum daily 8-hour ozone tmax    = maximum 1-hour temperature rh         = avg relative humidity (10am thru 4 pm) wsam    = scalar average wind speed (7am - 10am) wdam   = vector avg wind direction (7am - 10am) wspm   = vector avg wind speed (1pm - 4pm) jday      =  day of the year (1-365) yrf        =  year expressed as a factor ns (var,df)   = natural spline function of the variable with degrees of freedom = df (number of basis functions in the spline) bs.per.ek  = circular spine function The top left panel shows the raw ozone season values while the top right panel shows the seasonal values adjusted for meteorology. Values on the y-axis are on a log scale with the mean removed. The bottom two panels are just smooth splines fit to the data in the top two panels. The plots also include +/- twice the standard error of prediction. (Courtesy: Bill Cox, U.S. EPA) Section 9 – Pollutant Lifecycles and Trends

34 PEROXYACETYLNITRATE (PAN) AS RESERVOIR FOR LONG-RANGE TRANSPORT OF NOx
Section 9 – Pollutant Lifecycles and Trends

35 Particulate Matter Chemistry (4 of 4)
Sources Sample Collection PM Transport/Loss PM Formation Emissions Chemical Processes Mechanical Sea salt Dust Combustion Motor vehicles Industrial Fires Other gaseous Biogenic Anthropogenic Particles NaCl Crustal Soot Metals OC Gases NOx SO2 VOCs NH3 gases condense onto particles cloud/fog processes Measurement Issues Inlet cut points Vaporization of nitrate, H2O, VOCs Adsorption of VOCs Absorption of H2O transport sedimentation (dry deposition) wet deposition condensation and coagulation photochemical production cloud/fog processes Meteorological Processes Winds Clouds, fog Temperature Precipitation Relative humidity Solar radiation Vertical mixing Section 9 – Pollutant Lifecycles and Trends

36 Particulate Matter Chemistry (4 of 4)
Sources Sample Collection PM Transport/Loss PM Formation Emissions Chemical Processes Mechanical Sea salt Dust Combustion Motor vehicles Industrial Fires Other gaseous Biogenic Anthropogenic Particles NaCl Crustal Soot Metals OC Gases NOx SO2 VOCs NH3 gases condense onto particles cloud/fog processes Measurement Issues Inlet cut points Vaporization of nitrate, H2O, VOCs Adsorption of VOCs Absorption of H2O transport sedimentation (dry deposition) wet deposition condensation and coagulation photochemical production cloud/fog processes Meteorological Processes Winds Clouds, fog Temperature Precipitation Relative humidity Solar radiation Vertical mixing Section 9 – Pollutant Lifecycles and Trends

37 Particulate Matter Meteorology
How weather affects PM emissions, formation, and transport Phenomena Emissions PM Formation PM Transport/Loss Aloft Pressure Pattern No direct impact. Ridges tend to produce conditions conducive for accumulation of PM2.5. Troughs tend to produce conditions conducive for dispersion and removal of PM and ozone. In mountain-valley regions, strong wintertime inversions and high PM2.5 levels may not be altered by weak troughs. High PM2.5 concentrations often occur during the approach of a trough from the west. Winds and Transport In general, stronger winds disperse pollutants, resulting in a less ideal mixture of pollutants for chemical reactions that produce PM2.5. Strong surface winds tend to disperse PM2.5 regardless of season. Strong winds can create dust which can increase PM2.5 concentrations. Temperature Inversions Inversions reduce vertical mixing and therefore increase chemical concentrations of precursors. Higher concentrations of precursors can produce faster, more efficient chemical reactions that produce PM2.5. A strong inversion acts to limit vertical mixing allowing for the accumulation of PM2.5. Rain Rain can remove precursors of PM2.5. Rain can remove PM2.5. Moisture Moisture acts to increase the production of secondary PM2.5 including sulfates and nitrates. Temperature Warm temperatures are associated with increased evaporative, biogenic, and power plant emissions, which act to increase PM2.5. Cold temperatures can also indirectly influence PM2.5 concentrations (i.e., home heating on winter nights). Photochemical reaction rates increase with temperature. Although warm surface temperatures are generally associated with poor air quality conditions, very warm temperatures can increase vertical mixing and dispersion of pollutants. Warm temperatures may volatize Nitrates from a solid to a gas. Very cold surface temperatures during the winter may produce strong surface-based inversions that confine pollutants to a shallow layer. Clouds/Fog Water droplets can enhance the formation of secondary PM2.5. Clouds can limit photochemistry, which limits photochemical production. Convective clouds are an indication of strong vertical mixing, which disperses pollutants. Season Forest fires, wood burning, agriculture burning, field tilling, windblown dust, road dust, and construction vary by season. The sun angle changes with season, which changes the amount of solar radiation available for photochemistry. Section 9 – Pollutant Lifecycles and Trends

38 ORIGIN OF THE ATMOSPHERIC AEROSOL
Aerosol: dispersed condensed matter suspended in a gas Size range: mm (molecular cluster) to 100 mm (small raindrop) Soil dust Sea salt Environmental importance: health (respiration), visibility, radiative balance, cloud formation, heterogeneous reactions, delivery of nutrients… Section 9 – Pollutant Lifecycles and Trends

39 PEROXYACETYLNITRATE (PAN) AS RESERVOIR FOR LONG-RANGE TRANSPORT OF NOx
Section 9 – Pollutant Lifecycles and Trends

40 Lifetimes of ROGs Against Chemical Loss in Urban Air
ROG Species Phot. OH HO2 O NO3 O3 n-Butane h 1000 y 18 y 29 d 650 y trans-2-butene m 4 y 6.3 d 4 m 17 m Acetylene d y d Formaldehyde 7 h 6 h 1.8 h 2.5 y 2 d 3200 y Acetone 23 d 9.6 d Ethanol h Toluene h y 33 d 200 d Isoprene m d 5 m 4.6 h Table 4.3 Section 9 – Pollutant Lifecycles and Trends

41 Impacts of NOx emission
by mass, most NOx is emitted at the surface chemical impacts of NOx very non-linear limited impact in the continental PBL high OH and high NO2/NO ratio near surface result in a short photo-chemical lifetime NOx concentrations are already substantial per molecule, impact of NOx much greater in free troposphere venting to the free troposphere important emissions that occur in free troposphere aircraft, lightning Section 9 – Pollutant Lifecycles and Trends

42 Global tropospheric ozone
Seasonal cycle of O3 concentrations at different pressure levels, derived from ozonesonde data at eight different stations in the northern hemisphere. From Logan, J. Geophys. Res., , 1999. Remote northern stations spring-time maximum nearer to industrial emissions broader maximum stretching through summer Section 9 – Pollutant Lifecycles and Trends

43 Section 9 – Pollutant Lifecycles and Trends
Global distribution Spatial distribution of climatological O3 concentrations at 1000hPa. From Logan, J. Geophys. Res., , 1999. constructed from surface observations, ozonesondes and a bit of intuition note very low concentrations over tropical Pacific ocean Section 9 – Pollutant Lifecycles and Trends

44 Measurements from satellite
Data from asd-www.larc.nasa.gov/TOR/data.html See Fishman et al., Atmos. Chem. Phys., 3, , 2003. Tropospheric residual method total column (from TOMS) - stratospheric column (SBUV) Section 9 – Pollutant Lifecycles and Trends

45 Section 9 – Pollutant Lifecycles and Trends
Mission Overview July 1 to 25 Model CO A strong outflow event will appear from Saturday to Sunday Midwest Ohio etc NY-MA-MD TX-NM Southeast California Canada 2km wind field Section 9 – Pollutant Lifecycles and Trends

46 Section 9 – Pollutant Lifecycles and Trends

47 Section 9 – Pollutant Lifecycles and Trends
Aerosols in the East Asia Environment Have a Profound Impact on Resulting Secondary Pollution Formation Through Radiative Feedbacks Section 9 – Pollutant Lifecycles and Trends

48 GLOBAL DISTRIBUTION OF TROPOSPHERIC OZONE
Climatology of observed ozone at 400 hPa in July from ozonesondes and MOZAIC aircraft (circles) and corresponding GEOS-CHEM model results for 1997 (contours). GEOS-CHEM tropospheric ozone columns for July 1997. Li et al. [2001]

49 Short-Term Changes – Example (4 of 9)
Diurnal Pattern Categories Section 9 – Pollutant Lifecycles and Trends


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