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Keywords:

  • Temperature;
  • PM2.5;
  • Air quality modeling;
  • External Mixture

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[1] The direct effect of intraannual temperature variability on ozone and PM2.5 concentrations at the urban scale was simulated using a high-resolution air quality model that tracks the temperature-dependant formation of secondary organic and inorganic aerosol components. Calculations show that the concentration of ozone and non-volatile secondary particulate matter will generally increase at higher temperatures due to increased gas-phase reaction rates. The concentration of semi-volatile reaction products also will increase at higher temperatures, but the amount of this material that partitions to the particle-phase may decrease as equilibrium vapor pressures rise. Calculations performed for Southern California on September 25, 1996 predict that intraannual temperature variability may cause peak ozone and PM2.5 concentrations to fluctuate by up to 16% and 25% respectively. 24-hour average PM2.5 concentrations will decrease with increasing temperatures for inland portions of the South Coast air basin during most of the day. Slight increases in 24-hour average PM2.5 concentrations were predicted for coastal regions. The majority of the predicted shift in PM2.5 concentrations was related to increased production rates for nitric acid and condensable organic compounds balanced against increased volatilization of these products. Semi-volatile particulate ammonium nitrate concentrations are most sensitive to volatilization losses at hotter temperatures and when the ratio of gas-phase ammonia to nitric acid concentrations is approximately unity. Background sulfate particles and particles released from non-catalyst equipped gasoline-powered engines, diesel engines, and food cooking were shifted to smaller sizes as ammonium nitrate volatilized at hotter temperatures.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[2] Ozone and airborne particulate matter are the two primary ingredients of photochemical smog. It has long been recognized that high ozone concentrations have an adverse effect on human health [Schwartz, 1996; Anderson et al., 1997]. Epidemiological studies have also shown that the mass concentration of airborne particles with aerodynamic diameter smaller than 2.5 μm (PM2.5 - commonly referred to as fine particulate matter) is correlated with excess mortality and morbidity [Krewski et al., 2000; Dockery et al., 1993; Pope et al., 1995]. In response to these public health threats, many regions in the United States are in the process of designing emissions control programs to simultaneously reduce ozone and PM2.5 concentrations. Previous studies have considered the effect that reduced emissions rates have on ozone and PM2.5 concentrations [Nguyen and Dabdub, 2002; Chock et al., 1999; Meng et al., 1997]. Likewise, studies have been performed to consider the effect of temperature change on ozone concentrations in the troposphere [Klonecki and Levy, 1997; Sillman and Samson, 1995]. Relatively little work has been done to consider the simultaneous effect of variable meteorological conditions on ozone concentrations and the size and composition distribution of airborne particulate matter. This “natural” variability in ozone and PM2.5 concentrations must be understood before the effectiveness of emissions control programs designed to improve air quality can be evaluated.

[3] Temperature is one of the most important meteorological variables influencing air quality in urban atmospheres because it directly affects gas and heterogeneous chemical reaction rates and gas-to-particle partitioning. Meteorological data from the California Irrigation Management Information System (CIMIS) database indicate that air temperatures in the South Coast Air Basin surrounding Los Angeles, California, can fluctuate appreciably from year to year. During the late summer and early fall - historically the season for severe air quality episodes in the South Coast Air Basin- the monthly average maximum air temperatures at Riverside, California varied by as much as 5 K for consecutive years. The purpose of this study is to determine the first-order effects of this temperature variability on ozone production and on the size and composition of PM2.5 at the urban scale.

[4] The tool used to evaluate the effect of temperature variability on air quality is the CIT/UCD 3D Eulerian source-oriented external mixture air quality model that is capable of accurately predicting ozone concentrations while tracking the emissions, transformation, and ultimate fate of atmospheric particles released from separate emissions sources [Kleeman and Cass, 2001]. This model includes a detailed description of gas-phase reactions, heterogeneous reactions, aqueous-phase reactions, advection, turbulent diffusion, and deposition to the earth's surface. The source-oriented external mixture approach represents the most realistic simulation of the airborne particulate complex in an urban-scale air quality model to date. Calculations of this type allow scientists and decision-makers to directly evaluate the effect that different sources have on ozone and airborne particle concentrations, and consequently potential health effects. In the following sections, the source-oriented external mixture air quality model described above will be used to predict the effect of natural temperature variability on ozone concentrations and the detailed nature of the airborne particle size distribution in the South Coast Air Basin of California.

2. Background

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[5] Kim et al. [2000a] have determined that 52–59% of PM10 in the South Coast Air Basin is in the PM2.5 fraction on an annual average basis. The most intense and frequent episodes of high PM concentrations in the South Coast Air Basin occur in the late summer and early fall. These high PM concentration episodes are characterized by the formation of large amounts of secondary particulate matter in the accumulation mode (0.1–1.0 μm diameter) leading to significant visibility reduction and to adverse health effects. A yearlong monitoring program carried out by the South Coast Air Quality Management District (SCAQMD) from January 1995 to February 1996 measured peak 24-hour average PM2.5 concentrations of 129.3 μg m−3, which is approximately twice the 24-hour average EPA standard for PM2.5 of 65 μg m−3. [United States Environmental Protection Agency, 1996]. The major constituents of peak PM2.5 concentrations were identified as nitrate, ammonium ion, and organic compounds. Nitrate is a photochemical reaction product of NOx emissions that is produced by the same atmospheric chemistry that leads to high ozone concentrations. The highest particulate nitrate concentrations are observed in regions where ammonia is available to neutralize particles as nitrate builds up on them. The majority of ammonia in the Los Angeles atmosphere is released directly from sources such as livestock waste (42.5%), fertilizer applications (17.0%) and catalyst-equipped gasoline-powered vehicles (14.8%) [Mysliwiec and Kleeman, 2002]. The organic compounds contained in airborne particles may be emitted directly to the atmosphere [Kleeman et al., 1999, 2000] or this material may be produced when gas-phase organic compounds react to form less volatile products. Previous calculations have determined that 20–35% of the particulate organic compounds in the South Coast Air Basin are produced by secondary reaction pathways [Schauer et al., 1996; Hannigan, 1997]. The effect that seasonal temperature variability has on particulate nitrate, ammonium ion, and organic carbon will potentially affect the health of the more than 15 million residents in the South Coast Air Basin. A need exists to understand how temperature variability will affect the size and composition distribution of airborne particles during severe pollution episodes.

[6] It should be noted that temperature is linked to other ambient meteorological variables including humidity, wind speed, and mixing depth. The comprehensive analysis of how these other variables would change in response to increased temperature and the resultant secondary effect that these changes would have on air quality would require a fully coupled meteorology - air quality model. Previous calculations performed using such a model have predicted that soil moisture content in the South Coast Air Basin is linked to wind speed, mixing depth, and pollutant concentrations [Jacobsen, 1999]. While this result is interesting, it does not separately quantify the direct effect of a single important variable such as temperature from the indirect effect of other meteorological parameters. In the present study, the direct effect of temperature on airborne pollutant concentrations will be evaluated by considering temperature perturbations about a well-defined basecase episode during which wind fields, mixing depths, solar radiation, and absolute humidity are held constant. This analysis will reveal the simultaneous effect that temperature has on ozone and airborne particulate matter concentrations without confounding effects from other meteorological parameters. The sensitivity of model results to secondary effects such as changes in absolute humidity and mixing depth is discussed in Section 5.2.

3. Influence of Temperature on Aerosol Formation Processes

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

3.1. Gas-Phase Reaction Rates

[7] The rate of an elementary bimolecular reaction, r, is written as r = kcicj, where ci and cj are the concentrations of species i and j respectively. The rate constant, k, usually takes the form of an Arrhenius equation:

  • equation image

where A is the pre-exponential factor, E is the activation energy, R is the universal gas constant and T is the temperature. Since the activation energy (E) is a positive value, a rise in temperature will increase the rate of a bimolecular gas-phase reaction. The pre-exponential factor (A) may also change with temperature because the probability of reaction is affected by the translational kinetic energy and internal degrees of freedom of the two colliding molecules. In general this effect is minor compared to the effect of temperature in the denominator of the term E/RT in equation (1).

[8] Many tropospheric gas-phase reactions that are important to ozone and PM2.5 production are temperature dependent. Consider the bimolecular reactions that transform NO and NO2 shown in equations (2a) and (2b).

  • equation image
  • equation image

[9] The rate constants for both reactions are described by Arrhenius equations of the form shown in equation (1). A change in temperature can favor either the formation or destruction of NO2 depending on the magnitude of the pre-exponential factor (A) and the activation energy (E) as well as the concentration of the reactants.

[10] Gas-phase oxidation of a parent hydrocarbon by the hydroxyl radical (OH) can produce a range of secondary semi-volatile organics. For example, consider the oxidation of propene by the hydroxyl radical leading to the formation of two products:

  • equation image
  • equation image

[11] Under normal atmospheric conditions, the reaction pathway denoted by equation (3) is favored because it produces a more stable intermediate species [Seinfeld and Pandis, 1998]. The activation energy for each reaction is different, however, and so increasing temperatures will affect the distribution of product species produced by propene. Further reaction of the radical species will yield secondary organic products of differing volatility. This simple example illustrates that temperature variability can affect the amount of secondary particulate matter that can form from the parent hydrocarbon.

[12] Atmospheric chemistry in the lower troposphere is complex with many thousands of primary reactants and intermediate species undergoing numerous chemical reactions. Given the effect of temperature on reaction rates and product distribution, it is likely that an optimal temperature exists for the production of a specific semi-volatile reaction product given a set of boundary conditions. Fluctuations in air temperatures at the urban and regional scale could cause significant changes in the concentration of secondary pollutant species, which then condense onto airborne particles.

3.2. Gas-to-Particle Partitioning

[13] Semi-volatile gas-phase species tend to partition to the solid or liquid particle phase such that the Gibbs Free Energy of the entire system is minimized. Equilibrium is achieved when the chemical potential of the semi-volatile species in the gas phase is equal to the chemical potential of the semi-volatile species in the particle phase. The equilibrium partitioning of a semi-volatile species i between phases can be described using an equilibrium partitioning coefficient:

  • equation image

where Kiequilibrium is the partitioning coefficient, Ci,gas is the concentration of the semi-volatile species in the gas phase, and Ci,particle is the concentration of the semi-volatile species in the particle phase. Reference equilibrium partitioning coefficients for common semi-volatile inorganic species and the variation of these coefficients with temperature are well established [Wagman et al., 1982; Wexler and Seinfeld, 1990]. The temperature dependence of the partitioning coefficient for organic compounds is still an active research area at this time. Odum et al. [1996] measured reference equilibrium partitioning coefficients for a secondary organic aerosol (SOA) species i,Kom,i, in terms of the organic mass (om) concentration as:

  • equation image

where Kom,i has units of (m3 μg−1), Fi,om is the concentration of compound i (μg m−3) in the absorbing om phase, Ai is the gas phase concentration of compound i (μg m−3), and Mo is the absorbing organic mass concentration (μg m−3). The temperature dependence of such equilibrium constants can be described by the integrated form of the van't Hoff equation [Denbigh, 1981]:

  • equation image

where ΔHv,i is the enthalpy of vaporization that is assumed to be approximately constant over small temperature ranges and Tref is the temperature at which the reference equilibrium partitioning coefficient is measured. In the current study, the reference Kom,i values are based on the equilibrium partition coefficients for several classes of aromatic hydrocarbons determined by Odum et al. [1997] in a series of smog chamber experiments conducted at 308 K (Tref is 308 K in all subsequent calculations involving equation (7)).

[14] Tropospheric chemistry models group the many thousands of gas-phase organic species present in the atmosphere into a smaller number of lumped VOC species. The gas-phase lumping scheme used in the present study follows the treatment of Pandis et al. [1992] where organic compounds are lumped based on their functional groups and their reactivity with OH. These lumped organic compounds are oxidized by hydroxyl radical (OH), ozone (O3) and nitrate radical (NO3) to produce lumped semi-volatile compounds that then partition to the particle phase. The rate constants for reactions that produce lumped semi-volatile products are calculated based on the average of the rate constants for individual compounds within each lumped precursor category. Significant lumped organic precursors for secondary organic aerosol production include aromatics compounds (categories AAR5, AAR6 and AAR7 in order of increasing reactivity), α-pinene (APIN), β-pinene (BPIN), alkenes (OLE3) and toluene (TOLU). Odum et al. [1996] determined that the partitioning of semi-volatile VOC oxidation products could be satisfactorily explained using two lumped semi-volatile products: one less volatile and one more volatile. This approach is also used in the present study, and so each of the lumped organic precursor species described above produces both a less volatile and a more volatile lumped oxidation product that can partition to the particle phase. Hence the lumped organic precursors have the following lumped aerosol product categories: AEA5a and AEA5b (corresponding to AAR5 oxidation), AEA6a and AEA6b (AAR6 oxidation), AEA7a and AEA7b (AAR7 oxidation), AEAPa and AEAPb (APIN oxidation), AEBPa and AEBPb (BPIN oxidation), AEO3a and AEO3b (OLE3 oxidation), and AETLa and AETLb (TOLU oxidation). In order to characterize the behavior of the lumped semi-volatile reaction products, the specific compounds within each lumped category must be identified and then the properties of those compounds must be averaged.

[15] Studies have been conducted to determine the oxidation products of volatile organic compounds in the atmosphere [Atkinson, 1990; Seuwen and Warneck, 1996; Smith et al., 1998; Forstner et al., 1997; Yu et al., 1999; Smith et al., 1999; Sasaki et al., 1997; Grosjean et al., 1996]. Table 1 lists the detailed compounds that have been identified, the lumped species that they contribute to in model calculations, and their enthalpy of vaporization. In many cases experimentally measured enthalpies of vaporization were not available for the compounds of interest. Under these conditions enthalpies of vaporization (ΔHv) were estimated using the integrated form of the Clausius-Clapeyron equation:

  • equation image

where Pvp is the vapor pressure at a temperature T1; and Pb is the vapor pressure at the boiling point Tb. Experimental boiling points for individual compounds were taken from the literature when they were available. If experimental data was not available at any pressure, boiling points were estimated directly using Meissner's method [Meissner, 1949]. The vapor pressure of each specific compound in the desired temperature range (283–313 K) then was calculated using the modified Watson correlation [Watson, 1943]. Boiling points measured at reduced pressures were used to estimate vapor pressure using the Grain-Watson correction [Grain, 1982] when boiling point data at atmospheric pressure were not available. Enthalpies of vaporization for the lumped product species then were calculated as the average of the enthalpies of vaporization of identified product species within each lumped category (Table 2). Numerical averaging of the properties of detailed species to produce parameters for lumped model species introduce a small amount of error into model calculations. Over the temperature range studied this error is minor compared to the uncertainty associated with the parameters for the original detailed species. The enthalpies of vaporization for the lumped categories estimated in the current study have order of magnitude agreement with enthalpies of vaporization for high-yield aromatics and α-pinene oxidation products reported by Sheehan and Bowman [2001].

Table 1. Secondary Products of Organic Precursor Oxidation, Their Boiling Points and Enthalpies of Vaporizationa
Precursor (Model Species) Tb (C)NotesΔHvap J/mol
 Secondary Oxidation Product   
Toluene (TOLU)Benzaldehyde 149300.0
 Benzyl nitrate110254686.5
 o-Cresol 346975.9
 m- Cresol 461755.3
 p- Cresol 549567.5
 m-Nitrotoluene231.87654988.8
 Benzyl alcohol 750519.0
 Methyl-p-Benzoquinone178.58m51416.6
 o-Nitrotoluene221.7853701.5
 m-Nitrotoluene231.87654988.8
 p-Nitrotoluene 950200.0
 4-oxo-2-pentenal681049296.5
 α-angelica lactone1691148290.1
 2,5-furandione 1254847.1
 2-furaldehyde 1343248.3
 2-hydroxy-5-nitrobenzaldehyde197.26m58756.7
 2-methyl-1,4-benzoquinone164.64m49296.4
 2-methyl-4,6-dinitrophenol250m79690.5
 3-hydroxybenzaldehyde1911478166.6
 3-methyl-2(5H)-furanone801550252.7
 3-methyl-2,5-furandione2161652444.9
 3-methyl-4-nitrophenol205.75m60074.2
 4-methyl-2-nitrophenol1251767849.9
 2-methyl-4-nitrophenol1321892903.2
 5-methyl-2(3H)-furanone 1954800.0
 5-methyl-2-furancarboxaldehyde1802051568.3
 benzoic acid 2165445.4
 dihydro-2,5-furandione2612275727.2
 Phenol 447335.9
 glyoxal50.42331774.7
 methyl glyoxal722434671.6
α-pinene (APIN)pinic acid22525110065.0
 norpinic acid197.91m54236.7
 pinonic acid1942577133.9
 hydroxy pinonaldehyde194m, x77133.9
 norpinonic acid and isomer213.232654406.5
 norpinonaldehyde213.39m51083.2
 2,2-dimethyl-cyclobutane-1,3-dicarboxaldehyde159.67m48630.8
 hydroxy pinonic acid228.99m69260.9
 acetone 130800.0
 formaldehyde−19.252723320.5
 formic acid 2819904.0
β-pinene (BPIN)pinic acid22529110065.0
 norpinic acid197.91m54236.7
 pinonic acid1943077133.9
 norpinonic acid and isomer213.23m54406.5
 3-hydroxy-6,6-dimethyl-bicyclo[3.1.1]heptan-2-one192.27m53486.5
 6,6-dimethyl-bicyclo[3.1.1]heptane-2,3-dione184.25m52292.4
 2,2-dimethyl-cyclobutane-1,3-dicarboxaldehyde159.67m48630.8
 Nopinone2093151551.0
 hydroxy norpinonic acid183.39m52164.6
 hydroxy pinonic acid228.99m69260.9
 Acetone 130800.0
 Formaldehyde−19.252723320.5
 formic acid 2819904.0
ethylbenzene (AAR5)2,5-furandione 1254847.1
 2-acetyl-5-methylfuran1453246450.3
 2-ethyl-1,4-benzoquinone242.16m59783.2
 3,4-dimethylfurandione105.003377133.9
 3-ethyl-2,5-furandione1423460988.1
 3-methyl-2,5-furandione2161652444.9
 3′-nitroacetophenone1673571138.2
 4′-hydroxy-3′-nitroacetophenone253.69m73751.7
 4-ethylnitrobenzene246.53655307.7
 5-ethyl-2(3H)-furanone76.53750998.0
 5-ethyl-2-furaldehyde873851610.0
 3-methyl-2(5H)-furanone801550252.7
 5-methyl-2-furancarboxaldehyde1802051568.3
 acetophenone523951360.0
 benzaldehyde 149300.0
 dihydro-2,5-furandione2612275727.2
 dihydro-5-methyl-2(3H)-furanone 1954800.0
 ethyl-nitrophenol238.32m77263.0
 phenol 447335.9
 Sec-phenethyl alcohol504070191.9
 2-butenedial57.54147303.0
 glyoxal50.42331774.7
 2-ethyl-2-butenedial73.86m34255.9
 ethyl glyoxal904237040.8
m-xylene (AAR6)m-tolualdehyde1984349825.5
 2,6-dimethylphenol201.03559399.7
 2,4-dimethylphenol2104460796.5
 3,5-dimethylphenol219.54473359.8
 4-oxo-2-pentenal681049296.5
 3-methyl-2(5H)-furanone801550252.7
 glyoxal50.42331774.7
 methylglyoxal722434671.6
p-xylene (AAR6)p-tolualdehyde2044551917.0
 2,5-dimethylphenol211.13560933.6
 cis-3-hexene-2,5-dione904652146.4
 trans-3-hexene-2,5-dione904652146.4
 2-methyl-butenedial604748454.1
 2-nitro-p-xylene235.54855496.4
 2,5-dimethyl-2,5-cyclohexadiene-1,4-dione127.03m41910.2
 2,5-dimethylfuran934935719.3
 Glyoxal50.42331774.7
 Methylglyoxal722434671.6
1,2,4-trimethyl3,4-dimethylbenzaldehyde2245054517.9
Benzene (AAR6)2,5-dimethylbenzaldehyde219.55156075.9
 2,4-dimethylbenzaldehyde213.55255537.6
 2,3,6-trimethylphenol1205369991.7
 2,3,5-trimethylphenol235.335476561.7
 2,4,5-trimethylphenol2325575826.2
 cis-3-hexene-2,5-dione904652146.4
1,2,4-trimethyltrans-3-hexene-2,5-dione904652146.4
Benzene (AAR6)3-methyl-3-hexene-2,5-dione83.55651879.1
 2-methyl butenedial604748454.1
 Methylglyoxal50.42331774.7
 Glyoxal722434671.6
 Biacetyl895736583.8
1,3,5-trimethyl3,5-dimethylbenzaldehyde2245854013.1
Benzene (AAR7)2,4,6-trimethylphenol219.55967714.4
 3,5-dimethyl-2(3H)-furanone77.56046245.8
 3,5-dimethyl-5H-furan-2-one101.56150101.6
 3-methyl-5-methylene-2(5H)-furanone856249363.3
 2-methyl-4-oxo-2-pentanal57.56368534.3
 methyl maleic anhydride2161666550.9
 methyl glyoxal722434671.6
1-deceneFormaldehyde−19.252723320.5
 Octanal 6451340.0
 Nonanal 6456310.0
CyclohexenePentanal 6438100.0
Table 2. Partitioning Coefficients, Calculated Enthalpies of Vaporization and Uncertainty Estimates for Lumped Semi-Volatile Organic Species Used in Model Calculations
Gas-Phase PrecursoraSecondary OrganicPartitioning Coefficient, Kom (m3/μg)ΔHv (J/mol)Uncertainty (J/mol)
AAR5AEA5a0.09359112.610820.5
AEA5b0.0137593.66821.0
AAR6AEA6a0.04264204.718029.2
AEA6b0.001434343.42421.3
AAR7AEA7a0.09357503.413589.0
AEA7b0.0134671.63b0.0
APINAEAPa0.17167743.913589.0
AEAPb0.00424674.85572.8
BPINAEBPa0.17162322.819032.9
AEBPb0.00424674.85572.8
OLE3AEO3a0.17153825.03514.3
AEO3b0.00430710.210450.7
align="center"TOLUAETLa0.09357175.611598.9
AETLb0.0133223.216936.9

[16] Meissner's method claims an average error of ∼2% and a maximum error of <8% of the estimated boiling point in K. The modified Watson correlation is applicable to both liquid and solids and has a maximum error of 46.9% for vapor pressures estimates ranging from 10−7 to 10−3 mmHg [Grain, 1982]. Table 2 lists the overall uncertainties of the calculated enthalpies of vaporization for each of the lumped secondary organic products. The effect of this uncertainty on the formation of secondary organic aerosol will be discussed in the following section.

4. Test Case Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[17] Measurement programs have determined that the highest concentrations of airborne particulate matter in the Los Angeles area occur in the Eastern portion of the South Coast Air Basin [Hughes et al., 1999; Kim et al., 2000b]. Intensive particle measurements for model validation were collected at Riverside California on September 23–25, 1996 [Hughes et al., 2000]. To illustrate the effect of temperature on the competing aerosol formation processes in the atmosphere, the Lagrangian aerosol processes trajectory model described by Kleeman et al. [1999] was used to estimate the temperature response of secondary nitrate and SOA concentrations during this episode. The temperature dependant organic partitioning routine described in the previous section has been incorporated into these calculations. A description of initial concentrations, meteorological conditions, and emissions rates for this episode is provided by Kleeman et al. [1999]. Ambient temperature in the test cases reported in the current analysis were perturbed about basecase values by a constant factor that was adjusted between −10 K to +10 K for different sets of 24 hour simulations. As discussed previously, other meteorological variables such as wind fields, solar radiation, and absolute humidity were not perturbed during these tests so that the direct effect of temperature on gas-phase reaction and gas-to-particle partitioning rates for semi-volatile species could be evaluated. Relative humidity therefore increased with decreasing temperature during each simulation.

[18] Figure 1a shows the effect that temperature has on the total production, gas-phase concentration, particle-phase concentration, and deposition of secondary organic compounds (SOC) at Riverside, CA, on September 25, 1996. Each data point in Figure 1a represents the average of the concentration within 24 air parcels arriving at consecutive hours of the day at the receptor site after traveling across the polluted South Coast Air Basin for 2–3 days. To gauge the direct effect of enthalpies of vaporization on the partitioning of secondary organics to the condensed phase, two test cases (temperature invariant and conservative condensation) are formulated. The temperature invariant test case specifies that enthalpies of vaporization for all condensable organics are zero. According to equation (7), the secondary organic partitioning coefficient will then always assume the reference value. The conservative condensation test case sets the enthalpies of vaporization to the larger estimate (based on one standard deviation from the average shown in Table 2), which will lead to the partitioning of more secondary organic material to the condensed phase. Predicted 24-hour average particulate SOC concentrations ranged from 1.4 (+0.05, −0.17) μg m−3 to 1.9 (+0.26, −0.79) μg m−3 for the temperature range studied. Particulate SOC concentrations cannot be directly measured, but statistical source apportionment studies carried out for the South Coast Air Basin have determined that 65–85% of the total particulate organic compounds are accounted for by primary emissions on an annual average basis, with the remainder attributed to SOC formation or unidentified sources of primary organic compounds. Total particulate compound concentrations at Riverside averaged between 14:00–18:00 PST on September 25, 1996 were measured to be 10 μg m−3 [Hughes et al., 1999]. The annual average estimates described above suggest that an order of magnitude estimate for the concentration of particulate secondary organic compounds during this time period would be 2.0 to 3.5 μg m−3. Predictions for the concentration of particulate secondary organic compounds between 14:00–18:00 PST on September 25, 1996 are 1.7 μg m−3, showing order of magnitude agreement with estimated concentrations. Calculations predict that an increase in temperature will drive more of the SOC into the gas-phase, consistent with partitioning arguments presented previously. No maximum in particulate secondary organic compound concentrations was identified within the range of conditions studied in the present analysis.

image

Figure 1. Predicted concentration of secondary organics and nitrate at Riverside, California, on September 25, 1996 when temperature is perturbed about basecase values. Each data point represents the average of the concentration within 24 air parcels arriving at consecutive hours of the day at the receptor site.

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[19] Figure 1b shows the effect that temperature has on the total production, gas-phase concentration, particle-phase concentration, and deposition of nitrate aerosol at Riverside, CA, on September 25, 1996. Model calculations indicate that the total nitrate production increases with rising temperatures up to a maximum 24-hour average value of 113.5 μg m−3 at temperature perturbation of +2 K. Increases in relative humidity (Owing to decreased temperature and constant absolute humidity) lead to the activation of hygroscopic particles at temperature perturbations below −2 K. 24-hour average particulate nitrate concentrations reach a maximum value of 80 μg m−3 at a temperature perturbation of −4 K due to increased partitioning of nitrate to the aqueous phase. At temperatures perturbations below −4 K reduced production of nitrate and the deposition of large hygroscopic particles containing nitrate lowers the predicted 24-hour average particulate nitrate concentrations to 65 μg m−3 at a temperature perturbation of −10 K. At warmer temperatures nitric acid partitions to the gas-phase lowering 24-hour average particulate nitrate concentrations to 22 μg m−3 at a temperature perturbation of +10 K. Nitrate deposition increases at warmer temperature perturbations because gas-phase nitric acid has a high affinity for deposition to the earth's surface. This nonlinear behavior illustrates the complex nature of particulate air quality problems.

[20] In general, temperature has a more pronounced effect on particulate nitrate than on secondary organic compounds. Total nitrate production changed by 11% over the temperature range studied while whereas secondary organic production only increased by 5%. Similar trends also were observed for partitioning and deposition, since particulate SOA concentrations did not shift by more than 27% and deposition remained approximately constant. In contrast, nitrate responded strongly to temperature perturbation, with particulate nitrate concentrations changing by 73% and nitrate deposition changing by 52% over the temperature range studied.

[21] Figure 2 shows measured 24-hour average particulate nitrate concentrations for Rubidoux, California, plotted as a function of daily average air temperatures measured at the nearby Riverside monitoring site for days with weak onshore flow during the period spanning September to October 1995. Temperature and wind speed/direction data for this time period were obtained from the California Irrigation Management Information System (CIMIS) database. Days with weak onshore flow were identified by creating interpolated wind fields using the method of Goodin et al. [1979] and then integrating 3-day air parcel back trajectories from the Riverside monitoring site. Particulate nitrate concentrations during this time period were measured by the South Coast Air Quality Management District (SCAQMD) as a part of a yearlong monitoring effort [Kim et al., 2000a]. The scatter in nitrate concentrations as a function of temperature reflects the unique meteorological and emissions conditions experienced by each set of air parcels. In particular, those air parcels that were advected over the region of high ammonia emissions directly west of the Rubidoux/Riverside monitoring sites were characterized by significantly higher particulate nitrate concentrations. The trends shown in Figure 2 illustrate that the maximum nitrate concentrations recorded at the Rubidoux monitoring site (associated with high ammonia emissions) decrease with increasing temperature.

image

Figure 2. 24-hour average PM2.5 nitrate concentration at Rubidoux as a function of daily average air temperature at the nearby Riverside monitoring site on days with weak onshore flow during September to October 1995. Model predictions for Riverside on September 25, 1996 show good agreement with measurements on days when air parcel back trajectories cross over regions of high ammonia emissions.

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[22] The data shown in Figure 2 are the only available 24-hour average PM2.5 nitrate measurements that are reasonably close to the time and location of the simulated 24-hour PM2.5 nitrate concentrations shown in Figure 1. The comparison between these measured data and the model predictions shown in Figure 1 represent the best possible way to determine if model results are reasonable, since meteorological and emissions conditions are never exactly repeated with a uniform temperature perturbation applied. Model results from Figure 1 plotted alongside the measured data in Figure 2 show good agreement with the maximum observed nitrate concentration and with the trend of decreasing particulate nitrate concentrations at increasing temperature. These results support the conclusion that increased volatility of ammonium nitrate dominates increased production of nitric acid at higher temperatures leading to lower overall particulate nitrate concentrations.

[23] The nitrate results described above reflect the balance between enhanced gas-phase reaction rates and reduced partitioning to the particle-phase. In order to better understand the effect that increased temperature will have on the particulate nitrate formation, consider a closed system that initially has no particulate ammonium nitrate and some initial amount of gas-phase ammonia and nitric acid. The combined ammonium nitrate equilibrium constant [Mozurkewich, 1993] is graphically illustrated in Figure 3a. If the product of the gas-phase ammonia and nitric acid concentrations exceeds the equilibrium value, then particulate ammonium nitrate will be produced until the gas-phase concentrations are reduced to the equilibrium point. Note that the equilibrium condition is not immediately established due to limitations associated with gas-phase diffusion and interfacial mass transfer [Wexler and Seinfeld, 1990]. Analysis of the ambient conditions during the September 23–25 episode considered in the present analysis indicate that equilibrium conditions are established in a few seconds at inland sites. The amount of particulate ammonium nitrate [PN] produced at this equilibrium point will be

  • equation image

where [N(−III)] is the initial molar concentration of gas-phase ammonia, [N(V)] is the initial molar concentration of gas-phase nitric acid, [NH3]gas is the equilibrium molar concentration of gas-phase ammonia, and [HNO3]gas is the equilibrium molar concentration of gas-phase nitric acid. The derivative of the equilibrium particulate nitrate concentration with respect to temperature T is given by

  • equation image

where it is recognized that the derivative of [NH3]gas and [HNO3]gas with respect to temperature are stoichiometrically equal (i.e., every mole of particulate nitrate produced requires one mole of gas-phase nitric acid and one mole of gas-phase ammonia; therefore the derivatives of gas-phase nitric acid and ammonia with respect to temperature are equal). Differentiating the equation for the combined ammonium nitrate equilibrium constant Kp = [NH3] [HNO3] gives the expression

  • equation image

Substitution of Equation (11) into Equation (10) yields the following expression for the variation of particulate nitrate concentrations with temperature

  • equation image
image

Figure 3. (a) Dissociation constant of ammonium nitrate as a function of temperature when ambient relative humidity is less than deliquescence relative humidity and (b) isotherms for the formation rate of ammonium nitrate in response to changes in ammonia gas concentration.

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[24] Figure 3b illustrates the behavior of this function at various gas-phase ammonia concentrations and temperatures. As temperature increases, the sensitivity of particulate ammonium nitrate concentrations to temperature also increases. The greatest sensitivity to temperature occurs when gas-phase ammonia and nitric acid molar concentrations are equal. Under these conditions, each unit of temperature increase above 308 K reduces particulate ammonium nitrate concentrations by approximately 14 μg m−3. In comparison, each unit of temperature increase above 293 K reduces particulate ammonium nitrate concentrations by only 2 μg m−3. Note that particulate ammonium nitrate concentrations are much less sensitive to temperature change when the ratio of gas-phase ammonia to gas-phase nitric acid molar concentrations is very large or very small.

[25] The test case summarized in the current study is constructed for conditions relevant to Southern California where SO2 concentrations and secondary sulfate production are very low. Previous studies have shown increased particulate sulfate concentrations can reduce the concentration of particulate ammonium nitrate due to a reduction in the availability of gas-phase ammonia [Ansari and Pandis, 1998]. Model calculations in the present study account for this effect, but SO2 emissions and primary sulfate concentrations in Southern California are so small that enhanced sulfate production at hotter temperatures is negligible compared to other processes in the system. Under conditions of constant relative humidity, 24-hour average PM2.5 sulfate concentrations at Riverside, CA, on September 25, 1996 were approximately constant as temperatures were perturbed about basecase values by −10 to +10 K, with little effect on PM2.5 nitrate concentrations. Under conditions of constant absolute humidity, 24-hour average sulfate concentrations at Riverside on September 25, 1996 decreased as the temperature was perturbed lower than −2K due to the enhanced deposition of sulfate particles that had formed droplets at high relative humidity. These same hygroscopic sulfate droplets contained a significant amount of nitrate, and so particulate nitrate concentrations also decreased under these conditions. The results of a similar study conducted for regions such as the Eastern United States where SO2 emissions are larger would likely show that increased temperatures lead to higher particulate sulfate concentrations with an associated reduction in particulate nitrate.

5. Three Dimensional Air Quality Model Analysis

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[26] The primary objective of the current study is to analyze the impact of temperature variability on ozone and the concentration, size, and composition of airborne particulate matter at the urban and regional scale. The 3D Eulerian version of the source-oriented external mixture (CIT/UCD) air quality model described by Kleeman and Cass [2001] was used for this analysis for the period September 23–25, 1996. The CIT/UCD source-oriented external mixture air quality model tracks particles emitted from different sources separately through a simulation of atmospheric chemistry (gas and aqueous phase), advection, turbulent diffusion, and deposition [Kleeman et al., 1997, Kleeman and Cass, 1998]. Emitted particles do not interact with each other except via a common gas phase. This approach is a more realistic simulation of airborne particulate matter in an urban environment relative to previous models that make the internal mixture approximation. The initial conditions, meteorological parameters, and emissions inventory used for this analysis are generally the same as those used for the test cases described above and have been described elsewhere [Kleeman and Cass, 2001]. Very briefly, concentrations of routinely monitored gas-phase pollutants (O3, NOx, SOx, CO, CO2, RHC) were interpolated using measurements collected at 28 SCAQMD monitoring sites during the study period. Initial conditions of gas-phase nitric acid, gas-phase ammonia as well as particle size and composition were based on measurements made on September 23, 1996 at the following times and locations: 6:00–10:00 PST at Long Beach; 10:00–14:00 PST at Fullerton; and 14:00–18:00 PST at Riverside. These 4-hour measurements were back extrapolated to 0:00 PST on September 23, 1996 using the diurnal pollutant profile measured on September 8–9, 1993 [Fraser et al., 1996]. Gas-phase and particle-phase boundary conditions for the western edge of the study domain were measured using impactor and filter samplers at Santa Catalina Island on September 23, 1996 [Kleeman and Cass, 1999]. These measurements characterized the concentration of sea salt aerosol over the open ocean. The enhanced production of sea salt aerosol by the action of breaking waves at the coast was parameterized with a detailed sea spray emissions model [de Leeuw et al., 2000]. Gas and particle-phase pollutant emissions of anthropogenic origin were specified using an emissions modeling system described previously by Kleeman et al. [1999]. The mobile source inventory, point source inventory, and area source inventory are the three major components of the standard emissions inventory. The base mobile source inventory was calculated with the traffic emissions model EMFAC-7G featuring a day-specific temperature correction for evaporative emissions. Area source and small point source inventories within the modeling domain were based on the 1995 average-day emissions inventory provided by SCAQMD together with particle size and composition profiles defined by Kleeman et al. [1999]. Similarly, large point source inventories were based on the 1997 average-day emissions inventory provided by SCAQMD together with particle size and composition profiles defined by Kleeman et al. [1999]. To reflect changes in mobile sources, fertilizer and livestock emissions, the ammonia emissions inventory of Gharib and Cass [1984] was updated to 1996 and applied in the current study [Kleeman et al., 1999]. Routine hourly measurements made by the SCAQMD at their monitoring sites provided the meteorological inputs for the model. These measurements include wind speed (29 sites), temperature (10 sites), relative humidity (10 sites), total solar radiation (4 sites) and ultraviolet solar radiation (1 site). Upper level wind speed, wind direction and temperature were measured at hourly intervals using a lower atmospheric radar profiler at the Los Angeles International Airport (LAX). In the current study, wind fields were specified with greater numerical accuracy to improve self-consistency and reduce wind field divergence. Atmospheric mixing depths during the simulation were constructed using Holtzworth's method [Holtzworth, 1967], the interpolated surface temperature throughout the modeling domain, and the vertical temperature profile measured at LAX.

[27] Meteorological measurements made between September 15–October 14 of the years 1989–2001 at two receptor sites in the South Coast Air Basin (Riverside and Pomona) show that average maximum air temperatures range between 299–308 K. The observed meteorological data also indicated that the ambient air temperature during the September 15–October 14 interval could shift by 2 to 5K in consecutive years. On September 23–25, 1996, the maximum air temperature at Riverside was approximately 300 K. These results suggest that positive temperature perturbations should be considered to evaluate the effects of temperature variability on urban air pollution in the South Coast Air Basin during the study period. In the following sections the updated version of the CIT/UCD air quality model will be applied to predict the atmospheric particle concentration when the ambient temperature is raised by +2 and +5 K. The impact of temperature variability on ozone concentrations and the details of airborne particle size and composition distribution in South Coast Air Basin will be discussed, and the effect of mixing depth and absolute humidity variations on PM2.5 concentrations also will be considered.

5.1. Uniform Temperature Perturbation Results

[28] Ozone is the traditional indicator of photochemical smog in the South Coast Air Basin. Figure 2 of Kleeman and Cass [2001] illustrates that the CIT/UCD air quality model does an excellent job of simulating both the absolute magnitude and the time of peak ozone concentrations in the South Coast Air Basin on September 23–25, 1996. Figure 4a of the current paper shows the regional distribution of predicted ozone concentrations in the South Coast Air Basin at 15:00 PST on September 25, 1996. This time was chosen for analysis because ozone concentrations in the South Coast Air Basin are highest in the late afternoon hours. Model results predict that ozone concentrations are relatively uniform throughout the South Coast Air Basin at this time with the highest value of 100 ppb occurring near the northeast corner (460 Easting and 3805 Northing) of the study domain. (Figures 4b and 4c) show the change in ozone concentrations that are predicted to occur in response to uniform temperature perturbations of +2 K and +5 K. Peak ozone concentrations at 460 Easting and 3805 Northing increased by 7 ppb and 16 ppb for the +2 K and +5 K perturbation scenarios respectively. These results are consistent with previous studies that have shown increased ozone concentrations at higher temperatures [Johnson et al., 2001; Jonson et al., 2001]. Ozone concentrations were relatively unaffected by temperature perturbation in the western and central portions of the South Coast Air Basin. These areas are characterized by large amounts of fresh NO emissions, leading to reduced ozone concentrations.

image

Figure 4. (a) Hourly average ozone mixing ratio and (b, c) ozone mixing ratio difference in response to uniform temperature perturbations at 1500 PST on September 25, 1996. Units are ppb.

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[29] Kleeman and Cass [2001] have shown that the CIT/UCD air quality model predicts basecase particle size distributions that are in good agreement with measured concentration of the major aerosol species in the South Coast Air Basin on September 24–25, 1996. The basecase results in the current study are slightly improved relative to these previous model predictions due to the incorporation of temperature effects on SOA formation and increased accuracy of the wind fields used for model simulations as described above. Figure 5a of the current paper shows the simulated PM2.5 concentration field in the South Coast Air Basin on September 25, 1996. The highest predicted basecase (no temperature perturbation) concentrations of PM2.5 are found inland and downwind of Rubidoux, Riverside and Fontana. For September 25, 1996, model results predict a peak 24-hour average PM2.5 concentration of 125 μg m−3 at inland areas northeast of Riverside and Fontana. The largest contributors to peak PM2.5 concentrations at this location are nitrate and ammonium ion [Kleeman and Cass, 2001] caused largely by large livestock ammonia emissions just west of Rubidoux. Coastal regions of the South Coast Air Basin are generally predicted to have lower 24-hour average PM2.5 concentrations of <30 − 40 μg m−3 during the study period. These spatial patterns are in excellent agreement with the results of previous studies [Hughes et al., 1999, 2000; Kim et al., 2000b] that showed strong spatial variation in PM2.5 concentration - with low concentrations at coastal areas and high annual average concentrations at inland areas.

image

Figure 5. (a) 24-hour average PM2.5 concentration and (b, c) 24-hour average PM2.5 concentration difference in response to uniform temperature perturbations on September 25, 1996. Units are μg m−3.

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[30] Figures 5b and 5c show the predicted change in 24-hour averaged PM2.5 concentrations in the South Coast Air Basin in response to a +2 K and +5 K temperature perturbation respectively on September 25, 1996. PM2.5 concentrations averaged over this 24-hour period are predicted to decrease with increasing temperatures for most of the air basin, with the greatest reductions occurring in the northeast portion of the model domain. Previous studies by Kleeman et al. [1999] have shown that ammonium nitrate is the major contributor to PM2.5 mass in the northeast corner of the basin. Temperatures in this region are generally hotter than temperatures in the coastal areas and so ammonium nitrate concentrations are extremely sensitive to further temperature increases (see Figure 2b and associated discussion). The net result is that most areas in the inland portion of the study domain are predicted to undergo a reduction in 24-hour average PM2.5 concentrations of at least 2.5 μg m−3 in response to the temperature perturbations considered. The greatest reductions in 24-hour average PM2.5 concentration within the model domain (exclusive of boundary cells) were 6.1 μg m−3 (470 Easting and 3795 Northing) and 30.7 μg m−3 (485 Easting and 3805 Northing) for the +2 K and +5 K perturbation scenarios respectively. Note that ammonium nitrate concentrations in the area downwind of Rubidoux, Riverside, and Fontana did not respond as strongly to increased temperatures because gas-phase ammonia concentrations are much greater than gas-phase nitric acid concentrations in this region. Locations north of Fontana have gas-phase ammonia and nitric acid concentrations that are approximately equal, resulting in a greater reduction in particulate ammonium nitrate concentrations as temperature increases. Figures 5b and 5c also show that scattered locations in the offshore and coastal regions of the South Coast Air Basin are predicted to experience slightly higher 24-hour average PM2.5 concentration in response to increased ambient temperature. Positive temperature perturbations in these cool coastal areas lead to increased gas-phase reaction rates without reaching the threshold that drives particulate nitrate into the gas phase. In both of the uniform temperature perturbation scenarios, several locations along the Southern California coastline are predicted to experience increases in 24-hour averaged PM2.5 concentrations of 0.1 to 0.2 μg m−3, with more pronounced effects seen at higher temperatures.

[31] Figures 6a and 6b show the predicted airborne particle size distribution at 485 Easting 3785 Northing at 13:00 PST under basecase and +5K temperature scenarios. The large reduction in the amount of secondary ammonium nitrate aerosol under the +5 K temperature scenario accounts for virtually all of the predicted 46 μg m−3 PM2.5 reduction at this time and location. Primary particles originally released from mobile sources and food-cooking operations are shifted to smaller particle sizes under the increased temperature scenarios as semi-volatile compounds evaporate. Figures 6c and 6d show the predicted airborne particle size distribution at 485 Easting and 3790 Northing at 14:00 PST under basecase and +2 K temperature scenarios. The results are qualitatively similar to the +5 K temperature perturbation, with a reduction of 13.1 μg m−3 in PM2.5 concentrations associated with the evaporation of ammonium nitrate, and a related reduction in the size of the primary particle cores originally released from combustion sources. The bimodal submicron particle size distributions shown in Figures 6a–6d match qualitatively the results of previous aerosol measurements in the South Coast Air Basin [Hering et al., 1997]. For both the temperature-perturbed scenarios, the peak in the sub-micron size distribution at 0.2–0.3 μm particle diameter is associated primarily with particles released from non-catalyst-equipped gasoline engines, diesel engines, and food cooking that have become coated by ammonium nitrate. The peak in the sub-micron size distribution located at approximately 0.45 μm particle diameter is associated chiefly with background sulfate particles that have obtained coatings of ammonium nitrate as they are advected across the South Coast Air Basin [Kleeman and Cass, 2001].

image

Figure 6. Hourly average aggregate particle size distributions on September 25, 1996 corresponding to maximum increases/decreases in PM2.5 concentration in response to uniform temperature perturbations. (a) Basecase and (b) +5 K uniform perturbation case at 13:00 PST at 485 Easting, 3785 Northing; (c) basecase and (d) +2 K uniform perturbation case at 14:00 PST at 485 Easting 3790 Northing; (e) basecase and (f) +5 K uniform perturbation case at 5:00 PST at 285 Easting, 3800 Northing; (g) basecase and (h) +2 K uniform perturbation case at 3:00 PST at 485 Easting 3790 Northing.

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[32] PM2.5 concentrations at inland locations did not decrease uniformly throughout the day on September 25, 1996. During some hours of the day, PM2.5 concentrations actually increased at some locations under conditions of +2 K and +5 K temperature perturbation. Figures 6e and 6f show the predicted size distribution of airborne particulate matter at the location in the modeling domain (exclusive of boundary cells) with the greatest increase in hourly-average PM2.5 concentrations under basecase and +5 K temperature scenarios (285 Easting and 3800 Northing at 06:00 PST). Under these conditions, airborne sea salt particles at the coastal location that were originally greater than 2.5 μm in diameter shifted to smaller sizes as ammonium nitrate evaporated at hotter temperatures. This effect increased the concentration of airborne particulate matter in the PM2.5 size range by 2.1 μg m−3. Figures 6g and 6h show the predicted size distribution of airborne particulate matter at the location in the modeling domain (exclusive of boundary cells) with the greatest increase in hourly-average PM2.5 concentrations under basecase and +2 K temperature scenarios (485 Easting and 3790 Northing at 03:00 PST). The results shown in Figures 6g–6h illustrate that the increase in PM2.5 concentration at this time and location stems almost entirely from condensation of ammonium and nitrate onto particles in the accumulation mode. During the pre-dawn and early morning hours, temperatures are still low enough for appreciable quantities of these semi-volatile species to remain in the condensed phase at this location, giving rise to PM2.5 increase of 6 μg m−3 for the +2 K perturbation case. Note that increases in hourly average particulate matter concentrations are offset by concentration decreases during other parts of the day, resulting in an overall reduction in the 24-hour average PM2.5 concentration for both the +2 K and +5 K temperature scenarios at most locations.

5.2. Secondary Effects Associated With Other Meteorological Parameters

[33] The analysis described in the current paper holds meteorological parameters other than temperature fixed at their basecase values so that the direct effect of temperature on ozone and PM2.5 concentrations can be evaluated. In the real world, temperature change will likely be coupled to changes in other meteorological variables that will in turn affect airborne pollutant concentrations. Two of the most immediate and obvious meteorological variables that will respond to temperature change are ambient humidity and mixing depth. The sensitivity of predicted pollutant concentrations to these other variables will help to determine if they are significant relative to the direct effect of temperature.

[34] The model predictions presented in previous sections of the current paper have assumed constant absolute humidity in all simulations. Meteorological measurements from the CIMIS database indicate that there is a negative correlation (R squared values ranging between 0.31–0.41 for Pomona and Riverside respectively) between 24-hour average temperature and 24-hour average relative humidity at inland location in the South Coast Air Basin during the late summer and early fall months. The slope of this correlation is consistent with the assumption that absolute humidity is constant, while relative humidity changes with temperature. Nevertheless there is scatter in the data and so it is useful to consider how changes in absolute humidity may affect the results of the current analysis. One set of uniform temperature perturbations was performed under the condition of constant relative humidity to investigate the effect of humidity on predicted airborne pollutant concentrations. Ozone concentration trends under this condition were qualitatively similar to constant absolute humidity results for the uniform +2 K and +5 K temperature perturbations but the response to temperature was decreased. In the uniform +5 K temperature perturbation with constant relative humidity the maximum hourly-average ozone increase in the model domain was 14 ppb (compared to an increase of 16 ppb for the case with constant absolute humidity). PM2.5 concentration trends under the uniform +5 K temperature perturbation with constant relative humidity also were qualitatively similar to constant absolute humidity results but in this case there was a decreased response to temperature. In the uniform +5 K perturbation scenario with constant relative humidity, the 24-hour average PM2.5 concentration at Riverside California decreased by 9.2 μg m−3 (compared to a decrease of 18.7 μg m−3 for the case with constant absolute humidity). The 24-hour average PM2.5 concentration at offshore sites increased by 0.1 μg m−3 for both constant relative and constant absolute humidity cases. Thus the assumption of constant relative humidity appears to have little effect on predicted ozone trends, and somewhat larger effect on predicted PM2.5 trends. It should be noted that the assumption of constant relative humidity represents the extreme limiting case for uncertainty associated with this variable, and that the ambient data suggest that the condition of constant absolute humidity is more realistic.

[35] Previous studies have shown that predicted ozone concentrations in the South Coast Air Basin are affected by mixing depth when the inversion height was near the earth's surface during an episode in 1987 [Harley et al., 1993]. Mixing depths during the 1996 episode studied in the current paper were larger than mixing depths observed in 1987 with typical values greater than 1000 m during the day and greater than 100 m at night. Ground-level pollutant concentrations are not sensitive to variations in mixing depth under these conditions. Predicted 24-hour average PM2.5 concentrations in Southern California on September 25, 1996 changed by approximately 2% in response to a 20% change in mixing depth height.

[36] The analysis described above suggests that the direct effect of temperature on ozone and PM2.5 concentrations is significant and that likely corresponding changes in humidity and mixing depth will produce qualitatively similar results with slight variations in predicted ozone and PM2.5 concentrations for Southern California during the air quality episode that occurred on September 23–25, 1996. Future studies will consider the sensitivity of airborne pollutant concentrations to wind fields and emissions conditions.

6. Conclusion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[37] Increased temperatures will increase the concentration of ozone and non-volatile secondary particulate compounds in areas where complete conversion of precursor species has not yet occurred. The net effect that increased temperature has on airborne particle concentrations is a balance between increased production rates for secondary particulate matter (increases particulate concentrations) and increased equilibrium vapor pressures for semi-volatile particulate compounds (decreases particulate concentrations). Increased temperatures may either increase or decrease the concentration of semi-volatile secondary reaction products such as ammonium nitrate depending on ambient conditions. Regions with relatively cool initial temperatures (<290 K) and/or regions where the ratio of gas-phase ammonia to nitric acid concentrations is far from unity may experience minor reductions or even small increases in particulate ammonium nitrate concentrations as temperature increases. Regions with relatively hot initial temperatures (>290 K) will likely experience a reduction in particulate ammonium nitrate concentrations as temperature increases.

[38] Calculations for Southern California show that the direct effect of intraannual temperature variability can change peak ozone concentrations and 24-hour average PM2.5 concentrations by 16% and 25% respectively when secondary effects are not considered (i.e., other meteorological variables and emissions patterns are held constant). Under these conditions, an increase in ambient temperatures will lead to an increase in peak ozone concentrations but a reduction in the highest 24-hour average PM2.5 concentrations in the inland portion of the South Coast Air Basin surrounding Los Angeles during the late summer and early fall months. The PM2.5 component that responds most strongly to temperature change is ammonium nitrate. Areas with extremely high gas-phase ammonia concentrations in the eastern portion of the South Coast Air Basin will experience smaller PM2.5 changes due to temperature fluctuations than areas with approximately equal concentrations of gas-phase nitric acid and ammonia. Background sulfate particles and the primary particle cores released from non-catalyst-equipped gasoline-powered motor vehicles, diesel vehicles, and food cooking that act as condensation sites for ammonium nitrate aerosol are shifted to smaller particle sizes as ammonium nitrate volatilizes at hotter temperatures. Cooler coastal regions in the South Coast Air Basin are predicted to have slightly higher 24-hour average PM2.5 concentrations if ambient temperatures increase during the late summer and early fall months. The trends predicted by model calculations in the current study are consistent with observed nitrate concentrations trends in the South Coast Air Basin.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[39] This research was supported by U.S. Environmental Protection Agency Contract # R-82824201-01. This research has not yet been subjected to the U.S. Environmental Protection Agency's peer and policy review and therefore it does not necessarily reflect the views of the Agency. No official endorsement should be inferred.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Background
  5. 3. Influence of Temperature on Aerosol Formation Processes
  6. 4. Test Case Results
  7. 5. Three Dimensional Air Quality Model Analysis
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

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