Supporting Information

Transcription

1 Supporting Information On-road Chemical Transformation as an Important Mechanism of NO 2 Formation Bo Yang 1, K. Max Zhang 1, W. David Xu 1, Shaoun Zhang 1, Stuart Batterman 2, Richard W. Baldauf 3,4, Parikshit Deshmukh 5, Richard Snow 3, Ye Wu 6,7, Qiang Zhang 6, Zhenhua Li 6, Xian Wu 6 1 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA 2 School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA 3 U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Research Triangle Park, NC 27711, USA 4 U.S. EPA, Office of Transportation and Air Quality, National Vehicle and Fuels Emissions Laboratory, Ann Arbor, MI 48105, USA 5 Jacobs Technology, Inc., Durham, NC 27713, USA 6 School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beiing, , PR China 7 State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beiing, , PR China S1

2 S1. Curbside data analysis EH1 EH4 Allen Park (AP) a) NRGT SDK b) Figure S1. Curbside and ambient monitoring sites: a) Detroit, MI, EH1: curbside; EH4: nearroad upwind; AP: ambient O3; b) Atlanta, GA, NRGT: curbside; SDK: ambient O3, NO and NO2; The insert on each panel depicts the wind rose at each location. S2

3 Figure S2. The relationship between net NO2 concentrations (i.e., from both primary NO2 emissions and on-road chemical transformation) and ambient O3 from the Detroit dataset. S3

4 Figure S3. The correlations between curbside NO2/NOX ratio (RCS) and solar radiation for various ambient ozone concentration conditions in Detroit. Solar radiation (as a surrogate of NO2 photolysis rate) data were obtained NREL s U.S. National Solar Radiation Database (NSRDB) Update for the Detroit site location (42 23'09.6"N, 83 15'58.9"W) from Sep 27 th 2010 to Jun 19 th 2011 (daytime hours only). The daytime solar radiation values range up to 800 W m -2, with a mean of 223 W m -2. The correlations between RCS and solar radiation are not significant for any ozone levels, and all the correlation coefficients (R 2 ) below 0.1. The regression lines show a general downward trend. In other words, RCS decreases as solar radiation (and also NO2) increases, which is consistent with photolysis as a sink for NO2. S4

5 S2. Governing equations Modeling equations of the Realizable k-e model (one type of RANS models) are: éæ µ ö t k ù ( rk) + ( rku ) = ê µ ú + Gk + Gb - re -YM + Sk t x x ç + êë s è k ø x úû t x x µ ö e ù s e ø x úû t ( re ) + ( reu ) = êç µ + ú + rc1s e - rc2 + C1 e C3 egb + Se éæ ç êë è k + 2 e ne e k (S1) (S2) -. / where! " = $%& '0.43, , 4 = , and! 9 = 1.9,! "< = 1.44, = > = 1.0, = < = 1.2, /!?< = tanh D E G, where H is the component of the flow velocity parallel to the gravitational F vector, and is the component of the flow velocity perpendicular to the gravitational vector. The production of turbulence kinetic energy is represented by I > = J K 4 9 (S3) The generation of turbulence due to buoyancy is represented by I L = 1 N OPN PQ R S J K PQ 7 TU P& (S4) 7 where S 7 is the component of the gravitational vector in the ith direction. Compressibility effect on turbulence V W is normally neglected in the modeling of incompressible flows. The conservation equation for a chemical species, i, is: P PX (NV 7 ) + (N^V 7 ) = _ 7 + ` (S5) where V 7 is the mass fraction of species i, `7 is the net rate of production of species i by chemical reaction, and 4 7 is the rate of creation by sources. The diffusion flux of species i (_ 7 ) was computed as: _ 7 = ONa 7,b + J K 4c K R + V 7 where a 7,b is the diffusion coefficient for species i in the mixture, and the turbulent Schmidt number 4c K is 0.7. In the finite rate (FR) model, the molar reaction rate of creation/destruction of species i in reaction r is given by: w u w u kk k `7,d fg = Γi 7,d 7,d l mn o,d pq! 8,d r s v t,u n L,d pq! 8,d r s vv t,u y (S7) 8x" k kk Where 7,d is the stoichiometric coefficient for reactant i in reaction r; 7,d is the stoichiometric coefficient for product i in reaction r; Γ represents the effect of third bodies on the reaction rate; n o,d is the forward rate constant of reaction r; n L,d is the backward rate constant for reaction r; z d is the number of chemical species in reaction r;! 8,d is the molar concentration of each reactant and k product species in reaction r; { 8,d is the forward rate exponent for each reactant and product 8x" (S6) S5

6 kk species in reaction r; { 8,d is the backward rate exponent for each reactant and product species in reaction r. In the eddy dissipation method (EDM), the net rate of production of species i in reaction r, Ri is given by k `7,d }W = 7,d ~ 7 k N Ä n $ÅÇ É$ÅÇ É V g V k Ñ, Ö k á 7,d ~ g kk Ñ (S8) 7,d ~ á Where ~ 7 is molecular weight of species i; ~ g is molecular weight of reactant; ~ á is molecular weight of product; k is turbulent kinetic energy; Ä is turbulent dissipation rate; V g and V á are the mass fraction of reactants and products, respectively; k = 4.0, Ö k = 0.5 are default values of Magnussen constants for reactants and products, respectively. The reaction rate `7,d is the minimum of `7,d fg and `7,d }W. `7,d = $ÅÇi`7,d fg, `7,d }W l (S9) at low O3 (<15 ppb) and yearly averaged diurnal traffic pattern of I-96 at Detroit, MI S6

7 S3. Mesh sensitivity Three mesh resolutions were generated for the sensitivity analysis. The NO 2 concentration is an important quantity in this work. We normalized the spatial gradient of NO 2 concentration (ppb) by dividing the maximum gradient, and set the normalized gradient as the criterion of the mesh refinement. The local mesh would be refined if this normalized gradient is larger than Figure S4 showed the original mesh, the medium mesh after the first-round refinement, and the refined mesh after the second round refinement. Figure S5 showed the comparison among three different mesh resolutions. It can be seen that the medium resolution is refined enough. a) b) c) Figure S4. Three mesh resolution generated by adaptive mesh method, a) Coarse mesh 1M cells; b) Medium mesh 2M cells; c) Fine mesh 3M cells Figure S5. Mesh sensitivity, the horizontal axis represents the fine mesh results. It can be seen the medium mesh results (hollow circle) are almost the same as the fine mesh results (solid line). S7

8 S4. Chemical mechanism and photolysis rate sensitivity in on-road simulations Reaction Table S1. Simplified chemical mechanism Order Pre-exponential factor A a Temperature n (m (3(n-1) kmol (n-1) K -β s -1 ) exponent β a Activation energy E a (J/kmol) NO2 NO + O , 0.003, b 0 0 O + O2 O O3 + NO NO2 + O O + NO2 NO + O a : The reaction rate constant was represented by using Arrhenius form, n = Q â ä ã å çé b : Represented the low, the medium and the high Photolysis rates described as follows. We estimated the photolysis rate of NO2 from a polynomial relationship 1. The same relationship was adopted in our previous work 2. We obtained the solar radiation data from the U.S. National Solar Radiation Database (NSRDB) Update for the Detroit site location (42 23'09.6"N, 83 15'58.9"W) from Sep 27 th 2010 to Jun 19 th 2011 (daytime hours only). The daytime solar radiation values range up to 800 W m -2, with a mean of 223 W m -2, which corresponds to NO2~0.003 s -1. For the sensitivity analysis, we also tested NO2=0.001 s -1 and s -1. Figure S6 illustrates the results. Figure S6. R OR along the tailpipe centerline of different photolysis rates with the same 40 ppb ambient ozone concentration. Reference 1. Trebs, I.; Bohn, B.; Ammann, C.; Rummel, U.; Blumthaler, M.; Königstedt, R.; Meixner, F.; Fan, S.; Andreae, M., Relationship between the NO 2 photolysis frequency and the solar global irradiance. Atmospheric Measurement Techniques 2009, 2, (2), Wang, Y. J.; DenBleyker, A.; McDonald-Buller, E.; Allen, D.; Zhang, K. M., Modeling the chemical evolution of nitrogen oxides near roadways. Atmospheric Environment 2011, 45, (1), S8