Modeling atmospheric age distribution of elemental carbon using a

Subscriber access provided by the Henry Madden Library | California State University, Fresno

Environmental Modeling

Modeling atmospheric age distribution of elemental carbon using a regional age-resolved particle representation framework Hongliang Zhang, Hao Guo, Jianlin Hu, Qi Ying, and Michael J. Kleeman Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05895 • Publication Date (Web): 28 Nov 2018 Downloaded from http://pubs.acs.org on November 30, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 25

Environmental Science & Technology

Modeling atmospheric age distribution of elemental carbon using a regional age-resolved particle representation framework Hongliang Zhang1,*, Hao Guo1, Jianlin Hu2, Qi Ying3,2,*, Michael J. Kleeman4 1Department

of Civil and Environmental Engineering, Louisiana State University, Baton Rouge,

Louisiana, 70803 USA 2Jiangsu

Key Laboratory of Atmospheric Environment Monitoring and Pollution Control,

Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China 3Zachry

Department of Civil Engineering, Texas A&M University, College Station, Texas,

77843 USA 4Department

of Civil and Environmental Engineering, University of California at Davis, Davis,

California, 95616 USA *Corresponding authors: Hongliang Zhang, [email protected], +1-225-578-0140; Qi Ying, [email protected], +1-979-845-9709.

1 ACS Paragon Plus Environment


1

Abstract

2

The aging process of soot particles has significant implications when estimating their

3

impacts on air quality and climate. In this study, the source-oriented UCD/CIT model with

4

externally-mixed aerosol representation is expanded to track the age distribution of elemental

5

carbon (EC) in Southeast Texas. EC with the age of 0-3 hours (i.e. emitted less than 3 hours ago)

6

accounts for ~70-90% of total in urban Houston and 20-40% in rural areas of southeast Texas in

7

August 2000. Significant diurnal variations in the mean age of EC are predicted, with higher

8

contributions from fresh particles during the morning and early evening due to increased traffic

9

emission and reduced atmospheric mixing. Spatially, the mean age of EC decreases with proximity

10

to major sources. Ground-level EC with the age >6 hours are less than 20% of the first age group

11

over land, and background EC accounts for majority over the Gulf of Mexico. Differences in EC

12

spatial distribution indicate that age distribution could have regional impact on aerosol optical and

13

hygroscopic properties, and thus potentially affect cloud formation and radiation balance.

14

Appropriately accounting for the differential properties due to age distribution is needed to better

15

evaluate aerosol direct and indirect effects.

16

Key words: atmospheric age distribution, black carbon, extinction, UCD/CIT model, Houston

17


Page 2 of 25

Page 3 of 25

18


1. Introduction

19

Elemental carbon (EC, often used as a surrogate measure of black carbon (BC)) is one of

20

the dominant chemical components of atmospheric soot particles 1. EC has adverse effects on

21

visibility and human health, and can also affect atmospheric radiation as well as climate change

22

2-6.

23

atmosphere, fresh soot particles in long chained agglomerates can act as condensation sites for

24

inorganic and organic vapors, coagulate with existing particles, and act as reactive surfaces for

25

heterogeneous reactions 9, 10. The changes in EC aerosol morphology, hygroscopicity, and optical

26

properties due to these aging processes have been observed in laboratory experiments and ambient

27

measurements

28

effect of EC. For example, Liu et al. reported enhanced light absorption by mixed source EC

29

particles in UK winter16. The changes in EC properties also make it more hygroscopic leading to

30

enhanced EC uptake into cloud condensation nuclei (CCN), where it can absorb solar radiation to

31

impact cloud formation and the lifetime and albedo of clouds 4, 17. Thus, a proper understanding of

32

the EC aging process is essential to improve the model predictions of weather, air quality and

33

climate change.

34

EC is mainly generated from incomplete combustion processes

2, 9, 11-15.

4, 7, 8.

Once emitted into the

The enchantment of optical properties would affect the direct radiative

Atmospheric aging of EC and its impact on air quality, weather and climate have been 18

35

extensively investigated through modeling and experiments. Kleeman and Cass

created a

36

Lagrangian air quality model that represented the airborne particles as a source-oriented and age-

37

specific external mixture. The results showed the accumulation of secondary particulate nitrate

38

and secondary organic aerosols (SOA) onto diesel engine particles containing soot as they age in

39

the atmosphere.

40

models due to computational limitations, and it has been found that the climate impact of EC is

41

very sensitive to the chosen rates

42

explicitly treat aging process and found that the aging time scales significantly change when the

43

dominating aging processes switch. During the day, the absorption and condensation of secondary

Parameterized aging rates were commonly used over the last decade in climate

19-21.

Riemer et al.

22, 23


conducted model simulations that


Page 4 of 25

44

pollutants as well as coagulation are important processes and the time scales are from a few

45

minutes to less than 10 hours

46

decreasing of secondary pollutants formation and the time scales are about 10-50 hours. More

47

recently, by incorporating the gradual aging process of EC into AURAMS (A Unified Regional

48

Air-quality Modeling System), Park et al. 26 found that model performance on EC concentration

49

predictions was improved and wet deposition of EC was enhanced.

24, 25.

At night, coagulation dominates the aging process due to

50

Moffet and Prather 27 showed that in the Mexico city, fresh soot particles account for the

51

majority of the absorption coefficient at night and in the early morning because of the absence of

52

photochemistry, while aged soot particles are responsible for the majority of the midday absorption

53

when the solar irradiance is the highest. Cheng et al.

54

Beijing, the difference could be due to the different chemical processes and measurement

55

techniques. Photochemistry promotes the formation of secondary semi-volatile vapors that can

56

condense onto existing particles. Correct spatial and temporal distributions of the particles and

57

their aging status are needed to evaluate the impact of air quality on regional or global climate.

58

This information might be available in the future directly with satellite-based retrieval methods

59

but such remote sensing techniques have not been reported to date. Chemical transport models

60

(CTMs) can provide regional distributions of EC, but only a few studies have determined the

61

distribution of particle aging statues at regional/global scales. Matsui and Koike 28 incorporated a

62

process, age and source region chasing algorithm in the Community Multiscale Air Quality

63

(CMAQ) model, but it only tracked five particulate and gaseous species and did not show the

64

difference in aging of particles from different sources. In addition, EC emitted from different

65

sources have different morphology and hygroscopicity which will lead to different particle

66

behavior when going through various chemical and physical processes.

67

In this study, the aerosol operators used by Kleeman and Cass

24

showed a large fraction of aged soot in

18

are used in the source-

68

resolved University of California at Davis / California Institute of Technology (UCD/CIT) air

69

quality model 29, 30 with externally mixed particle representation to firstly track the age distribution, 4 ACS Paragon Plus Environment

Page 5 of 25


70

i.e. the time since emission, of airborne particles in a regional calculation. Particles from different

71

sources are lumped into two externally mixed particle types based on the fraction of EC in the

72

fresh emissions – one represents particles with higher EC fractions and other represents particles

73

with lower EC fractions. The differential aging (i.e. accumulation of secondary components) of

74

those two types are further distinguished by resolving the atmospheric age of the emitted particles

75

in each time bin so that freshly emitted particles from each type are not internally mixed with older

76

particles of the same type that have been in the air for a much longer time. This model development

77

would provide a basic understanding on the age distribution of EC in addition to differential aging

78

by secondary formation and eventually improve air quality and climate models to understand the

79

health effects and feedbacks of particles.

80

2. Methods

81

This section is an overview of methods of this study and full details can be found in the

82

Supporting Information (SI).

83

2.1 Model description

84

In most existing online and offline air quality models 31, the internal mixture representation

85

of particles is used, thus particles of the same size have identical chemical compositions and optical

86

properties at a given location and time32, 33.

87

of WRF-Chem that tracks primary particles from different sources separately through the

88

atmosphere, however, this does not account for the effects of atmospheric ages. Assuming that

89

freshly emitted and aged soot particles are internally mixed could lead to significant biases in the

90

overall hygroscopicity and light absorbing estimations 13, 16, 34, 35.

Zhang et al. 34 developed a source-oriented version

91

Kleeman and Cass29 expanded the source-oriented and age-specific external mixture

92

representation of particles into a 3D Eulerian framework but calculations for age-specific aerosols

93

were not enabled. The UCD/CIT model is being developed continuously for better performance

94

36-43.

95

source-oriented external mixture framework so that particles with different ages are also tracked

In this study, the age-resolved UCD/CIT model is established by further expanding the



Page 6 of 25

96

separately. As shown in Figure 1, the aerosol module in the externally-mixed UCD/CIT model is

97

configured to include to 2 general emissions types and expanded to track n (n=7 in this study) time

98

bins. Type 1 particles include the emissions from sources which have low EC content (EC 6 hours) is negligible in urban areas but very

225

important in suburban and rural areas. Generally, particle age decreases with proximity to major

226

emissions sources.

227

30-40% at non-traffic-peak hours while the contribution from particles with age ≥6 hours increases

228

to up to 50%. Note this is only the case in Houston for this episode. In other urban areas, long

229

range transport of EC has been shown to be very important 56.

230

3.2 Effects on secondary PM formation

In rural areas, the contributions from particles with age ≤3 hours decrease to

231

SI Figure S7 shows the episode-averaged model representation of Type 1 PM2.5 aerosols

232

at BAYP. The average size of the particles in all bins grows larger as they age in the atmosphere,

233

with the diameters of freshly emitted particles (≤1 hour of atmospheric aging time) increasing by

234

10-30% after 5-6 hours of aging time. The mass and number concentrations decrease with age due

235

to atmospheric removal processes including deposition and mixing. EC accounts for 6 hours, OC contributions decrease to 10-15% of total

241

PM mass. Secondary inorganic components including sulfate, nitrate, and ammonium are minimal

242

in the freshly emitted particles and increase gradually to a total of ~10% in particles of with an

243

atmospheric age of 5-6 hours. For particles with age > 6hrs, average combined contributions from

244

these three components increase to 25%, with nitrate accounting for ~15% of the mass and sulfate

245

and ammonia accounting for ~5% of the mass each. Other primary components account for 6 hours old.



Page 12 of 25

248

Figure 5 shows the episode-averaged model representation of Type 2 aerosols, which are

249

particles emitted from diesel engines and coal combustion at BAYP. In this type, the particles grow

250

very slowly in their first 6 hours in the atmosphere. Even after 6 hours, the diameters of all size

251

bins increase only a few percent. The mass concentrations decrease dramatically by more than 90%

252

after 6 hours. EC was the main chemical component for particles of all sizes and time bins,

253

followed by OC. Their relative contributions remain unchanged until the particles are 5-6 hours

254

old. After 6 hours, the relative contributions of sulfate, nitrate, ammonia and other components

255

increase slightly. SI Figures S8-S11 show the results in CONR and HVSL, the particles show

256

similar trend from fresh to age of 5-6 hours, except the decrease is smaller and age of >6 hours has

257

higher concentrations.

258

Type 1 and Type 2 particles behavior differently as much less secondary components are

259

formed on Type 2 particles. Internally mixed representations would artificially mix hydrophyllic

260

Type 1 components with hydrophobic Type 2 particles, leading to an over-prediction of secondary

261

components condensing on EC from diesel engines and coal combustion.

262

on Type 2 particles would be thinner due to reduced secondary formation, and the extinction

263

coefficients would not be enhanced as much as predicted using the internally mixed particle

264

representation. The overall formation of secondary PM is similar in the AR and base case even

265

though the distribution of secondary materials among primary particle cores differs in these two

266

cases.

267

oxidation rates to form less volatile products. This finding is consistent with previous simulations

268

in the South Coast Air Basin surrounding Los Angeles, California 18.

In reality, the shell

The amount of secondary PM is still limited by the availability of precursor gases and

269

Type 1 and Type 2 particles defined in the current study have very different hygroscopicity.

270

However, the two types have similar age distributions, probably because the simulated episode is

271

a relatively dry period with mostly clear sky so that the difference in the secondary PM components

272

is small and the resulting diameter change does not lead to significantly different deposition rates.

273

However, one would expect that the difference in the relative fraction of more hygroscopic 12 ACS Paragon Plus Environment

Page 13 of 25


274

secondary components would affect the aerosol’s ability to act as CNN, and thus could have

275

significant implication in weather and climate predictions. The impact of representing particles as

276

source and time resolved ensemble should be evaluated in meteorology and chemistry coupled

277

models.

278

3.3 Effects on aerosol optical properties

279

Aerosol optical properties change as they age in the atmospheric through physical and

280

chemical processes. Figure 6 shows the extinction coefficient (bext), single scattering albedo, and

281

asymmetry factor of aerosols in the AR case and differences between the AR case and the base

282

case. The asymmetry factor is a measure of the preferred scattering direction. It approaches +1 for

283

scattering strongly peaked in the forward direction and -1 for scattering strongly peaked in the

284

backward direction. The predicted bext in the AR case is as high as 0.16 km-1 (corresponding to a

285

visual range of ~24 km) in urban Houston and northeast of the domain, and is ~0.08-0.1 km-1 in

286

other regions during the simulated time period. The difference in bext between the AR case and the

287

base case is up to 0.06 km-1 in urban Houston (~35% difference) and 0.02-0.03 km-1 in other land

288

areas. Coastal and ocean areas show no difference as the majority of the particles are well aged in

289

these regions. Single scattering albedo is ~0.88 in urban area and close to 1 in other areas. The

290

difference between the AR case and the base case is as high as 0.12 in urban Houston and 0.04 m-1

291

in the surrounding areas and other small urban areas. The base case results have lower values of

292

the asymmetry factor as the source-oriented external mixture of fresh and aged EC leads to less

293

absorption. The asymmetry factor is between 0.6-0.7 within the domain and large differences of

294

0.2 are observed between the AR case and base case in urban areas. Separate representation of EC

295

particles from other particles changed the particles asymmetry factor by up to 30% in urban areas

296

like Houston. The Houston area is cleaner than more polluted areas in China and India which

297

emphasizes the need to properly treat the aging of EC in models with online photolysis calculations.

298

With the differences in age distribution, secondary formation, optical and hygroscopic properties

299

of EC from different sources after emission, the roles of EC in atmospheric radiation, air quality 13 ACS Paragon Plus Environment


300

and cloud formation would be different for different source origins. It is important to accounting

301

for the differential aerosol physical and chemical properties due to atmospheric age distribution in

302

air quality and climate model for more accurate estimation of aerosol effects.

303

Overall, this study expanded the source-oriented UCD/CIT model with multiple time bins

304

to track the contribution of particles with different atmospheric aging times to EC in Southeast

305

Texas. The age distribution of particles from different sources is similar in the relatively dry and

306

clean period, but it does lead to significant differences in optical properties including up to 35%

307

difference in extinction coefficient in urban Houston. Considering the differences in the optical

308

and hygroscopic properties between freshly emitted and aged EC shown by experimental studies,

309

current results provide valuable information on separately tracking particles based on their

310

atmospheric ages. Potentially, it is important in presenting radiation balance and cloud formation

311

in climate models, which will improve the evaluation of direct and indirect effects of aerosols on

312

climate. Future studies include applying the model in severely polluted environments in episodes

313

with significant observations of aging distribution for model evaluation and improving the model

314

performance on secondary formation.

315

Supporting Information

316

Detailed Methods section showing model description, model application and changes made to the

317

codes, a table showing the model vertical structure, and figures showing model configuration,

318

emissions, regional age distribution and components of each particle group at different sites.

319

Acknowledgments

320

This work was partially supported by the National Key R&D Program of China

321

(2016YFC0203500, Task #2). Portions of this research were conducted with high performance

322

computing resources provided by Louisiana State University (http://www.hpc.lsu.edu). Authors

323

thank Mark Estes of the Texas Commission of Environmental Quality for providing emissions and

324

meteorology inputs and Yosuke Kimura and David Allen from University of Texas at Austin for

325

providing wildfire emissions. 14 ACS Paragon Plus Environment

Page 14 of 25

Page 15 of 25


326

References

327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364

1. Andreae, M. O.; Gelencsér, A., Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. Atmos. Chem. Phys. 2006, 6, (10), 3131-3148. 2. Ramanathan, V.; Carmichael, G., Global and regional climate changes due to black carbon. Nature geoscience 2008, 1, 221-227. 3. Bond, T. C.; Sun, H., Can reducing black carbon emissions counteract global warming? Environmental Science & Technology 2005, 39, 5921-5926. 4. Ackerman, A. S.; Toon, O. B.; Stevens, D. E.; Heymsfield, A. J.; Ramanathan, V.; Welton, E. J., Reduction of tropical cloudiness by soot. Science 2000, 288, 1042-1047. 5. Bond, T. C.; Bergstrom, R. W., Light absorption by carbonaceous particles: an investigative review Aerosol Science and Technology 2006, 40, 27-67. 6. Menon, S.; Hansen, J.; Nazarenko, L.; Luo, Y., Climate Effects of Black Carbon Aerosols in China and India. Science 2002, 297, 2250-2253. 7. Chameides, W. L.; Bergin, M., Soot takes center stage. Science 2002, 297, 2214-2215. 8. Jacobson, M. Z., Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols. Nature 2001, 409, 695-697. 9. Moffet, R. C.; Prather, K. A., In-situ measurements of the mixing state and optical properties of soot with implications for radiative forcing estimates. Proceedings of the National Academy of Sciences of the United States of America 2009, 106, 11872-11877. 10. Zhang, R.; Khalizov, A. F.; Pagels, J.; Zhang, D.; Xue, H.; McMurry, P. H., Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing. Proceedings of the National Academy of Sciences of the United States of America 2008, 105, 10291-10296. 11. Zhang, D.; Zhang, R., Laboratory investigation of heterogeneous interaction of sulfuric acid with soot. Environmental Science and Technology 2005, 39, 5722-5728. 12. Zhang, R. Y.; Khalizov, A. F.; Pagels, J.; Zhang, D.; Xue, H. X.; McMurry, P. H., Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, (30), 10291-10296. 13. Liu, D.; Whitehead, J.; Alfarra, M. R.; Reyes-Villegas, E.; Spracklen, Dominick V.; Reddington, Carly L.; Kong, S.; Williams, Paul I.; Ting, Y.-C.; Haslett, S.; Taylor, Jonathan W.; Flynn, Michael J.; Morgan, William T.; McFiggans, G.; Coe, H.; Allan, James D., Black-carbon absorption enhancement in the atmosphere determined by particle mixing state. Nature Geoscience 2017, 10, 184. 14. Cross, E. S.; Onasch, T. B.; Ahern, A.; Wrobel, W.; Slowik, J. G.; Olfert, J.; Lack, D. A.; Massoli, P.; Cappa, C. D.; Schwarz, J. P.; Spackman, J. R.; Fahey, D. W.; Sedlacek, A.; Trimborn, A.; Jayne, J. T.; Freedman, A.; Williams, L. R.; Ng, N. L.; Mazzoleni, C.; Dubey, M.; Brem, B.; Kok, G.; Subramanian, R.; Freitag, S.; Clarke, A.; Thornhill, D.; Marr, L. C.; Kolb, C. E.; Worsnop, D. R.; Davidovits, P., Soot Particle Studies—Instrument Inter-Comparison—Project Overview. Aerosol Science and Technology 2010, 44, (8), 592-611. 15 ACS Paragon Plus Environment


365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403

15. Lee, A. K. Y.; Willis, M. D.; Healy, R. M.; Onasch, T. B.; Abbatt, J. P. D., Mixing state of carbonaceous aerosol in an urban environment: single particle characterization using the soot particle aerosol mass spectrometer (SP-AMS). Atmos. Chem. Phys. 2015, 15, (4), 1823-1841. 16. Liu, S.; Aiken, A. C.; Gorkowski, K.; Dubey, M. K.; Cappa, C. D.; Williams, L. R.; Herndon, S. C.; Massoli, P.; Fortner, E. C.; Chhabra, P. S.; Brooks, W. A.; Onasch, T. B.; Jayne, J. T.; Worsnop, D. R.; China, S.; Sharma, N.; Mazzoleni, C.; Xu, L.; Ng, N. L.; Liu, D.; Allan, J. D.; Lee, J. D.; Fleming, Z. L.; Mohr, C.; Zotter, P.; Szidat, S.; Prévôt, A. S. H., Enhanced light absorption by mixed source black and brown carbon particles in UK winter. Nature Communications 2015, 6, 8435. 17. McMeeking, G. R.; Good, N.; Petters, M. D.; McFiggans, G.; Coe, H., Influences on the fraction of hydrophobic and hydrophilic black carbon in the atmosphere. Atmos. Chem. Phys. 2011, 11, (10), 5099-5112. 18. Kleeman, M. J.; Cass, G. R., Source contributions to the size and composition distribution of urban particulate air pollution. Atmospheric Environment 1998, 32, (16), 2803-2816. 19. Cooke, W. F.; Wilson, J. N., A global black carbon aerosol model. Journal of Geophysical Research 1996, 101, 19395-19408. 20. Wilson, J.; Cuvelier, C.; Raes, F., A modeling study of global mixed aerosol fields. Journal of Geophysical Research 2001, 106, 34081-34108. 21. Croft, B.; Lohmann, U.; von Salzen, K., Black carbon ageing in the Canadian Centre for Climate modelling and analysis atmospheric general circulation model. Atmos. Chem. Phys. 2005, 5, (7), 1931-1949. 22. Riemer, N.; Vogel, H.; Vogel, B., Soot aging time scales in polluted regions during day and night. Atmospheric Chemistry and Physics 2004, 4, 1885-1893. 23. Riemer, N.; West, M.; Zaveri, R.; Easter, R., Estimating black carbon aging time-scales with a particle=resolved aerosol model. Aerosol Science 2010, 41, 143-158. 24. Cheng, Y. F.; Su, H.; Rose, D.; Gunthe, S. S.; Berghof, M.; Wehner, B.; Achtert, P.; Nowak, A.; Takegawa, N.; Kondo, Y.; Shiraiwa, M.; Gong, Y. G.; Shao, M.; Hu, M.; Zhu, T.; Zhang, Y. H.; Carmichael, G. R.; Wiedensohler, A.; Andreae, M. O.; Pöschl, U., Size-resolved measurement of the mixing state of soot in the megacity Beijing, China: diurnal cycle, aging and parameterization. Atmos. Chem. Phys. 2012, 12, (10), 4477-4491. 25. Tsai, I.-C.; Chen, J.-P.; Lin, Y.-C.; Chou, C. C.-K.; Chen, W.-N., Numerical investigation of the coagulation mixing between dust and hygroscopic aerosol particles and its impacts. Journal of Geophysical Research: Atmospheres 2015, 120, (9), 4213-4233. 26. Park, S. H.; Gong, S. L.; Bouchet, V. S.; Gong, W.; Makar, P. A.; Moran, M. D.; Stroud, C. A.; Zhang, J., Effects of black carbon aging on air quality predictions and direct radiative forcing estimation. Tellus B 2011, 63, (5), 1026-1039. 27. Moffet, R. C.; Prather, K. A., In-situ measurements of the mixing state and optical properties of soot with implications for radiative forcing estimates. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, (29), 11872-11877. 16 ACS Paragon Plus Environment

Page 16 of 25

Page 17 of 25

404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440


28. Matsui, H.; Koike, M., New source and process apportionment method using a threedimensional chemical transport model: Process, Age, and Source region Chasing ALgorithm (PASCAL). Atmospheric Environment 2012, 55, 399-409. 29. Kleeman, M. J., Cass, G. R., A 3d Eulerian source-oriented model for an externally mixed aerosol. Environmental Science and Technology 2001, 35, (4834-4867), 4834. 30. Zhang, H.; Ying, Q., Source apportionment of airborne particulate matter in Southeast Texas using a source-oriented 3D air quality model. Atmospheric Environment 2010, 44, (29), 35473557. 31. Grell, G. A.; Peckham, S. E.; Schmitz, R.; McKeen, S. A.; Frost, G.; Skamarock, W. C.; Eder, B., Fully coupled "online" chemistry within the WRF model. Atmospheric Environment 2005, 39, (37), 6957-6975. 32. Nordmann, S.; Cheng, Y. F.; Carmichael, G. R.; Yu, M.; Denier van der Gon, H. A. C.; Zhang, Q.; Saide, P. E.; Pöschl, U.; Su, H.; Birmili, W.; Wiedensohler, A., Atmospheric black carbon and warming effects influenced by the source and absorption enhancement in central Europe. Atmos. Chem. Phys. 2014, 14, (23), 12683-12699. 33. Klingmüller, K.; Steil, B.; Brühl, C.; Tost, H.; Lelieveld, J., Sensitivity of aerosol radiative effects to different mixing assumptions in the AEROPT 1.0 submodel of the EMAC atmosphericchemistry–climate model. Geosci. Model Dev. 2014, 7, (5), 2503-2516. 34. Zhang, H.; DeNero, S. P.; Joe, D. K.; Lee, H. H.; Chen, S. H.; Michalakes, J.; Kleeman, M. J., Development of a source oriented version of the WRF/Chem model and its application to the California regional PM10 / PM2.5 air quality study. Atmos. Chem. Phys. 2014, 14, (1), 485-503. 35. Thamban, N. M.; Tripathi, S. N.; Moosakutty, S. P.; Kuntamukkala, P.; Kanawade, V. P., Internally mixed black carbon in the Indo-Gangetic Plain and its effect on absorption enhancement. Atmospheric Research 2017, 197, 211-223. 36. Zapata, C. B.; Yang, C.; Yeh, S.; Ogden, J.; Kleeman, M. J., Low-carbon energy generates public health savings in California. Atmos. Chem. Phys. 2018, 18, (7), 4817-4830. 37. Hu, J.; Jathar, S.; Zhang, H.; Ying, Q.; Chen, S. H.; Cappa, C. D.; Kleeman, M. J., Long-term particulate matter modeling for health effect studies in California – Part 2: Concentrations and sources of ultrafine organic aerosols. Atmos. Chem. Phys. 2017, 17, (8), 5379-5391. 38. Wang, M.; Sampson, P. D.; Hu, J.; Kleeman, M.; Keller, J. P.; Olives, C.; Szpiro, A. A.; Vedal, S.; Kaufman, J. D., Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5. Environmental Science & Technology 2016, 50, (10), 5111-5118. 39. Cappa, C. D.; Jathar, S. H.; Kleeman, M. J.; Docherty, K. S.; Jimenez, J. L.; Seinfeld, J. H.; Wexler, A. S., Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 2: Assessing the influence of vapor wall losses. Atmos. Chem. Phys. 2016, 16, (5), 3041-3059.



441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478

40. Hu, J.; Zhang, H.; Chen, S.-H.; Wiedinmyer, C.; Vandenberghe, F.; Ying, Q.; Kleeman, M. J., Predicting Primary PM2.5 and PM0.1 Trace Composition for Epidemiological Studies in California. Environmental Science & Technology 2014, 48, (9), 4971-4979. 41. Hu, J.; Zhang, H.; Ying, Q.; Chen, S. H.; Vandenberghe, F.; Kleeman, M. J., Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations. Atmos. Chem. Phys. 2015, 15, (6), 3445-3461. 42. Ying, Q.; Fraser, M. P.; Griffin, R. J.; Chen, J. J.; Kleeman, M. J., Verification of a sourceoriented externally mixed air quality model during a severe photochemical smog episode. Atmospheric Environment 2007, 41, (7), 1521-1538. 43. Kleeman, M. J., Cass, G. R., Eldering, A., Modeling the airborne particle complex as a sourceoriented external mixture. Journal of Geophysical Research 1997, 102, 21355-21372. 44. Wexler, A. S.; Seinfeld, J. H., Second-generation inorganic aerosol model. Atmospheric Environment. Part A. General Topics 1991, 25, (12), 2731-2748. 45. Odum, J. R.; Hoffmann, T.; Bowman, F.; Collins, D.; Flagan, R. C.; Seinfeld, J. H., Gas/particle partitioning and secondary organic aerosol yields. Environmental Science & Technology 1996, 30, (8), 2580-2585. 46. Ying, Q.; Mysliwiec, M.; Kleeman, M. J., Source apportionment of visibility impairment using a three-dimensional source-oriented air quality model. Environmental Science & Technology 2004, 38, (4), 1089-1101. 47. Toon, O. B.; Ackerman, T. P., Algorithms for the calculation of scattering by stratified spheres. Appl. Opt. 1981, 20, (20), 3657-3660. 48. Stelson, A. W., Urban aerosol refractive index prediction by partial molar refraction approach. Environmental Science & Technology 1990, 24, (11), 1676-1679. 49. Yang, F. Radiative forcing and climate impact of the Mount Pinatubo volcanic eruption. University of Illinois at Urbana-Champaign, 2000. 50. Byun, D. W.; Kim, S. T.; Kim, S. B., Evaluation of air quality models for the simulation of a high ozone episode in the Houston metropolitan area. Atmospheric Environment 2007, 41, (4), 837-853. 51. Nopmongcol, U.; Khamwichit, W.; Fraser, M. P.; Allen, D. T., Estimates of heterogeneous formation of secondary organic aerosol during a wood smoke episode in Houston, Texas. Atmospheric Environment 2007, 41, (14), 3057-3070. 52. Ying, Q.; Krishnan, A., Source contributions of volatile organic compounds to ozone formation in southeast Texas. J. Geophys. Res. 2010, 115, (D17), D17306. 53. Nielsen-Gammon, J. Evaluation and comparison of preliminary meteorological modeling for the August 2000 Houston-Galveston ozone episode; 2002. 54. Junquera, V.; Russell, M. M.; Vizuete, W.; Kimura, Y.; Allen, D., Wildfires in eastern Texas in August and September 2000: Emissions, aircraft measurements, and impact on photochemistry. Atmospheric Environment 2005, 39, (27), 4983-4996


Page 18 of 25

Page 19 of 25


479 480 481 482

55. Boylan, J. W.; Russell, A. G., PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmospheric Environment 2006, 40, 4946-4959. 56. Sobhani, N.; Kulkarni, S.; Carmichael, G. R., Source Sector and Region Contributions to Black Carbon and PM2.5 in the Arctic. Atmos. Chem. Phys. Discuss. 2018, 2018, 1-43.

483 484 485 486

Figure 1. Schematic diagram of source- and age-resolved particle representation (n is the total number of time bins). In this representation, the particles are represented as a source-oriented external mixture with 2*n independent bins. In this study, n=7.



487 488 489 490 491 492

Figure 2. Regional distribution of episode-averaged EC concentrations in Southeast Texas for different age groups. Panels (a-g) show the EC concentrations at different age groups. Panel (h) shows the contribution of boundary conditions (BCs) to EC with the locations of sites for comparison in this study (a) BAYP, (b) CONR and (c) HSVL. Panel (i) shows the total EC concentrations. The scales are different on panels. Units are µg m-3.


Page 20 of 25

Page 21 of 25

493 494 495


Figure 3. Episode-averaged EC concentrations for two types and 7 time bins at three stations. Contributions of boundary conditions are not included.



496 497 498

Figure 4. Episode-averaged diurnal variation (local time) of the mass fractional (%) contributions of time bins to EC concentrations at three stations.


Page 22 of 25

Page 23 of 25


499 500 501 502 503 504 505

Figure 5. Episode-averaged model representation of Type 2 aerosols at BAYP. Each of the pie charts illustrates the composition of a single particle for a certain type and age group with a certain size. OTHER represents the components in PM2.5 other than the explicit components. Only PM2.5 particles are shown. The averaged diameter and total concentrations of the particles are shown as labels. The size of each pie chart relatively shows the size of actual particle diameter.



506 507 508 509 510 511

Figure 6. Extinction coefficient (bext, m−1), single albedo, and asymmetry factor (bscat/bext) of aerosols calculate by the age tracking case (a, b and c) and differences between the age tracking case and base case (d, e and f) averaged over the simulation episode in the 4-km southeast Texas domain.


Page 24 of 25

Page 25 of 25


512 513

For TOC art only


Modeling atmospheric age distribution of elemental carbon using a

Recommend Documents