Proteins and Amino Acids in Fine Particulate Matter in Rural Guangzhou, Southern China: Seasonal Cycles, Sources, and Atmospheric Processes Tianli Song, Shan Wang, Yingyi Zhang, *, Junwei Song, Fobang Liu, Pingqing Fu, Manabu Shiraiwa, Zhiyong Xie, Dingli Yue, Liuju Zhong, Junyu Zheng, Senchao Lai *, Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, School of Environment and Energy, South China University of Technology, Guangzhou 510006, China Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz 55128, Germany State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Department of Chemistry, University of California, Irvine, California 92697-2025, United States Helmholtz-Zentrum Geesthacht, Centre for Materials and Coastal Research, Institute of Coastal Research, Geesthacht 21502, Germany Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangzhou 510308, China S1
*Phone: +86-135-7097-4216. E-mail: sclai@scut.edu.cn. *Phone: +86-156-9242-3889. E-mail: zhyy@scut.edu.cn. Table of Contents Positive matrix factorization (PMF) analysis... S2 Table S1. The method detection limits (MDLs), precision and recoveries of AA analysis.... S4 Table S2. Seasonal mass concentration of PM 2.5 and the associated chemical components in PM 2.5 at Tianhu (µg m -3 ).... S5 Table S3. Molar and mass concentrations of proteins and FAAs (arithmetic mean, standard deviation and range) in PM 2.5 at Tianhu.... S6 Table S4. Correlation coefficients (r) of proteinaceous matter and major ions as well as CM in PM 2.5.... S7 Figure S1. The location of the sampling site: Tianhu, Guangzhou (23.65 o N, 113.63 o E).... S8 Figure S2. Typical chromatogram of AA standard solution (0.09 nmol µl -1 ).... S8 Figure S3. PMF results: (a) source profile: percentage contribution of each sources to the concentration of each species; (b) source contributions to proteins and TFAAs during the sampling period.... S9 Reference... S10 Positive matrix factorization (PMF) analysis Positive matrix factorization (PMF) model (EPA PMF 5.0) was used for the source S2
apportionment of proteins and FAAs in PM 2.5. Proteins, total FAAs, and other 13 selected species, including water-soluble ions (Na +, NH + 4, K +, Cl -, NO - 3 and SO 2-4 ), OC, EC and soil-related elements (Si, Al, Fe, Ca and Ti), were applied for the model input. Half of the MDLs were used for the values below MDLs and 5/6 of the MDLs were used as their uncertainties. The uncertainties for normal data points were used as the sum of 10% of the values and two times the MDLs of the corresponding species. Considering the MDL of total FAAs can t be obtained, we adopted 15% of the concentration values of total FAAs for its uncertainties. PMF model was performed with 4 8 factors and the 5 factor was considered to be the optimal one. The source profiles of the 5 factors are shown in Figure S3. As shown in Figure S3 a, factor 1 was characterized by high levels of soil-related elements (larger than 55.0%), indicating the influence of fugitive dust/soil. Factor 2 was characterized by the dominance of nss-k + (48.0%), which is a tracer of biomass burning activities. 1, 2 Factor 3 was dominated by Cl - (53.8%) and Na + (34.6%), which are the signatures of sea salt. Factor 4 had high levels of SO 2-4 (73.0%) and NH + 4 (68.3%), suggesting the influence of - secondary sulfate formation process. Factor 5 was characterized by high level of NO 3 (80.6%), suggesting the influence of secondary nitrate formation process. 3 The individual contributions of the various sources to the measured proteins and total FAAs are shown in Figure S3 b. The two factors associated with the secondary particles formation processes (secondary sulfate and nitrate), contributed 48.8% for proteins and 41.4% for total FAAs, respectively. Sulfate and nitrate are mainly formed by the S3
oxidation of SO 2 and NO x, which then form particles through the reaction with the atmospheric NH 4 +. 4 NH 4 + is mainly derived from the NH 3 releasing from agricultural activities, such as livestock excreta and fertilizer application. 5, 6 Therefore, the high contributions of secondary sulfate and nitrate factors to proteins and total FAAs may linked to the major contribution of intensive agricultural activities to proteinaceous matter in PM 2.5 at Tianhu. Additionally, biomass burning is also an important source of proteinaceous matter, and its contributions to the measured proteins and total FAAs were 21.0% and 28.9%, respectively. Nevertheless, the contributions of fugitive dust/soil to proteins and total FAAs showed a big difference (proteins 26.7%, total FAAs 7.0%), which may be attributed to the polymeric state of organic nitrogen in soil (e.g, peptides, proteins and protein-humic complex). 7, 8 The sea salt only accounted 3.5% for proteins, while it can contribute up to 22.7% for the total FAAs. It suggests that the oceanic emission was also a source of proteinaceous matter, especially for FAAs. 9, 10 Additionally, the lifetimes of some amino acids (e.g., Glu and Gly) in the atmosphere are from several days to weeks, 11 and therefore could be subjected to long-range transport. Thus, the influence of oceanic air masses through long-range transport could not be neglected in this region. Table S1. The method detection limits (MDLs), precision and recoveries of AA analysis. AAs MDL MDL a EMDL b Precision Recovery (pmol μl -1 ) (ng ul -1 ) (ng m -3 ) (%) (%) Aspartic acid (Asp) 5.59 0.74 12.4 0.89 62.8 Glutamic acid (Glu) 6.14 0.90 15.1 0.99 65.5 Asparagine (Asn) 6.16 0.81 13.6 3.03 70.4 S4
Serine (Ser) 6.27 0.66 11.0 0.49 79.4 Glutamine (Gln) 13.5 1.98 33.0 2.83 65.4 Histidine (His) 3.87 0.60 10.0 1.92 70.4 Glycine (Gly) 4.67 0.35 5.84 4.16 74.9 Threonine (Thr) 6.44 0.77 12.8 4.08 65.8 Arginine (Arg) 6.72 1.17 19.5 9.16 65.2 Alanine (Ala) 10.2 0.91 15.2 2.96 67.5 Tyrosine (Tyr) 4.74 0.86 14.3 1.24 69.0 Valine (Val) 0.35 0.04 0.69 4.76 69.5 Methionine (Met) 5.30 0.79 13.2 8.29 62.6 Tryptophane (Trp) 2.54 0.52 8.63 2.95 66.4 Phenylalanine (Phe) 1.92 0.32 5.28 8.26 68.0 Isoleucine (Ile) 5.87 0.77 12.8 7.06 65.6 Leucine (Leu) 6.03 0.79 13.2 1.21 67.3 Lysine (Lys) 1.32 0.19 3.23 1.31 68.2 a MDLs converted from pmol μl -1 to ng μl -1 with their indivdual molecular weight of each AAs; b The corresponding effective limit in the aerosol samples (EMDL). Table S2. Seasonal mass concentration of PM 2.5 and the associated chemical components in PM 2.5 at Tianhu (µg m -3 ). Component Annual Spring a Summer a Autumn a Winter a PM 2.5 44.2 ± 25.8 31.3 ± 12.4 36.8 ± 18.6 67.1 ± 33.2 40.8 ± 20.2 OC 6.1 ± 4.0 4.5 ± 3.1 4.9 ± 3.6 7.6 ± 3.8 7.5 ± 4.7 EC 0.8 ± 0.4 0.7 ± 0.3 0.6 ± 0.3 0.9 ± 0.4 1.0 ± 0.4 Cl - 0.2 ± 0.2 0.2 ± 0.1 0.2 ± 0.1 0.2 ± 0.1 0.3 ± 0.2 - NO 3 2- SO 4 2.0 ± 2.4 1.8 ± 1.7 1.6 ± 2.0 1.4 ± 2.0 2.9 ± 3.4 12.2 ± 8.0 9.1 ± 2.8 9.5 ± 5.2 17.8 ± 11.5 12.6 ± 7.6 Na + 0.5 ± 0.2 0.5 ± 0.3 0.5 ± 0.2 0.6 ± 0.2 0.5 ± 0.2 NH 4 + 5.2 ± 2.9 3.4 ± 0.9 3.7 ± 2.1 7.6 ± 3.4 5.8 ± 2.6 K + 0.7 ± 0.3 0.6 ± 0.2 0.7 ± 0.3 0.8 ± 0.3 0.7 ± 0.3 CM b 2.1 ± 1.5 2.1 ± 1.9 1.3 ± 0.9 2.8 ± 1.5 2.0 ± 1.3 Non-CM c 0.5 ± 0.4 0.4 ± 0.3 0.3 ± 0.3 0.8 ± 0.3 0.5 ± 0.4 a the sampling period was classified into four seasons: spring (Mar.-May), summer (Jun.-Aug.), autumn (Sep.- Nov.), and winter (Dec.-Feb.) b CM (crustal material) = 2.2 [Al]+2.49 [Si]+1.63 [Ca]+2.42 [Fe]+1.94 [Ti] S5
c non-cm (non-crustal material), the sum of the elements of S, Cl, K, Mn, Cu, Zn, Br and Pb. Table S3. Molar and mass concentrations of proteins and FAAs (arithmetic mean, standard deviation and range) in PM 2.5 at Tianhu. Compounds Mean SD Range Mean SD Range Protein (µg m -3 ) 0.79 0.47 0.20-1.86 FAAs pmol m -3 10-3 µg m -3 Aspartic acid (Asp) 18.1 5.85 10.2-36.6 2.47 0.80 1.40-5.01 Glutamic acid (Glu) 24.6 6.59 16.6-54.3 3.37 0.90 2.27-7.44 Asparagine (Asn) 21.3 4.87 14.1-49.1 2.91 0.67 1.93-6.72 Serine (Ser) 34.1 10.8 21.9-78.2 4.68 1.49 3.00-10.7 Glutamine (Gln) 21.0 5.48 14.3-54.1 2.87 0.75 1.96-7.42 Histidine (His) 3.13 1.41 1.63-8.79 0.43 0.19 0.22-1.20 Glycine (Gly) 193 109 60.9-526 26.4 14.9 8.34-72.1 Threonine (Thr) 38.8 30.0 6.56-205 5.31 4.12 0.90-28.1 Arginine (Arg) 25.1 17.0 10.7-84.5 3.44 2.33 1.46-11.6 Alanine (Ala) 33.6 20.7 8.31-99.7 4.60 2.84 1.14-13.7 Tyrosine (Tyr) 14.7 4.93 9.06-29.5 2.04 0.68 1.24-4.03 Valine (Val) 179 100 50.4-498 24.6 13.7 6.91-68.2 Methionine (Met) 156 69.0 41.9-367 21.4 9.44 5.74-50.2 Phenylalanine (Phe) 131 48.0 53.9-328 18.0 6.58 7.39-45.0 Lysine (Lys) 76.3 46.3 6.21-235 10.5 6.35 0.85-32.2 Total FAAs 970 353 432-2038 133 48.4 59.3-279 S6
Table S4. Correlation coefficients (r) of proteinaceous matter and major ions as well as CM in PM 2.5. proteins Total FAAs Gly Val Met Phe Lys Thr Ser Ala Arg Na + Cl - + NH 4 proteins 1 Total FAAs 0.74 a 1 Gly 0.81 0.89 1 Val 0.70 0.92 0.84 1 Met 0.44 0.78 0.58 0.66 1 Phe 0.45 0.73 0.44 0.61 0.53 1 Lys 0.09 0.46 0.12 0.27 0.41 0.69 1 Thr 0.43 0.48 0.42 0.34 0.20 0.34 0.21 1 Ser 0.12 0.28 0.34 0.21 0.31 0.06 0.03-0.01 1 Ala 0.74 0.66 0.70 0.58 0.41 0.35 0.08 0.32 0.30 1 Arg 0.64 0.84 0.89 0.80 0.53 0.44 0.20 0.40 0.36 0.61 1 Na + 0.31 0.44 0.47 0.36 0.38 0.22 0.16 0.11 0.09 0.28 0.42 1 Cl - 0.18 0.28 0.08 0.09 0.22 0.65 0.51 0.18-0.05 0.08 0.06 0.45 1 + NH 4 - NO 3 2- SO 4-2- NO 3 SO 4 0.74 0.80 0.79 0.79 0.53 0.55 0.19 0.40-0.00 0.54 0.71 0.45 0.27 1 0.47 0.39 0.27 0.23 0.39 0.55 0.33 0.18-0.08 0.28 0.13 0.35 0.67 0.51 1 0.68 0.70 0.76 0.71 0.49 0.34 0.04 0.33 0.09 0.44 0.69 0.41 0.12 0.90 0.37 1 nss-k + CM nss-k + 0.79 0.69 0.72 0.55 0.53 0.48 0.20 0.39 0.09 0.55 0.59 0.53 0.35 0.73 0.63 0.63 1 CM b 0.73 0.52 0.57 0.51 0.39 0.30-0.05 0.14 0.11 0.66 0.52 0.35 0.14 0.47 0.35 0.43 0.65 1 a Bold is significant at p < 0.01. b CM = 2.20[Al] + 2.49[Si] + 1.63[Ca] + 2.42[Fe] + 1.94[Ti] S7
Figure S1. The location of the sampling site: Tianhu, Guangzhou (23.65 o N, 113.63 o E). Figure S2. Typical chromatogram of AA standard solution (0.09 nmol µl -1 ). S8
Protein TFAAs OC EC Na+ NH4+ nss-k+ Cl- NO3- SO42- Al Si Ca Ti Fe Fraction of species apportioned to factors (%) (a) 100 Factor 1 (Crustal dust) 80 60 40 20 0 60 Factor 2 (Biomass burning) 40 20 0 60 Factor 3 (Sea salt) 40 20 0 80 Factor 4 (Secondary sulfate) 60 40 20 0 80 Factor 5 (Secondary nitrate) 60 40 20 0 TFAAs (b) Proteins 8.8% 2.2% 7.0% Fugitive dust/soil 40.0% 26.7% 21.0% 3.5% 39.2% 22.7% 28.9% Biomass burning Sea salt Secondary sulfate Figure S3. PMF results: (a) source profile: percentage contribution of each sources to the concentration of each species; (b) source contributions to proteins and TFAAs during the sampling period. S9
Reference (1) Ho, K. F.; Engling, G.; Ho, S. S. H.; Huang, R.; Lai, S.; Cao, J.; Lee, S. C., Seasonal variations of anhydrosugars in PM2.5 in the Pearl River Delta Region, China. Tellus. B 2014, 66, 1-14; DOI: 10.3402/tellusb.v66.22577. (2) Kunwar, B.; Kawamura, K., One-year observations of carbonaceous and nitrogenous components and major ions in the aerosols from subtropical Okinawa Island, an outflow region of Asian dusts. Atmos. Chem. Phys. 2014, 14 (1), 1819-1836; DOI: 10.5194/acp-14-1819-2014. (3) Huang, X. H. H.; Bian, Q.; Ng, W. M.; Louie, P. K. K.; Yu, J. Z., Characterization of PM 2.5 major components and source investigation in suburban Hong Kong: a one year monitoring study. Aerosol Air Qual. Res. 2014, 14 (1), 237-250; DOI: 10.4209/aaqr.2013.01.0020. (4) Lai, S.; Zou, S.; Cao, J.; Lee, S.; Ho, K. F., Characterizing ionic species in PM 2.5 and PM 10 in four Pearl River Delta cities, South China. J. Environ. Sci-China 2007, 19 (8), 939-947; DOI: 10.1016/s1001-0742(07)60155-7. (5) Zhang, Y.; Dore, A. J.; Ma, L.; Liu, X. J.; Ma, W. Q.; Cape, J. N.; Zhang, F. S., Agricultural ammonia emissions inventory and spatial distribution in the North China Plain. Environ. Pollut. 2010, 158 (2), 490-501; DOI: 10.1016/j.envpol.2009.08.033. (6) Zhang, Y.; Luan, S.; Chen, L.; Shao, M., Estimating the volatilization of ammonia from synthetic nitrogenous fertilizers used in China. J. Environ. Manage. 2011, 92 (3), 480-493; DOI: 10.1016/j.jenvman.2010.09.018. (7) Roberts, P.; Jones, D. L., Critical evaluation of methods for determining total protein in soil solution. Soil Biol. Biochem. 2008, 40 (6), 1485-1495; DOI: 10.1016/j.soilbio.2008.01.001. (8) Warren, C., Organic N molecules in the soil solution: what is known, what is unknown and the path forwards. Plant Soil 2014, 375 (1-2), 1-19; DOI: 10.1007/s11104-013-1939-y. (9) Scalabrin, E.; Zangrando, R.; Barbaro, E.; Kehrwald, N. M.; Gabrieli, J.; Barbante, C.; Gambaro, A., Amino acids in Arctic aerosols. Atmos. Chem. Phys. 2012, 12 (21), 10453-10463; DOI: 10.5194/acp-12-10453-2012. (10) Wedyan, M. A.; Preston, M. R., The coupling of surface seawater organic nitrogen and the marine aerosol as inferred from enantiomer-specific amino acid analysis. Atmos. Environ. 2008, 42 (37), 8698-8705; DOI: 10.1016/j.atmosenv.2008.04.038. (11) McGregor, K. G.; Anastasio, C., Chemistry of fog waters in California's Central Valley: 2. Photochemical transformations of amino acids and alkyl amines. Atmos. Environ. 2001, 35 (6), 1091-1104; DOI: 10.1016/S1352-2310(00)00282-X. S10